Lithium battery SOC estimation method, device, storage medium and apparatus

Abstract

The present invention discloses a lithium battery SOC estimation method, equipment, storage medium and device. The present invention corrects the error of the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm, determines whether to use the ampere-hour integration method to estimate the SOC of the target lithium battery according to the corrected first change amplitude and the preset change amplitude; estimates the SOC by switching to the ampere-hour integration method; and when the SOC estimation error is greater than the preset error value, performs the step of performing error correction on the SOC based on the deviation-compensated least squares method and the extended set membership estimation algorithm. Since the present invention can switch between the extended set membership estimation algorithm and the ampere-hour integration algorithm to correct the SOC value when estimating the battery SOC online, compared with the commonly used estimation method in the prior art, which leads to low calculation efficiency, the present invention switches the algorithm, and the correction process takes a short time to meet the requirements of SOC estimation accuracy and estimation efficiency in different application scenarios.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a lithium battery SOC estimation method, equipment, storage medium and device. Background Art

[0002] In addition to the detection and protection functions of battery overvoltage, overcurrent, undervoltage, overtemperature, etc., the onboard battery management system (BMS) for the cascade utilization of retired power batteries is also responsible for the online estimation of battery parameters such as SOC and SOH. However, the current onboard battery management system (BMS) for the cascade utilization of retired power batteries has limited computing power and cannot complete complex online calculations, which results in the inability to use high-precision SOC estimation methods online in the onboard battery management system. Commonly used SOC estimation methods, such as the ampere-hour integration method and the open-circuit voltage method, although they occupy less computing resources, have low SOC accuracy obtained online.

[0003] For the application areas of retired power batteries in low-speed electric vehicles, such as food delivery electric vehicles, express delivery electric vehicles, factory low-speed logistics trailers, etc., the discharge current of the battery changes rapidly during operation and the amplitude of the change is large. It is difficult to obtain an accurate SOC value using ampere-hour integration. At the same time, battery users in this type of application field have high requirements on the economy of energy storage devices. For this reason, the battery management systems usually equipped in this type of application field have low cost and low computing power.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of the present invention is to provide a lithium battery SOC estimation method, equipment, storage medium and device, aiming to solve the technical problem in the prior art of how to accurately obtain the SOC value of retired power batteries in the application field of low-speed electric vehicles, and effectively prevent overcharging or over-discharging, ensure the safe and cascade utilization of retired power batteries, and extend the service life of retired power batteries.

[0006] To achieve the above object, the present invention provides a lithium battery SOC estimation method, the lithium battery SOC estimation method comprising the following steps:

[0007] Based on the deviation-compensated least squares method and the extended set membership estimation algorithm, the SOC of the target lithium battery is corrected to obtain a first variation amplitude of the corrected target SOC;

[0008] Determining whether to use the ampere-hour integration method to estimate the SOC of the target lithium battery according to the first change amplitude and a preset change amplitude;

[0009] When the ampere-hour integration method is switched to estimate the SOC of the target lithium battery, an SOC estimation error corresponding to the ampere-hour integration method is obtained in real time;

[0010] When the SOC estimation error is greater than a preset error value, the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC is performed.

[0011] Optionally, the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC includes:

[0012] Based on the deviation compensation least squares method, the battery model characteristic parameters corresponding to the target lithium battery are compensated for deviations to obtain optimal estimated values ​​corresponding to the battery model characteristic parameters;

[0013] The SOC of the target lithium battery is error corrected according to the optimal estimate value and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC.

[0014] Optionally, before the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain the first change amplitude of the corrected target SOC, the step further includes:

[0015] Obtain voltage signal, current signal and temperature signal of the target lithium battery load;

[0016] Constructing a model mathematical relationship based on the voltage signal, the current signal, the temperature signal and a preset equivalent circuit;

[0017] The mathematical relationship of the model is: ;

[0018] in, is the state of charge value of the battery at time k+1, is the state of charge value of the battery at time k, is the voltage value of the polarization resistance and polarization capacitance of the battery at time k+1, is the battery polarization resistance value, is the battery polarization capacitance, is the battery charge and discharge capacity, is the battery's ohmic internal resistance, is the battery load current, is the open circuit voltage of the battery at time k under ambient temperature T;

[0019] A battery model corresponding to the target lithium battery is constructed according to the model mathematical relationship.

[0020] Optionally, after the step of constructing the battery model corresponding to the target lithium battery according to the model mathematical relationship, the method further includes:

[0021] Performing Laplace transformation on the mathematical relationship of the model, we obtain:

[0022] ;

[0023] in, , , is a function of the battery characteristic parameters, k is the current value, k-1 is the previous value, For system input, Output for the system;

[0024] ;

[0025] The battery model characteristic parameters corresponding to the target lithium battery are iteratively calculated using the least squares algorithm, and the iterative calculation relationship is:

[0026]

[0027] in, , ;

[0028] is the reference value estimated by the system at the previous moment, is the size of the observation at this moment, As the actual observed value of the system, The prediction error is obtained after subtraction. Multiply to obtain the correction of the current prediction value to obtain the least squares estimate at the current moment ;

[0029] Based on the deviation compensation least squares method, the least squares estimation value at the current moment is compensated for the deviation to obtain the optimal estimation value corresponding to the characteristic parameter of the battery model;

[0030] The step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC includes:

[0031] The SOC of the target lithium battery is error corrected according to the optimal estimate value and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC.

