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CN112895979B - Self-adaptive vehicle battery energy management method and device - Google Patents

Self-adaptive vehicle battery energy management method and device Download PDF

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Publication number
CN112895979B
CN112895979B CN202110392977.2A CN202110392977A CN112895979B CN 112895979 B CN112895979 B CN 112895979B CN 202110392977 A CN202110392977 A CN 202110392977A CN 112895979 B CN112895979 B CN 112895979B
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data
vehicle
determining
type
energy management
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CN112895979A (en
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杨仁立
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Beijing CHJ Automobile Technology Co Ltd
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Beijing CHJ Automobile Technology Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/24—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries for controlling the temperature of batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
    • B60L3/12—Recording operating variables ; Monitoring of operating variables
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/24—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries for controlling the temperature of batteries
    • B60L58/27—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries for controlling the temperature of batteries by heating
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles

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  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Secondary Cells (AREA)

Abstract

The present disclosure provides a method and apparatus for adaptive vehicle battery energy management, comprising: acquiring driving data of a vehicle; determining the type of the vehicle based on the driving data, wherein different types of vehicles have different vehicle using habits and control habit data; a vehicle battery energy management strategy corresponding to the type of vehicle is determined based on the type of vehicle, with different vehicle battery energy management strategies having different warm-up thresholds. The vehicle battery heat management strategy management system can classify according to the use data of different vehicles at the cloud end, different management strategies are constructed according to different classifications, the constructed management strategies are output to the vehicles meeting the classifications, after the use state of the vehicles changes, the battery heat management strategies of the vehicles can be updated through the cloud end battery heat management strategies, the battery heat management strategies of the vehicles can be automatically updated, and the adaptability of the power performance or the driving mileage of the vehicle batteries is improved.

Description

Self-adaptive vehicle battery energy management method and device
Technical Field
The disclosure relates to the technical field of electric vehicles, in particular to a self-adaptive vehicle battery energy management method and device.
Background
The battery is a power source of the electric automobile, and the heat management strategy of the battery directly influences the output performance of the battery energy, so that the power performance, the driving mileage and other use conditions of the electric automobile are influenced.
The existing battery thermal management strategies of the electric automobile are often single, when the electric automobile leaves a factory, the thermal management strategies of the battery of the electric automobile, such as a thermal management threshold value and an SOC (system on chip) use range of the battery, are fixedly configured, so that the thermal management strategies of the battery of the electric automobile can be determined only according to the temperature and the use condition of the battery, the management mode is single, the thermal management strategies cannot be intelligently configured according to multiple factors such as use habits of different users and use environments (such as different seasons and different regions) of the vehicle, the power performance or the mileage of the vehicle cannot be different from person to person, and the user experience is poor.
Disclosure of Invention
The invention aims to provide an adaptive vehicle battery energy management method, an adaptive vehicle battery energy management device, a vehicle controller and an electric vehicle, which can solve the technical problem of adaptively performing thermal management on a battery. The specific scheme is as follows:
according to a specific embodiment of the present disclosure, in a first aspect, the present disclosure provides an adaptive vehicle battery energy management method, including:
acquiring driving data of a vehicle, wherein the driving data is vehicle using habit and control habit data formed in the driving process of the vehicle;
determining the type of the vehicle based on the driving data, wherein different types of vehicles have different vehicle using habits and control habit data;
determining a vehicle battery energy management strategy corresponding to the type of the vehicle based on the type of the vehicle, different vehicle battery energy management strategies having different heating thresholds.
Optionally, the driving data includes pedal stroke data and single-trip mileage data.
Optionally, the determining the type of the vehicle based on the driving data includes:
determining all frequency data meeting a pedal travel threshold within a preset time range based on the pedal travel data;
determining the statistical distribution values of all the frequency data and the single-time mileage data;
and determining the type of the vehicle according to the statistical distribution value of the frequency data and the statistical distribution value of the single-time mileage data.
Optionally, the determining the statistical distribution value of the frequency data includes:
determining a statistical distribution of the frequency data;
and determining the maximum value and the sub-maximum value in the statistical distribution data of all the frequency data as the statistical distribution value of the frequency data.
Optionally, the determining the type of the vehicle according to the statistical distribution value of the frequency data and the statistical distribution value of the single trip mileage data includes:
constructing a triangular model according to the statistical distribution value of the frequency data determined by the maximum value in the statistical distribution data of all the frequency data, the statistical distribution value of the frequency data determined by the secondary maximum value in the statistical distribution data of all the frequency data and the statistical distribution value of the single-time mileage data;
determining the type of the vehicle through the triangular model.
Optionally, the determining the type of the vehicle through the triangular model includes:
obtaining an angle threshold value, wherein the angle threshold value comprises an angle threshold value corresponding to at least one angle in the triangular model;
determining the type of the vehicle based on the magnitude relationship of the angle in the triangular model and the angle threshold.
Optionally, the determining the type of the vehicle based on the magnitude relationship between the angle in the triangular model and the angle threshold includes:
determining the type of the vehicle based on the magnitude relation between a first angle and a second angle in the triangular model and the angle threshold, wherein the first angle is an angle corresponding to a statistical distribution value determined by the maximum value in the statistical distribution data of all the frequency data in the triangular model, and the second angle is an angle corresponding to the statistical distribution value of the single trip mileage data.
