WO2024187166A1

WO2024187166A1 - System and method of determining economic decisions based on lifetime performance of a battery by a real-time battery management system

Info

Publication number: WO2024187166A1
Application number: PCT/US2024/019268
Authority: WO
Inventors: Jeffrey J. ROMNEY; William FLURY; John Cronin
Original assignee: Lifetime Energy Inc.
Priority date: 2023-03-08
Filing date: 2024-03-08
Publication date: 2024-09-12

Abstract

A system and method for determining economic decisions based on a battery management system, that may include: providing a battery, having a warranty and providing a battery management system for the battery, the battery management system being in communication with the battery, and the battery management system is configured to collect data from the battery in real-time. The system and method may also include a cloud server, where the battery management system can communicate with the cloud server, including: transferring the data from the battery; wherein the cloud server includes a data collection module, an economic module, a notification module, and an economic database. The economic database may include predetermined economic recommendations based on performance of the battery and battery data. The battery management system may measure performance of the battery, resulting in battery performance measurements, and collect battery data.

Description

SYSTEM AND METHOD OF DETERMINING ECONOMIC DECISIONS BASED ON LIFETIME PERFORMANCE OF A BATTERY BY A REAL-TIME BATTERY

MANAGEMENT SYSTEM

FIELD OF THE DISCLOSURE

The present disclosure is generally related to determining economic decisions based on lifetime performance of a battery by a real-time battery management system.

BACKGROUND

Currently, battery⁷ management systems or BMS measure battery data and determines how the battery is performing. Also, BMS are used to monitor the current state of a battery to ensure the proper function and use of the battery and may notify the user or administrator if any of the performance metrics are outside of a desired range. Lastly, there are no BMS that utilize the collected performance data as well as user data, such as warranty information, to determine if the battery needs maintenance or be replaced while the user’s warranty is still active. Thus, there is a need in the prior art to determine economic decisions based on lifetime performance of a battery by a real-time battery management system.

DESCRIPTIONS OF THE DRAWINGS

The features and advantages of the disclosure will become apparent from a consideration of the subsequent detailed description presented in connection with the accompanying drawings in which:

FIG. 1 : Illustrates a method of determining economic decisions based on lifetime performance of a battery by a real-time battery management system, according to an embodiment;

FIG. 2: Illustrates a Data Collection Module, according to an embodiment;

FIG. 3: Illustrates an Economic Module, according to an embodiment;

FIG. 4: Illustrates a Notification Module, according to an embodiment;

FIG. 5: Illustrates a BMS Database, according to an embodiment;

FIG. 6: Illustrates an Economic Database, according to an embodiment;

FIG. 7: Illustrates an Alert Module, according to an embodiment.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles in accordance with the disclosure, reference will now be made to the embodiments illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Any alterations and further modifications of the inventive features illustrated herein, and any additional applications of the principles of the disclosure as illustrated herein, which would normally occur to one skilled in the relevant art and having possession of this disclosure, are to be considered within the scope of the disclosure claimed.

Before the present systems and methods for determining economic recommendations using a battery management system are disclosed and described, it is to be understood that this disclosure is not limited to the particular systems, configurations, process or method steps, and materials disclosed herein as such configurations, process or method steps, and materials may vary somewhat. It is also to be understood that the terminology employed herein is used for the purpose of describing particular embodiments only and is not intended to be limiting since the scope of the present disclosure will be limited only by the appended claims and equivalents thereof.

It must be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

In describing and claiming the present disclosure, the following terminology’ will be used in accordance with the definitions set out below.

As used herein, the terms “comprising,” “including,” “containing,” “characterized by,” and grammatical equivalents thereof are inclusive or open-ended terms that do not exclude additional, unrecited elements or method steps.

As used herein, the phrase “consisting of’ and grammatical equivalents thereof exclude any element, step, or ingredient not specified in the claim.

Embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings in which like numerals represent like elements throughout the several figures, and in which example embodiments are shown. Embodiments of the claims may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. The examples set forth herein are non-limiting examples and are merely examples among other possible examples.