[0032] Optionally, the step of performing error correction on the SOC of the target lithium battery according to the optimal estimate and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC includes:

[0033] Constructing a parameter matrix according to the optimal estimate and the extended set membership estimation algorithm; ;

[0034] In the formula, is the current measurement noise, is the voltage measurement noise, A, B, C, and D are all parameter matrices;

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] Initialize the extended set membership estimation algorithm parameters and obtain the target parameters;

[0040] The SOC of the target lithium battery is error corrected according to the target parameter and a parameter matrix corresponding to the preset type to obtain a first variation amplitude of the corrected target SOC.

[0041] Optionally, when the ampere-hour integration method is switched to estimate the SOC of the target lithium battery, the step of acquiring the SOC estimation error corresponding to the ampere-hour integration method in real time includes:

[0042] When the ampere-hour integration method is switched to estimate the SOC of the target lithium battery, an error estimation is performed according to a preset formula and the corrected target SOC to obtain an SOC estimation error corresponding to the ampere-hour integration method;

[0043] The preset formula is: , ;

[0044] in, is the initial value of SOC, is the battery capacity, Coulomb efficiency, is the SOC value at the next moment, is the current SOC value, It is the working current of the battery at the current moment.

[0045] Optionally, when the SOC estimation error is greater than a preset error value, the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC includes:

[0046] Setting a corresponding preset error value according to the current SOC estimation algorithm;

[0047] When the SOC estimation error is greater than a preset error value, the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC is performed.

[0048] In addition, to achieve the above-mentioned purpose, the present invention also proposes a lithium battery SOC estimation device, which includes a memory, a processor, and a lithium battery SOC estimation program stored in the memory and executable on the processor, and the lithium battery SOC estimation program is configured to implement the steps of lithium battery SOC estimation as described above.

[0049] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a lithium battery SOC estimation program is stored. When the lithium battery SOC estimation program is executed by a processor, the steps of the lithium battery SOC estimation method described above are implemented.

[0050] In addition, to achieve the above object, the present invention also proposes a lithium battery SOC estimation device, the lithium battery SOC estimation device comprising:

[0051] An error correction module, used to perform error correction on the SOC of the target lithium battery based on a deviation-compensated least squares method and an extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC;

[0052] an algorithm switching module, configured to determine whether to use the ampere-hour integration method to estimate the SOC of the target lithium battery according to the first change amplitude and a preset change amplitude;

[0053] An SOC estimation module, for obtaining in real time an SOC estimation error corresponding to the ampere-hour integration method when switching to the ampere-hour integration method to estimate the SOC of the target lithium battery;

[0054] The error correction module is also used to execute the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first change amplitude of the corrected target SOC when the SOC estimation error is greater than a preset error value.

[0055] The present invention performs error correction on the SOC of a target lithium battery based on a deviation-compensated least squares method and an extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC; determines whether to use the ampere-hour integration method to estimate the SOC of the target lithium battery based on the first variation amplitude and a preset variation amplitude; when switching to the ampere-hour integration method to estimate the SOC of the target lithium battery, obtains the SOC estimation error corresponding to the ampere-hour integration method in real time; when the SOC estimation error is greater than the preset error value, performs the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain the first variation amplitude of the corrected target SOC. Since the present invention can switch between the extended set membership estimation algorithm and the ampere-hour integration algorithm to correct the SOC value when estimating the battery SOC online, compared with the commonly used accurate SOC estimation methods (H-infinity filter algorithm, Kalman filter algorithm, set membership estimation algorithm, etc.) in the prior art, the calculation efficiency is high and the calculation resource occupation is low; the present invention corrects the SOC value by algorithm switching, the correction process is time-consuming, and the SOC convergence speed is fast. The algorithm in the present invention can set different switching correction times to meet the requirements of SOC estimation accuracy and estimation efficiency in different application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a structural schematic diagram of a lithium battery SOC estimation device in a hardware operating environment involved in an embodiment of the present invention;

[0057] Figure 2 It is a flow chart of the first embodiment of the lithium battery SOC estimation method of the present invention;

[0058] Figure 3 Schematic diagram of the implementation process of the first embodiment of the lithium battery SOC estimation method of the present invention

[0059] Figure 4 It is a flow chart of a second embodiment of a lithium battery SOC estimation method according to the present invention;

[0060] Figure 5 A schematic diagram of a preset equivalent circuit of a second embodiment of a lithium battery SOC estimation method according to the present invention;

[0061] Figure 6 FIG. 4 is a structural block diagram of a first embodiment of a lithium battery SOC estimation device according to the present invention.

[0062] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0063] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0064] Reference Figure 1 , Figure 1 The figure is a schematic diagram of the structure of a lithium battery SOC estimation device in the hardware operating environment involved in the embodiment of the present invention.

[0065] like Figure 1 As shown, the lithium battery SOC estimation device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), and the user interface 1003 may optionally include a standard wired interface and a wireless interface. The wired interface of the user interface 1003 may be a USB interface in the present invention. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) or a stable memory (Non-volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0066] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the lithium battery SOC estimation device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0067] like Figure 1 As shown, the memory 1005 identified as a computer storage medium may include an operating system, a network communication module, a user interface module, and a lithium battery SOC estimation program.

[0068] exist Figure 1 In the lithium battery SOC estimation device shown, the network interface 1004 is mainly used to connect to the background server and communicate data with the background server; the user interface 1003 is mainly used to connect to the user device; the lithium battery SOC estimation device calls the lithium battery SOC estimation program stored in the memory 1005 through the processor 1001, and executes the lithium battery SOC estimation method provided in the embodiment of the present invention.