Optionally, the determining a vehicle battery energy management strategy corresponding to the type of the vehicle based on the type of the vehicle includes: determining a temperature threshold corresponding to the type of the vehicle based on the type of the vehicle; and acquiring the ambient temperature, and determining the heating threshold according to the relationship between the ambient temperature and the temperature threshold.
Optionally, after determining the vehicle battery energy management policy corresponding to the type of the vehicle based on the type of the vehicle, the method further includes:
acquiring actual power consumption data and actual battery temperature range data of the vehicle within a preset time range;
determining power consumption data and battery temperature range data for the vehicle at different types based on vehicle driving parameters;
and when the actual power consumption data is smaller than the power consumption data and the coincidence degree of the actual battery temperature range data and the battery temperature range data is larger than a preset coincidence threshold value, determining the battery energy management strategy under the type as the battery energy management strategy of the vehicle.
Optionally, the method further includes: and when the actual power consumption data is larger than or equal to the power consumption data and/or the coincidence degree of the actual battery temperature range data and the battery temperature range data is smaller than or equal to a preset coincidence threshold value, determining that the battery energy management strategy of the vehicle is the battery energy management strategy when the vehicle leaves a factory.
Optionally, the power consumption data and the battery temperature range data include theoretical power consumption data and theoretical battery temperature range data, or the power consumption data and the battery temperature range data include operating power consumption data and operating battery temperature range data.
According to a second aspect thereof, the present disclosure provides an adaptive vehicle battery energy management apparatus, comprising:
the data acquisition unit is used for acquiring the driving data of the vehicle, wherein the driving data is the vehicle usage habit and control habit data formed in the driving process of the vehicle;
a first determination unit configured to determine a type of the vehicle based on the travel data, different types of vehicles having different vehicle usage habits and handling habit data;
a second determination unit to determine a vehicle battery energy management strategy corresponding to the type of the vehicle based on the type of the vehicle, different vehicle battery energy management strategies having different heating thresholds.
Optionally, the driving data includes pedal stroke data and single-trip mileage data.
Optionally, the first determining unit is further configured to:
determining all frequency data meeting a pedal travel threshold within a preset time range based on the pedal travel data;
determining the statistical distribution values of all the frequency data and the single-time mileage data;
and determining the type of the vehicle according to the statistical distribution value of the frequency data and the statistical distribution value of the single-time mileage data.
In a third aspect, the present disclosure provides a vehicle controller having stored thereon one or more instructions that, when executed by the vehicle controller, implement the method of the first aspect.
In a fourth aspect, the present disclosure provides an electric vehicle comprising the vehicle controller according to the third aspect.
Compared with the prior art, the scheme of the embodiment of the disclosure at least has the following beneficial effects:
according to the vehicle battery energy management method and device, the driving data sent by the vehicle are obtained, statistical analysis is carried out on the basis of the driving data, the vehicle is classified according to the result of the statistical analysis, and finally a vehicle battery energy management strategy corresponding to the classification is formed on the basis of the classification. The vehicle battery heat management strategy management system can classify at the cloud according to the use data of different vehicles, construct different management strategies according to different classifications, output the constructed management strategies to the vehicle meeting the classification, and update the battery heat management strategy of the vehicle through the cloud battery heat management strategy after the use state of the vehicle changes, so that the battery heat management strategy of the vehicle can be automatically updated, and the adaptability of the power performance or the driving range of the vehicle battery is improved.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and components are not necessarily drawn to scale. In the drawings:
FIG. 1 shows a flow chart of an adaptive vehicle battery energy management method according to an embodiment of the disclosure;
FIG. 2 illustrates a vehicle data acquisition analysis management flow diagram according to an embodiment of the present disclosure;
FIG. 3 shows a schematic diagram of a vehicle classification method according to an embodiment of the disclosure;
FIG. 4 shows a block diagram of elements of an adaptive vehicle battery energy management apparatus, according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more complete and thorough understanding of the present disclosure. It should be understood that the drawings and the embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The vehicle-mounted power battery provides a power source for the electric automobile and determines the power performance and the endurance mileage of the whole automobile. In the using process of the battery, the thermal management strategy of the battery needs to be adjusted according to the temperature of the battery and the actual using state of the vehicle, so as to improve the power performance and the driving range of the vehicle.
How to adaptively manage the vehicle battery energy is described in detail below with reference to the accompanying drawings.
Example one
According to an embodiment of the present disclosure, as shown in fig. 1, the present disclosure provides an adaptive vehicle battery energy management method, comprising the following method steps:
step S102: acquiring driving data of a vehicle, wherein the driving data is vehicle using habit and control habit data formed in the driving process of the vehicle; as shown in fig. 2.
The vehicle is all vehicles which are involved in data collection, analysis and management, and the vehicles comprise but are not limited to pure electric vehicles, hybrid electric vehicles and other electric vehicles which completely or partially need batteries for power supply. The use area of the vehicle is not limited, and the vehicle can be used in any area with different environmental temperatures. The vehicle comprises a data receiving and transmitting unit which is used for collecting driving data required in a vehicle central control system, transmitting the driving data to a cloud server in real time and receiving management or control data transmitted by the cloud server.