FIG. 1 illustrates a method of determining economic decisions based on lifetime performance of a battery by a real-time battery management system. This method comprises of a battery' platform 102 that may reside in the cloud 116 may include a data collection module 104, economic module 106, notification module 108, BMS database 110, economic database 112, and a communication network 114. The battery platform 102 may continuously collect BMS 118 data from a plurality of BMS 118 systems and receive performance data associated with battery packs 120 to determine an economic decision, such as a recommendation, that is sent to a user 132 to inform the user 132 if the battery' pack 120 needs to have maintenance performed, replaced, malfunctioning, inspected, etc. prior to the ending of a warranty on the battery pack 120. Further, embodiments may include a data collection module 104 which begins by connecting to the BMS 1 18. The data collection module 104 sends a request to the BMS 118 for the performance data. The data collection module 104 receives the performance data from the BMS 118. The data collection module 104 stores the performance data from the BMS 118 in the BMS database 110. The data collection module 104 initiates the economic module 106 and returns to sending a request to the BMS 118 to receive the performance data. Further, embodiments may include an economic module 106 which begins by being initiated by the data collection module 104. The economic module 106 extracts the data entry from the BMS database 110. The economic module 106 compares the extracted data entry from the BMS database 110 to the economic database 1 12. The economic module 106 determines if there is a corresponding recommendation stored in the economic database 112 based upon the performance data from the data entry from the BMS database 110. If it is determined that there is a corresponding recommendation the economic module 106 extracts the recommendation from the economic database 112. The economic module 106 sends the recommendation to the notification module 108. If it is determined that there is no corresponding recommendation, or the recommendation has been sent to the notification module 108 the economic module 106 initiates the notification module 108. Further, embodiments may include a notification module 108 which begins by being initiated by the economic module 106. The notification module 108 connects to the alert module 136. The notification module 108 extracts the performance data from the BMS database 110. The notification module 108 sends the performance data notification to the alert module 136. The notification module 108 determines if a recommendation was received from the economic module 106. If it is determined a recommendation was received the notification module 108 receives the recommendation from the economic module 106. The notification module 108 sends the extracted recommendation to the alert module 136. If it is determined that the notification module 108 did not receive a recommendation or after the recommendation is sent to the alert module 136 the notification module 108 returns to the economic module 106. Further, embodiments may include a BMS database 110 which is created through the process described in the data collection module 104 in which the battery platform 102 receives the performance data from the BMS 1 18. The database may contain the date and time the data was received, the user ID, the battery ID or a BMS ID, the warranty length of the battery 120, the manufactured date of the battery 120, the warranty status of the battery 120, the date the battery 120 was first used or activated, the time it takes for the battery 120 to be fully charged or the charge capacity of the battery 120, the rate of change in the charge levels of the battery 120, etc. In some embodiments, the data may be collected and stored periodically over the course of a day, week, month, year, etc. In some embodiments, the user ID may be used to determine information about the user, such as the location of the BMS 118 system, the warranty information about the user’s system, maintenance data about the user’s system, etc. In some embodiments, the battery ID may be used to determine information about batten- 120, such as the make and model of the battery- 120, the length of time the battery 120 has been use, the normal life expectancy of the battery 120, etc. In some embodiments, the performance data stored in the database may also include voltage, including total voltage, voltages of individual cells, or voltage of periodic taps, temperature, including average temperature, coolant intake temperature, coolant output temperature, or temperatures of individual cells, coolant flow including for liquid cooled batteries, current including current in or out of the battery, health of individual cells, state of balance of cells, etc. In some embodiments, the performance data stored may also include calculations by the BMS 118 such as voltage, including minimum and maximum cell voltage, state of charge (SoC) or depth of discharge (DoD), to indicate the charge level of the battery, state of health (SoH), for example a measurement of the remaining capacity of the battery- as percentage of the original capacity, state of power (SoP), for example the amount of power available for a defined time interval given the current power usage, temperature and other conditions, state of safety (SOS), maximum charge current as a charge current limit (CCL), maximum discharge current as a discharge current limit (DCL), energy delivered since last charge or charge cycle, internal impedance of a cell, such as to determine open circuit voltage, charge delivered or stored, total energy delivered since first use, total operating time since first use, total number of cycles, temperature monitoring, coolant flow for air or liquid cooled batteries. The data stored in the BMS database 110 may be used in machine learning algorithms to adjust the recommendations stored in the economic database 112. For example, the BMS database 110 may store historical data from a plurality of users 134, including the user's 134 locations, the amount of batteries 120 the user's 134 have, the make and model of the batteries 120, the current age of the batteries 120, etc. The BMS database 110 may be filtered on the make and model of the battery 120 and the first parameter, such as user’s 134 locations, the amount of batteries 120 the user’s 134 have, the current age of the batteries 120, etc., then the economic module 106 may perform correlations on the performance data stored in the BMS database 110, such as the time to charge the battery 120, the maximum charge level of the battery 120, etc. An example of highly correlated data may be the time to charge the battery 120 and the maximum charge level of the battery 120, such as users 134 in the Boston, MA area have a maximum charge level of 90% which takes 70 minutes to charge to during the winter months. There may be a predetermined correlation coefficient, such as 0.75, that the data would need to meet to become a recommendation in the economic database 112. If the predetermined threshold is exceeded, the most re-occurring data point in the data set would be extracted and would be stored in the economic database 112 as a recommendation to show that this is a normal occurrence for user’s 134 in the Boston, MA area during the winter months. An example of data that is not highly correlated may be the amount of batteries 120 the user 134 has and the charge level of the battery, such as there is no trend for the batteries 120 to consistently have the same charge level if the user 134 has multiple batteries 120, with a correlation coefficient of 0.60. In this example the correlation coefficient does not exceed the predetermined threshold, so the next parameter is selected, and correlations are performed again on a newly selected parameter. Further, embodiments may include an economic database 112 which may be stored on the batten- platform 102 to be used to determine if a user should receive a recommendation based on performance data collected from the BMS 118. The database contains the BMS 118 data, the current warranty status of the battery 120, and the corresponding recommendation based upon the BMS data. For example, if the BMS data collected and stored in the BMS database 110 is that the first time the batten 120 is used is within six months of the battery 120 being manufactured then there is no action, however, if the first time the battery 120 is used is greater than one year of the batten’ 120 being manufactured and still under warranty the recommendation may be to schedule maintenance on the battery' 120 to ensure it is functioning properly. In some embodiments, the recommendations may be based on overall performance of the battery 120, such as the decrease in charge capacity since the battery 120 was installed. In some embodiments, the recommendations may be based on the performance of the battery 120 over a certain time period, such as a decrease in charge capacity' over a predetermined time period, such as an hour, day, week, month, year, etc. In some embodiments, the recommendations may be based upon other performance data of the battery 120 collected by the BMS 118, such as voltage, including total voltage, voltages of individual cells, or voltage of periodic taps, temperature, including average temperature, coolant intake temperature, coolant output temperature, or temperatures of individual cells, coolant flow including for liquid cooled batteries, current including current in or out of the battery, health of individual cells, state of balance of cells, etc. In some embodiments, the recommendations may be based on calculations performed on the performance data such as voltage, including minimum and maximum cell voltage, state of charge (SoC) or depth of discharge (DoD), to indicate the charge level of the battery, state of health (SoH), for example a measurement of the remaining capacity of the battery as percentage of the original capacity, state of power (SoP), for example the amount of power available for a defined time interval given the current power usage, temperature and other conditions, state of safety⁷ (SOS), maximum charge current as a charge current limit (CCL), maximum discharge current as a discharge current limit (DCL), energy delivered since last charge or charge cycle, internal impedance of a cell, such as to determine open circuit voltage, charge delivered or stored, total energy⁷ delivered since first use, total operating time since first use, total number of cycles, temperature monitoring, coolant flow for air or liquid cooled batteries. The database may include warranty calculations in which the economic recommendation is sent to the user based upon the historical performance of the battery 120. For example, if the battery takes longer than one hour to fully charge the database may⁷ include the required amount of instances over a predetermined amount of time to send the economic recommendation based upon the user’s warranty status. For example, the first instance in which the battery 120 takes longer than one hour to fully charge the economic recommendation may not be extracted and sent to the user 134 since there is only one occurrence of the battery 120 exceeding one hour to fully charge. If the battery takes longer than one hour to charge over the course of 4 days and each day it takes more time to reach a fully a charged state the economic recommendation may be extracted and sent to the user 134 to use the benefits of the warranty⁷. For example, if for four consecutive days the battery 120 takes 65 minutes, 70 minutes, 90 minutes, and 120 minutes, then the battery 120 is malfunctioning and the economic recommendation to schedule maintenance may be extracted and sent to the user 134. In some embodiments, the warranty calculations may include the amount of times it takes longer than expected to fully charge the battery 120 over a given time period, the amount of times the battery 120 does not reach a full charged state over a given time period, etc. The data stored in the BMS database 110 may be used in machine learning algorithms to adjust the recommendations stored in the economic database 112. For example, the BMS database 1 10 may store historical data from a plurality of users 134, including the user’s 134 locations, the amount of batteries 120 the user’s 134 have, the make and model of the batteries 120, the current age of the batteries 120, etc. The BMS database 110 may be filtered on the make and model of the battery 120 and the first parameter, such as user's 134 locations, the amount of batteries 120 the user’s 134 have, the current age of the batteries 120, etc., then the economic module 106 may perform correlations on the performance data stored in the BMS database 110, such as the time to charge the battery' 120, the maximum charge level of the battery 120, etc. An example of highly correlated data may be the time to charge the battery 120 and the maximum charge level of the battery 120, such as users 134 in the Boston, MA area have a maximum charge level of 90% which takes 70 minutes to charge to during the winter months. There may be a predetermined correlation coefficient, such as 0.75, that the data would need to meet to become a recommendation in the economic database 112. If the predetermined threshold is exceeded, the most reoccurring data point in the data set would be extracted and would be stored in the economic database 112 as a recommendation to show that this is a normal occurrence for user’ s 134 in the Boston, MA area during the winter months. An example of data that is not highly- correlated may be the amount of batteries 120 the user 134 has and the charge level of the battery-, such as there is no trend for the batteries 120 to consistently have the same charge level if the user 134 has multiple batteries 120, with a correlation coefficient of 0.60. In this example the correlation coefficient does not exceed the predetermined threshold, so the next parameter is selected, and correlations are performed again on a newly selected parameter. Further, embodiments may include a comms 114 or communication network 114 may be a wired and/or a wireless network. The communication network 114, if wireless, may be implemented using communication techniques such as Visible Light Communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE), Wireless Local Area Network (WLAN), Infrared (IR) communication, Public Switched Telephone Network (PSTN), Radio waves, and other communication techniques known in the art. The communication network 114 may allow ubiquitous access to shared pools of configurable system resources and higher-level sendees that can be rapidly provisioned with minimal management effort, often over Internet and relies on sharing of resources to achieve coherence and economies of scale, like a public utility, while third-party clouds enable organizations to focus on their core businesses instead of expending resources on computer infrastructure and maintenance. Further, embodiments may include a cloud 1 16 which is a distributed netw ork of computers comprising servers and databases. A cloud 116 may be a private cloud 116, where access is restricted by isolating the network such as preventing external access, or by using encryption to limit access to only authorized users. Alternatively, a cloud 1 16 may be a public cloud 1 16 where access is widely available via the internet. A public cloud 116 may not be secured or may be include limited security features. Further, embodiments may include a BMS 118 or battery' management system which may be an electronic system that manages a rechargeable battery, such as a cell or batten’ pack 120, by protecting the battery 120 from operating outside its safe operating area, monitoring its state, calculating secondary data, reporting that data, controlling its environment, authenticating it and/or balancing it. A BMS 118 may monitor the state of the battery' as represented by various items, such as voltage, including total voltage, voltages of individual cells, or voltage of periodic taps, temperature, including average temperature, coolant intake temperature, coolant output temperature, or temperatures of individual cells, coolant flow including for liquid cooled batteries, current including current in or out of the battery', health of individual cells, state of balance of cells, etc. Additionally, a BMS 118 may calculate values, such as voltage, including minimum and maximum cell voltage, state of charge (SoC) or depth of discharge (DoD), to indicate the charge level of the battery, state of health (SoH), for example a measurement of the remaining capacity of the battery' as percentage of the original capacity', state of power (SoP), for example the amount of power available for a defined time interval given the current power usage, temperature and other conditions, state of safety (SOS), maximum charge current as a charge current limit (CCL), maximum discharge current as a discharge current limit (DCL), energy delivered since last charge or charge cycle, internal impedance of a cell, such as to determine open circuit voltage, charge delivered or stored, total energy' delivered since first use, total operating time since first use, total number of cycles, temperature monitoring, coolant flow for air or liquid cooled batteries. The BMS 1 18 may utilize the comms 124 to connect to the battery' platform 102 which resides in the cloud 116 to send data collected and calculated by the BMS 118. For example, when the BMS 118 is originally activated there may be a connection module that connects to the battery platform 102 using the comms 124 through the cloud 11 , then the BMS 118 may continuously collect, calculate, and send data to the battery' platform 102. Further, embodiments may include a plurality' of battery' packs 120 which may be a set of any number of identical batteries or individual battery cells, for example a battery pack 120 may contain two battery cells for every one BMS 118. They may be configured in a series, parallel or a mixture of both to deliver the desired voltage, capacity⁷, or power density. In some embodiments, the battery' pack 120 may be built together with the BMS 118 with an external communication data bus. Further, embodiments may include a plurality’ of sensors 122 that may be used by the BMS 118 such as the voltage sensors, current sensors, and temperature sensors for each cell and send it to the BMS 118 to analyze the sensor 122 data to ensure that each cell operates within the prescribed limits. Further, embodiments may include a comms 124 or communication network 124 may be a wired and/or a wireless network. The communication network 124, if wireless, may be implemented using communication techniques such as Visible Light Communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE), Wireless Local Area Network (WLAN), Infrared (IR) communication, Public Switched Telephone Network (PSTN), Radio waves, and other communication techniques known in the art. The communication network 124 may allow ubiquitous access to shared pools of configurable system resources and higher-level services that can be rapidly provisioned with minimal management effort, often over Internet and relies on sharing of resources to achieve coherence and economies of scale, like a public utility, while third-party clouds enable organizations to focus on their core businesses instead of expending resources on computer infrastructure and maintenance. Further, embodiments may include a controller 146 for performing computations and communicates with a memory’ 144 for storing data. The controller 146 is in communication with a communications interface 156. The controller 146 may be a commercially available central processing unit (CPU) or graphical processing unit (GPU) or may be a proprietary, purposebuild design. More than one controller 146 may operate in tandem and may be of different types, such as a CPU and a GPU. A GPU is not restricted to only processing graphics or image data and may be used for other computations. Further, embodiments may include a charger 128 which may be an AC -DC converter which can be plugged into the main power supply. For example, when the battery 120 is running low, the charger 128 can be used to charge the batten- 120. In some embodiments, the charger 128 may supply the load bypassing the battery 120 when fully charged to preserve the charge for later use when mains supply is not available. The BMS 118 must control the energy conversion process and the charging parameters of the battery. The charging current and voltage of the batten 120 is continuously measured and fed back to the controller 126 of the BMS. Which in turn generates the control signal to control the output of the charger 128. Further, embodiments may include a converter 130 such as a DC-DC converter which is used to interface the battery 120 to the load. The battery 120 rating may not be the same as the load demands. The DC-DC converter 130 may provide appropriate voltage and current as per the load demands to ensure efficient use of the battery- 120 and monitors the discharge current. In some embodiments, the converter 130 may be controlled by a microcontroller. The controller senses the battery 120 parameters to be controlled and load requirements and generates appropriate control signals to adjust the converter output. Further, embodiments may include a plurality- of users 132 which are the owners of the BMS 118 systems that monitor their battery pack 120 or cells and may utilize a platform application 134 to monitor the performance of the system, receive notifications, etc. Further, embodiments may include a platform application 134 which provides the user 134 with the ability to monitor the system including the performance of the batteries 120, receive notifications from the battery platform 102, such as schedule maintenance, replace a battery, be notified of a malfunction, etc. In some embodiments, the user 134 may be able to schedule assistance from the battery- platform 102 through the platform application 134, order additional parts or supplies, purchase subscription services to access additional performance data, etc. Further, embodiments may include an alert module 136 which begins byconnecting to the battery platform 102. The alert module 136 continuously polls for a performance data notification from the notification module 108. The alert module 136 receives the performance data notification from the notification module 108. The alert module 136 displays the received performance data notification from the notification module 108 on the user interface 138. The alert module 136 determines if a recommendation was received from the notification module 108. If it is determined that a recommendation was received from the notification module 108 the alert module 136 receives the recommendation from the notification module 108. The alert module 136 displays the received recommendation from the notification module 108 on the user interface 138 and the process returns to continuously polling to receive a performance data notification. Further, embodiments may include a user interface 138 which may either accept inputs from users 132 or provide outputs to the users 132 or may perform both the actions. In one case, a user 132 can interact with the interface(s) using one or more user- interactive objects and devices. The user-interactive objects and devices may comprise user input buttons, switches, knobs, levers, keys, trackballs, touchpads, cameras, microphones, motion sensors, heat sensors, inertial sensors, touch sensors, or a combination of the above. Further, the interface(s) may either be implemented as a Command Line Interface (CLI), a Graphical User Interface (GUI), a voice interface, or a web-based user-interface.