[0069] Based on the above hardware structure, an embodiment of the lithium battery SOC estimation method of the present invention is proposed.

[0070] Reference Figure 2 , Figure 21 is a flow chart of a first embodiment of a lithium battery SOC estimation method according to the present invention, and provides a first embodiment of a lithium battery SOC estimation method according to the present invention.

[0071] In this embodiment, the lithium battery SOC estimation method includes the following steps:

[0072] Step S10: performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC.

[0073] It should be noted that the execution subject of this embodiment can be a lithium battery device including an onboard BMS lithium-ion battery state of charge online estimation system with low computing resource usage. The device can be an onboard computer or a device connected to the vehicle by wireless or wired means, such as a computer, a notebook, a mobile phone, etc. The system can realize accurate and efficient online estimation of lithium-ion battery SOC. In this embodiment, a data set of A123 model lithium-ion batteries can be selected as a training set to improve the accuracy of system estimation data. The data set is data collected under three temperature conditions of 0°C, 25°C, and 45°C. Under the same temperature conditions, different loads simulating automobile driving states are applied to the lithium-ion battery, including dynamic stress test (Dynamic Stress Test, DST) and Federal Urban Driving Schedule (Federal Urban Driving Schedule, FUDS). The detailed information of the lithium-ion battery is shown in Table 1, and the detailed information of the data set is shown in Table 1 below.

[0074] Table 1A123 lithium-ion battery specific parameters

[0075]

[0076] It should be understood that the deviation-compensated least squares method and the extended set membership estimation algorithm in this embodiment are algorithms for online estimation of the SOC value of a lithium battery and error correction of the estimated SOC value. When estimating the SOC of a lithium battery, the deviation-compensated least squares method is used to perform deviation compensation on the battery model characteristic parameters corresponding to the target lithium battery. Compared with the traditional least squares method, the deviation-compensated least squares method has a smaller calculation error, thereby improving the accuracy of the battery model characteristic parameters. The current SOC value of the lithium battery is calculated based on the extended set membership estimation algorithm and the battery model characteristic parameters after deviation compensation, thereby accurately obtaining the battery SOC value at the current moment, and determining the first change amplitude of the corrected target SOC based on the battery SOC value at the current moment and the battery SOC value at the previous moment.

[0077] Step S20: judging whether to use the ampere-hour integration method to estimate the SOC of the target lithium battery according to the first change amplitude and the preset change amplitude.

[0078] It should be noted that the preset change amplitude may be a preset amplitude used to determine whether the SOC change amplitude exceeds the amplitude corresponding to the preset change rate. The preset change rate is a preset amplitude change rate, which may be set according to the battery type. Different amplitude change rates may be set according to different battery types, so as to achieve battery SOC estimation for different scenarios.

[0079] It should be understood that when estimating the SOC by the ampere-hour integration method, the computing resource usage can be reduced and the computing efficiency can be improved. Therefore, the first change amplitude and the preset change amplitude are used to determine whether the current battery system has reached the preset condition, so that the ampere-hour integration method is used to estimate the SOC of the target lithium battery to ensure that the computing accuracy is guaranteed while reducing the computing resource usage.

[0080] Step S30: when switching to the ampere-hour integration method to estimate the SOC of the target lithium battery, obtaining the SOC estimation error corresponding to the ampere-hour integration method in real time.

[0081] It should be noted that when the ampere-hour integration method is used to estimate the battery SOC online, in order to avoid the influence of the estimation error of the ampere-hour integration method on the battery SOC output result, it is necessary to obtain the SOC estimation error corresponding to the ampere-hour integration method in real time, so as to switch to the extended set membership estimation algorithm to estimate the battery SOC online in time after the SOC error estimated online by the ampere-hour integration method increases, so as to correct the SOC estimation error caused by the ampere-hour integration method.

[0082] Step S40: When the SOC estimation error is greater than a preset error value, the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC is executed.

[0083] It should be noted that for the cascade application of retired power batteries in low-speed electric vehicles, such as takeaway electric vehicles, express delivery electric vehicles, and factory low-speed logistics trailers, the discharge current of the battery changes rapidly during operation, and the change range is large. It is difficult to obtain an accurate SOC value using ampere-hour integration. At the same time, battery users in such application fields have high requirements for the economy of energy storage devices, and such application fields are usually equipped with battery management systems with low computing power. In order to obtain a high-precision battery SOC value and reduce the computing resource occupancy rate, the battery SOC can be estimated by switching algorithms.

[0084] It can be understood that the preset error value is a preset error threshold corresponding to the battery SOC estimation algorithm when the system switches the algorithm. For example, if the system adopts the ampere-hour integration method to estimate the battery SOC online, the algorithm switching threshold is set according to the ampere-hour integration method. If the SOC estimation error is greater than the preset error value, it is necessary to switch the online estimation algorithm to the extended set membership estimation algorithm to correct the SOC error, and execute the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain the first change amplitude of the corrected target SOC.

[0085] Furthermore, the step S40 includes: setting a corresponding preset error value according to the current SOC estimation algorithm; when the SOC estimation error is greater than the preset error value, executing the error correction step based on the deviation-compensated least squares method and the extended set membership estimation algorithm to correct the SOC of the target lithium battery, and obtaining a first change amplitude of the corrected target SOC.