Data collection may be performed in units of a predetermined collection time period, such as a quarter or a month, and then statistically analyzed.
The driving data includes, but is not limited to, pedal travel data and single trip mileage data, which are related to the user's usage habits and handling habits. The pedal travel data refers to the maximum travel opening of a pedal which is depressed once in the vehicle running process, and the central control system of the automobile acquires the opening value of each time through a pedal sensor, for example, the pedal is 100% depressed, the pedal is 90% depressed, the pedal is 70% depressed, the pedal is 30% depressed, the pedal is 10% depressed, and the like. The single trip mileage data refers to a distance traveled once in a period from the start of the vehicle to the flameout, and may be, for example, 10 km, 50 km, 1 km, or the like.
After the vehicle acquires the driving data through the central control system, the driving data are sent to the cloud server through the receiving and sending unit, and the cloud server receives and processes the related driving data.
Step S104: determining a type of the vehicle based on the travel data, different types of vehicles having different usage habits and handling habit data.
The determining the type of the vehicle based on the travel data comprises the sub-steps of:
step S104-2: determining all frequency data satisfying a pedal travel threshold within a preset time range based on the pedal travel data.
The preset time range is a period of time for collecting vehicle data, such as a quarter or a month, and is not particularly limited.
The frequency data of the pedal stroke data is statistics of the number of times that the pedal stroke data is pressed within a preset design range, for example, if 90% of the pedal stroke is counted, the frequency data can be recorded as 1 as long as 90% of the pedal stroke is pressed once, and finally all frequency data based on the pedal stroke data within a time range, such as 90% of 100 times, 70% of 500 times, 30% of 200 times and the like, are counted, and specific values of the statistics of the pedal stroke are not limited, and statistics of any pedal stroke data from 1% to 100% can be counted, or statistics of pedal stroke data within a range spaced from 1% to 100%, such as 1% to 10%, 11% to 20%, and \ 8230, and 90% to 100%.
Step S104-4: and determining the statistical distribution value of all the frequency data and the single-time mileage data.
Wherein, the determining the statistical distribution value of the frequency data comprises the following substeps:
step S104-4-2: determining a statistical distribution of the frequency data;
statistics are performed on the frequency data collected within a certain time range (e.g., a month or a quarter), and a normal distribution of all frequency data within the time range is determined, such as 90% of 100 times, 70% of 500 times, 30% of 200 times, and so on.
Step S104-4-4: and determining the maximum value and the sub-maximum value in the statistical distribution data of all the frequency data as the statistical distribution value of the frequency data.
According to the normal statistical distribution data of all frequency data determined in the above steps, the maximum value and the sub-maximum value in the normal distribution data are obtained, for example, 500 times with the maximum value of 70%, 200 times with the sub-maximum value of 30%, or the maximum value and the sub-maximum value in a certain range, for example, 1000 times with the maximum value of 70% -90%, 800 times with the sub-maximum value of 30% -50%, and the maximum value and the sub-maximum value are different according to the difference of data statistics.
Step S104-6: and determining the type of the vehicle according to the statistical distribution value of the frequency data and the statistical distribution value of the single-time mileage data.
Determining a normal distribution value of the single trip mileage data based on the single trip mileage data.
Counting the single-time mileage data collected within a certain time range (for example, one month or one quarter), and determining the normal distribution of all data within the time range, for example, the value of the single-time mileage data distributed in the range of 48-52 kilometers is 1000 times, and is the maximum value of the statistical distribution, then the normal distribution value for the period of time can be determined to be 50 kilometers by an averaging method.
The determining the type of the vehicle according to the statistical distribution value of the frequency data and the statistical distribution value of the single-time mileage data comprises the following substeps:
step S104-6-2: constructing a triangular model according to the statistical distribution value of the frequency data determined by the maximum value in the statistical distribution data of all the frequency data, the statistical distribution value of the frequency data determined by the secondary maximum value in the statistical distribution data of all the frequency data and the statistical distribution value of the single-time driving mileage data;
as shown in fig. 3, according to the collected and statistically calculated data, the statistical distribution value of the frequency data (e.g., N = 1000) determined by the maximum value (e.g., 90%) of the statistical distribution data of all the frequency data is one vertex, the statistical distribution value of the frequency data (e.g., M = 800) determined by the maximum value (e.g., 70%) of the statistical distribution data of all the frequency data is one vertex, and the normal distribution value (e.g., 50 km) of the single trip mileage data is one vertex, a triangular model is constructed, so that the collected data is classified according to the triangular model. The vehicle type classification method has the advantages that the classification is carried out according to the triangular model, the vehicle can be accurately classified according to the vehicle driving data, and the vehicle type classification efficiency is improved.
Step S104-6-4: determining the type of the vehicle through the triangular model.
Wherein the determining the type of the vehicle through the triangular model comprises: obtaining an angle threshold value, wherein the angle threshold value comprises an angle threshold value corresponding to at least one angle in the triangular model; determining the type of the vehicle based on the magnitude relationship of the angle in the triangular model and the angle threshold.
