CN112578282B - Method for estimating battery SOC, electric equipment and storage medium - Google Patents
Method for estimating battery SOC, electric equipment and storage medium Download PDFInfo
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- CN112578282B CN112578282B CN202011400560.8A CN202011400560A CN112578282B CN 112578282 B CN112578282 B CN 112578282B CN 202011400560 A CN202011400560 A CN 202011400560A CN 112578282 B CN112578282 B CN 112578282B
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- 238000000034 method Methods 0.000 title claims abstract description 77
- 230000008569 process Effects 0.000 claims abstract description 16
- 238000004891 communication Methods 0.000 claims abstract description 12
- 125000004122 cyclic group Chemical group 0.000 claims abstract description 11
- 230000010354 integration Effects 0.000 claims description 14
- 208000028659 discharge Diseases 0.000 description 23
- 238000007599 discharging Methods 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 238000007726 management method Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 4
- 229910052744 lithium Inorganic materials 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
- 238000012545 processing Methods 0.000 description 4
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 3
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 3
- 238000004146 energy storage Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 229910001416 lithium ion Inorganic materials 0.000 description 3
- 229910052710 silicon Inorganic materials 0.000 description 3
- 239000010703 silicon Substances 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
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- 229920000642 polymer Polymers 0.000 description 1
- 239000007774 positive electrode material Substances 0.000 description 1
- 238000004064 recycling Methods 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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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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
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current 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]
- G01R31/385—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
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Abstract
The application provides a method for estimating battery SOC of a battery, which is applied to electric equipment, wherein the electric equipment is in communication connection with a cloud big data platform, and the method comprises the following steps: collecting historical state data of the battery in the cyclic use process; the historical state data is sent to a cloud big data platform, wherein the cloud big data platform estimates and obtains a first SOC according to the historical state data and the pre-stored initial parameters of the battery; and receiving the first SOC sent by the cloud big data platform, and taking the first SOC as the SOC of the battery. According to the method for estimating the battery SOC of the battery, the electric equipment and the storage medium provided by the application, the estimation accuracy of the battery SOC can be improved.
Description
Technical Field
The present application relates to the field of battery technologies, and in particular, to a method for estimating SOC of a battery, an electric device, and a storage medium.
Background
Current policies are strongly pushing the development of new energy automobiles. There are many large enterprises on the market to share the development of two-wheeled electric vehicles, so the demand for high-capacity and high-power batteries is increasing. Lithium batteries are used without a Battery management system (Battery MANAGEMENT SYSTEM, BMS). The BMS is used as a key component for ensuring normal and safe use of the battery and improving the service life of the battery, and one of the core functions is State of Charge (SOC) estimation. The SOC is a state of charge and reflects the current remaining charge of the battery. Which is defined as the percentage of the current remaining capacity of the battery to the total capacity. SOC has high non-linear characteristics, which are difficult to measure accurately. In the prior art, the SOC estimation is carried out by measuring parameters such as voltage, current, temperature, internal resistance and the like of a battery. For example, discharge measurement, open circuit voltage, ampere-hour integration, kalman filter, etc. However, the estimation method in the prior art has the problems of low estimation precision, unrealistic vehicle endurance mileage and poor customer experience.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method for estimating a battery SOC, an electric device, and a storage medium, which can improve the accuracy of battery SOC estimation.
The embodiment of the application provides a method for estimating a battery SOC, which is applied to electric equipment, wherein the electric equipment is in communication connection with a cloud big data platform, and the method comprises the following steps: collecting historical state data of the battery in the cyclic use process; the historical state data is sent to a cloud big data platform, wherein the cloud big data platform estimates and obtains a first SOC according to the historical state data and the pre-stored initial parameters of the battery; and receiving the first SOC sent by the cloud big data platform, and taking the first SOC as the SOC of the battery.
According to some embodiments of the application, the method further comprises: estimating and obtaining a second SOC by combining an ampere-hour integration method and an open-circuit voltage method; and determining an SOC of the battery based on the first SOC and the second SOC.
According to some embodiments of the application, the first SOC has a higher priority than the second SOC.
According to some embodiments of the application, the historical state data includes historical voltage data, historical current data, historical temperature data, historical SOC data, and cycle times, and the initial parameters include a factory date and an initial capacity.
According to some embodiments of the application, the estimating the first SOC by the cloud big data platform according to the historical state data and the pre-stored initial parameters of the battery includes: dynamically updating the current SOC-OCV corresponding relation of the battery based on the historical state data; correcting the rated capacity of the battery according to the historical state data and the initial parameters of the battery; and estimating the first SOC according to the updated SOC-OCV corresponding relation, the corrected rated capacity and the received state data of the battery in the last cyclic use process.