[0086] It should be noted that since two algorithms are used to estimate the battery SOC online in this solution, the number of algorithm switches can be adjusted by setting different algorithm switching thresholds, and the algorithm's computational efficiency and computing resource occupancy rate can be adjusted at the same time. Different error values ​​are set for different algorithms. When the preset error value is set to a smaller value, the number of algorithm switches will increase, and the algorithm's computational efficiency will decrease, and the computing resource occupancy rate will increase; when the preset error value is set to a larger value, the number of algorithm switches will decrease, and the algorithm's computational efficiency will increase, and the computing resource occupancy rate will decrease. In order to meet the algorithm switching requirements in various scenarios, different preset error values ​​can be set according to different battery types under different estimation algorithms, so that battery SOC estimation in various scenarios can be achieved.

[0087] In the specific implementation, for further explanation, please refer to Figure 3Schematic diagram of the implementation process. BMS collects the working voltage and current of the lithium-ion battery in real time, and determines whether the battery SOC error has been corrected. When the SOC error is not corrected, the deviation compensation least squares method of the SOC error correction module is used to identify the battery model parameter value online, and the battery SOC is estimated online according to the extended set membership estimation algorithm. After the SOC error has been corrected, it can be determined whether the SOC change amplitude meets the preset change rate (such as 1%), so that the algorithm switches to the ampere-hour integration to estimate the battery SOC. If it meets the requirements, that is, when the ampere-hour integration method is used to estimate the battery SOC online, the error value can be set according to the fault tolerance rate corresponding to the ampere-hour integration method. When the SOC error estimated online by the ampere-hour integration method increases and is greater than the set error value, the extended set membership estimation algorithm is switched to estimate the SOC to correct the error estimated by the ampere-hour integration method. This solution estimates the battery SOC online by regularly switching to the extended set membership estimation algorithm to correct the SOC estimation error caused by the ampere-hour integration method and improve the estimation accuracy. After correcting the SOC error, the method of the present invention automatically switches to the ampere-hour integration method to estimate the SOC online, so as to reduce the computing resource occupation of the SOC estimation algorithm and improve the computing efficiency.

[0088] This embodiment performs error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first change amplitude of the corrected target SOC; determines whether to use the ampere-hour integration method to estimate the SOC of the target lithium battery based on the first change amplitude and the preset change amplitude; when switching to the ampere-hour integration method to estimate the SOC of the target lithium battery, obtains the SOC estimation error corresponding to the ampere-hour integration method in real time; when the SOC estimation error is greater than the preset error value, executes the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain the first change amplitude of the corrected target SOC. Since the present embodiment can correct the SOC value by switching between the extended set membership estimation algorithm and the ampere-hour integration algorithm when estimating the battery SOC online, compared with the commonly used accurate SOC estimation methods in the prior art (H-infinity filter algorithm, Kalman filter algorithm, set membership estimation algorithm, etc.), which result in high computing resource usage and low computing efficiency, the present embodiment corrects the SOC value by algorithm switching, and the correction process is time-saving and the SOC convergence speed is fast. The algorithm of the present embodiment can set different switching correction times to meet the requirements of SOC estimation accuracy and estimation efficiency in different application scenarios.

[0089] Reference Figure 4 , Figure 4 This is a flow chart of a second embodiment of a lithium battery SOC estimation method according to the present invention. Figure 2 The first embodiment shown is a second embodiment of the lithium battery SOC estimation method of the present invention.

[0090] In this embodiment, the step S10 includes:

[0091] Step S101: performing deviation compensation on the battery model characteristic parameters corresponding to the target lithium battery based on the deviation compensation least squares method to obtain the optimal estimated values ​​corresponding to the battery model characteristic parameters.

[0092] It should be noted that the battery model characteristic parameters include characteristic parameters corresponding to ohmic resistance, polarization resistance and polarization capacitance. When the deviation of the battery model characteristic parameters is compensated by the least square method of deviation compensation, the optimal estimated values ​​of the characteristic parameters corresponding to ohmic resistance, polarization resistance and polarization capacitance are output, so as to obtain accurate battery model characteristic parameters, so as to estimate the battery SOC later.

[0093] Step S102: performing error correction on the SOC of the target lithium battery according to the optimal estimate value and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC.

[0094] In the specific implementation, after obtaining the optimal estimated values ​​corresponding to the ohmic resistance, polarization resistance and polarization capacitance, the SOC of the target lithium battery is error corrected through the extended set membership estimation algorithm, so as to accurately obtain the battery SOC value at the current moment, and determine the first change amplitude of the corrected target SOC based on the battery SOC value at the current moment and the battery SOC value at the previous moment.

[0095] Furthermore, the step before S10 includes: obtaining a voltage signal, a current signal and a temperature signal of a target lithium battery load; constructing a model mathematical relationship based on the voltage signal, the current signal, the temperature signal and a preset equivalent circuit; and constructing a battery model corresponding to the target lithium battery according to the model mathematical relationship.

[0096] The mathematical relationship of the model is: ;

[0097] in, is the state of charge value of the battery at time k+1, is the state of charge value of the battery at time k, is the voltage value of the polarization resistance and polarization capacitance of the battery at time k+1, is the battery polarization resistance value, is the battery polarization capacitance, is the battery charge and discharge capacity, is the battery's ohmic internal resistance, is the battery load current, is the open circuit voltage of the battery at time k under an ambient temperature of temperature T; ; The coefficient K can be shown in Table 2 below:

[0098] Table 2 Coefficient K table

[0099]

[0100] In the specific implementation, the battery management system obtains the voltage signal, current signal and battery temperature signal of the battery load in real time, and Figure 5 The preset equivalent circuit schematic diagram shown in the figure constructs a model mathematical relationship to determine the battery model corresponding to the target lithium battery;