The determining the type of the vehicle based on the magnitude relation between the angle in the triangular model and the angle threshold comprises:
determining the type of the vehicle based on the magnitude relation between a first angle and a second angle in the triangular model and the angle threshold, wherein the first angle is an angle corresponding to a statistical distribution value determined by the maximum value in the statistical distribution data of all the frequency data in the triangular model, and the second angle is an angle corresponding to the statistical distribution value of the single trip mileage data.
For example, a triangle is drawn according to the collected data, wherein a top angle is an angle corresponding to a statistical distribution value determined by a maximum value in the statistical distribution data of all frequency data in the triangle model, one bottom angle is an angle corresponding to a statistical distribution value determined by a second maximum value in the statistical distribution data of all frequency data in the triangle model, and the other bottom angle is an angle corresponding to a statistical distribution value of single-trip mileage data, as an example, for example, the top angle belongs to type one when the top angle is less than 30 degrees and the bottom right angle is less than 60 degrees, the top angle belongs to type two when the top angle is greater than 30 degrees and the bottom right angle is greater than 60 degrees, and the rest belongs to type three. The criteria for type classification are not so limited, and collected travel data may be classified according to any reasonable partitioning strategy. Through the division of angle, can divide current vehicle directly perceivedly and fast.
Step S106: determining a vehicle battery energy management strategy corresponding to the type of the vehicle based on the type of the vehicle, different vehicle battery energy management strategies having different heating thresholds.
Determining a temperature threshold corresponding to the type of the vehicle based on the type of the vehicle; the method comprises the steps of obtaining an ambient temperature, determining a heating threshold value according to the relation between the ambient temperature and the temperature threshold value, wherein the heating threshold value is used for indicating that heating is stopped when the temperature of a battery reaches a preset threshold value. The temperature threshold is preset according to different vehicle types, and the temperature threshold of the same vehicle type can be one or more. The heating threshold value is predetermined according to an ambient temperature value.
As an alternative embodiment, as shown in fig. 2, determining a vehicle battery energy management strategy corresponding to the type of the vehicle based on the type of the vehicle, different vehicle battery energy management strategies having different heating thresholds includes:
when the vehicle is of type one, the vehicle battery energy management policy is: when the ambient temperature is less than zero, presetting a first heating threshold value; when the ambient temperature is greater than or equal to zero and less than or equal to a first preset temperature, presetting a second heating threshold; stopping heating when the ambient temperature is higher than the first preset temperature, wherein the preset first heating threshold is higher than the preset second heating threshold; zero, the first temperature threshold value that predetermines all is type one vehicle, through predetermineeing two temperature threshold values, divides the ambient temperature into three intervals, can all predetermine a heating threshold value in every interval.
Specifically, the thermal management heating threshold is set as follows:
Te<0 ℃ thermal management heating threshold T bat1 ≥25℃；
Te is more than or equal to 0 and less than or equal to 20 ℃, and the thermal management heating threshold T bat2 >20℃；
Te >20 ℃, no heating.
Wherein Te represents the ambient temperature of the battery, T bat1 The method is characterized by representing a preset first heating threshold value, namely when the preset first heating threshold value is reached, the heating of the battery is stopped, and the battery has enough power performance and endurance mileage. T is bat2 And the heating control unit is used for indicating a preset second heating threshold, namely when the preset second heating threshold is reached, the heating of the battery is stopped, the battery has enough power performance and endurance mileage, and when the ambient temperature is high enough, the battery is not heated, and the battery also has enough power performance and endurance mileage.
When the vehicle is of type two, the vehicle battery energy management policy is: when the ambient temperature is less than zero, presetting a third heating threshold; when the ambient temperature is greater than or equal to zero and less than or equal to a second preset temperature, presetting a fourth heating threshold; stopping heating when the ambient temperature is higher than the second preset temperature, wherein the preset third heating threshold is higher than the preset fourth heating threshold; zero, the second temperature of predetermineeing all is the temperature threshold value of type two vehicles, through predetermineeing two temperature threshold values, divides the ambient temperature into three intervals, can all predetermine a heating threshold value in every interval.
Specifically, the thermal management heating threshold is set as follows:
Te<0 ℃ thermal management heating threshold T bat3 ≥10℃；
Te is more than or equal to 0 and less than or equal to 10 ℃, and the thermal management heating threshold T bat4 >5℃；
Te is more than 10 ℃ and is not heated.
Wherein Te represents the ambient temperature of the battery, T bat3 And indicating a preset third heating threshold, namely stopping heating the battery when the preset third heating threshold is reached, wherein the battery has enough power performance and endurance mileage. T is bat4 And the preset fourth heating threshold is represented, namely when the preset fourth heating threshold is reached, the battery is stopped being heated, the battery has enough power performance and endurance mileage, and when the environment temperature is high enough, the battery is not heated, and the battery also has enough power performance and endurance mileage.
When the vehicle is of type three, the vehicle battery energy management strategy is: when the ambient temperature is less than zero, presetting a fifth heating threshold; when the ambient temperature is greater than or equal to zero and less than or equal to a third preset temperature, presetting a sixth heating threshold; and when the ambient temperature is higher than the third preset temperature, stopping heating, wherein the preset fifth heating threshold is higher than the preset sixth heating threshold. Zero, the third temperature of predetermineeing all is the temperature threshold value of three vehicles of type, through predetermineeing two temperature threshold values, divides the ambient temperature into three intervals, can all predetermine a heating threshold value in every interval.