FIG. 2 illustrates the data collection module 104. The process begins with the data collection module 104 connecting, at step 200, to the BMS 118. For example, the data collection module 104 may connect to a plurality of BMS 118 systems that are owned by the users 134 through the cloud 116. The data collection module 104 sends, at step 202, a request to the BMS 1 18 for the performance data. For example, the data collection module 104 sends a request to the BMS 118 for the performance data collected and calculated by the BMS 118. In some embodiments, the data collection module 104 may continuously send a request to receive real-time data from the BMS 118. In some embodiments, the data collection module 104 may send a request periodically such as every minute, hour, day, week, month, quarter, year, etc. The data collection module 104 receives, at step 204, the performance data from the BMS 118. For example, the data collection may receive the performance data from the BMS 118 such as the date and time the data was received, the user ID, the battery ID or a BMS ID, the warranty length of the battery 120, the manufactured date of the battery 120, the warranty status of the battery 120, the date the battery 120 was first used or activated, the time it takes for the battery 120 to be fully charged or the charge capacity of the battery 120, the rate of change in the charge levels of the battery’ 120, etc. In some embodiments, the performance data may include voltage, including total voltage, voltages of individual cells, or voltage of periodic taps, temperature, including average temperature, coolant intake temperature, coolant output temperature, or temperatures of individual cells, coolant flow including for liquid cooled batteries, current including current in or out of the battery, health of individual cells, state of balance of cells, etc. The data collection module 104 stores, at step 206, the performance data from the BMS 118 in the BMS database 110. For example, the data collection module 104 stores the performance data in the BMS database 110 such as the date and time the data was received, the user ID, the battery ID or a BMS ID, the warranty length of the battery 120, the manufactured date of the battery 120, the warranty status of the battery 120, the date the battery 120 was first used or activated, the time it takes for the battery 120 to be fully charged or the charge capacity of the battery 120, the rate of change in the charge levels of the battery 120, etc. In some embodiments, the performance data may include voltage, including total voltage, voltages of individual cells, or voltage of periodic taps, temperature, including average temperature, coolant intake temperature, coolant output temperature, or temperatures of individual cells, coolant flow including for liquid cooled batteries, current including current in or out of the battery, health of individual cells, state of balance of cells, etc. In some embodiments, the performance data stored may also include calculations by the BMS 118 such as voltage, including minimum and maximum cell voltage, state of charge (SoC) or depth of discharge (DoD), to indicate the charge level of the battery, state of health (SoH), for example a measurement of the remaining capacity of the battery as percentage of the original capacity, state of power (SoP), for example the amount of power available for a defined time interval given the current power usage, temperature and other conditions, state of safety (SOS), maximum charge current as a charge current limit (CCL), maximum discharge current as a discharge current limit (DCL), energy delivered since last charge or charge cycle, internal impedance of a cell, such as to determine open circuit voltage, charge delivered or stored, total energy delivered since first use, total operating time since first use, total number of cycles, temperature monitoring, coolant flow for air or liquid cooled batteries. The data collection module 104 initiates, at step 208, the economic module 106 and returns to sending a request to the BMS 118 to receive the performance data. For example, the data collection module 104 initiates the economic module 106 to determine if the performance data corresponds to any recommendation stored in the economic database 112 that should be sent to the user 134.

FIG. 3 illustrates the economic module 106. The process begins with the economic module 106 being initiated, at step 300, by the data collection module 104. For example, the economic module 106 may be initiated once the data collection module 104 receives and stores the performance data from the BMS 118 in the BMS database 110. In some embodiments, the economic module 106 may be continuously querying the BMS database 110 for a new data entry and be initiated once a new data entry is stored in the BMS database 110. The economic module 106 extracts, at step 302, the data entry from the BMS database 110. For example, the economic module 106 extracts the data entry from the BMS database 110 such as the date and time the data was received, the user ID, the battery ID or a BMS ID, the warranty length of the battery 120, the manufactured date of the battery 120, the warranty status of the battery 120, the date the battery 120 was first used or activated, the time it takes for the battery 120 to be fully charged or the charge capacity of the battery 120, the rate of change in the charge levels of the battery 120, etc. In some embodiments, the economic module 106 may extract performance data such as voltage, including total voltage, voltages of individual cells, or voltage of periodic taps, temperature, including average temperature, coolant intake temperature, coolant output temperature, or temperatures of individual cells, coolant flow including for liquid cooled batteries, current including current in or out of the battery, health of individual cells, state of balance of cells, etc. in some embodiments, the economic module 106 may extract calculations performed by the BMS 118 stored in the BMS database 110 such as voltage, including minimum and maximum cell voltage, state of charge (SoC) or depth of discharge (DoD), to indicate the charge level of the battery, state of health (SoH), for example a measurement of the remaining capacity of the battery as percentage of the original capacity, state of power (SoP), for example the amount of power available for a defined time interval given the current power usage, temperature and other conditions, state of safety (SOS), maximum charge current as a charge current limit (CCL), maximum discharge current as a discharge current limit (DCL), energy delivered since last charge or charge cycle, internal impedance of a cell, such as to determine open circuit voltage, charge delivered or stored, total energy' delivered since first use, total operating time since first use, total number of cycles, temperature monitoring, coolant flow for air or liquid cooled batteries. The economic module 106 compares, at step 304, the extracted data entry from the BMS database 110 to the economic database 112. For example, the economic module 106 compares the extracted data from the data entry stored in the BMS database 110 to the economic database 112 to determine if any of the data has a corresponding recommendation. For example, if the BMS data collected and stored in the BMS database 110 is that the first time the battery 120 is used is within six months of the battery 120 being manufactured then there is no action, however, if the first time the battery' 120 is used is greater than one year of the battery' 120 being manufactured and still under warranty the recommendation may be to schedule maintenance on the battery 120 to ensure it is functioning properly. If the first time the battery' 120 is used is greater than three years since the battery⁷ 120 was manufactured the recommendation may be to notify the user of a potential malfunction of the battery 120. For example, if the BMS 118 data that is stored indicates that the time for the battery 120 to completely charge takes less than one hour than there may be no action needed, however if the battery 120 takes longer than one hour to completely charge then the recommendation may be to schedule maintenance on the battery 120 if it is still under warranty. Also, if the battery’ 120 is not fully charging and is only charging up to or lower than 95% capacity then the recommendation may be to schedule a replacement of the battery’ 120. If the change in charge rate of the battery’ 120 is two percent over multiple ten minute intervals then no action may be required, but if the charge rate fluctuates by five percent over multiple ten minute intervals then the battery 120 may be charging erratically and the corresponding recommendation may be to schedule maintenance on the battery 120. In some embodiments, the economic module 106 may perform calculations, input the BMS 1 18 data into algorithms, or further analyze the data collected from the BMS 118 to determine if the battery' 120 is functioning correctly and compare the results of the calculations, algorithms, or analysis, to the economic database 112 to determine if there is a corresponding recommendation that should be sent to the notification module 108 to alert the user 132. The economic module 106 determines, at step 306, if there is a corresponding recommendation stored in the economic database 112 based upon the performance data from the data entry from the BMS database 110. For example, if the BMS data collected and stored in the BMS database 110 is that the first time the battery 120 is used is within six months of the battery 120 being manufactured then there is no action, however, if the first time the battery' 120 is used is greater than one year of the battery' 120 being manufactured and still under warranty the recommendation may be to schedule maintenance on the battery 120 to ensure it is functioning properly. If the first time the battery' 120 is used is greater than three years since the battery 120 was manufactured the recommendation may be to notify the user of a potential malfunction of the battery 120. For example, if the BMS 118 data that is stored indicates that the time for the battery' 120 to completely charge takes less than one hour than there may be no action needed, however if the battery 120 takes longer than one hour to completely charge then the recommendation may be to schedule maintenance on the battery 120 if it is still under warranty. Also, if the battery' 120 is not fully charging and is only charging up to or lower than 95% capacity' then the recommendation may be to schedule a replacement of the battery 120. If the change in charge rate of the battery 120 is two percent over multiple ten minute intervals then no action may be required, but if the charge rate fluctuates by five percent over multiple ten minute intervals then the battery 120 may be charging erratically and the corresponding recommendation may be to schedule maintenance on the battery