[0101] Furthermore, after the step of constructing the battery model corresponding to the target lithium battery according to the model mathematical relationship, it also includes:

[0102] Performing Laplace transformation on the mathematical relationship of the model, we obtain:

[0103] ;

[0104] in, , , is a function of the battery characteristic parameters, k is the current value, k-1 is the previous value, For system input, Output for the system;

[0105] ;

[0106] The battery model characteristic parameters corresponding to the target lithium battery are iteratively calculated using the least squares algorithm, and the iterative calculation relationship is:

[0107]

[0108] in, , ;

[0109] is the reference value estimated by the system at the previous moment, is the size of the observation at this moment, As the actual observed value of the system, The prediction error is obtained after subtraction. Multiply to obtain the correction of the current prediction value to obtain the least squares estimate at the current moment ;

[0110] Based on the deviation compensation least squares method, the least squares estimation value at the current moment is compensated for the deviation to obtain the optimal estimation value corresponding to the characteristic parameter of the battery model;

[0111] It should be noted that the calculation steps are:

[0112] Compute the bias-compensated least squares estimation error :

[0113] = ;

[0114] Compute bias-compensated least squares covariance :

[0115] = ;

[0116] Calculate the bias-compensated least squares gain :

[0117] = ;

[0118] Calculates the bias-compensated least squares error criterion function :

[0119] = ;

[0120] Calculate the bias-compensated least squares noise variance :

[0121] = ;

[0122] In the formula, is the deviation-compensated least squares estimate of the system at the previous moment, is the least squares estimate of the system at the current moment, is the correlation matrix, .

[0123] Compute bias-compensated least squares estimates : = ;

[0124] Get parameters , , , combined with the formula ;

[0125] The optimal estimated values ​​corresponding to the characteristic parameters of the battery model can be calculated, including the battery polarization resistance , battery polarization capacitance , battery ohmic internal resistance .

[0126] Furthermore, the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC includes:

[0127] The SOC of the target lithium battery is error corrected according to the optimal estimate value and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC.

[0128] In the specific implementation, the battery polarization resistance calculated according to the above formula is , battery polarization capacitance , battery ohmic internal resistance And the extended set membership estimation algorithm performs error correction on the SOC of the target lithium battery, so as to accurately obtain the battery SOC value at the current moment, and determine the first change amplitude of the corrected target SOC according to the battery SOC value at the current moment and the battery SOC value at the previous moment.

[0129] This embodiment performs deviation compensation on the battery model characteristic parameters corresponding to the target lithium battery based on the deviation compensation least squares method to obtain the optimal estimation value corresponding to the battery model characteristic parameter; performs error correction on the SOC of the target lithium battery according to the optimal estimation value and the extended set membership estimation algorithm to obtain the first change amplitude of the corrected target SOC; determines whether to use the ampere-hour integration method to estimate the SOC of the target lithium battery according to the first change amplitude and the preset change amplitude; when switching to the ampere-hour integration method to estimate the SOC of the target lithium battery, obtains the SOC estimation error corresponding to the ampere-hour integration method in real time; when the SOC estimation error is greater than the preset error value, executes the step of performing error correction on the SOC of the target lithium battery based on the deviation compensation least squares method and the extended set membership estimation algorithm to obtain the first change amplitude of the corrected target SOC. Since the present embodiment can correct the SOC value by switching between the extended set membership estimation algorithm and the ampere-hour integration algorithm when estimating the battery SOC online, compared with the commonly used accurate SOC estimation methods in the prior art (H-infinity filter algorithm, Kalman filter algorithm, set membership estimation algorithm, etc.), the computing resources are high and the computing efficiency is low; the present embodiment corrects the SOC value by algorithm switching, the correction process is time-saving, and the SOC convergence speed is fast. The algorithm of the present embodiment can set different switching correction times to meet the requirements of SOC estimation accuracy and estimation efficiency in different application scenarios.

[0130] Based on the above Figure 4 The first embodiment shown is a third embodiment of the lithium battery SOC estimation method of the present invention.

[0131] In this embodiment, the step of performing error correction on the SOC of the target lithium battery according to the optimal estimate and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC includes: constructing a parameter matrix according to the optimal estimate and the extended set membership estimation algorithm; initializing the extended set membership estimation algorithm parameters to obtain target parameters; performing error correction on the SOC of the target lithium battery according to the target parameters and the parameter matrix corresponding to the preset type to obtain a first variation amplitude of the corrected target SOC.

[0132] According to the mathematical relationship of the model and combining A, B, C, and D, we can know , And derive the equation:

[0133] ;

[0134] In the formula, is the current measurement noise, is the voltage measurement noise, A, B, C, and D are all parameter matrices;

[0135] ;

[0136] ;

[0137] ;

[0138] ;

[0139] Initialize the extended set membership estimation algorithm parameters: , P, Q, R, ,

[0140] in, Refers to the above The value with the subscript 0 represents the initial value, P is the positive definite envelope matrix, Q is the positive definite matrix describing the system noise ellipsoid, and R is the positive definite matrix describing the measurement noise ellipsoid. represents noise, is the radius.

[0141] System status estimation: ;

[0142] Positive definite envelope matrix estimate:

[0143] calculate ,get ;

[0144] calculate ,get ;

[0145] calculate ,get ;

[0146] Calculate the innovation matrix: ;

[0147] Calculate the gain matrix: ;

[0148] In the formula, According to the minimum ellipsoid radius calculate;

[0149] Update system status: ;(get That is, the required estimation result is obtained. The following formula is used to update the system parameters for the estimation at time k+1)

[0150] Update the positive definite envelope matrix: ;

[0151] Update the ellipsoid "generalized radius":

[0152] ;

[0153] In the formula, .