Specifically, the thermal management heating threshold is set as follows:
Te<0 ℃ thermal management heating threshold T bat5 ≥15℃；
Te is more than or equal to 0 and less than or equal to 15 ℃, and the thermal management heating threshold T bat6 >10℃；
Te is greater than 15 ℃ and is not heated.
Wherein Te represents the ambient temperature of the battery, T bat5 And indicating a preset fifth heating stop threshold value, namely stopping heating the battery when the preset fifth heating stop threshold value is reached, wherein the battery has enough power performance and endurance mileage. T is bat6 And when the ambient temperature is high enough, the battery is not heated, and the battery also has enough power performance and driving mileage.
Optionally, the first preset temperature is higher than the third preset temperature, and the third preset temperature is higher than the second preset temperature.
Optionally, after determining the vehicle battery energy management strategy corresponding to the type of the vehicle based on the type of the vehicle, the method further includes the following steps:
step S108: acquiring actual power consumption data and actual battery temperature range data of the vehicle within a preset time range;
as one embodiment, after the vehicle leaves the factory, after the vehicle is actually used within a preset time range (for example, one month or one quarter), the actual power consumption data and the actual battery temperature range data of the vehicle are acquired and used for evaluating the thermal management of the vehicle, and the actual power consumption data and the actual battery temperature range data are used as evaluation indexes to evaluate the thermal management strategy of the current vehicle simply and intuitively.
As another embodiment, after the vehicle is given the thermal management strategy (which may be the case after the first time or after N updates), after the vehicle is actually used for a preset time range (for example, one month or one quarter), the actual power consumption data and the actual battery temperature range data of the vehicle are obtained for evaluating the current thermal management strategy of the vehicle, and the thermal management strategy of the vehicle can be optimized according to the specific use condition of the vehicle by evaluating the thermal management strategy of the vehicle again.
Step S110: power consumption amount data and battery temperature range data of the vehicle in different types are determined based on vehicle running parameters.
The driving parameters of the vehicle are vehicle data collected by a vehicle central control system during the running process of the vehicle, the vehicle data include, but are not limited to, a battery current value/voltage value, an accessory consumption current value/voltage value, and the like, and the power consumption data and the battery temperature range data of the vehicle are determined by vehicle data calculation. The above calculation manner is only a simple example, and cannot be used for limiting the present application, and the calculation of the cloud power consumption data and the cloud battery temperature range is also only a calculation method listing part of the driving parameters, and the actual situation is often complex and may include calculation rules of more driving parameters, and in general situations, the more driving parameters are used, the more accurate the calculation is, and the calculation is not listed one by one here.
As an alternative embodiment, the power consumption data and the battery temperature range data include theoretical power consumption data and theoretical battery temperature range data, or the power consumption data and the battery temperature range data include operational power consumption data and operational battery temperature range data. The theoretical power consumption data and the theoretical battery temperature range data are calculated and determined at the cloud end according to the driving parameters of the vehicle, the operation power consumption data and the operation battery temperature range data are the power consumption data and the battery temperature range data formed in the actual operation process after the battery management strategy is given by the vehicle operation cloud end, and the data can be calculated and determined by obtaining the vehicle operation parameters.
Step S112: and when the actual power consumption data is smaller than the power consumption data and the coincidence degree of the actual battery temperature range data and the battery temperature range data is larger than a preset coincidence threshold value, determining the battery energy management strategy under the type as the battery energy management strategy of the vehicle.
According to the steps, different thermal management strategies and cloud power consumption data and cloud battery temperature range data of the vehicles under different types can be formed through the collected vehicle data and serve as evaluation indexes, and at the moment, the actual power consumption data and the actual battery temperature range data of the vehicles are actually obtained to evaluate the thermal management strategies of the vehicles. The actual power consumption data and the actual battery temperature range data comprise power consumption data and a battery temperature range of the vehicle determined according to a thermal management strategy configured when the vehicle leaves a factory, or the power consumption data and the battery temperature range of the vehicle determined after the cloud thermal management strategy is given.
As an embodiment, when the actual power consumption amount data is smaller than the cloud-side power consumption amount data of the type-one vehicle, and the coincidence degree of the actual battery temperature range data and the cloud-side battery temperature range data of the type-one vehicle is larger than a first coincidence threshold (for example, 80%), determining the battery energy management strategy of the type-one vehicle as the battery energy management strategy of the current vehicle; compared with the vehicle leaving strategy, the dynamic property is not weakened and the driving range is increased.
When the actual power consumption data are smaller than the cloud power consumption data of the second type of vehicle, and the coincidence degree of the actual battery temperature range data and the cloud battery temperature range data of the second type of vehicle is larger than a second coincidence threshold (for example, 20%), determining the battery energy management strategy of the second type of vehicle as the battery energy management strategy of the current vehicle; the driving range is increased relative to the vehicle-measuring-out strategy.
And when the actual power consumption data is smaller than the cloud power consumption data of the three types of vehicles, and the coincidence degree of the actual battery temperature range data and the cloud battery temperature range data of the three types of vehicles is larger than a third coincidence threshold (for example, 60%), determining the battery energy management strategy of the three types of vehicles as the battery energy management strategy of the current vehicle. Compared with the vehicle delivery strategy, the dynamic property is not weakened, and the driving range is increased.
