CN112798968B - Method for estimating SOC of battery parallel system, electric equipment and medium - Google Patents
Method for estimating SOC of battery parallel system, electric equipment and medium Download PDFInfo
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- CN112798968B CN112798968B CN202011553360.6A CN202011553360A CN112798968B CN 112798968 B CN112798968 B CN 112798968B CN 202011553360 A CN202011553360 A CN 202011553360A CN 112798968 B CN112798968 B CN 112798968B
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- 238000000034 method Methods 0.000 title claims abstract description 97
- 238000007726 management method Methods 0.000 claims description 44
- 125000004122 cyclic group Chemical group 0.000 claims description 18
- 230000010354 integration Effects 0.000 claims description 13
- 208000028659 discharge Diseases 0.000 description 25
- 238000004891 communication Methods 0.000 description 12
- 238000007599 discharging Methods 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 238000004146 energy storage Methods 0.000 description 4
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 4
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 229910001416 lithium ion Inorganic materials 0.000 description 3
- 101000984533 Homo sapiens Ribosome biogenesis protein BMS1 homolog Proteins 0.000 description 2
- IVUGBSGLHRJSSP-UHFFFAOYSA-N LimKi 3 Chemical compound S1C(NC(=O)C(C)C)=NC=C1C1=CC(C(F)F)=NN1C1=C(Cl)C=CC=C1Cl IVUGBSGLHRJSSP-UHFFFAOYSA-N 0.000 description 2
- 102100027057 Ribosome biogenesis protein BMS1 homolog Human genes 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 239000006183 anode active material Substances 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000006182 cathode active material Substances 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
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- 238000002360 preparation method Methods 0.000 description 1
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- 229910052710 silicon Inorganic materials 0.000 description 1
- 239000010703 silicon Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- General Physics & Mathematics (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
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Abstract
The application provides a battery parallel connection method, which comprises the following steps: collecting a first voltage of a master battery pack and a plurality of second voltages of a plurality of slave battery packs; and if the voltage difference between any two of the first voltage and the plurality of second voltages is smaller than or equal to a preset voltage, controlling the two battery packs corresponding to the voltage difference smaller than or equal to the preset voltage to be connected in parallel, so as to obtain a battery parallel system. The application also provides a method and equipment for estimating the SOC of the battery parallel system constructed by the battery parallel method. The application can improve the estimation precision of the state of charge of the battery parallel system.
Description
Technical Field
The present application relates to the field of battery technologies, and in particular, to a method, an electric device, and a medium for estimating an SOC of a battery parallel system.
Background
In order to solve the problems of difficult charging and slow charging of new energy automobiles, the current policy is greatly advancing the development of a new energy automobile power conversion mode so as to realize the common development and application of charging and power conversion, and the high-efficiency cyclic utilization system of the power battery is emphasized and built, so that innovative application of the power battery echelon products in the fields of energy storage, energy preparation, charging and power conversion is supported, and the modularization, standardization and generalization of the battery are realized.
The existing box-dividing power-changing technology adopts a serial connection mode of a battery pack, and the lowest value of the charge states in the serial battery pack is used as the charge state of the whole serial system in the using process. In the practical use process, the short-circuit effect of the discharge capacity of the series connection mode of the battery packs is very obvious, namely, the capacity of one battery pack in the series connection system is inconsistent with the capacity of other battery packs, so that the finally-increased electric quantity of the other battery packs cannot be utilized. Therefore, in order to realize the most efficient utilization of the electric quantity of each split-box battery pack as possible, the low-voltage battery pack serial connection mode has high requirements on the capacity consistency and the voltage consistency of the split-box battery packs, and the battery packs of different batches cannot be mixed.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, apparatus and medium for estimating SOC of a battery parallel system.
An embodiment of the present application provides a method of estimating a battery parallel system SOC, including: collecting a first voltage of a master battery pack and a plurality of second voltages of a plurality of slave battery packs, wherein the master battery pack comprises a master battery management system and the slave battery packs comprise slave battery management systems; if the voltage difference between any two of the first voltage and the plurality of second voltages is smaller than or equal to a preset voltage, controlling two battery packs corresponding to the voltage difference smaller than or equal to the preset voltage to be connected in parallel, and obtaining a battery parallel system; the main battery management system collects first historical state data of the main battery pack in the cyclic use process; the first historical state data is sent to a cloud big data platform, wherein the cloud big data platform obtains a first capacity and a first state of charge according to the first historical state data and a prestored first initial parameter of the main battery pack through estimation, and the cloud big data platform comprises: dynamically updating the current SOC-OCV corresponding relation of the main battery pack based on the first historical state data; correcting the rated capacity of the main battery pack according to the first historical state data and the first initial parameter of the main battery pack to obtain the first capacity; estimating the first state of charge according to the updated SOC-OCV correspondence, the first capacity and the received state data of the main battery pack in the last cyclic use process; the secondary battery management system collects second historical state data of the secondary battery pack in the cyclic use process; the second historical state data is sent to a cloud big data platform, wherein the cloud big data platform obtains a second capacity and a second state of charge according to the second historical state data and the prestored second initial parameter estimation of the secondary battery pack; the main battery management system receives the first capacity and the first state of charge sent by the cloud big data platform, and the secondary battery management system receives the second capacity and the second state of charge sent by the cloud big data platform; and the main battery management system calculates the current state of charge SOC And is combined with of the parallel system according to the first capacity and the first state of charge and the second capacity and the second state of charge.