[0154] In this embodiment, the step S40 includes: when switching to the ampere-hour integration method to estimate the SOC of the target lithium battery, performing error estimation according to a preset formula and the corrected target SOC to obtain an SOC estimation error corresponding to the ampere-hour integration method;

[0155] The preset formula is: , ;

[0156] in, is the initial value of SOC, is the battery capacity, Coulomb efficiency, is the SOC value at the next moment, is the current SOC value, It is the working current of the battery at the current moment.

[0157] In the specific implementation, the SOC is estimated by the above formula. When the change in SOC reaches the set value △SOC, the SOC estimation algorithm switches to calculating the extended set membership estimation algorithm to estimate the battery SOC online.

[0158] This embodiment performs deviation compensation on the battery model characteristic parameters corresponding to the target lithium battery based on the deviation compensation least squares method to obtain the optimal estimation value corresponding to the battery model characteristic parameter; performs error correction on the SOC of the target lithium battery according to the optimal estimation value and the extended set membership estimation algorithm to obtain the first change amplitude of the corrected target SOC; determines whether to use the ampere-hour integration method to estimate the SOC of the target lithium battery according to the first change amplitude and the preset change amplitude; when switching to the ampere-hour integration method to estimate the SOC of the target lithium battery, performs error estimation according to the preset formula and the corrected target SOC to obtain the SOC estimation error corresponding to the ampere-hour integration method; when the SOC estimation error is greater than the preset error value, performs the step of performing error correction on the SOC of the target lithium battery based on the deviation compensation least squares method and the extended set membership estimation algorithm to obtain the first change amplitude of the corrected target SOC. Since the present embodiment can correct the SOC value by switching between the extended set membership estimation algorithm and the ampere-hour integration algorithm when estimating the battery SOC online, compared with the commonly used accurate SOC estimation methods in the prior art (H-infinity filter algorithm, Kalman filter algorithm, set membership estimation algorithm, etc.), the computing resources are high and the computing efficiency is low; the present embodiment corrects the SOC value by algorithm switching, the correction process is time-saving, and the SOC convergence speed is fast. The algorithm of the present embodiment can set different switching correction times to meet the requirements of SOC estimation accuracy and estimation efficiency in different application scenarios.

[0159] In addition, to achieve the above-mentioned purpose, the present invention also proposes a lithium battery SOC estimation device, which includes a memory, a processor, and a lithium battery SOC estimation program stored in the memory and executable on the processor, and the lithium battery SOC estimation program is configured to implement the steps of lithium battery SOC estimation as described above.

[0160] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a lithium battery SOC estimation program is stored. When the lithium battery SOC estimation program is executed by a processor, the steps of the lithium battery SOC estimation method described above are implemented.

[0161] Reference Figure 6 , Figure 6 FIG. 4 is a structural block diagram of a first embodiment of a lithium battery SOC estimation device according to the present invention.

[0162] like Figure 6 As shown, the lithium battery SOC estimation device proposed in the embodiment of the present invention includes:

[0163] The error correction module 10 is used to perform error correction on the SOC of the target lithium battery based on the deviation compensation least square method and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC;

[0164] An algorithm switching module 20, configured to determine whether to use the ampere-hour integration method to estimate the SOC of the target lithium battery according to the first change amplitude and a preset change amplitude;

[0165] The SOC estimation module 30 is used to obtain the SOC estimation error corresponding to the ampere-hour integration method in real time when the ampere-hour integration method is switched to estimate the SOC of the target lithium battery;

[0166] The error correction module 10 is also used to execute the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first change amplitude of the corrected target SOC when the SOC estimation error is greater than a preset error value.

[0167] This embodiment performs error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first change amplitude of the corrected target SOC; determines whether to use the ampere-hour integration method to estimate the SOC of the target lithium battery based on the first change amplitude and the preset change amplitude; when switching to the ampere-hour integration method to estimate the SOC of the target lithium battery, obtains the SOC estimation error corresponding to the ampere-hour integration method in real time; when the SOC estimation error is greater than the preset error value, executes the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain the first change amplitude of the corrected target SOC. Since the present embodiment can correct the SOC value by switching between the extended set membership estimation algorithm and the ampere-hour integration algorithm when estimating the battery SOC online, compared with the commonly used accurate SOC estimation methods in the prior art (H-infinity filter algorithm, Kalman filter algorithm, set membership estimation algorithm, etc.), the computing resources are high and the computing efficiency is low; the present embodiment corrects the SOC value by algorithm switching, the correction process is time-saving, and the SOC convergence speed is fast. The algorithm of the present embodiment can set different switching correction times to meet the requirements of SOC estimation accuracy and estimation efficiency in different application scenarios.

[0168] Furthermore, the error correction module 10 is also used to perform deviation compensation on the battery model characteristic parameters corresponding to the target lithium battery based on the deviation compensation least squares method to obtain the optimal estimation value corresponding to the battery model characteristic parameter; perform error correction on the SOC of the target lithium battery according to the optimal estimation value and the extended set membership estimation algorithm to obtain the first change amplitude of the corrected target SOC.