Optionally, for different types, the first coincidence threshold is greater than the third coincidence threshold, and the third coincidence threshold is greater than the second coincidence threshold.
Optionally, the method further comprises the following steps: and when the factory power consumption data is larger than or equal to the cloud power consumption data and/or the coincidence degree of the factory battery temperature range data and the cloud battery temperature range data is smaller than or equal to a preset coincidence threshold value, determining the battery energy management strategy as the battery energy management strategy of the vehicle when the vehicle leaves the factory.
As an optional implementation manner, when the actual power consumption data is smaller than the operating power consumption data, and the coincidence degree of the actual battery temperature range data and the operating battery temperature range data is greater than a preset coincidence threshold value, the battery energy management policy of the operation under the type is determined as the battery energy management policy of the vehicle. According to the embodiment, after the vehicle is endowed with the battery energy management strategy under a certain type, the currently endowed battery energy management strategy can be corrected after the operation parameters are changed in the actual operation process so as to meet the optimal battery energy management strategy, and the vehicle operation state is more matched with the vehicle operation parameters.
According to the vehicle battery energy management method and device, the driving data sent by the vehicle are obtained, statistical analysis is carried out on the basis of the driving data, the vehicle is classified according to the result of the statistical analysis, and finally a vehicle battery energy management strategy corresponding to the classification is formed on the basis of the classification. The vehicle battery heat management strategy management system can classify at the cloud according to the use data of different vehicles, construct different management strategies according to different classifications, output the constructed management strategies to the vehicle meeting the classification, and update the battery heat management strategy of the vehicle through the cloud battery heat management strategy after the use state of the vehicle changes, so that the battery heat management strategy of the vehicle can be automatically updated, and the adaptability of the power performance or the driving range of the vehicle battery is improved.
Example two
Corresponding to the first embodiment provided by the disclosure, the disclosure also provides a second embodiment, namely an adaptive vehicle battery energy management device. The embodiment is used for implementing the method described in the embodiment, and the same features have the same technical effects, which are not described herein again. The device embodiments described below are merely illustrative.
As shown in fig. 4, the present disclosure provides an adaptive vehicle battery energy management apparatus, comprising:
the data acquisition unit 402 is configured to acquire driving data of the vehicle, where the driving data is vehicle usage habit and control habit data formed during driving of the vehicle.
The driving data includes, but is not limited to, pedal travel data and single trip mileage data, which are related to the user's usage habits and handling habits.
A first determining unit 404 for determining the type of the vehicle based on the driving data, different types of vehicles having different usage habits and handling habit data.
A first determining unit 404, further configured to:
determining all frequency data satisfying a pedal travel threshold within a preset time range based on the pedal travel data.
And determining the statistical distribution value of all the frequency data and the single-time mileage data.
Wherein the determining the statistical distribution value of the frequency data comprises:
determining a statistical distribution of the frequency data;
and determining the maximum value and the sub-maximum value in the statistical distribution data of all the frequency data as the statistical distribution value of the frequency data.
A first determining unit 404, further configured to:
and determining the type of the vehicle according to the statistical distribution value of the frequency data and the statistical distribution value of the single-time mileage data.
Determining a normal distribution value of the single trip mileage data based on the single trip mileage data.
Determining the type of the vehicle according to the statistical distribution value of the frequency data and the statistical distribution value of the single-time mileage data, including:
constructing a triangular model according to the statistical distribution value of the frequency data determined by the maximum value in the statistical distribution data of all the frequency data, the statistical distribution value of the frequency data determined by the secondary maximum value in the statistical distribution data of all the frequency data and the statistical distribution value of the single-time mileage data; determining the type of the vehicle through the triangular model.
Wherein the determining the type of the vehicle through the triangular model comprises: obtaining an angle threshold value, wherein the angle threshold value comprises an angle threshold value corresponding to at least one angle in the triangular model; determining the type of the vehicle based on the magnitude relation of the angle in the triangular model and the angle threshold.
The determining the type of the vehicle based on the magnitude relation of the angle in the triangular model and the angle threshold comprises:
determining the type of the vehicle based on the magnitude relation between a first angle and a second angle in the triangular model and the angle threshold, wherein the first angle is an angle corresponding to a statistical distribution value determined by the maximum value in the statistical distribution data of all the frequency data in the triangular model, and the second angle is an angle corresponding to the statistical distribution value of the single-time mileage data.
A second determining unit 406, further configured to determine a vehicle battery energy management strategy corresponding to the type of the vehicle based on the type of the vehicle, different vehicle battery energy management strategies having different heating thresholds.
Determining a temperature threshold corresponding to the type of the vehicle based on the type of the vehicle; acquiring the ambient temperature, and determining the heating stop threshold according to the relationship between the ambient temperature and the temperature threshold, wherein the heating stop threshold is used for indicating that heating is stopped when the temperature of the battery reaches a preset threshold.