120. In some embodiments, the economic module 106 may perform calculations, input the BMS 118 data into algorithms, or further analyze the data collected from the BMS 118 to determine if the battery 120 is functioning correctly and compare the results of the calculations, algorithms, or analysis, to the economic database 112 to determine if there is a corresponding recommendation that should be sent to the notification module 108 to alert the user 132. If it is determined that there is a corresponding recommendation the economic module 106 extracts, at step 308, the recommendation from the economic database 112. For example, if the BMS data collected and stored in the BMS database 110 is that the first time the battery 120 is used is within six months of the battery 120 being manufactured then there is no action, however, if the first time the battery 120 is used is greater than one year of the batten- 120 being manufactured and still under warranty the recommendation may be to schedule maintenance on the battery 120 to ensure it is functioning properly. If the first time the battery 120 is used is greater than three years since the battery 120 was manufactured the recommendation may be to notify the user of a potential malfunction of the battery 120. For example, if the BMS 118 data that is stored indicates that the time for the battery' 120 to completely charge takes less than one hour than there may be no action needed, however if the battery 120 takes longer than one hour to completely charge then the recommendation may be to schedule maintenance on the battery 120 if it is still under warranty. Also, if the battery 120 is not fully charging and is only charging up to or lower than 95% capacity' then the recommendation may be to schedule a replacement of the battery' 120. If the change in charge rate of the battery' 120 is two percent over multiple ten minute intervals then no action may be required, but if the charge rate fluctuates by five percent over multiple ten minute intervals then the battery 120 may be charging erratically and the corresponding recommendation may be to schedule maintenance on the battery 120. In some embodiments, the economic module 106 may perform calculations, input the BMS 118 data into algorithms, or further analyze the data collected from the BMS 118 to determine if the battery 120 is functioning correctly and compare the results of the calculations, algorithms, or analysis, to the economic database 112 to determine if there is a corresponding recommendation that should be sent to the notification module 108 to alert the user 132. In some embodiments, warranty calculations may be used to determine if the economic recommendation is extracted and sent to the user based upon the historical performance of the battery 120. For example, if the battery takes longer than one hour to fully charge the economic database 112 may include the required amount of instances over a predetermined amount of time to send the economic recommendation based upon the user’s warranty status. For example, the first instance in which the battery 120 takes longer than one hour to fully charge the economic recommendation may not be extracted and sent to the user 134 since there is only one occurrence of the battery 120 exceeding one hour to fully charge. If the battery takes longer than one hour to charge over the course of 4 days and each day it takes more time to reach a fully a charged state the economic recommendation may be extracted and sent to the user 134 to use the benefits of the warranty. For example, if for four consecutive days the battery 120 takes 65 minutes, 70 minutes, 90 minutes, and 120 minutes, then the battery 120 is malfunctioning and the economic recommendation to schedule maintenance may be extracted and sent to the user 134. In some embodiments, the warranty calculations may include the amount of times it takes longer than expected to fully charge the battery 120 over a given time period, the amount of times the battery 120 does not reach a full charged state over a given time period, etc. The economic module 106 sends, at step 310, the recommendation to the notification module 108. For example, the economic module 106 sends the extracted corresponding recommendation to the notification module 108 such as to schedule maintenance, schedule a replacement, notify the user of a malfunction, etc. If it is determined that there is no corresponding recommendation or after the recommendation has been sent to the notification module 108 the economic module 106 initiates, at step 312, the notification module 108. For example, the economic module 106 initiates the notification module 108 to connect to the user’s 132 platform application 134 to send the extracted corresponding recommendation. In some embodiments, the economic module 106 may perform machine learning algorithms to update the recommendations stored in the economic database 112. For example, the BMS database 110 may store historical data from a plurality of users 134, including the user’s 134 locations, the amount of batteries 120 the user’s 134 have, the make and model of the batteries 120, the current age of the batteries 120, etc. The BMS database 110 may be filtered on the make and model of the battery 120 and the first parameter, such as user’s 134 locations, the amount of batteries 120 the user’s 134 have, the current age of the batteries 120, etc., then the economic module 106 may perform correlations on the performance data stored in the BMS database 110, such as the time to charge the battery 120, the maximum charge level of the battery 120, etc. An example of highly correlated data may be the time to charge the battery 120 and the maximum charge level of the battery 120, such as users 134 in the Boston, MA area have a maximum charge level of 90% which takes 70 minutes to charge to during the winter months. There may be a predetermined correlation coefficient, such as 0.75, that the data would need to meet to become a recommendation in the economic database 112. If the predetermined threshold is exceeded, the most re-occurring data point in the data set would be extracted and would be stored in the economic database 112 as a recommendation to show that this is a normal occurrence for user’s 134 in the Boston, MA area during the winter months. An example of data that is not highly correlated may be the amount of batteries 120 the user 134 has and the charge level of the battery, such as there is no trend for the batteries 120 to consistently have the same charge level if the user 134 has multiple batteries 120, with a correlation coefficient of 0.60. In this example the correlation coefficient does not exceed the predetermined threshold, so the next parameter is selected, and correlations are performed again on a newly selected parameter.

FIG. 4 illustrates the notification module 108. The process begins with the notification module 108 being initiated, at step 400, by the economic module 106. For example, the notification module 108 may be initiated by the economic module 106 once it is determined if there is a recommendation for the user. The notification module 108 connects, at step 404, to the alert module 136. For example, the notification module 108 may connect to the alert module 136 through the cloud 116. In some embodiments, the economic module 106 may extract and send the user ID from the data entry that was compared to the economic database 112 to the notification module 108 to determine which user 132 to send the performance data notification and/or recommendation to. The notification module 108 extracts, at step 404, the performance data from the BMS database 110. For example, the notification module 108 may extract performance data from the BMS database 110 such as the time it takes for the battery 120 to be fully charged or the charge capacity of the battery 120, the rate of change in the charge levels of the battery 120, etc. to inform the user 132 of the performance of the battery 120. In some embodiments, the performance data may include the make and model of the battery' 120, the length of time the battery 120 has been use, the normal life expectancy of the battery 120, voltage, including total voltage, voltages of individual cells, or voltage of periodic taps, temperature, including average temperature, coolant intake temperature, coolant output temperature, or temperatures of individual cells, coolant flow including for liquid cooled batteries, current including current in or out of the battery, health of individual cells, state of balance of cells, calculations by the BMS 118 such as voltage, including minimum and maximum cell voltage, state of charge (SoC) or depth of discharge (DoD), to indicate the charge level of the battery', state of health (SoH), for example a measurement of the remaining capacity of the battery' as percentage of the original capacity, state of power (SoP), for example the amount of power available for a defined time interval given the current power usage, temperature and other conditions, state of safety (SOS), maximum charge current as a charge current limit (CCL), maximum discharge current as a discharge current limit (DCL), energy delivered since last charge or charge cycle, internal impedance of a cell, such as to determine open circuit voltage, charge delivered or stored, total energy delivered since first use, total operating time since first use, total number of cycles, temperature monitoring, coolant flow for air or liquid cooled batteries, etc. The notification module 108 sends, at step 406, the performance data notification to the alert module 136. For example, the notification module 108 sends the performance data notification to the alert module 136 to inform the user 132 of the current performance of the battery 120. In some embodiments, the user 132 may manually request the performance data from the battery’ platform 102. In some embodiments, the user 132 may input a schedule to receive the performance data notifications through the settings in the platform application 134 such as receiving the notifications hourly, daily, weekly, monthly, quarterly, yearly, etc. and/or the time of day the desire to receive the performance data notifications. In some embodiments, the user 132 may select which performance data notifications to receive through the platform application 134, such as the length of time the battery 120 has been use, the normal life expectancy of the battery 120, voltage, including total voltage, voltages of individual cells, or voltage of periodic taps, temperature, including average temperature, coolant intake temperature, coolant output temperature, or temperatures of individual cells, coolant flow including for liquid cooled batteries, current including current in or out of the battery', health of individual cells, state of balance of cells, calculations by the BMS 1 18 such as voltage, including minimum and maximum cell voltage, state of charge (SoC) or depth of discharge (DoD), to indicate the charge level of the battery⁷, state of health (SoH), for example a measurement of the remaining capacity of the battery as percentage of the original capacity, state of power (SoP), for example the amount of power available for a defined time interval given the current power usage, temperature and other conditions, state of safety⁷ (SOS), maximum charge current as a charge current limit (CCL), maximum discharge current as a discharge current limit (DCL), energy delivered since last charge or charge cycle, internal impedance of a cell, such as to determine open circuit voltage, charge delivered or stored, total energy delivered since first use, total operating time since first use, total number of cycles, temperature monitoring, coolant flow for air or liquid cooled batteries, etc. The notification module 108 determines, at step 408, if a recommendation was received from the economic module 106. For example, the notification module 108 may determine if a recommendation was received from the economic module 106 by continuously polling for a predetermined time period, such as 10 seconds, to receive the recommendation and if no recommendation is received within the predetermined time period then the notification module 108 may determine that there is no recommendation from the economic module 108. The notification module 108 receives, at step 410, the extracted recommendation from the economic module 106. For example, the notification module 108 receives the extracted corresponding recommendation determined by economic module 106 such as to schedule maintenance, schedule a replacement, notify the user of a malfunction, etc. The notification module 108 sends, at step 412, the extracted recommendation to the alert module 136. For example, the notification module 108 sends the extracted recommendation to the alert module 136 such as to schedule maintenance, schedule a replacement, notify the user of a malfunction, etc. The notification module 108 returns, at step 414, to the data collection module 104. For example, the notification module 108 returns to the data collection module 104 or in some embodiments the notification module 108 may return to the economic module 106.

FIG. 5 illustrates the BMS database 110. The database is created through the process described in the data collection module 104 in which the battery platform 102 receives the performance data from the BMS 118. The database may contain the date and time the data was received, the user ID, the battery' ID or a BMS ID, the warranty length of the battery' 120, the manufactured date of the battery 120, the warranty status of the battery 120, the date the battery 120 was first used or activated, the time it takes for the battery 120 to be fully charged or the charge capacity of the battery 120, the rate of change in the charge levels of the battery 120, etc. In some embodiments, the data may be collected and stored periodically over the course of a day, week, month, year, etc. In some embodiments, the user ID may be used to determine information about the user, such as the location of the BMS 118 system, the warranty information about the user’s system, maintenance data about the user’s system, etc. In some embodiments, the battery ID may be used to determine information about battery 120, such as the make and model of the battery 120, the length of time the battery 120 has been use, the normal life expectancy of the battery 120, etc. In some embodiments, the performance data stored in the database may also include voltage, including total voltage, voltages of individual cells, or voltage of periodic taps, temperature, including average temperature, coolant intake temperature, coolant output temperature, or temperatures of individual cells, coolant flow including for liquid cooled batteries, current including current in or out of the battery, health of individual cells, state of balance of cells, etc. In some embodiments, the performance data stored may also include calculations by the BMS 118 such as voltage, including minimum and maximum cell voltage, state of charge (SoC) or depth of discharge (DoD), to indicate the charge level of the battery, state of health (SoH), for example a measurement of the remaining capacity of the battery as percentage of the original capacity⁷, state of power (SoP), for example the amount of power available for a defined time interval given the current power usage, temperature and other conditions, state of safety (SOS), maximum charge current as a charge current limit (CCL), maximum discharge current as a discharge current limit (DCL), energy' delivered since last charge or charge cycle, internal impedance of a cell, such as to determine open circuit voltage, charge delivered or stored, total energy delivered since first use, total operating time since first use, total number of cycles, temperature monitoring, coolant flow' for air or liquid cooled batteries. The data stored in the BMS database 110 may be used in machine learning algorithms to adjust the recommendations stored in the economic database 112. For example, the BMS database 110 may store historical data from a plurality of users 134, including the user's 134 locations, the amount of batteries 120 the user's 134 have, the make and model of the batteries 120, the current age of the batteries 120, etc. The BMS database 110 may be filtered on the make and model of the battery 120 and the first parameter, such as user’s 134 locations, the amount of batteries 120 the user’s 134 have, the current age of the batteries 120, etc., then the economic module 106 may perform correlations on the performance data stored in the BMS database 110, such as the time to charge the battery 120, the maximum charge level of the battery 120, etc. An example of highly correlated data may be the time to charge the battery' 120 and the maximum charge level of the battery 120, such as users 134 in the Boston, MA area have a maximum charge level of 90% which takes 70 minutes to charge to during the winter months. There may be a predetermined correlation coefficient, such as 0.75, that the data would need to meet to become a recommendation in the economic database 112. If the predetermined threshold is exceeded, the most re-occurring data point in the data set would be extracted and would be stored in the economic database 112 as a recommendation to show that this is a normal occurrence for user’s 134 in the Boston, MA area during the winter months. An example of data that is not highly correlated may be the amount of batteries 120 the user 134 has and the charge level of the battery⁷, such as there is no trend for the batteries 120 to consistently have the same charge level if the user 134 has multiple batteries 120, with a correlation coefficient of 0.60. In this example the correlation coefficient does not exceed the predetermined threshold, so the next parameter is selected, and correlations are performed again on a newly selected parameter.