According to some embodiments of the application, said modifying the rated capacity of the battery according to the historical state data and the initial parameters of the battery comprises: counting the times and duration of charge and discharge exceeding the rated multiplying power in the historical state data; determining a decay in battery capacity based on the number of times and the duration; and correcting the rated capacity of the battery according to the attenuation of the battery capacity and the initial capacity.
According to some embodiments of the application, the modifying the rated capacity of the battery based on the historical state data and the initial parameters of the battery further comprises: combining the date of leaving the factory of the battery with historical temperature data to obtain the standing time length and the standing temperature of the battery in the cyclic use process; calculating according to the standing temperature and the standing time to obtain the attenuation of the battery capacity; and correcting the rated capacity of the battery according to the attenuation of the battery capacity and the initial capacity.
According to some embodiments of the application, the estimating the second SOC by a combination of an ampere-hour integration method and an open circuit voltage method includes: dividing an OCV-SOC curve corresponding to the battery into a voltage platform area, a low-voltage area and a high-voltage area; calculating the second SOC in the high-voltage area and the low-voltage area by an open circuit voltage method; and calculating the second SOC in the voltage platform region by an ampere-hour integration method.
An embodiment of the present application provides a powered device including a battery and a processor for performing the method of estimating a battery 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 performing a method of estimating a battery SOC of a battery as described above.
According to the embodiment of the application, the influence of SOC estimation is corrected according to the change of the historical state data of the whole life cycle range of the battery, and the SOC estimation precision of the lithium battery is improved. The method has the advantages that the collected historical state data are uploaded to a cloud big data platform, and the local offline calculation is combined through the big data platform calculation, so that the problems of insufficient local calculation power and weak data storage and analysis capability are solved, and the estimation accuracy of the SOC is improved.
Drawings
Fig. 1 is a schematic structural diagram of a powered device according to an embodiment of the present application.
Fig. 2 is a flowchart of a method of estimating a battery SOC of a battery according to an embodiment of the present application.
FIG. 3 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
Processor 11
Battery 12
Battery management system 120
Acquisition module 101
Transmitting module 102
Receiving module 103
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. It will be apparent to those skilled in the art that the specific meaning of the terms described above in the present application may be set forth immediately according to 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, an estimation system 100 operates in a powered device 1. The powered device 1 includes, but is not limited to, a communication unit 10, at least one processor 11, and a battery 12, where the above components may be connected by a bus or may be directly connected.
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.
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.
In one embodiment, the battery 12 is a rechargeable battery for providing electrical energy to the powered device 1. For example, the battery 12 may be a lithium ion battery, a lithium polymer battery, a lithium iron phosphate battery, or the like. The battery 12 includes at least one cell, and the battery 12 may be rechargeable in a recyclable manner.
The battery 12 is used for storing electricity, and the positive electrode and the negative electrode of the battery 12 can be separated and receive energy-carrying particles. According to the application scenario of the battery 12, the battery 12 in the embodiment of the present application may include a power battery and an energy storage battery, where the power battery may be applied to, for example, electric vehicles, electric bicycles, and other electric tool fields, and the energy storage battery may be applied to, for example, fields of energy storage power stations, renewable energy grid connection, micro-grids, and the like. Taking a power battery as an example, from the battery 12 types, the battery 12 may be, but not limited to, a lithium iron phosphate system battery or a silicon adding system battery, wherein the lithium iron phosphate system battery is a lithium ion battery containing lithium iron phosphate as a positive electrode active material, and the silicon adding system battery is a lithium ion battery containing silicon as a negative electrode active material.
In this embodiment, the Battery 12 includes a Battery management system (Battery MANAGEMENT SYSTEM, BMS) 120, and the Battery management system 120 can manage the charge and discharge of the Battery 12.
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.
Referring to fig. 2, fig. 2 is a flowchart of a method of estimating a battery SOC according to an embodiment of the present application. The method of estimating the battery SOC may include the steps of:
Step S21: historical state data of the battery in the cyclic use process is collected.
In this embodiment, in order to more accurately estimate the SOC of the battery, the battery management system of the battery may collect the historical state data during the cyclic use of the battery. Wherein the historical state data includes historical voltage data, historical current data, historical temperature data, historical SOC data, and cycle times.
Step S22: and sending the historical state data to a cloud big data platform, wherein the cloud big data platform estimates and obtains a first SOC according to the historical state data and the pre-stored initial parameters of the battery.
In this embodiment, the cloud big data platform receives the historical state data and stores all the historical state data of the battery from factory operation. The cloud big data platform also stores initial parameters of the batteries in advance, wherein the initial parameters comprise delivery date, initial capacity and the like of each battery. The cloud big data platform can estimate and obtain a first SOC according to the historical state data and initial parameters of the battery.