According to some embodiments of the application, the method further comprises: if the voltage difference between any two of the first voltage and the second voltages is larger than the preset voltage, controlling the battery pack with the highest voltage in the master battery pack and the slave battery packs to be in a working state; continuing to acquire a first voltage of the master battery pack and a plurality of second voltages of a plurality of slave battery packs; if the voltage difference between any two of the first voltage and the plurality of second voltages is smaller than or equal to the preset voltage; and controlling the corresponding two battery packs to be connected in parallel when the voltage difference value is smaller than or equal to the preset voltage until the master battery pack and the plurality of slave battery packs are connected in parallel to obtain the battery parallel system.
According to some embodiments of the application, the master battery pack has a different capacity than the slave battery packs.
According to some embodiments of the present application, the current state of charge SOC And is combined with of the parallel system is calculated by the following formula:
Wherein n is the sum of the master battery pack and the plurality of slave battery packs, C 1 is the first capacity, SOC 1 is the first state of charge, C 2…Cn is the second capacity, and SOC 2…SOCn is the second state of charge.
According to some embodiments of the application, the first historical state data includes first historical voltage data, first historical current data, first historical temperature data, first historical state of charge data, and a first number of cycles, the first initial parameter includes a first factory date and a first initial capacity; the second historical state data includes second historical voltage data, second historical current data, second historical temperature data, second historical state of charge data, and a second number of cycles, and the second initial parameters include a second factory date and a second initial capacity.
According to some embodiments of the present application, the estimating, by the cloud big data platform, the first capacity and the first state of charge according to the first historical state data and the prestored first initial parameter of the main battery pack includes: dynamically updating the current SOC-OCV corresponding relation of the main battery pack based on the first historical state data; correcting the rated capacity of the main battery pack according to the first historical state data and the first initial parameter of the main battery pack to obtain the first capacity; and estimating the first charge state according to the updated SOC-OCV corresponding relation, the first capacity and the received state data of the main battery pack in the last cyclic use process.
According to some embodiments of the application, correcting the rated capacity of the battery based on the first historical state data and the first initial parameter of the main battery pack to obtain the first capacity includes: counting the times and duration of charge and discharge exceeding a rated multiplying power in the first historical state data; determining a decay in capacity of the primary battery pack based on the number of times and duration; and correcting the rated capacity of the main battery pack according to the attenuation of the capacity of the main battery pack and the first initial capacity to obtain the first capacity.
According to some embodiments of the present application, the estimating, by the cloud big data platform, the second capacity and the second state of charge according to the second historical state data and the pre-stored initial parameter of the secondary battery pack includes: dynamically updating the current SOC-OCV corresponding relation of the slave battery pack based on the second historical state data; correcting the rated capacity of the battery according to the second historical state data and the initial parameters of the secondary battery pack to obtain the second capacity; and estimating the second charge state according to the updated SOC-OCV corresponding relation, the second capacity and the received state data of the slave battery pack in the last cyclic use process.
According to some embodiments of the application, correcting the rated capacity of the battery based on the second historical state data and the initial parameter of the slave battery pack to obtain the second capacity includes: counting the times and duration of charge and discharge exceeding a rated multiplying power in the second historical state data; determining a decay in capacity of the slave battery pack based on the number of times and duration; and correcting the rated capacity of the secondary battery pack according to the attenuation of the capacity of the secondary battery pack and the second initial capacity to obtain the second capacity.
According to some embodiments of the application, the method further comprises: estimating and obtaining the actual state of charge of the battery parallel system by combining an ampere-hour integration method and an open-circuit voltage method; and determining a target state of charge of the battery parallel system based on the current state of charge and the actual state of charge, wherein the current state of charge has a higher priority than the actual state of charge.
According to some embodiments of the present application, the estimating the actual state of charge of the parallel battery system by combining the ampere-hour integration method and the open circuit voltage method includes: dividing the SOC-OCV curves corresponding to the master battery pack and the slave battery pack into a voltage platform area, a low-voltage area and a high-voltage area respectively; calculating the actual states of charge of the main battery pack and the auxiliary battery pack respectively in the high-voltage area and the low-voltage area by an open circuit voltage method, and calculating the actual states of charge of the battery parallel system based on the actual states of charge of the main battery pack and the auxiliary battery pack; and respectively calculating the actual states of charge of the master battery pack and the slave battery pack in the voltage platform area by an ampere-hour integration method, and calculating the actual states of charge of the battery parallel system based on the actual states of charge of the master battery pack and the slave battery pack.
An embodiment of the present application provides a powered device including a battery parallel system and a processor for performing the method of estimating a battery parallel system SOC as described above.
An embodiment of the present application provides a storage medium having stored thereon at least one computer instruction loaded by a processor and for executing the method of estimating a battery parallel system SOC as described above.
According to the embodiment of the application, the batteries are connected in parallel according to the voltage difference to construct a battery parallel system, and the historical state data of the master battery pack and the slave battery pack in the battery parallel system are received through the cloud big data platform, so that the state analysis of the battery parallel system in the full life cycle is realized, and the latest maximum available capacity, different from the rated capacity when leaving a factory, and the more accurate SOC value of the battery parallel system in practical application are obtained.
Drawings
Fig. 1 is an application environment diagram for estimating a battery parallel system SOC according to an embodiment of the present application.
Fig. 2 is a schematic view of a battery parallel system according to an embodiment of the present application.
Fig. 3 is a flowchart of a battery parallel method according to an embodiment of the present application.
Fig. 4 is a flowchart of a method of estimating a battery parallel system SOC according to an embodiment of the present application.
FIG. 5 is a block diagram of an estimation system according to one embodiment of the application.
Description of the main reference signs
Electric equipment 1
Cloud big data platform 2
Estimation System 100
Communication unit 10
Battery parallel system 11
Load 12
Main battery pack 111
From the battery pack 112
Acquisition module 101
Control module 102
Transmitting module 103
Receiving module 104
Calculation module 105
The present application will be described in further detail with reference to the following detailed description and the accompanying drawings.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the present application.