FIG. 6 illustrates the economic database 112. The database may be stored on the battery' platform 102 to be used to determine if a user should receive a recommendation based on performance data collected from the BMS 118. The database contains the BMS 118 data, the current warranty' status of the battery 120, and the corresponding recommendation based upon the BMS data. For example, if the BMS data collected and stored in the BMS database 1 10 is that the first time the battery 120 is used is within six months of the battery 120 being manufactured then there is no action, however, if the first time the battery' 120 is used is greater than one year of the battery' 120 being manufactured and still under warranty the recommendation may be to schedule maintenance on the battery’ 120 to ensure it is functioning properly. In some embodiments, the recommendations may be based on overall performance of the battery 120, such as the decrease in charge capacity' since the battery' 120 was installed. In some embodiments, the recommendations may be based on the performance of the battery 120 over a certain time period, such as a decrease in charge capacity over a predetermined time period, such as an hour, day, week, month, year, etc. In some embodiments, the recommendations may be based upon other performance data of the battery' 120 collected by the BMS 118, such as voltage, including total voltage, voltages of individual cells, or voltage of periodic taps, temperature, including average temperature, coolant intake temperature, coolant output temperature, or temperatures of individual cells, coolant flow including for liquid cooled batteries, current including current in or out of the battery, health of individual cells, state of balance of cells, etc. In some embodiments, the recommendations may be based on calculations performed on the performance data such as voltage, including minimum and maximum cell voltage, state of charge (SoC) or depth of discharge (DoD), to indicate the charge level of the battery', state of health (SoH), for example a measurement of the remaining capacity of the battery' as percentage of the original capacity, state of power (SoP), for example the amount of power available for a defined time interval given the current power usage, temperature and other conditions, state of safety (SOS), maximum charge current as a charge current limit (CCL), maximum discharge current as a discharge current limit (DCL), energy delivered since last charge or charge cycle, internal impedance of a cell, such as to determine open circuit voltage, charge delivered or stored, total energy' delivered since first use, total operating time since first use, total number of cycles, temperature monitoring, coolant flow for air or liquid cooled batteries. The database may include warrant}' calculations in which the economic recommendation is sent to the user based upon the historical performance of the battery 120. For example, if the battery takes longer than one hour to fully charge the database may include the required amount of instances over a predetermined amount of time to send the economic recommendation based upon the user’s warranty status. For example, the first instance in which the battery' 120 takes longer than one hour to fully charge the economic recommendation may not be extracted and sent to the user 134 since there is only one occurrence of the battery 120 exceeding one hour to fully charge. If the battery takes longer than one hour to charge over the course of 4 days and each day it takes more time to reach a fully a charged state the economic recommendation may be extracted and sent to the user 134 to use the benefits of the warranty. For example, if for four consecutive days the battery 120 takes 65 minutes, 70 minutes, 90 minutes, and 120 minutes, then the battery 120 is malfunctioning and the economic recommendation to schedule maintenance may be extracted and sent to the user 134. In some embodiments, the warranty' calculations may include the amount of times it takes longer than expected to fully charge the battery 120 over a given time period, the amount of times the battery 120 does not reach a full charged state over a given time period, etc. The data stored in the BMS database 110 may be used in machine learning algorithms to adjust the recommendations stored in the economic database 112. For example, the BMS database 110 may store historical data from a plurality of users 134, including the user’s 134 locations, the amount of batteries 120 the user’s 134 have, the make and model of the batteries 120, the current age of the batteries 120, etc. The BMS database 110 may be filtered on the make and model of the battery 120 and the first parameter, such as user’s 134 locations, the amount of batteries 120 the user’ s 134 have, the current age of the batteries 120, etc., then the economic module 106 may perform correlations on the performance data stored in the BMS database 110, such as the time to charge the battery 120, the maximum charge level of the battery' 120, etc. An example of highly correlated data may⁷ be the time to charge the battery' 120 and the maximum charge level of the battery' 120, such as users 134 in the Boston, MA area have a maximum charge level of 90% which takes 70 minutes to charge to during the winter months. There may be a predetermined correlation coefficient, such as 0.75, that the data would need to meet to become a recommendation in the economic database 112. If the predetermined threshold is exceeded, the most reoccurring data point in the data set would be extracted and would be stored in the economic database 112 as a recommendation to show that this is a normal occurrence for user’s 134 in the Boston, MA area during the winter months. An example of data that is not highly correlated may be the amount of batteries 120 the user 134 has and the charge level of the battery’, such as there is no trend for the batteries 120 to consistently have the same charge level if the user 134 has multiple batteries 120, with a correlation coefficient of 0.60. In this example the correlation coefficient does not exceed the predetermined threshold, so the next parameter is selected, and correlations are performed again on a newly selected parameter.

FIG. 7 illustrates the alert module 136. The process begins with the alert module 136 connecting, at step 700, to the battery platform 102. For example, the alert module 136 may connect to the battery platform 102 through the cloud 116. In some embodiments, the alert module 136 may be a module that is continuously running on the platform application 134 on a user 132 device, such as a smartphone, laptop, computer, iPad, tablet, smart watch, etc. The alert module 136 continuously polls, at step 702, for a performance data notification from the notification module 108. For example, the alert module 136 may be continuously polling to receive a performance data notification from the notification module 108, such as the time it takes for the battery’ 120 to be fully charged or the charge capacity of the battery’ 120, the rate of change in the charge levels of the battery’ 120, etc. to inform the user 132 of the performance of the battery' 120. In some embodiments, the performance data may' include the make and model of the battery 120, the length of time the battery' 120 has been use, the normal life expectancy of the battery’ 120, voltage, including total voltage, voltages of individual cells, or voltage of periodic taps, temperature, including average temperature, coolant intake temperature, coolant output temperature, or temperatures of individual cells, coolant flow including for liquid cooled batteries, current including current in or out of the battery', health of individual cells, state of balance of cells, calculations by the BMS 118 such as voltage, including minimum and maximum cell voltage, state of charge (SoC) or depth of discharge (DoD), to indicate the charge level of the battery, state of health (SoH), for example a measurement of the remaining capacity of the battery' as percentage of the original capacity⁷, state of power (SoP), for example the amount of power available for a defined time interval given the current power usage, temperature and other conditions, state of safety (SOS), maximum charge current as a charge current limit (CCL), maximum discharge current as a discharge current limit (DCL), energy⁷ delivered since last charge or charge cycle, internal impedance of a cell, such as to determine open circuit voltage, charge delivered or stored, total energy⁷ delivered since first use, total operating time since first use, total number of cycles, temperature monitoring, coolant flow for air or liquid cooled batteries, etc. The alert module 136 receives, at step 704, the performance data notification from the notification module 108. For example, the alert module 136 may receive a performance data notification from the notification module 108, such as the time it takes for the battery 120 to be fully charged or the charge capacity of the battery 120, the rate of change in the charge levels of the battery 120, etc. to inform the user 132 of the performance of the battery 120. In some embodiments, the performance data may include the make and model of the battery 120, the length of time the battery 120 has been use, the normal life expectancy of the battery 120, voltage, including total voltage, voltages of individual cells, or voltage of periodic taps, temperature, including average temperature, coolant intake temperature, coolant output temperature, or temperatures of individual cells, coolant flow including for liquid cooled batteries, current including current in or out of the battery, health of individual cells, state of balance of cells, calculations by the BMS 118 such as voltage, including minimum and maximum cell voltage, state of charge (SoC) or depth of discharge (DoD), to indicate the charge level of the battery, state of health (SoH), for example a measurement of the remaining capacity of the battery as percentage of the original capacity, state of power (SoP), for example the amount of power available for a defined time interval given the current power usage, temperature and other conditions, state of safety (SOS), maximum charge current as a charge current limit (CCL), maximum discharge current as a discharge current limit (DCL), energy delivered since last charge or charge cycle, internal impedance of a cell, such as to determine open circuit voltage, charge delivered or stored, total energy delivered since first use, total operating time since first use, total number of cycles, temperature monitoring, coolant flow for air or liquid cooled batteries, etc. The alert module 136 displays, at step 706, the received performance data notification from the notification module 108 on the user interface 138. For example, the alert module 136 may display the performance data notification as a notification on the user interface 138 in the platform application 134. In some embodiments, the user 132 may manually request the performance data from the battery' platform 102. In some embodiments, the user 132 may input a schedule to receive the performance data notifications through the settings in the platform application 134 such as receiving the notifications hourly, daily, weekly, monthly, quarterly, yearly, etc. and/or the time of day the desire to receive the performance data notifications. In some embodiments, the user 132 may select which performance data notifications to receive through the platform application 134. The alert module 136 determines, at step 708, if a recommendation was received from the notification module 108. For example, the alert module 136 may determine if a recommendation was received from the economic module 106 by continuously polling for a predetermined time period, such as 10 seconds, to receive the recommendation and if no recommendation is received within the predetermined time period then the alert module 136 may determine that there is no recommendation from the notification module 108. The alert module 136 receives, at step 710, the recommendation from the notification module 108. For example, the alert module 136 may receive a recommendation from the notification module 108, such as a recommendation related to the user’s 132 battery 120, for example to schedule maintenance, schedule a replacement, notify the user of a malfunction, etc. In some embodiments, the recommendation received by the alert module 136 may prompt the alert module 136 to display an icon on the user 132 device to notify the user of the recommendation, such as an icon depicting required maintenance on the user's 132 battery 120, for example by a wrench over a battery. The alert module 136 displays, at step 712, the received recommendation from the notification module 108 on the user interface 138 and the process returns to continuously polling to receive a performance data notification. For example, the alert module 136 may display the recommendation as a notification on the user interface 138 in the platform application 134. In some embodiments, the recommendation received by the alert module 136 may prompt the alert module 136 to display an icon on the user 132 device to notify⁷ the user of the recommendation, such as an icon depicting required maintenance on the user’s 132 battery 120, for example by a wrench over a battery.

In another embodiment, a system and method for determining economic decisions based on a battery management system may include the use of multiple batteries 120 from multiple users in multiple geographic regions, this can add to a database of information gained about battery use and performance in similar climate regions and with under similar use circumstances. Each of these batteries 120 may include a plurality of individual cells and also be cooled by flowing coolant. Additionally, each battery⁷ 120 may include user or manufactures warranty. Each of the multiple batteries 120 may include a corresponding battery management system 1 18 and each battery management system 118 may be in realtime communication with its corresponding battery 120. Additionally, each battery⁷ management system 118 may be designed and configured to collect data from the corresponding battery 120 in real-time.

This system and method for determining economic decisions may also include a cloud server 116, that may be in periodic or constant communication with each battery management system 118. Each battery management system 118 can communicate with the cloud server 116, and may transfer data from each of the corresponding batteries 120. The cloud server 116 may include a data collection module 104, an economic module 106, a notification module 136 and an economic database 112. The economic database 112 may include predetermined economic recommendations that may be based on performance of one or a plurality of batteries 120 and battery data from one or a plurality of batteries 120.