[0169] Furthermore, the lithium battery SOC estimation device also includes: a model construction module, the model construction is used to obtain the voltage signal, current signal and temperature signal of the target lithium battery load; based on the voltage signal, the current signal, the temperature signal and the preset equivalent circuit to construct a model mathematical relationship; according to the model mathematical relationship to construct the battery model corresponding to the target lithium battery;

[0170] The mathematical relationship of the model is: ;

[0171] in, is the state of charge value of the battery at time k+1, is the state of charge value of the battery at time k, is the voltage value of the polarization resistance and polarization capacitance of the battery at time k+1, is the battery polarization resistance value, is the battery polarization capacitance, is the battery charge and discharge capacity, is the battery's ohmic internal resistance, is the battery load current, is the open circuit voltage of the battery at time k when the ambient temperature is T.

[0172] Furthermore, the model building module is also used to perform Laplace transformation on the mathematical relationship of the model to obtain: ;

[0173] in, , , is a function of the battery characteristic parameters, k is the current value, k-1 is the previous value, For system input, Output for the system;

[0174] ;

[0175] The battery model characteristic parameters corresponding to the target lithium battery are iteratively calculated using the least squares algorithm, and the iterative calculation relationship is:

[0176]

[0177] in, , ;

[0178] is the reference value estimated by the system at the previous moment, is the size of the observation at this moment, As the actual observed value of the system, The prediction error is obtained after subtraction. Multiply to obtain the correction of the current prediction value to obtain the least squares estimate at the current moment ;

[0179] Based on the deviation compensation least squares method, the least squares estimation value at the current moment is compensated for the deviation to obtain the optimal estimation value corresponding to the characteristic parameter of the battery model;

[0180] The error correction module 10 is further used to perform error correction on the SOC of the target lithium battery according to the optimal estimation value and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC.

[0181] Furthermore, the error correction module 10 is further used to construct a parameter matrix according to the optimal estimation value and the extended set membership estimation algorithm; initialize the extended set membership estimation algorithm parameters to obtain target parameters; perform error correction on the SOC of the target lithium battery according to the target parameters and the parameter matrix corresponding to the preset type to obtain a first variation amplitude of the corrected target SOC;

[0182] ;

[0183] In the formula, is the current measurement noise, is the voltage measurement noise, A, B, C, and D are all parameter matrices;

[0184] ;

[0185] ;

[0186] ;

[0187] .

[0188] Furthermore, the SOC estimation module 30 is further configured to perform error estimation according to a preset formula and the corrected target SOC when switching to the ampere-hour integration method to estimate the SOC of the target lithium battery, and obtain the SOC estimation error corresponding to the ampere-hour integration method;

[0189] The preset formula is: , ;

[0190] in, is the initial value of SOC, is the battery capacity, Coulomb efficiency, is the SOC value at the next moment, is the current SOC value, It is the working current of the battery at the current moment.

[0191] Furthermore, the error correction module 10 is also used to set a corresponding preset error value according to the current SOC estimation algorithm; when the SOC estimation error is greater than the preset error value, the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first change amplitude of the corrected target SOC is executed.

[0192] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present invention. In specific applications, technicians in this field can make settings as needed, and the present invention does not limit this.

[0193] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0194] In addition, for technical details not fully described in this embodiment, reference can be made to the lithium battery SOC estimation method provided in any embodiment of the present invention, and will not be repeated here.

[0195] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0196] The serial numbers of the embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In a unit claim that lists several means, several of these means may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order and these words may be interpreted as names.

[0197] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory image (ROM) / random access memory (RAM), a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0198] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A lithium battery SOC estimation method, It is characterized in that The lithium battery SOC estimation method comprises: Based on the deviation-compensated least squares method and the extended set membership estimation algorithm, the SOC of the target lithium battery is corrected to obtain a first variation amplitude of the corrected target SOC; Determining whether to use the ampere-hour integration method to estimate the SOC of the target lithium battery according to the first change amplitude and a preset change amplitude; When the ampere-hour integration method is switched to estimate the SOC of the target lithium battery, an SOC estimation error corresponding to the ampere-hour integration method is obtained in real time; Setting a corresponding preset error value according to the current SOC estimation algorithm; When the SOC estimation error is greater than a preset error value, performing the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC; Before the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain the first change amplitude of the corrected target SOC, the method further includes: Obtain voltage signal, current signal and temperature signal of the target lithium battery load; Constructing a model mathematical relationship based on the voltage signal, the current signal, the temperature signal and a preset equivalent circuit; The mathematical relationship of the model is: ; in, is the state of charge value of the battery at time k+1, is the state of charge value of the battery at time k, is the voltage value of the polarization resistance and polarization capacitance of the battery at time k+1, is the voltage value of the polarization resistance and polarization capacitance of the battery at time k, is the battery polarization resistance value, is the battery polarization capacitance, is the battery charge and discharge capacity, is the battery's ohmic internal resistance, is the battery load current, is the open circuit voltage of the battery at time k under ambient temperature T; Constructing a battery model corresponding to the target lithium battery according to the model mathematical relationship; Performing Laplace transformation on the mathematical relationship of the model, we obtain: ； in, , , is a function of the characteristic parameters of the battery model, k is the current value, k-1 is the previous value, For system input, Output for the system; ； Based on the deviation compensation least squares method, the least squares estimation value at the current moment is subjected to deviation compensation to obtain the optimal estimation value corresponding to the characteristic parameter of the battery model. The specific calculation steps are as follows: Compute the bias-compensated least squares estimation error : = ， in, , It is the reference value estimated by the system at the previous moment; Compute bias-compensated least squares covariance : = ； Calculate the bias-compensated least squares gain : = ； Calculates the bias-compensated least squares error criterion function : = ； Calculate the bias-compensated least squares noise variance : = ； In the formula, is the deviation-compensated least squares estimate of the system at the previous moment, is the least squares estimate of the system at the current moment, is the correlation matrix, ; Compute bias-compensated least squares estimates : = , get the parameters , , The value of according to , , Values ​​and formulas Calculate and obtain the optimal estimated value corresponding to the characteristic parameter of the battery model, wherein the optimal estimated value includes the battery polarization resistance , battery polarization capacitance , battery ohmic internal resistance ; The step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC includes: The SOC of the target lithium battery is error corrected according to the optimal estimate value and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC.