As an alternative embodiment, determining a vehicle battery energy management strategy corresponding to the type of the vehicle based on the type of the vehicle, different vehicle battery energy management strategies having different heating thresholds, includes:
when the vehicle is of type one, the vehicle battery energy management policy is: when the ambient temperature is less than zero, presetting a first heating stop threshold value; when the ambient temperature is greater than or equal to zero and less than or equal to a first preset temperature, presetting a second heating stop threshold; stopping heating when the ambient temperature is higher than the first preset temperature, wherein the preset first heating stopping threshold value is higher than the preset second heating stopping threshold value;
when the vehicle is of type two, the vehicle battery energy management policy is: when the ambient temperature is less than zero, presetting a third heating stop threshold; when the ambient temperature is greater than or equal to zero and less than or equal to a second preset temperature, presetting a fourth heating stop threshold; when the ambient temperature is higher than the second preset temperature, stopping heating, wherein the preset third heating stop threshold is higher than the preset fourth heating stop threshold;
when the vehicle is of type three, the vehicle battery energy management strategy is: when the ambient temperature is less than zero, presetting a fifth heating stop threshold; when the ambient temperature is greater than or equal to zero and less than or equal to a third preset temperature, presetting a sixth heating stop threshold; and when the ambient temperature is higher than the third preset temperature, stopping heating, wherein the preset fifth heating stopping threshold value is higher than the preset sixth heating stopping threshold value.
Optionally, the first preset temperature is higher than the third preset temperature, and the third preset temperature is higher than the second preset temperature.
Optionally, the apparatus further includes a third determining unit, configured to:
acquiring actual power consumption data and actual battery temperature range data of the vehicle within a preset time range; power consumption amount data and battery temperature range data of the vehicle in different types are determined based on vehicle running parameters. As an alternative embodiment, the power consumption data and the battery temperature range data include theoretical power consumption data and theoretical battery temperature range data, or the power consumption data and the battery temperature range data include operating power consumption data and operating battery temperature range data. The theoretical power consumption data and the theoretical battery temperature range data are calculated and determined at the cloud end according to the driving parameters of the vehicle, the operation power consumption data and the operation battery temperature range data are the power consumption data and the battery temperature range data formed in the actual operation process after the battery management strategy is given by the vehicle operation cloud end, and the data can be calculated and determined by obtaining the vehicle operation parameters.
And when the actual power consumption data is smaller than the power consumption data and the coincidence degree of the actual battery temperature range data and the battery temperature range data is larger than a preset coincidence threshold value, determining the battery energy management strategy as the battery energy management strategy of the vehicle.
As an embodiment, when the actual power consumption amount data is smaller than the power consumption amount data of the type-one vehicle and the coincidence of the actual battery temperature range data and the battery temperature range data of the type-one vehicle is larger than a first coincidence threshold (for example, 80%), the vehicle battery energy management strategy of the type-one vehicle is determined as the battery energy management strategy of the current vehicle; compared with the vehicle leaving strategy, the dynamic property is not weakened and the driving range is increased.
When the actual power consumption data is smaller than the cloud power consumption data of the type two vehicle, and the coincidence degree of the actual battery temperature range data and the battery temperature range data of the type two vehicle is larger than a second coincidence threshold value (for example, 20%), determining the vehicle battery energy management strategy of the type two as the battery energy management strategy of the current vehicle; the driving range is increased relative to the vehicle-measuring-out strategy.
And when the actual power consumption data is smaller than the cloud power consumption data of the three types of vehicles, and the coincidence degree of the actual battery temperature range data and the battery temperature range data of the three types of vehicles is larger than a third coincidence threshold (for example, 60%), determining the battery energy management strategy of the three types of vehicles as the battery energy management strategy of the current vehicle. Compared with the vehicle delivery strategy, the dynamic property is not weakened, and the driving range is increased.
Optionally, for different types, the first coincidence threshold is greater than the third coincidence threshold, and the third coincidence threshold is greater than the second coincidence threshold.
Optionally, the method further includes: and when the factory power consumption data is larger than or equal to the cloud power consumption data and/or the coincidence degree of the factory battery temperature range data and the cloud battery temperature range data is smaller than or equal to a preset coincidence threshold value, determining the battery energy management strategy as the battery energy management strategy of the vehicle when the vehicle leaves the factory.
According to the vehicle battery energy management method and device, the driving data sent by the vehicle are obtained, statistical analysis is carried out on the basis of the driving data, the vehicle is classified according to the result of the statistical analysis, and finally a vehicle battery energy management strategy corresponding to the classification is formed on the basis of the classification. The vehicle battery heat management strategy management system can classify according to the use data of different vehicles at the cloud end, different management strategies are constructed according to different classifications, the constructed management strategies are output to the vehicles meeting the classifications, after the use state of the vehicles changes, the battery heat management strategies of the vehicles can be updated through the cloud end battery heat management strategies, the battery heat management strategies of the vehicles can be automatically updated, and the adaptability of the power performance or the driving mileage of the vehicle batteries is improved.
EXAMPLE III
The third embodiment of the present disclosure provides a vehicle controller, which has one or more instructions stored thereon, and when the one or more instructions are executed by the vehicle controller, the method according to the first embodiment is implemented.