In describing embodiments of the present application, it should be noted that the term "coupled" should be interpreted broadly, unless otherwise indicated and limited thereto, such as a fixed connection, a removable connection, or an integral connection; can be mechanically connected, electrically connected or can be communicated with each other; either directly or indirectly through a centering assembly, or in communication with the interior of the two elements or in interaction with the two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.
The terms "first," "second," and "third" in the description of the application and in the above figures, etc. are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the term "include" and any variations thereof is intended to cover a non-exclusive inclusion.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
Referring to fig. 1, the application provides a method for estimating a battery parallel system SOC, which is applied in an application environment composed of an electric device 1 and a cloud big data platform 2. The electric equipment 1 comprises, but is not limited to, a communication unit 10, a battery parallel system 11 and a load 12, wherein the communication unit 10, the battery parallel system 11 and the load 12 can be connected through buses or can be directly connected.
In this embodiment, the communication unit 10 may provide wired or wireless network communication for the electric device 1. In this embodiment, the wired network may be any type of conventional wired communication, such as the internet, a local area network. The wireless network may be of any type of conventional wireless communication, such as radio, wireless fidelity (WIRELESS FIDELITY, WIFI), cellular, satellite, broadcast, etc. For example, the electric equipment 1 can be in communication connection with the cloud big data platform 2 through the communication unit 10. It will be appreciated that the cloud big data platform 2 page includes a communication unit (not shown in the figure) to provide a communication function for the cloud big data platform 2.
As shown in fig. 2, the battery parallel system 11 includes one master battery pack 111 and a plurality of slave battery packs 112 connected in parallel. In this embodiment, a Battery management system (Battery MANAGEMENT SYSTEM, BMS) may be disposed in each of the master Battery pack 111 and the plurality of slave Battery packs 112, where each Battery pack is managed by a corresponding Battery management system. For example, the master battery pack 111 includes a master battery management system BMS1, and the slave battery pack 112 includes slave battery management systems BMS2, BMS3 … BMSn. The master battery management system BMS1 is communicatively connected to the slave battery management systems BMS2, BMS3 … BMSn. In the present application, a battery parallel method is also disclosed to construct the battery parallel system 11, and the detailed description of the battery parallel method is referred to fig. 3.
In one embodiment, each of the master battery pack 111 and the plurality of slave battery packs 112 is a rechargeable battery for providing power to the powered device 1. For example, the battery pack may be a lithium ion battery, a lithium polymer battery, a lithium iron phosphate battery, or the like. The battery pack includes at least one cell, and the battery pack can be repeatedly charged in a recyclable manner.
Each of the master battery pack 111 and the plurality of slave battery packs 112 is configured to store an electric power, and the positive and negative poles of the battery packs are each capable of releasing and receiving energy-carrying particles. According to the application scenario of the battery pack, the battery pack in the embodiment of the application can comprise a power battery and an energy storage battery, wherein the power battery can be applied to the fields of electric automobiles, electric bicycles and other electric tools, and the energy storage battery can be applied to the fields of energy storage power stations, renewable energy grid connection, micro-grids and the like. Taking a power battery as an example, the battery pack may be, but not limited to, a lithium iron phosphate system battery or a silicon-added system battery, wherein the lithium iron phosphate system battery is a lithium ion battery containing lithium iron phosphate as an anode active material, and the silicon-added system battery is a lithium ion battery containing silicon as a cathode active material.
Although not shown, the powered device 1 may further include a wireless fidelity (WIRELESS FIDELITY, WIFI) unit, a bluetooth unit, a speaker, and other components, which are not described in detail herein.
It should be noted that fig. 1 is only an example of the electric device 1. In other embodiments, powered device 1 may also include more or fewer elements, or have a different configuration of elements. The powered device 1 may be an electric motorcycle, an electric bicycle, an electric automobile, a mobile phone, a tablet computer, a digital assistant, a personal computer, or any other suitable rechargeable device.
Referring to fig. 3, fig. 3 is a flowchart of a method for connecting batteries in parallel according to an embodiment of the application. In this embodiment, in order to solve the problem that the short-circuit effect is easy to occur in the existing battery serial system, the present application provides a method for constructing a battery parallel system according to the pressure difference condition between the battery packs. Specifically, the method for connecting the batteries in parallel may include the following steps:
step S31: a first voltage of a master battery pack and a plurality of second voltages of a plurality of slave battery packs are collected.
In the present embodiment, it is assumed that N battery packs need to be connected in parallel, and one of the N battery packs is set as a master battery pack, and the other N-1 battery packs are set as slave battery packs. The primary battery pack includes a primary battery management system that collects a first voltage of the primary battery pack.
In this embodiment, each of the N-1 slave battery packs includes a slave battery management system that may collect the second voltage of each slave battery pack to obtain N-1 second voltages. The slave battery management system transmits the N-1 second voltages to the master battery management system.
Step S32: comparing whether the voltage difference between any two of the first voltage and the plurality of second voltages is smaller than or equal to a preset voltage. If the voltage difference is less than or equal to the preset voltage, the flow proceeds to step S33; if the voltage differences are all greater than the preset voltage, the flow proceeds to step S34.
In this embodiment, before the master battery pack is connected in parallel with the plurality of slave battery packs, it is necessary to determine how to construct the battery parallel system based on the first voltage of the master battery pack and the second voltages of the plurality of slave battery packs. Only when the voltage difference between any two of the first voltage and the plurality of second voltages is smaller than or equal to a preset voltage, two corresponding battery packs can be connected in parallel when the voltage difference is smaller than or equal to the preset voltage.