Each batten- management system 118 may measure performance of a corresponding battery- 120, and these battery performance characteristics may including each of, or at least one of voltage, temperature of the battery, coolant intake temperature, coolant output temperature, temperatures of individual cells, current in and out of the battery, health of individual cells, state of balance of cells, minimum and maximum cell voltage, state of charge, depth of discharge, state of health, state of power, temperature and other conditions, state of safety, maximum charge current as a charge current limit, maximum discharge current as a discharge current limit, energy delivered since a last charge or charge cycle, internal impedance of a cell, charge delivered or stored, energy delivered since first use, total operating time since first use and total number of charge cycles.

Each battery management system 118 may also collect battery data from one or a plurality- of batteries 120, and such battery data may' include all, or at least one of the following: a user ID, a battery- ID, a battery- management system ID, a warranty- length of the battery, a manufactured date of the battery, a warranty status of the battery, a date the battery- was first used or activated, a time it takes for the battery to be fully charged, a charge capacity of the battery-, a rate of change in the charge levels of the battery, geographic location of the battery- was used, the geographic location of where the battery is stored.

The battery management systems 118 may' send or transmit the battery performance measurements and/ or battery- data to the data collection module 104 in the cloud sen- er 116 and the economic module 106 can be used to compare the battery performance measurements and battery- data of a single battery or multiple batteries 120 stored in the data collection module 104, with predetermined economic recommendations based on performance of a battery- and battery data in the economic database 112. Once this comparison by the economic module 106 is completed, the economic module 106 may identify an economic recommendation.

The battery- management system 118 may also collect historical battery- data, which may include the battery performance measurements and battery data in the data collection module 104 from the multiple batteries 120 from the multiple users, and these battery performance measurements and battery data can be stored as historical data in the data collection module 104. The economic module 106 may then perform a correlation analysis using machine learning algorithms, which may compare the historical data from the multiple batteries 120 from the multiple users, with the battery performance data and batten’ data of the specific batten- being analyzed and measured.

The economic module 106 may then adjust the economic recommendation based on the correlation analysis and generate an adjusted economic recommendation. The adjusted economic recommendations may include any or all of: replacement of the battery, service of the battery, or maintenance of the battery. Once the adjusted economic recommendation has been determined, then the notification module 136 may send a notification to a user of the battery being analyzed or measured, and the notification may include the adjusted economic recommendation.

Functions performed in the processes and methods may be implemented in differing order. Furthermore, the outlined steps and operations are only provided as examples, and some of the steps and operations may be optional, combined into fewer steps and operations, or expanded into additional steps and operations without detracting from the essence of the disclosed embodiments.

Additional Specification Support

Embodiment 1. A method for determining economic decisions based on a batterymanagement system, including the steps of: providing multiple batteries, each battery having a warranty; provrdrng a battery management system for each of the multiple battenes, respectively, wherein each battery management system is disposed in communication with a corresponding battery-, each battery- management system being configured and arranged to collect data from the corresponding battery- in real-time; providing a cloud server, wherein each batery management system can communicate with the cloud server, including: transferring at least some of the data collected from the corresponding batery; wherein the cloud server includes a data collection module, an economic module, a notification module, and an economic database; wherein the economic database includes predetermined economic recommendations based on performance of a batery and batery data; measuring performance of the multiple bateries, resulting in batery performance measurements, using the battery management system; collecting batery data using the baten' management system; transmiting at least some of the battery’ performance measurements and at least some of the batery data from the batery management system to the data collection module; comparing at least some of the batery performance measurements and batery' data in the data collection module to the predetermined economic recommendations based on at least some of the batery performance measurements and batery’ data in the economic database, and identify ing an economic recommendation; collecting historical batery data, including at least some of the batery' performance measurements and batery data in the data collection module from the multiple bateries, and storing at least some of the historical data in the data collection module; performing a correlation analysis using machine learning algorithms in the economic module, comparing at least some of the historical data from the multiple bateries; adjusting the economic recommendation based on the correlation analysis and generating an adjusted economic recommendation using the economic module; and sending a notification to a user of the batery providing the adjusted economic recommendation, using the notification module.

Embodiment 2. The method of embodiment 1, wherein the multiple bateries are owned by multiple users.

Embodiment 3. The method of embodiment 1, wherein multiple bateries are located in in multiple geographic regions.

Embodiment 4. The method of embodiment 1, wherein each of the multiple bateries includes a plurality of cells and coolant. Embodiment 5. The method of embodiment 1, wherein the measuring performance of a battery using the battery management system, includes measuring at least one of the following battery performance characteristics: voltage, temperature of the battery, coolant intake temperature, coolant output temperature, temperatures of individual cells, current in and out of the battery, health of individual cells, state of balance of cells, minimum and maximum cell voltage, state of charge, depth of discharge, state of health, state of power, temperature and other conditions, state of safety, maximum charge current as a charge current limit, maximum discharge current as a discharge current limit, energy delivered since a last charge or charge cycle, internal impedance of a cell, charge delivered or stored, energy delivered since first use, total operating time since first use and total number of charge cycles.

Embodiment 6. The method of embodiment 1 , wherein the battery data includes at least one of the following: a user ID, a battery ID, a battery management system ID, a warranty length of the battery, a manufactured date of the battery, a warranty status of the battery, a date the batten- was first used or activated, a time it takes for the batten- to be fully charged, a charge capacity of the battery-, a rate of change in the charge levels of the battery, geographic location of the battery was used, the geographic location of where the battery is stored.

Embodiment 7. The method of embodiment 1, wherein the adjusted economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

Embodiment 8. The method of embodiment 1, wherein the predetermined economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

Embodiment 9. A method for determining economic decisions based on a batterymanagement system, including: providing a battery, having a warranty; providing a battery management system for the battery, the battery' management system is disposed in communication with the battery, and the battery management system is configured and arranged to collect data from the battery in real-time; providing a cloud server, wherein the battery' management system can communicate with the cloud server, including: transferring at least some of the data from the battery'; wherein the cloud server includes a data collection module, an economic module, a notification module, and an economic database; wherein the economic database includes predetermined economic recommendations based on performance of the battery' and battery' data; measuring performance of the battery', resulting in battery performance measurements, using the battery management system; collecting battery data using the battery' management system; transmitting at least some of the battery' performance measurements and at least some of the battery' data from the battery' management system to the data collection module; comparing at least some of the battery performance measurements and battery’ data in the data collection module to the predetermined economic recommendations based on at least some of the performance of the battery' and the battery' data in the economic database, and identifying an economic recommendation; and sending a notification to a user of the battery, providing the economic recommendation, using the notification module.

Embodiment 10. The method of embodiment 9, further including: providing multiple batteries from multiple users.

Embodiment 11. The method of embodiment 10, further including: collecting historical battery' data, including the battery performance measurements and battery data in the data collection module from the multiple batteries, and storing the historical data in the data collection module.

Embodiment 12. The method of embodiment 11, further including: performing a correlation analysis using machine learning algorithms in the economic module, comparing the historical data from the multiple batteries. Embodiment 13. The method of embodiment 12, further including: adjusting the economic recommendation based on the correlation analysis and generating an adjusted economic recommendation using the economic module.

Embodiment 14. The method of embodiment 10, wherein the multiple batteries are owned by multiple users.

Embodiment 15. The method of embodiment 14, wherein multiple batteries are located in in multiple geographic regions.

Embodiment 16. The method of embodiment 15, wherein each of the multiple batteries includes a plurality of cells and coolant.

Embodiment 17. The method of embodiment 9, wherein the measuring performance of a battery using the battery management system, includes measuring at least one of the following battery performance characteristics: voltage, temperature of the battery, coolant intake temperature, coolant output temperature, temperatures of individual cells, current in and out of the battery, health of individual cells, state of balance of cells, minimum and maximum cell voltage, state of charge, depth of discharge, state of health, state of power, temperature and other conditions, state of safety, maximum charge current as a charge current limit, maximum discharge current as a discharge current limit, energy delivered since a last charge or charge cycle, internal impedance of a cell, charge delivered or stored, energy delivered since first use, total operating time since first use and total number of charge cycles.

Embodiment 18. The method of embodiment 9, wherein the battery data includes at least one of the following: a user ID, a battery ID, a battery management sy stem ID, a warranty length of the battery, a manufactured date of the battery, a warranty status of the battery, a date the battery was first used or activated, a time it takes for the battery to be fully charged, a charge capacity of the battery, a rate of change in the charge levels of the battery, geographic location of the battery was used, the geographic location of where the battery is stored. Embodiment 19. The method of embodiment 13, wherein the adjusted economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

Embodiment 20. The method of embodiment 9, wherein the predetermined economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

Embodiment 21. A battery management system for determining economic decisions, including: multiple batteries, each battery having a warranty; a battery management system for each of the multiple batteries, each batten management system is configured and arranged to be disposed in communication with a corresponding battery, each battery management system is also configured and arranged to collect data from the corresponding batten’ in real-time; a cloud server, wherein each battery management system can communicate with the cloud sen' er, including: transferring at least some of the data from the corresponding battery; wherein the cloud server includes a data collection module, an economic module, a notification module, and an economic database; wherein the economic database includes predetermined economic recommendations based on at least some of the performance of a battery and battery' data; wherein the battery management system is configured and arranged to measure performance of the multiple batteries, resulting in battery' performance measurements, and collect battery' data from the multiple batteries; wherein the battery management system is configured and arranged to transmit at least some of the battery’ performance measurements and the battery data to the data collection module; wherein the economic module is configured to compare at least some of the battery' performance measurements and battery data in the data collection module to the predetermined economic recommendations based on at least some of the performance of the battery and the battery data in the economic database, and wherein the economic module is also configured and arranged to identify an economic recommendation; wherein the data collection module is configured and arranged to store historical battery data, including at least some of the battery performance measurements and battery data in the data collection module from the multiple batteries; wherein the economic module is configured and arranged to perform a correlation analysis using machine learning algorithms and compare at least some of the historical data from the multiple batteries, and then adjust the economic recommendation based on the correlation analysis, generating an adjusted economic recommendation, and wherein the notification module is configured and arranged to send a notification to a user of the battery, providing the adjusted economic recommendation.

Embodiment 22. The system of embodiment 21 , wherein the multiple batteries are owned by multiple users.

Embodiment 23. The system of embodiment 21, wherein multiple batteries are located in in multiple geographic regions.

Embodiment 24. The system of embodiment 21, wherein each of the multiple batteries includes a plurality of cells and coolant.