Specifically, the estimating, by the cloud big data platform, the first SOC according to the historical state data and the prestored initial parameters of the battery includes:
(1) And dynamically updating the current SOC-OCV corresponding relation of the battery based on the historical state data. In this embodiment, the stored historical voltage data and the historical SOC data are subjected to matching analysis, so that the current SOC-OCV correspondence can be dynamically updated.
(2) And correcting the rated capacity of the battery according to the historical state data and the initial parameters of the battery.
The capacity fade of the battery 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 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 corresponding multiplying power values, 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, the modifying the rated capacity of the battery according to the historical state data and the initial parameter of the battery includes: counting the times and duration of charge and discharge exceeding the rated multiplying power in the historical state data; determining a decay in battery capacity based on the number of times and the duration; and correcting the rated capacity of the battery according to the attenuation of the battery capacity and the initial capacity. It should be noted that, the capacity attenuation of the battery under different discharge multiplying factors 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 a battery using a rate of 2C can result 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 battery is in actual recycling, 3600 times of current with 2C multiplying power appear, and each time lasting for 1 second. Then the capacity of the corresponding battery should be the second preset capacity after 3600 charge-discharge processes at 2C magnification.
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, said modifying the rated capacity of the battery based on the historical state data and the initial parameters of the battery further comprises: combining the date of leaving the factory of the battery with historical temperature data to obtain the standing time length and the standing temperature of the battery in the cyclic use process; calculating according to the standing temperature and the standing time to obtain the attenuation of the battery capacity; and correcting the rated capacity of the battery according to the attenuation of the battery capacity and the initial capacity.
(3) And estimating the first SOC according to the updated SOC-OCV corresponding relation, the corrected rated capacity and timely battery state data (for example, the state data of the battery in the last cycle use process) of the battery received by the platform in the use process. Specifically, according to the current latest SOC-OCV correspondence, the corrected rated capacity, and the timely battery state data of the battery received by the platform in the use process, the cloud big data platform calculates an SOC estimation value (i.e., a first SOC) more conforming to the actual SOC of the battery, and issues the first SOC to the BMS of the corresponding battery.
Step S23: and receiving the first SOC sent by the cloud big data platform, and taking the first SOC as the SOC of the battery.
In this embodiment, since the SOC of the battery currently calculated by the electric device does not consider the influence of the historical data, there is a certain error. Therefore, after receiving the first SOC, the first SOC is taken as the SOC of the battery.
In the present application, the method of estimating the SOC of the battery further includes: estimating and obtaining a second SOC (namely, the SOC of the battery currently calculated by the electric equipment) by combining an ampere-hour integration method and an open-circuit voltage method; an SOC of the battery is determined based on the first SOC and the second SOC. Wherein the priority of the first SOC is higher than the priority of the second SOC. When the electric equipment receives the first SOC sent by the cloud big data platform and estimates the second SOC, the first SOC is preferentially used as the SOC of the battery.
In this embodiment, the second SOC may be used as the SOC of the battery when the electric device is in the network-free state. Or when the electric equipment considers the network charge problem, the second SOC can be used as the SOC of the battery under the condition that the first SOC sent by the cloud big data platform is not received any more.
In this embodiment, the OCV-SOC curve of the battery is characterized by a steep at both ends and a gentle at the middle. The battery has the characteristics that the change of the capacity of the battery in the early and later stages of charge and discharge is obvious, and the change of the capacity in the middle stage of charge and discharge is not obvious in voltage change. And calculating the second SOC by adopting different methods in different charge and discharge phases.
Specifically, estimating the second SOC by a combination of an ampere-hour integration method and an open circuit voltage method includes:
(1) And dividing the OCV-SOC curve corresponding to the battery 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 SOC span corresponding to the platform area is larger; the slope of the OCV-SOC curve varies greatly in the low-voltage region and the high-voltage region.
(2) And calculating the second SOC in the high-voltage area and the low-voltage area through an open circuit voltage method.
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 SOC value corresponding to the value can be determined to be more consistent with the current real SOC, and the open circuit voltage method is higher in priority than the ampere-hour integration method. The second SOC 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.
(3) And calculating the second SOC in the voltage platform region by an ampere-hour integration method.
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 SOC 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.
It should be noted that, the ampere-hour integration method and the open-circuit voltage method are both existing methods for estimating SOC, and are not described in detail in the present application.
According to the method, the influence of SOC estimation is corrected according to the change of the historical state data of the whole life cycle range of the battery, and the SOC estimation precision of the lithium battery is improved. The method has the advantages that the collected historical state data are uploaded to a cloud big data platform, and the local offline calculation is combined through the big data platform calculation, so that the problems of insufficient local calculation power and weak data storage and analysis capability are solved, and the estimation accuracy of the SOC is improved.
Referring to fig. 3, in this embodiment, the estimation system 100 may be divided into one or more modules, and the one or more modules may be stored in the processor 11, and the method for estimating the SOC of the battery according to the embodiment of the present application is performed by the processor 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 transmission module 102, and a reception module 103 in fig. 3.