Step S33: and controlling the voltage difference value to be smaller than or equal to the preset voltage, and connecting the two battery packs in parallel to obtain a battery parallel system.
In this embodiment, when the voltage difference satisfies a battery parallel condition, that is, the voltage difference is less than or equal to the preset voltage, two battery packs corresponding to the voltage difference is controlled to be parallel, so as to obtain a battery parallel system. It is understood that the voltage difference value being less than or equal to the preset voltage describes that the voltage difference between the two battery packs is not large or the voltage is the same.
Step S34: and controlling the battery pack with highest voltage in the master battery pack and the plurality of slave battery packs to be in a working state, and returning the flow to the step S31.
In this embodiment, if the voltage difference between the first voltage and the plurality of second voltages is greater than the preset voltage, it is determined that the master battery pack and the slave battery packs do not meet the parallel condition, and the battery packs with high voltages need to be powered on first, so that the battery packs are in a working state for a preset time, and then it is determined whether the voltage difference between any one of the battery packs with high voltages and another battery pack is less than or equal to the preset voltage; and if the voltage difference between the battery pack with high voltage and any one of the other battery packs is smaller than or equal to the preset voltage, confirming that the battery parallel connection condition is met, controlling the battery pack with high voltage to be connected with any one of the other battery packs in parallel, and the like until the master battery pack is connected with the plurality of slave battery packs in parallel.
Specifically, the battery parallel connection method further comprises the following steps: if the voltage difference between any two of the first voltage and the plurality of second voltages is greater than the preset voltage; controlling the battery pack with highest voltage in the master battery pack and the plurality of slave battery packs to be in a working state; continuing to acquire a first voltage of the master battery pack and a plurality of second voltages of a plurality of slave battery packs; if the voltage difference between any two of the first voltage and the plurality of second voltages is smaller than or equal to the preset voltage; and controlling the corresponding two battery packs to be connected in parallel when the voltage difference value is smaller than or equal to the preset voltage until the master battery pack and the plurality of slave battery packs are connected in parallel to obtain the battery parallel system.
For example, if three battery packs need to be connected in parallel, voltages of the three battery packs are collected respectively, and if a voltage difference value between any two of the three battery packs is smaller than or equal to the preset voltage, any two battery packs with voltage difference values smaller than or equal to the preset voltage are connected in parallel, so that the battery parallel system is obtained. And if the voltage difference between any two of the three battery packs is larger than the preset voltage, controlling the battery pack with the highest voltage among the three battery packs to be in a working state. After the preset time, continuously comparing whether the voltage difference between any two of the first voltage and the plurality of second voltages is smaller than or equal to the preset voltage. I.e. continuously collecting the first voltage of the master battery pack and the second voltages of the slave battery packs; if the voltage difference between any two of the first voltage and the plurality of second voltages is smaller than or equal to the preset voltage; and controlling the corresponding two battery packs to be connected in parallel when the voltage difference value is smaller than or equal to the preset voltage until the master battery pack and the plurality of slave battery packs are connected in parallel to obtain the battery parallel system.
The capacities of the master battery pack and the plurality of slave battery packs do not need to be identical in the parallel connection process, and the capacities of the master battery pack and the slave battery packs may be different or the same. The capacity difference between any two parallel battery packs is allowed, and the battery packs with capacity difference in different batches can be used together. Specifically, the capacity difference may be a difference in design itself, or may be a difference caused by different use conditions under the same design.
The battery parallel system can be obtained by the battery parallel method, and the battery parallel system can bring more convenience and flexibility for use. Different numbers of battery packs and the like can be placed according to different electric equipment (such as electric automobiles). By adopting the mode of parallel connection of the batteries, only the rationality of the pressure difference between the battery packs is controlled, the capacity difference requirement between the battery packs is not as high as that of the serial connection mode, and the battery packs with capacity differences in different batches can be used together. Meanwhile, the capacity of the battery pack can be reasonably configured according to different vehicle types and different endurance mileage requirements.
Referring to fig. 4, fig. 4 is a flowchart illustrating a method of estimating a battery state of charge of the battery parallel system constructed as in fig. 3 according to an embodiment of the present application. The battery parallel system includes a master battery pack including a master battery management system and a plurality of slave battery packs including slave battery management systems, and the method of estimating a battery and associating his state of charge may include the steps of:
step S41: the main battery management system collects first historical state data of the main battery pack in the cyclic use process.
In this embodiment, in order to more accurately estimate the state of charge of the battery parallel system, the primary battery management system may collect the first historical state data during the cyclic use of the battery parallel system. The first historical state data comprises first historical voltage data, first historical current data, first historical temperature data, first historical state of charge data and first cycle times.
Step S42: and sending the first historical state data to a cloud big data platform, wherein the cloud big data platform obtains a first capacity and a first state of charge according to the first historical state data and a prestored first initial parameter of the main battery pack.
In this embodiment, the cloud big data platform receives the first historical state data and stores all the first historical state data of the main battery pack from factory operation. The cloud big data platform also stores first initial parameters of the main battery pack in advance, wherein the first initial parameters comprise a first delivery date, a first initial capacity and the like. The cloud big data platform can estimate and obtain a first capacity and a first state of charge according to the first historical state data and a first initial parameter of the main battery pack.
Specifically, the cloud big data platform estimating a first capacity and a first state of charge according to the first historical state data and a prestored first initial parameter of the main battery pack includes:
(1) And dynamically updating the current SOC-OCV corresponding relation of the main battery pack based on the first historical state data. In this embodiment, the stored first historical voltage data and first historical state of charge data are subjected to matching analysis, so that the current state of charge-OCV correspondence can be dynamically updated.