Embodiment 25. The system of embodiment 21, wherein the performance of the battery being measured by the battery management system includes at least one of the following battery performance characteristics: voltage, temperature of the battery, coolant intake temperature, coolant output temperature, temperatures of individual cells, current in and out of the battery, health of individual cells, state of balance of cells, minimum and maximum cell voltage, state of charge, depth of discharge, state of health, state of power, temperature and other conditions, state of safety, maximum charge current as a charge current limit, maximum discharge current as a discharge current limit, energy’ delivered since a last charge or charge cycle, internal impedance of a cell, charge delivered or stored, energy delivered since first use, total operating time since first use and total number of charge cycles.

Embodiment 26. The system of embodiment 21, wherein the battery data includes at least one of the follow ing: a user ID, a battery ID, a battery management system ID, a warranty length of the battery, a manufactured date of the battery, a warranty status of the battery, a date the battery was first used or activated, a time it takes for the battery to be fully charged, a charge capacity of the battery', a rate of change in the charge levels of the battery, geographic location of the battery' was used, the geographic location of where the battery is stored.

Embodiment 27. The system of embodiment 21, wherein the adjusted economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

Embodiment 28. The system of embodiment 21, wherein the predetermined economic recommendation includes at least one of the following recommendations: replacement of the battery', service of the battery, or maintenance of the battery.

Embodiment 29. A battery management system for determining economic decisions, including: a battery' having a yvarranty; a battery’ management system for the battery, the battery management system is configured and arranged to be disposed in communication with the battery, and the battery management system is configured and arranged to collect data from the battery in real-time; a cloud server, wherein the cloud server includes a data collection module, an economic module, a notification module, and an economic database, and wherein the battery management system is configured and arranged to communicate with the cloud serv er, including: transferring at least some of the data from the battery; wherein the economic database includes predetermined economic recommendations based performance of the battery' and battery' data; wherein the battery management system is configured and arranged to measure performance of the battery, resulting in battery performance measurements, and collect battery data; wherein the battery management system is also configured and arranged to transmit at least some of the batten- performance measurements and the battery⁷ data to the data collection module; wherein the economic module is configured and arranged to compare at least some of the battery performance measurements and battery data in the data collection module to the predetermined economic recommendations based on at least some of the performance of the battery and the battery data in the economic database, and then identify an economic recommendation; and wherein the notification module is configured and arranged to send a notification to a user of the battery and provide the economic recommendation.

Embodiment 30. The system of embodiment 29, further including: multiple batteries from multiple users.

Embodiment 31. The system of embodiment 30, wherein the data collection module is configured to collect historical battery data, including the battery⁷ performance measurements and battery data from the multiple batteries, and then store the historical data.

Embodiment 32. The system of embodiment 31 , wherein the economic module is configured to perform a correlation analysis using machine learning algorithms, and then compare the historical data from the multiple batteries.

Embodiment 33. The system of embodiment 32, wherein the economic module is configured to adjust the economic recommendation based on the correlation analysis and then generate an adjusted economic recommendation.

Embodiment 34. The system of embodiment 30, wherein the multiple batteries are owned by multiple users.

Embodiment 35. The system of embodiment 34, wherein multiple batteries are located in in multiple geographic regions. Embodiment 36. The system of embodiment 35, wherein each of the multiple batteries includes a plurality of cells and coolant.

Embodiment 37. The system of embodiment 9, wherein the performance of a battery being measured includes at least one of the following battery performance characteristics: voltage, temperature of the battery, coolant intake temperature, coolant output temperature, temperatures of individual cells, current in and out of the battery, health of individual cells, state of balance of cells, minimum and maximum cell voltage, state of charge, depth of discharge, state of health, state of power, temperature and other conditions, state of safety, maximum charge current as a charge current limit, maximum discharge current as a discharge current limit, energy delivered since a last charge or charge cycle, internal impedance of a cell, charge delivered or stored, energy delivered since first use, total operating time since first use and total number of charge cycles.

Embodiment 38. The system of embodiment 29, wherein the battery data includes at least one of the following: a user ID, a battery ID, a battery management system ID, a warrant) length of the battery, a manufactured date of the battery⁷, a warranty' status of the battery', a date the battery was first used or activated, a time it takes for the battery to be fully charged, a charge capacity of the battery’, a rate of change in the charge levels of the battery, geographic location of the battery was used, the geographic location of where the battery is stored.

Embodiment 39. The system of embodiment 33, wherein the adjusted economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

Embodiment 40. The system of embodiment 29, wherein the predetermined economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery. Embodiment 41. A method for determining economic decisions based on a battery management system, including: providing multiple batteries from multiple users in multiple geographic regions, wherein each battery includes individual cells, coolant, and a warranty; providing a battery management system for each of the multiple batteries, each battery management system is configured and arranged to be disposed in communication with a corresponding battery, each battery management system is configured and arranged to collect data from the corresponding battery in real-time; providing a cloud server, wherein each battery management system can communicate with the cloud server, including transferring the data from the corresponding battery; wherein the cloud server includes a data collection module, an economic module, a notification module and an economic database; wherein the economic database includes predetermined economic recommendations based on performance of a battery and battery data; measuring performance of a battery using the battery management system, including: voltage, temperature of the battery, coolant intake temperature, coolant output temperature, temperatures of individual cells, current in and out of the battery', health of individual cells, state of balance of cells, minimum and maximum cell voltage, state of charge, depth of discharge, state of health, state of power, temperature and other conditions, state of safety, maximum charge current as a charge current limit, maximum discharge current as a discharge current limit, energy delivered since a last charge or charge cycle, internal impedance of a cell, charge delivered or stored, energy' delivered since first use, total operating time since first use and total number of charge cycles; collecting battery data using the battery management, including: a user ID, a battery ID, a battery management system ID, a warranty' length of the battery', a manufactured date of the battery', a warranty' status of the battery', a date the battery was first used or activated, a time it takes for the battery to be fully charged, a charge capacity of the battery, a rate of change in the charge levels of the battery, geographic location of the battery was used, the geographic location of where the battery' is stored; sending the battery performance measurements and battery' data to the data collection module, comparing the battery performance measurements and battery data in the data collection module to the predetermined economic recommendations based on performance of a battery and battery data in the economic database, and identifying an economic recommendation; collecting historical battery' data, including the battery' performance measurements and battery data to the data collection module from the multiple batteries from the multiple users, and storing this historical data in the data collection module; performing a correlation analysis using machine learning algorithms, comparing the historical data from the multiple batteries from the multiple users, adjusting the economic recommendation based on the correlation analysis and generating an adjusted economic recommendation using the economic module, including: replacement of the battery, service of the battery, or maintenance of the battery; and sending a notification to a user of the battery providing the adjusted economic recommendation, using the notification module.

In the foregoing Detailed Description, various features of the present disclosure are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed disclosure requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the following claims are hereby incorporated into this Detailed Description of the Disclosure by this reference, with each claim standing on its ow n as a separate embodiment of the present disclosure.

It is to be understood that the above-described arrangements are only illustrative of the application of the principles of the present disclosure. Numerous modifications and alternative arrangements may be devised by those skilled in the art without departing from the spirit and scope of the present disclosure, and the appended claims are intended to cover such modifications and arrangements. Thus, while the present disclosure has been shown in the drawings and described above with particularity and detail, it will be apparent to those of ordinary skill in the art that numerous modifications, including, but not limited to, variations in size, materials, shape, form, function and manner of operation, assembly and use may be made without departing from the principles and concepts set forth herein. The appended claims are intended to cover, and do cover, any such modifications.

Claims

CLAIMS What is claimed is:

1. A method for determining economic decisions based on a battery management system, comprising the steps of: providing multiple batteries, each battery having a warranty; providing a battery management system for each of the multiple batteries, respectively, wherein each battery management system is disposed in communication with a corresponding battery, each battery management system being configured and arranged to collect data from the corresponding battery in real-time; providing a cloud server, wherein each batten- management system can communicate with the cloud server, including: transferring at least some of the data collected from the corresponding batten'; wherein the cloud server includes a data collection module, an economic module, a notification module, and an economic database; wherein the economic database includes predetermined economic recommendations based on performance of a battery and battery data; measuring performance of the multiple batteries, resulting in battery performance measurements, using the batten- management system; collecting battery data using the battery management system; transmitting at least some of the battery performance measurements and at least some of the battery data from the battery management system to the data collection module; comparing at least some of the battery' performance measurements and battery' data in the data collection module to the predetermined economic recommendations based on at least some of the battery performance measurements and battery data in the economic database, and identifying an economic recommendation; collecting historical battery data, including at least some of the battery performance measurements and battery data in the data collection module from the multiple bateries, and storing at least some of the historical data in the data collection module; performing a correlation analysis using machine learning algorithms in the economic module, comparing at least some of the historical data from the multiple batteries; adjusting the economic recommendation based on the correlation analysis and generating an adjusted economic recommendation using the economic module; and sending a notification to a user of the batery providing the adjusted economic recommendation, using the notification module.

2. The method of claim 1, wherein the multiple batteries are owned by multiple users.

3. The method of claim 1, wherein multiple bateries are located in in multiple geographic regions.

4. The method of claim 1, wherein each of the multiple batteries includes a plurality of cells and coolant.

5. The method of claim 1, wherein the measuring performance of a batery using the battery management system, includes measuring at least one of the following batten,' performance characteristics: voltage, temperature of the batery, coolant intake temperature, coolant output temperature, temperatures of individual cells, current in and out of the batery, health of individual cells, state of balance of cells, minimum and maximum cell voltage, state of charge, depth of discharge, state of health, state of power, temperature and other conditions, state of safety, maximum charge current as a charge current limit, maximum discharge current as a discharge current limit, energy delivered since a last charge or charge cycle, internal impedance of a cell, charge delivered or stored, energy delivered since first use, total operating time since first use and total number of charge cycles.

6. The method of claim 1, wherein the battery data includes at least one of the following: a user ID, a battery ID, a batten⁷ management system ID, a w arranty length of the batten , a manufactured date of the battery, a warranty status of the battery, a date the battery' w as first used or activated, a time it takes for the battery' to be fully charged, a charge capacity of the battery, a rate of change in the charge levels of the battery, geographic location of the battery was used, the geographic location of where the battery is stored.