2. The lithium battery SOC estimation method according to claim 1, It is characterized in that The step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC includes: Based on the deviation compensation least squares method, the battery model characteristic parameters corresponding to the target lithium battery are compensated for deviations to obtain optimal estimated values ​​corresponding to the battery model characteristic parameters; The SOC of the target lithium battery is error corrected according to the optimal estimate value and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC.

3. The lithium battery SOC estimation method according to claim 1, It is characterized in that The step of performing error correction on the SOC of the target lithium battery according to the optimal estimate value and the extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC includes: Constructing a parameter matrix according to the optimal estimate and the extended set membership estimation algorithm; ; In the formula, is the current measurement noise, is the voltage measurement noise, A, B, C, and D are all parameter matrices; ； ； ； ； Initialize the extended set membership estimation algorithm parameters and obtain the target parameters; The SOC of the target lithium battery is error corrected according to the target parameter and a parameter matrix corresponding to the preset type to obtain a first variation amplitude of the corrected target SOC.

4. The lithium battery SOC estimation method according to claim 1, It is characterized in that The step of obtaining the SOC estimation error corresponding to the ampere-hour integration method in real time when the ampere-hour integration method is switched to estimate the SOC of the target lithium battery includes: When the ampere-hour integration method is switched to estimate the SOC of the target lithium battery, an error estimation is performed according to a preset formula and the corrected target SOC to obtain an SOC estimation error corresponding to the ampere-hour integration method; The preset formula is: , ; in, is the initial value of SOC, is the battery capacity, Coulomb efficiency, is the SOC value at the next moment, is the current SOC value, It is the working current of the battery at the current moment.

5. A lithium battery SOC estimation device, It is characterized in that The lithium battery SOC estimation device includes: a memory, a processor, and a lithium battery SOC estimation program stored in the memory and executable on the processor. When the lithium battery SOC estimation program is executed by the processor, the steps of lithium battery SOC estimation as described in any one of claims 1 to 4 are implemented.

6. A storage medium, It is characterized in that The storage medium stores a lithium battery SOC estimation program, which, when executed by a processor, implements the steps of the lithium battery SOC estimation method according to any one of claims 1 to 4.

7. A lithium battery SOC estimation device, It is characterized in that The lithium battery SOC estimation device comprises: An error correction module, used to perform error correction on the SOC of the target lithium battery based on a deviation-compensated least squares method and an extended set membership estimation algorithm to obtain a first variation amplitude of the corrected target SOC; an algorithm switching module, configured to determine whether to use the ampere-hour integration method to estimate the SOC of the target lithium battery according to the first change amplitude and a preset change amplitude; An SOC estimation module, for obtaining in real time an SOC estimation error corresponding to the ampere-hour integration method when switching to the ampere-hour integration method to estimate the SOC of the target lithium battery; The error correction module is further used to set a corresponding preset error value according to the current SOC estimation algorithm; when the SOC estimation error is greater than the preset error value, the step of performing error correction on the SOC of the target lithium battery based on the deviation-compensated least squares method and the extended set membership estimation algorithm to obtain a first change amplitude of the corrected target SOC is executed; The error correction module is also used to obtain the voltage signal, current signal and temperature signal of the target lithium battery load; based on the voltage signal, the current signal, the temperature signal and the preset equivalent circuit, a model mathematical relationship is constructed; the model mathematical relationship is: ; in, is the state of charge value of the battery at time k+1, is the state of charge value of the battery at time k, is the voltage value of the polarization resistance and polarization capacitance of the battery at time k+1, is the voltage value of the polarization resistance and polarization capacitance of the battery at time k, is the battery polarization resistance value, is the battery polarization capacitance, is the battery charge and discharge capacity, is the battery's ohmic internal resistance, is the battery load current, is the open circuit voltage of the battery at time k under an ambient temperature of T; constructing a battery model corresponding to the target lithium battery according to the model mathematical relationship; performing Laplace transformation on the model mathematical relationship to obtain: ;in, , , is a function of the characteristic parameters of the battery model, k is the current value, k-1 is the previous value, For system input, Output for the system; ； Based on the deviation compensation least squares method, the least squares estimation value at the current moment is subjected to deviation compensation to obtain the optimal estimation value corresponding to the characteristic parameter of the battery model. The specific calculation steps are as follows: Compute the bias-compensated least squares estimation error : = ,in, , It is the reference value estimated by the system at the previous moment; Compute bias-compensated least squares covariance : = ； Calculate the bias-compensated least squares gain : = ； Calculates the bias-compensated least squares error criterion function : = ； Calculate the bias-compensated least squares noise variance : = ； In the formula, is the deviation-compensated least squares estimate of the system at the previous moment, is the least squares estimate of the system at the current moment, is the correlation matrix, ; Compute bias-compensated least squares estimates : = , get the parameters , , The value of according to , , Values ​​and formulas Calculate and obtain the optimal estimated value corresponding to the characteristic parameter of the battery model, wherein the optimal estimated value includes the battery polarization resistance , battery polarization capacitance , battery ohmic internal resistance .

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