(2) And correcting the rated capacity of the main battery pack according to the first historical state data and the first initial parameter of the main battery pack to obtain the first capacity.
The capacity fade of the battery pack is affected by the rate charge and rate discharge during use, with larger rates affecting more. Therefore, it is necessary to count the number of times and time of charge and discharge exceeding the rated rate. In the application, all first historical current data are analyzed, all multiplying power charging and multiplying power discharging exceeding the rated multiplying power in the using process are classified and accumulated according to the multiplying power values corresponding to the multiplying power charging and multiplying power discharging, and the time of all multiplying power charging and multiplying power discharging exceeding the rated multiplying power under each multiplying power value is counted, so that the current rated capacity can be corrected according to the influence relation of the charging and discharging of different multiplying power on the capacity.
Specifically, correcting the rated capacity of the battery according to the first historical state data and the first initial parameter of the main battery pack, and obtaining the first capacity includes: counting the times and duration of charge and discharge exceeding a rated multiplying power in the first historical state data; determining a decay in capacity of the primary battery pack based on the number of times and duration; and correcting the rated capacity of the main battery pack according to the attenuation of the capacity of the main battery pack and the first initial capacity to obtain the first capacity. It should be noted that, the capacity attenuation of the main battery pack under different discharge rates is stored in the cloud big data platform in advance.
For example, experiments have demonstrated that performing a charge-discharge cycle (half-hour charge and half-hour discharge) on the main battery pack using a rate of 2C results in a decay in the capacity of the battery from a first preset capacity to a second preset capacity. And storing the capacity attenuation of the battery after charging and discharging for a preset time under the multiplying power of 2C to the cloud big data platform. If the main battery pack is in the actual recycling process, 3600 times of current with 2C multiplying power appear, and the duration of each time is 1 second. Then the capacity of the corresponding main battery pack should be the second preset capacity after 3600 times of 2C-rate charge and discharge processes.
The term "C" refers to a charge/discharge ratio, which is a current value required for charging to or discharging from a rated capacity in a predetermined period of time, and is equal in value to a charge/discharge current/a rated capacity of the battery. For example, when a battery having a rated capacity of 10Ah is discharged at 2A, its discharge rate is 0.2C; when discharging at 20A, the discharge rate was 2C.
In another embodiment, the correcting the rated capacity of the battery according to the first historical state data and the first initial parameter of the main battery pack to obtain the first capacity further includes: combining the first delivery date and the first historical temperature data of the main battery to obtain the standing time and the standing temperature of the main battery pack in the cyclic use process; calculating to obtain the capacity attenuation of the main battery pack according to the standing temperature and the standing time; and correcting the rated capacity of the main battery pack according to the attenuation of the capacity of the main battery pack and the first initial capacity to obtain the first capacity.
(3) And estimating the first state of charge according to the updated SOC-OCV corresponding relation, the corrected rated capacity and timely state data (such as state data of the main battery pack in the last cyclic use process) of the main battery pack received by the platform in the use process. Specifically, according to the current latest SOC-OCV correspondence, the corrected rated capacity, and the state data of the main battery set received by the platform in time during the use process of the main battery set, the cloud big data platform calculates a state of charge estimated value more in line with the actual state of charge of the main battery set, and issues the state of charge estimated value to the corresponding main battery management system.
Step S43: the secondary battery management system collects second historical state data of the secondary battery pack in the cyclic use process.
In this embodiment, the second historical state data includes second historical voltage data, second historical current data, second historical temperature data, second historical state of charge data, and a second number of cycles.
Step S44: and sending the second historical state data to a cloud big data platform, wherein the cloud big data platform obtains a second capacity and a second state of charge according to the second historical state data and the prestored second initial parameter estimation of the secondary battery pack.
In this embodiment, the method for estimating the second capacity and the second state of charge by the cloud big data platform is the same as the method for estimating the first capacity and the first state of charge.
Specifically, the cloud big data platform estimating, according to the second historical state data and the pre-stored initial parameters of the slave battery pack, a second capacity and a second state of charge, including: dynamically updating the current SOC-OCV corresponding relation of the slave battery pack based on the second historical state data; correcting the rated capacity of the battery according to the second historical state data and the initial parameters of the secondary battery pack to obtain the second capacity; and estimating the second charge state according to the updated SOC-OCV corresponding relation, the second capacity and the received state data of the slave battery pack in the last cyclic use process.
In this embodiment, correcting the rated capacity of the battery based on the second history state data and the initial parameter of the slave battery pack, the obtaining the second capacity includes: counting the times and duration of charge and discharge exceeding a rated multiplying power in the second historical state data; determining a decay in capacity of the slave battery pack based on the number of times and duration; and correcting the rated capacity of the secondary battery pack according to the attenuation of the capacity of the secondary battery pack and the second initial capacity to obtain the second capacity.
Step S45: the main battery management system receives the first capacity and the first state of charge sent by the cloud big data platform, and the secondary battery management system receives the second capacity and the second state of charge sent by the cloud big data platform.
The cloud data scattering platform calculates and stores the first historical state data and the second historical state data according to the first historical state data
Step S46: and the main battery management system calculates the current state of charge SOC And is combined with of the parallel system according to the first capacity and the first state of charge and the second capacity and the second state of charge.