Example four
The embodiment of the present disclosure provides an embodiment four, that is, an electric vehicle including the vehicle controller according to the embodiment three.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims (13)

1. An adaptive vehicle battery energy management method, comprising:
acquiring driving data of a vehicle, wherein the driving data is vehicle using habit and control habit data formed in the driving process of the vehicle;
determining the type of the vehicle based on the driving data, wherein different types of vehicles have different vehicle using habits and control habit data;
determining a vehicle battery energy management strategy corresponding to the type of the vehicle based on the type of the vehicle, different vehicle battery energy management strategies having different heating thresholds;
after determining the vehicle battery energy management strategy corresponding to the type of the vehicle based on the type of the vehicle, the method further comprises the following steps:
acquiring actual power consumption data and actual battery temperature range data of the vehicle within a preset time range;
determining power consumption data and battery temperature range data for the vehicle at different types based on vehicle driving parameters;
when the actual power consumption data are smaller than the power consumption data and the coincidence degree of the actual battery temperature range data and the battery temperature range data is larger than a preset coincidence threshold value, determining the battery energy management strategy under the type as the battery energy management strategy of the vehicle;
and when the actual power consumption data is larger than or equal to the power consumption data and/or the coincidence degree of the actual battery temperature range data and the battery temperature range data is smaller than or equal to a preset coincidence threshold value, determining that the battery energy management strategy of the vehicle is the battery energy management strategy when the vehicle leaves a factory.
2. The method of claim 1,
the driving data includes pedal travel data and single trip mileage data.
3. The method of claim 2, wherein the determining the type of the vehicle based on the travel data comprises:
determining all frequency data meeting a pedal travel threshold within a preset time range based on the pedal travel data;
determining the statistical distribution value of all the frequency data and the single-time mileage data;
and determining the type of the vehicle according to the statistical distribution values of all the frequency data and the statistical distribution value of the single-time mileage data.
4. The method of claim 3, wherein the determining the statistical distribution value of all frequency data comprises:
determining statistical distribution data of all frequency data;
and determining the maximum value and the sub-maximum value in the statistical distribution data of all the frequency data as the statistical distribution value of all the frequency data.
5. The method of claim 4,
determining the type of the vehicle according to the statistical distribution values of all the frequency data and the statistical distribution value of the single-time mileage data, including:
constructing a triangular model according to the statistical distribution value of all the frequency data determined by the maximum value in the statistical distribution data of all the frequency data, the statistical distribution value of all the frequency data determined by the secondary maximum value in the statistical distribution data of all the frequency data and the statistical distribution value of single-time driving mileage data;
determining the type of the vehicle through the triangular model.
6. The method of claim 5, wherein the determining the type of the vehicle from the triangular model comprises:
obtaining an angle threshold value, wherein the angle threshold value comprises an angle threshold value corresponding to at least one angle in the triangular model;
determining the type of the vehicle based on the magnitude relationship of the angle in the triangular model and the angle threshold.
7. The method of claim 6, wherein the determining the type of the vehicle based on the magnitude relationship of the angle in the triangular model to the angle threshold comprises:
determining the type of the vehicle based on the magnitude relation between a first angle and a second angle in the triangular model and the angle threshold, wherein the first angle is an angle corresponding to the statistical distribution value of all frequency data determined by the maximum value in the statistical distribution data of all frequency data in the triangular model, and the second angle is an angle corresponding to the statistical distribution value of the single-time mileage data.
8. The method of claim 1, wherein the determining a vehicle battery energy management strategy corresponding to the type of vehicle based on the type of vehicle comprises: determining a temperature threshold corresponding to the type of the vehicle based on the type of the vehicle;
and acquiring the ambient temperature, and determining the heating threshold according to the relationship between the ambient temperature and the temperature threshold.
9. The method of claim 1, wherein the power consumption data comprises theoretical power consumption data and the battery temperature range data comprises theoretical battery temperature range data, or wherein the power consumption data comprises operational power consumption data and the battery temperature range data comprises operational battery temperature range data.
10. An adaptive vehicle battery energy management apparatus, comprising:
the data acquisition unit is used for acquiring the driving data of the vehicle, wherein the driving data is the vehicle usage habit and control habit data formed in the driving process of the vehicle;
a first determination unit configured to determine a type of the vehicle based on the travel data, different types of vehicles having different vehicle usage habits and handling habit data;
a second determination unit configured to determine a vehicle battery energy management strategy corresponding to the type of the vehicle based on the type of the vehicle, different vehicle battery energy management strategies having different warm-up thresholds;
a third determination unit, configured to acquire actual power consumption amount data and actual battery temperature range data of the vehicle within a preset time range;
determining power consumption data and battery temperature range data for the vehicle at different types based on vehicle driving parameters;
when the actual power consumption data is smaller than the power consumption data and the coincidence degree of the actual battery temperature range data and the battery temperature range data is larger than a preset coincidence threshold value, determining the battery energy management strategy under the type as the battery energy management strategy of the vehicle;
and when the actual power consumption data is larger than or equal to the power consumption data and/or the coincidence degree of the actual battery temperature range data and the battery temperature range data is smaller than or equal to a preset coincidence threshold value, determining that the battery energy management strategy of the vehicle is the battery energy management strategy when the vehicle leaves a factory.
11. The apparatus of claim 10,
the driving data comprises pedal travel data and single-trip mileage data.
12. A vehicle controller having stored thereon one or more instructions that, when executed by the vehicle controller, implement the method of any one of claims 1 to 9.
13. An electric vehicle characterized by comprising the vehicle controller according to claim 12.
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