The acquisition module 101 is used for acquiring historical state data of the battery in the cyclic use process; the sending module 102 is configured to send the historical state data to a cloud big data platform, where the cloud big data platform estimates and obtains a first SOC according to the historical state data and a pre-stored initial parameter of the battery; the receiving module 103 is configured to receive a first SOC sent by the cloud big data platform, and take the first SOC as an SOC of the battery.
By the estimation system 100, the SOC of the battery can be estimated according to the historical data of the battery in the charging and discharging processes, and the estimation accuracy of the SOC of the battery is improved. For details, reference may be made to the above-described embodiments of the method for estimating the SOC of the battery, and details thereof will not be provided herein.
In one embodiment, the Processor 11 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor 11 may be any other conventional processor or the like.
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 (8)
1. The method for estimating the SOC of the battery is applied to electric equipment, and the electric equipment is in communication connection with a cloud big data platform, and is characterized by comprising the following steps:
collecting historical state data of the battery in the cyclic use process;
The historical state data is sent to a cloud big data platform, wherein the cloud big data platform estimates and obtains a first SOC according to the historical state data and the pre-stored initial parameters of the battery, and the method comprises the following steps: dynamically updating the current SOC-OCV corresponding relation of the battery based on the historical state data;
correcting the rated capacity of the battery according to the historical state data and the initial parameters of the battery;
Estimating the first SOC according to the updated SOC-OCV corresponding relation, the corrected rated capacity and the received state data of the battery in the last cyclic use process;
and receiving the first SOC sent by the cloud big data platform, and taking the first SOC as the SOC of the battery.
2. The method of estimating a battery SOC of claim 1, further comprising:
estimating and obtaining a second SOC by combining an ampere-hour integration method and an open-circuit voltage method; and
When the electric equipment receives the first SOC sent by the cloud big data platform, the first SOC is preferentially used as the SOC of the battery.
3. The method of estimating a battery SOC of claim 1, wherein the historical state data includes historical voltage data, historical current data, historical temperature data, historical SOC data, and number of cycles, and the initial parameters include a factory date and an initial capacity.
4. The method of estimating a battery SOC of claim 3, wherein the modifying the rated capacity of the battery based on the historical state data and the initial parameters of the battery includes:
Counting the times and duration of charge and discharge exceeding the rated multiplying power in the historical state data;
determining a decay in battery capacity based on the number of times and the duration; and
And correcting the rated capacity of the battery according to the attenuation of the battery capacity and the initial capacity.
5. The method of estimating a battery SOC of claim 4, wherein said modifying the rated capacity of the battery based on the historical state data and the initial parameters of the battery further comprises:
combining the date of leaving the factory of the battery with historical temperature data to obtain the standing time length and the standing temperature of the battery in the cyclic use process;
Calculating according to the standing temperature and the standing time to obtain the attenuation of the battery capacity;
and correcting the rated capacity of the battery according to the attenuation of the battery capacity and the initial capacity.
6. The method of estimating a battery SOC of claim 2, wherein the estimating the second SOC by a combination of an ampere-hour integration method and an open circuit voltage method includes:
dividing an OCV-SOC curve corresponding to the battery into a voltage platform area, a low-voltage area and a high-voltage area;
Calculating the second SOC in the high-voltage area and the low-voltage area by an open circuit voltage method; and
And calculating the second SOC in the voltage platform region by an ampere-hour integration method.
7. An electrical device, the electrical device comprising:
A battery;
And a processor for executing the method of estimating the battery SOC according to any one of claims 1 to 6.
8. 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 battery SOC according to any of claims 1 to 6.
Priority Applications (1)
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| CN102662148B (en) * | 2012-05-09 | 2014-06-18 | 中国农业大学 | On-line feedback battery state of charge (SOC) predicting method |
| US9643511B2 (en) * | 2014-12-17 | 2017-05-09 | Samsung Electronics Co., Ltd. | Method and apparatus for estimating state of charge (SOC) of battery in electric vehicle |
| CN105021996A (en) * | 2015-08-04 | 2015-11-04 | 深圳拓普科新能源科技有限公司 | Battery SOH (section of health) estimation method of energy storage power station BMS (battery management system) |
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| CN109828215A (en) * | 2019-02-26 | 2019-05-31 | 清华大学 | A kind of method and system promoting battery cell SOC estimation precision |
| CN110133526B (en) * | 2019-05-31 | 2021-12-14 | 蜂巢能源科技有限公司 | Method, device and equipment for determining SOC of battery and storage medium |
| CN110673037B (en) * | 2019-09-11 | 2022-02-22 | 国网河北省电力有限公司石家庄供电分公司 | Battery SOC estimation method and system based on improved simulated annealing algorithm |
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