In this embodiment, ideally, no matter how many batteries are connected in parallel, the capacity of the battery parallel system is the single battery capacity multiplied by the number of batteries; the state of charge of the battery parallel system is the state of charge of one of the battery packs. However, in practical situations, the working condition of each battery pack is different, so that the battery packs are identical even when leaving the factory, the capacity of each battery pack is different after a period of use, then the state of charge is also different due to different internal resistances, and in addition, the battery packs with the same voltage platform but different capacities are allowed to be connected in parallel, and the capacity and the state of charge of the whole parallel system obtained by calculation cannot be calculated according to the ideal situation. Therefore, in the present application, the current state of charge SOC And is combined with of the parallel system is calculated by the following formula:
Wherein n is the sum of the master battery pack and the plurality of slave battery packs, C 1 is the first capacity, SOC 1 is the first state of charge, C 2…Cn is the second capacity, and SOC 2…SOCn is the second state of charge.
In this embodiment, since the state of charge of the battery parallel system calculated by the electric device itself does not consider the influence of the history data, there is a certain error. Thus, upon receiving the current state of charge SOC And is combined with , the current state of charge SOC And is combined with is taken as the state of charge of the battery parallel system.
It should be noted that, in the case of the case-division power-change mode of the high-voltage battery parallel connection mode (i.e., the battery parallel connection system), estimation of the execution state of charge based on the number of case-division modules (the number of battery packs) may be achieved. For example, a vehicle having only 1 battery pack is configured, the total battery capacity calculated by the main battery management system is the battery pack capacity, and the state of charge calculated by the main battery management system is the state of charge of the battery. For more than 1 battery pack, the total capacity of the battery calculated by the main battery management system should be the sum of the total capacities of all the battery packs participating in parallel connection, and the state of charge calculated by the main battery management system should be the ratio of the remaining value to the total capacity after subtracting the current consumed electric quantity from the sum of the total actual available electric quantity.
Therefore, when the total battery capacity is adjusted in a modularized mode based on the split battery power-changing mode, the main battery management system needs to realize self-adaptive matching of the capacity and the charge state according to the data uploaded from the battery management system in the battery packs to ensure that the battery-changing vehicle obtains better power and cruising experience when in use, and the quantity of the battery packs is different and the capacity difference of each battery pack is considered.
In the present application, the method for estimating the state of charge of the battery parallel system further includes: estimating the actual state of charge of the battery parallel system (namely the state of charge of the battery parallel system calculated by the electric equipment) by combining an ampere-hour integration method and an open-circuit voltage method; and determining a target state of charge of the battery parallel system based on the current state of charge and the actual state of charge, wherein the priority of the current state of charge is higher than the priority of the actual state of charge. That is, when the electric equipment receives the current state of charge SOC And is combined with sent by the cloud big data platform and estimates the actual state of charge, the current state of charge SOC And is combined with is preferentially used as the state of charge of the battery parallel system.
In this embodiment, the actual state of charge may be used as the state of charge of the parallel battery system when the electric device is in a network-free state. Or when the electric equipment considers the network charge problem, the actual state of charge can be used as the state of charge of the battery under the condition that the current state of charge SOC And is combined with sent by the cloud big data platform is not received any more.
In this embodiment, the SOC-OCV curve of the battery is characterized by a steep at both ends and a gentle at the middle. The method has the characteristics that the change of the voltage corresponding to the change of the capacity of the master battery and the slave battery pack in the early stage and the later stage of charge and discharge is obvious, and the change of the capacity in the middle stage of charge and discharge is not obvious in the voltage change. And adopting different methods to calculate the actual charge state in different charge and discharge stages.
Specifically, estimating the actual state of charge by combining the ampere-hour integration method and the open-circuit voltage method includes:
(1) And dividing the SOC-OCV curves corresponding to the master battery pack and the slave battery pack into a voltage platform area, a low-voltage area and a high-voltage area.
In this embodiment, the low voltage region corresponds to a battery discharge late stage, the platform region corresponds to a battery charge and discharge intermediate stage, and the high voltage region corresponds to a battery charge late stage. The slope of the curve corresponding to the voltage platform area changes slowly, and the charge state span corresponding to the platform area is larger; in the low-voltage region and the high-voltage region, the slope of the SOC-OCV curve varies greatly. And dividing the SOC-OCV curves corresponding to the master battery pack and the slave battery pack into a voltage platform area, a low-voltage area and a high-voltage area.
(2) And respectively calculating the actual states of charge of the main battery pack and the auxiliary battery pack in the high-voltage area and the low-voltage area through an open circuit voltage method, and calculating the actual states of charge of the battery parallel system based on the actual states of charge of the main battery pack and the auxiliary battery pack.
At each charge, if the OCV of the battery satisfies the distinct feature point in the late stage of charge, or each discharge satisfies the distinct feature point in the late stage of discharge. When the voltage reaches a certain value, the state of charge value corresponding to the value can be determined to be more consistent with the current real state of charge, and the open circuit voltage method has higher priority than the ampere-hour integrating method. The actual state of charge is calculated by an open circuit voltage method. Therefore, the accumulated error of the ampere-hour integrating method can be eliminated, which is equivalent to zero clearing of the error when the charging is finished or the discharging is finished, and the error accumulation is avoided to be larger and larger.
Specifically, the actual states of charge of the master battery pack and the slave battery pack are calculated in the high-voltage area and the low-voltage area through an open circuit voltage method respectively, and the actual states of charge of the battery parallel system are calculated based on the actual states of charge of the master battery pack and the slave battery pack.
(3) And respectively calculating the actual states of charge of the master battery pack and the slave battery pack in the voltage platform area by an ampere-hour integration method, and calculating the actual states of charge of the battery parallel system based on the actual states of charge of the master battery pack and the slave battery pack.