7. The method of claim 1, wherein the adjusted economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

8. The method of claim 1, wherein the predetermined economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

9. A method for determining economic decisions based on a battery⁷ management system, comprising: providing a battery, having a warranty; providing a battery management system for the battery, the battery management system is disposed in communication with the battery, and the battery' management system is configured and arranged to collect data from the battery in real-time; providing a cloud server, wherein the battery management system can communicate with the cloud server, including: transferring at least some of the data from the battery⁷; wherein the cloud server includes a data collection module, an economic module, a notification module, and an economic database; wherein the economic database includes predetermined economic recommendations based on performance of the battery⁷ and battery' data; measuring performance of the battery, resulting in battery' performance measurements, using the battery management system; collecting battery data using the battery management system; transmitting at least some of the battery performance measurements and at least some of the battery data from the battery management system to the data collection module; comparing at least some of the battery performance measurements and battery' data in the data collection module to the predetermined economic recommendations based on at least some of the performance of the battery and the battery data in the economic database, and identifying an economic recommendation; and sending a notification to a user of the battery, providing the economic recommendation, using the notification module.

10. The method of claim 9, further comprising: providing multiple batteries from multiple users.

11. The method of claim 10, further comprising: collecting historical battery data, including the battery performance measurements and battery data in the data collection module from the multiple batteries, and storing the historical data in the data collection module.

12. The method of claim 11, further comprising: performing a correlation analysis using machine learning algorithms in the economic module, comparing the historical data from the multiple batteries.

13. The method of claim 12, further comprising: adjusting the economic recommendation based on the correlation analysis and generating an adjusted economic recommendation using the economic module.

14. The method of claim 10, wherein the multiple batteries are owned by multiple users.

15. The method of claim 14, wherein multiple batteries are located in in multiple geographic regions.

16. The method of claim 15, wherein each of the multiple batteries includes a plurality of cells and coolant.

17. The method of claim 9, wherein the measuring performance of a batten- using the battery management system, includes measuring at least one of the following battery performance characteristics: voltage, temperature of the battery, coolant intake temperature, coolant output temperature, temperatures of individual cells, current in and out of the battery-, health of individual cells, state of balance of cells, minimum and maximum cell voltage, state of charge, depth of discharge, state of health, state of power, temperature and other conditions, state of safety, maximum charge current as a charge current limit, maximum discharge current as a discharge current limit, energy delivered since a last charge or charge cycle, internal impedance of a cell, charge delivered or stored, energy- delivered since first use, total operating time since first use and total number of charge cycles.

18. The method of claim 9, wherein the battery data includes at least one of the following: a user ID, a battery ID, a battery management system ID, a warranty length of the battery, a manufactured date of the battery, a warranty status of the battery, a date the battery was first used or activated, a time it takes for the battery to be fully charged, a charge capacity- of the battery-, a rate of change in the charge levels of the battery, geographic location of the battery was used, the geographic location of where the battery is stored.

19. The method of claim 13, wherein the adjusted economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

20. The method of claim 9, wherein the predetermined economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

21. A batten- management system for determining economic decisions, comprising: multiple batteries, each battery having a warrant}'; a battery' management system for each of the multiple batteries, each batter}’ management system is configured and arranged to be disposed in communication with a corresponding battery, each battery management system is also configured and arranged to collect data from the corresponding battery in real-time; a cloud server, wherein each battery management system can communicate with the cloud sen' er, including: transferring at least some of the data from the corresponding battery; wherein the cloud server includes a data collection module, an economic module, a notification module, and an economic database; wherein the economic database includes predetermined economic recommendations based on at least some of the performance of a batter}’ and batter ’ data; wherein the battery management system is configured and arranged to measure performance of the multiple batteries, resulting in battery performance measurements, and collect batter}' data from the multiple batteries; wherein the battery management system is configured and arranged to transmit at least some of the battery performance measurements and the battery data to the data collection module; wherein the economic module is configured to compare at least some of the batter}' performance measurements and battery data in the data collection module to the predetermined economic recommendations based on at least some of the performance of the battery and the battery data in the economic database, and wherein the economic module is also configured and arranged to identify an economic recommendation; wherein the data collection module is configured and arranged to store historical battery data, including at least some of the battery performance measurements and batter}' data in the data collection module from the multiple batteries; wherein the economic module is configured and arranged to perform a correlation analysis using machine learning algorithms and compare at least some of the historical data from the multiple batteries, and then adjust the economic recommendation based on the correlation analysis, generating an adjusted economic recommendation, and wherein the notification module is configured and arranged to send a notification to a user of the battery, providing the adjusted economic recommendation.

22. The system of claim 21 , wherein the multiple batteries are owned by multiple users.

23. The system of claim 21, wherein multiple batteries are located in in multiple geographic regions.

24. The system of claim 21, wherein each of the multiple batteries includes a plurality of cells and coolant.

25. The system of claim 21, wherein the performance of the battery being measured by the battery management system includes at least one of the following battery performance characteristics: voltage, temperature of the battery, coolant intake temperature, coolant output temperature, temperatures of individual cells, current in and out of the batten,', health of individual cells, state of balance of cells, minimum and maximum cell voltage, state of charge, depth of discharge, state of health, state of power, temperature and other conditions, state of safety, maximum charge current as a charge current limit, maximum discharge current as a discharge current limit, energy delivered since a last charge or charge cycle, internal impedance of a cell, charge delivered or stored, energy' delivered since first use, total operating time since first use and total number of charge cycles.

26. The system of claim 21, wherein the battery data includes at least one of the following: a user ID, a battery ID, a battery management system ID, a warranty length of the battery, a manufactured date of the battery, a warranty status of the battery, a date the battery was first used or activated, a time it takes for the battery to be fully charged, a charge capacity’ of the battery, a rate of change in the charge levels of the battery, geographic location of the battery was used, the geographic location of where the battery is stored.

27. The system of claim 21, wherein the adjusted economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

28. The system of claim 21, wherein the predetermined economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

29. A battery’ management system for determining economic decisions, comprising: a battery having a warranty; a battery management system for the battery, the battery management system is configured and arranged to be disposed in communication with the battery, and the battery' management system is configured and arranged to collect data from the battery' in real-time; a cloud server, wherein the cloud server includes a data collection module, an economic module, a notification module, and an economic database, and wherein the battery management system is configured and arranged to communicate with the cloud ser er, including: transferring at least some of the data from the battery; wherein the economic database includes predetermined economic recommendations based performance of the battery’ and battery' data; wherein the battery’ management system is configured and arranged to measure performance of the battery, resulting in battery' performance measurements, and collect battery’ data; wherein the battery management system is also configured and arranged to transmit at least some of the battery' performance measurements and the battery' data to the data collection module; wherein the economic module is configured and arranged to compare at least some of the battery performance measurements and battery data in the data collection module to the predetermined economic recommendations based on at least some of the performance of the battery and the battery data in the economic database, and then identify an economic recommendation; and wherein the notification module is configured and arranged to send a notification to a user of the battery and provide the economic recommendation.

30. The system of claim 29, further comprising: multiple batteries from multiple users.

31. The system of claim 30, wherein the data collection module is configured to collect historical battery data, including the battery performance measurements and battery data from the multiple batteries, and then store the historical data.

32. The system of claim 31, wherein the economic module is configured to perform a correlation analysis using machine learning algorithms, and then compare the historical data from the multiple batteries.

33. The system of claim 32, wherein the economic module is configured to adjust the economic recommendation based on the correlation analysis and then generate an adjusted economic recommendation.

34. The system of claim 30, wherein the multiple batteries are owned by multiple users.

35. The system of claim 34, wherein multiple batteries are located in in multiple geographic regions.

36. The system of claim 35, wherein each of the multiple batteries includes a plurality of cells and coolant.

37. The system of claim 29, wherein the performance of a battery being measured includes at least one of the following battery performance characteristics: voltage, temperature of the battery', coolant intake temperature, coolant output temperature, temperatures of individual cells, current in and out of the battery, health of individual cells, state of balance of cells, minimum and maximum cell voltage, state of charge, depth of discharge, state of health, state of power, temperature and other conditions, state of safety⁷, maximum charge current as a charge current limit, maximum discharge current as a discharge current limit, energy delivered since a last charge or charge cycle, internal impedance of a cell, charge delivered or stored, energy delivered since first use, total operating time since first use and total number of charge cycles.

38. The system of claim 29, wherein the battery data includes at least one of the following: a user ID, a battery ID, a battery management system ID, a warranty⁷ length of the battery, a manufactured date of the battery⁷, a warranty⁷ status of the battery⁷, a date the battery was first used or activated, a time it takes for the battery to be fully⁷ charged, a charge capacity⁷ of the battery, a rate of change in the charge levels of the battery, geographic location of the battery was used, the geographic location of where the battery⁷ is stored.

39. The system of claim 33, wherein the adjusted economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

40. The system of claim 29, wherein the predetermined economic recommendation includes at least one of the following recommendations: replacement of the battery, service of the battery, or maintenance of the battery.

41. A method for determining economic decisions based on a battery management system, comprising: providing multiple batteries from multiple users in multiple geographic regions, wherein each battery⁷ includes individual cells, coolant, and a warranty; providing a battery⁷ management system for each of the multiple batteries, each battery management system is configured and arranged to be disposed in communication with a corresponding battery, each battery management system is configured and arranged to collect data from the corresponding batten- in realtime; providing a cloud server, wherein each battery management system can communicate with the cloud server, including transferring the data from the corresponding battery'; wherein the cloud server includes a data collection module, an economic module, a notification module and an economic database; wherein the economic database includes predetermined economic recommendations based on performance of a battery and battery' data; measuring performance of a battery using the battery management system, including: voltage, temperature of the battery, coolant intake temperature, coolant output temperature, temperatures of individual cells, current in and out of the battery', health of individual cells, state of balance of cells, minimum and maximum cell voltage, state of charge, depth of discharge, state of health, state of power, temperature and other conditions, state of safety, maximum charge current as a charge current limit, maximum discharge current as a discharge current limit, energy' delivered since a last charge or charge cycle, internal impedance of a cell, charge delivered or stored, energy delivered since first use, total operating time since first use and total number of charge cycles; collecting battery data using the battery' management, including: a user ID, a battery' ID, a battery' management system ID, a warranty length of the battery, a manufactured date of the battery, a warranty status of the battery, a date the battery was first used or activated, a time it takes for the battery to be fully charged, a charge capacity of the battery, a rate of change in the charge levels of the battery', geographic location of the battery' was used, the geographic location of where the battery is stored; sending the battery performance measurements and battery data to the data collection module, comparing the battery' performance measurements and battery data in the data collection module to the predetermined economic recommendations based on performance of a battery and battery data in the economic database, and identifying an economic recommendation; collecting historical battery data, including the battery performance measurements and battery’ data to the data collection module from the multiple batteries from the multiple users, and storing this historical data in the data collection module; performing a correlation analysis using machine learning algorithms, comparing the historical data from the multiple batteries from the multiple users, adjusting the economic recommendation based on the correlation analysis and generating an adjusted economic recommendation using the economic module, including: replacement of the battery’, service of the battery’, or maintenance of the battery'; and sending a notification to a user of the battery providing the adjusted economic recommendation, using the notification module.