In this embodiment, the processing is performed with a high priority by the ampere-hour integration method at the stage of the charge and discharge late stage of the charge and discharge removal. I.e. in the voltage plateau region, the second state of charge is calculated by ampere-hour integration. Thus, large deviations of the open circuit voltage method when the battery voltage falls near its voltage plateau can be avoided. And respectively calculating the actual states of charge of the master battery pack and the slave battery pack in the voltage platform area by an ampere-hour integration method, and calculating the actual states of charge of the battery parallel system based on the actual states of charge of the master battery pack and the slave battery pack.
It should be noted that the ampere-hour integration method and the open-circuit voltage method are both existing methods for estimating the state of charge, and are not described in detail in the present application.
According to the application, the historical state data of the master battery pack and the slave battery pack are received through the cloud big data platform, so that the state analysis of the battery parallel system in the full life cycle is realized, and the latest maximum available capacity, which is different from the rated capacity when leaving a factory, and a more accurate SOC value of the battery parallel system in practical application are obtained; meanwhile, the battery packs with the same voltage and different capacities are allowed to be mixed, and the main battery management system during mixing can obtain a capacity value and an SOC value which are more in line with the current battery parallel system according to data reported by the battery management system. And the capacity adjustment of the battery parallel system can be realized by changing the number of battery packs in the battery parallel system or changing the capacity of the battery packs, and the main battery management system can dynamically calculate the capacity and the SOC of the whole battery parallel system according to the number and the capacity of the battery packs.
Referring to fig. 5, in the present embodiment, the estimation system 100 may be divided into one or more modules, which may be stored in the battery parallel system 11, and the method of estimating the battery parallel system SOC according to the embodiment of the present application is performed by the battery parallel system 11. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, the instruction segments describing the execution of the estimation system 100 in the powered device 1. For example, the estimation system 100 may be divided into an acquisition module 101, a control module 102, a transmission module 103, a reception module 104, and a calculation module 105 in fig. 5.
The acquisition module 101 is configured to acquire a first voltage of a master battery pack and a plurality of second voltages of a plurality of slave battery packs; the control module 102 is configured to control two battery packs corresponding to the voltage difference value smaller than or equal to the preset voltage to be connected in parallel if the voltage difference value between the first voltage and any two of the plurality of second voltages is smaller than or equal to the preset voltage, so as to obtain a battery parallel system.
The acquisition module 101 is further configured to acquire first historical state data of the main battery pack during a cyclic use process; the sending module 103 is configured to send the first historical state data to a cloud big data platform, where the cloud big data platform estimates to obtain a first capacity and a first state of charge according to the first historical state data and a first initial parameter of the main battery pack stored in advance; the acquisition module 101 is further configured to acquire second historical state data of the slave battery pack during a cyclic use process; the sending module 103 is further configured to send the second historical state data to a cloud big data platform, where the cloud big data platform estimates and obtains a second capacity and a second state of charge according to the second historical state data and the pre-stored second initial parameter of the slave battery pack; the receiving module 104 is configured to receive the first capacity and the first state of charge sent by the cloud big data platform, and the second capacity and the second state of charge sent by the cloud big data platform and received from the battery management system; the calculating module 105 is configured to calculate a current state of charge SOC And is combined with of the parallel system according to the first capacity and the first state of charge, and the second capacity and the second state of charge.
The estimation system 100 can estimate the state of charge of the battery parallel system according to the historical data of the master battery pack and the slave battery pack in the battery parallel system in the charge and discharge process, so that the estimation accuracy of the state of charge of the battery is improved. For details, reference may be made to the embodiments of the method for estimating the state of charge of a parallel battery system for the above-mentioned battery, and details thereof will not be described herein.
The modules in the estimation system 100, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
It will be appreciated that the above-described division of modules into a logical function division may be implemented in other ways. In addition, each functional module in the embodiments of the present application may be integrated in the same processing unit, or each module may exist alone physically, or two or more modules may be integrated in the same unit. The integrated modules may be implemented in hardware or in hardware plus software functional modules.
In another embodiment, the powered device 1 may further comprise a memory (not shown), and the one or more modules may also be stored in the memory and executed by the processor 11. The memory may be an internal memory of the powered device 1, i.e. a memory built into the powered device 1. In other embodiments, the memory may also be an external memory of the powered device 1, i.e. a memory external to the powered device 1.
In some embodiments, the memory is used to store program codes and various data, for example, program codes of the estimation system 100 installed in the powered device 1, and to implement high-speed, automatic access to programs or data during operation of the powered device 1.
The memory may include random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card (SMART MEDIA CARD, SMC), secure Digital (SD) card, flash memory card (FLASH CARD), at least one disk storage device, flash memory device, or other volatile solid state storage device.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The above-described embodiments of the application are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (11)
1. A method of estimating a battery parallel system SOC, the method comprising:
Collecting a first voltage of a master battery pack and a plurality of second voltages of a plurality of slave battery packs, the capacity of the master battery pack being different from the capacity of the slave battery packs, wherein the master battery pack comprises a master battery management system and the slave battery packs comprise slave battery management systems;
If the voltage difference between any two of the first voltage and the plurality of second voltages is smaller than or equal to a preset voltage, controlling two battery packs corresponding to the voltage difference smaller than or equal to the preset voltage to be connected in parallel, and obtaining a battery parallel system;
The main battery management system collects first historical state data of the main battery pack in the cyclic use process;
The first historical state data is sent to a cloud big data platform, wherein the cloud big data platform obtains a first capacity and a first state of charge according to the first historical state data and a prestored first initial parameter of the main battery pack through estimation, and the cloud big data platform comprises: dynamically updating the current SOC-OCV corresponding relation of the main battery pack based on the first historical state data; correcting the rated capacity of the main battery pack according to the first historical state data and the first initial parameter of the main battery pack to obtain the first capacity; estimating the first state of charge according to the updated SOC-OCV correspondence, the first capacity and the received state data of the main battery pack in the last cyclic use process;
The secondary battery management system collects second historical state data of the secondary battery pack in the cyclic use process;
The second historical state data is sent to a cloud big data platform, wherein the cloud big data platform obtains a second capacity and a second state of charge according to the second historical state data and the prestored second initial parameter estimation of the secondary battery pack;
The main battery management system receives the first capacity and the first state of charge sent by the cloud big data platform, and the secondary battery management system receives the second capacity and the second state of charge sent by the cloud big data platform;
And the main battery management system calculates the current state of charge SOC And is combined with of the parallel system according to the first capacity and the first state of charge and the second capacity and the second state of charge.
2. The method of estimating a battery parallel system SOC of claim 1, further comprising:
If the voltage difference between any two of the first voltage and the second voltages is larger than the preset voltage, controlling the battery pack with the highest voltage in the master battery pack and the slave battery packs to be in a working state;
continuing to acquire a first voltage of the master battery pack and a plurality of second voltages of a plurality of slave battery packs;
If the voltage difference between any two of the first voltage and the plurality of second voltages is smaller than or equal to the preset voltage;
And controlling the corresponding two battery packs to be connected in parallel when the voltage difference value is smaller than or equal to the preset voltage until the master battery pack and the plurality of slave battery packs are connected in parallel to obtain the battery parallel system.
3. The method of estimating a battery parallel system SOC of claim 1, wherein the current state of charge SOC And is combined with of the parallel system is calculated by the following equation:
Wherein n is the sum of the master battery pack and the plurality of slave battery packs, C 1 is the first capacity, SOC 1 is the first state of charge, C 2…Cn is the second capacity, and SOC 2…SOCn is the second state of charge.
4. The method of estimating a battery parallel system SOC of claim 1, wherein:
The first historical state data comprises first historical voltage data, first historical current data, first historical temperature data, first historical state of charge data and first cycle times, and the first initial parameters comprise a first delivery date and a first initial capacity;
The second historical state data includes second historical voltage data, second historical current data, second historical temperature data, second historical state of charge data, and a second number of cycles, and the second initial parameters include a second factory date and a second initial capacity.
5. The method of estimating a battery parallel system SOC of claim 4, wherein modifying the rated capacity of the battery based on the first historical state data and a first initial parameter of the main battery pack to obtain the first capacity includes:
Counting the times and duration of charge and discharge exceeding a rated multiplying power in the first historical state data;
determining a decay in capacity of the primary battery pack based on the number of times and duration; and
And correcting the rated capacity of the main battery pack according to the attenuation of the capacity of the main battery pack and the first initial capacity to obtain the first capacity.
6. The method of estimating a battery parallel system SOC of claim 4, wherein the cloud big data platform estimating a second capacity and a second state of charge from the second historical state data and the pre-stored initial parameter of the slave battery pack comprises:
Dynamically updating the current SOC-OCV corresponding relation of the slave battery pack based on the second historical state data;
Correcting the rated capacity of the battery according to the second historical state data and the initial parameters of the secondary battery pack to obtain the second capacity;
and estimating the second charge state according to the updated SOC-OCV corresponding relation, the second capacity and the received state data of the slave battery pack in the last cyclic use process.
7. The method of estimating a battery parallel system SOC of claim 6, wherein modifying the rated capacity of the battery based on the second historical state data and the initial parameter of the slave battery pack to obtain the second capacity includes:
counting the times and duration of charge and discharge exceeding a rated multiplying power in the second historical state data;
determining a decay in capacity of the slave battery pack based on the number of times and duration; and
And correcting the rated capacity of the secondary battery pack according to the attenuation of the capacity of the secondary battery pack and the second initial capacity to obtain the second capacity.
8. The method of estimating a battery parallel system SOC of claim 7, further comprising:
estimating and obtaining the actual state of charge of the battery parallel system by combining an ampere-hour integration method and an open-circuit voltage method; and
And determining a target state of charge of the battery parallel system based on the current state of charge and the actual state of charge, wherein the priority of the current state of charge is higher than the priority of the actual state of charge.
9. The method for estimating SOC of a battery parallel system of claim 8, wherein estimating the actual state of charge of the battery parallel system by a combination of ampere-hour integration and open circuit voltage comprises:
Dividing the SOC-OCV curves corresponding to the master battery pack and the slave battery pack into a voltage platform area, a low-voltage area and a high-voltage area respectively;
Calculating the actual states of charge of the main battery pack and the auxiliary battery pack respectively in the high-voltage area and the low-voltage area by an open circuit voltage method, and calculating the actual states of charge of the battery parallel system based on the actual states of charge of the main battery pack and the auxiliary battery pack; and
And respectively calculating the actual states of charge of the master battery pack and the slave battery pack in the voltage platform area by an ampere-hour integration method, and calculating the actual states of charge of the battery parallel system based on the actual states of charge of the master battery pack and the slave battery pack.
10. An electrical device, the electrical device comprising:
A battery parallel system;
And a processor for performing the method of estimating the battery parallel system SOC according to any one of claims 1 to 9.
11. A storage medium having stored thereon at least one computer instruction, wherein the instructions are loaded by a processor and are used to perform the method of estimating a battery parallel system SOC according to any of claims 1 to 9.
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| WO2023050377A1 (en) * | 2021-09-30 | 2023-04-06 | 宁德时代新能源科技股份有限公司 | Method, apparatus and system for determining state of charge |
| CN116995785B (en) * | 2023-09-26 | 2023-12-22 | 杭州华塑科技股份有限公司 | Battery pack parallel connection method, device and system |
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