Method for estimating battery system state of charge value, battery management system, battery system, and storage medium

Abstract

The application relates to the technical field of battery management, in particular to a battery system state of charge value estimation method, a battery management system, a battery system and a storage medium. According to the health state value, the state of charge value and the internal resistance of a battery pack, the first weight value and the second weight value of the battery pack are determined, and the voltage of the battery system is comprehensively considered, so that an accurate target state of charge value of the battery system can be estimated, the accuracy of the battery system state of charge estimation is significantly improved, and the accuracy of the control strategy can be ensured.

Description

Technical Field

[0001] This application relates to the field of battery management technology, and in particular to a method for estimating the state of charge of a battery system, a battery management system, a battery system, and a storage medium. Background Technology

[0002] Battery systems composed of multiple battery packs connected in series are widely used in electric vehicles, energy storage power stations, and secondary battery packs. Among them, the state of charge (SOC) of the battery system is the core control parameter of the battery management system (BMS), which directly determines the charging and discharging strategy, driving range assessment, and safe operation of the battery system.

[0003] Most methods for estimating the SOC of a battery system assume that the SOH of each battery pack is consistent and use a uniform estimation model and weights. This leads to a large deviation in the SOC estimation of battery packs with severe SOH decay, and the error accumulates continuously, thereby reducing the accuracy of the battery system SOC estimation. Summary of the Invention

[0004] In view of this, one objective of the embodiments of this application is to provide a method for estimating the state of charge (SOC) value of a battery system, a battery management system, a battery system, and a storage medium, so as to improve the low accuracy of SOC estimation in related technologies.

[0005] In a first aspect, embodiments of this application provide a method for estimating the state of charge (SOC) value of a battery system. The battery system includes multiple battery packs connected in series. The estimation method includes: acquiring first state data and second state data, wherein the first state data includes the health state value, SOC value, and internal resistance of the battery packs, and the second state data includes the voltage of the battery system; determining a first weight value for the battery packs based on the health state value and internal resistance of the battery packs; multiplying the SOC deviation value of the battery packs by the health state difference value to obtain a deviation product of the battery packs, wherein the SOC deviation value is the difference between the SOC value of the battery packs and the mean SOC value, the mean SOC value is the average value of the SOC values ​​of all battery packs, and the health state difference value is the difference between the standard health state value and the health state value of the battery packs; subtracting a correction amount from a preset standard weight value to obtain a second weight value for the battery packs, wherein the correction amount is the product of a preset deviation correction coefficient and the deviation product; and obtaining a target SOC value for the battery system based on the voltage of the battery system, the SOC value of the battery packs, the first weight value, and the second weight value.

[0006] Secondly, embodiments of this application provide a battery management system, including a processor and a memory. The processor is communicatively connected to the memory, and the memory stores computer program instructions executable by the processor. When the computer program instructions are executed by the processor, the battery management system performs the battery system state-of-charge estimation method provided in the first aspect.

[0007] Thirdly, embodiments of this application provide a battery system including multiple battery packs connected in series and a battery management system provided in the second aspect, wherein the battery management system is connected to each of the battery packs.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing processor-executable computer program instructions, which, when executed by the processor, cause the processor to perform the method for estimating the state of charge of a battery system provided in the first aspect.

[0009] The embodiments of this application have the following beneficial effects: Unlike related technologies, the embodiments of this application utilize health status values ​​and internal resistance to jointly determine the first weight, thereby dynamically distinguishing between new and old batteries and achieving adaptive compensation for capacity decay; simultaneously, a second weight value is determined based on the battery pack's state of charge deviation value and health status value, automatically identifying abnormal battery packs that deviate from the mean or have the risk of overcharging or over-discharging, preventing extreme state of charge values ​​from affecting the overall result, effectively suppressing the error diffusion caused by the weakest link effect, and significantly improving the estimation robustness of the system when consistency is poor; in addition, the actual total voltage of the battery system is introduced as a global macroscopic constraint, and the weighted result is pulled back and corrected when calculating the target state of charge value, solving the long-term problem of integral drift in pure software methods, so that the estimated value always converges to the true physical value. Overall, by constructing an intelligent arbitration model that "respects the strong (healthy batteries), restricts the weak (aged abnormal batteries), and obeys the whole (system voltage)," the target state of charge value of the battery system ultimately has both the sensitivity at the individual unit level and the accuracy at the system level. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the accompanying drawings used in the description of the related technologies or embodiments will be briefly introduced below. Obviously, the drawings described below only show some embodiments of this application and should not be considered as limiting the scope of protection. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1A This is a schematic diagram illustrating an application scenario for estimating the state of charge (SOC) value of a battery system in some embodiments of this application; Figure 1B This is a schematic diagram of the structure of a battery system provided in some embodiments of this application; Figure 2 This is a schematic diagram of the structure of a battery management system provided in some embodiments of this application; Figure 3A This is a flowchart illustrating a method for estimating the state of charge (SOC) of a battery system according to some embodiments of this application. Figure 3B yes Figure 3A A schematic diagram of a sub-process of step S32 in the method for estimating the state of charge of the battery system shown in the embodiment; Figure 4 yes Figure 3A A schematic diagram of a sub-process of step S34 in the method for estimating the state of charge of a battery system shown in the embodiment. Detailed Implementation

[0012] To make the objectives and advantages of the embodiments of this application more readily understood, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. The detailed description of the embodiments of this application in the accompanying drawings is not intended to limit the scope of protection claimed by this application, but only represents selected embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] It should be noted that, unless there is a conflict, the various technical features involved in the embodiments of this application described below can be combined with each other, and all are within the protection scope of this application. Furthermore, although functional modules are divided in the device or structural schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. In addition, the terms "first," "second," "third," and other similar expressions used herein do not limit the data or execution order, but are only for illustrative purposes and to distinguish identical or similar items with substantially the same function and effect, and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features.

[0014] Unless otherwise defined, the technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. It should be understood that the term "and / or" as used in this specification includes any and all combinations of one or more of the listed items.

[0015] Battery systems composed of multiple battery packs connected in series are widely used in electric vehicles, energy storage power stations, and secondary battery packs. Among them, the state of charge (SOC) of the battery system is the core control parameter of the battery management system (BMS), which directly determines the charging and discharging strategy, driving range assessment, and safe operation of the battery system.

[0016] The existing methods for estimating the SOC of a battery system do not consider the differences in SOH degradation and the unevenness of initial SOC among the various battery packs, resulting in at least the following drawbacks: 1) Assuming that all battery packs have the same SOH and using a uniform estimation model and weights, the differences in the energy characteristics of battery packs with different degrees of degradation are not distinguished. This leads to large deviations in the SOC estimation of battery packs with severe SOH degradation, and the errors accumulate continuously, thereby reducing the accuracy of the battery system SOC estimation; 2) The methods do not adapt to the unevenness of the initial SOC of battery packs. In battery systems that are used in cascaded applications or restarted after long-term idleness, the initial SOC of each battery pack often differs significantly. The existing methods directly use averaging or minimum values ​​to estimate the battery system SOC, which cannot reflect the true remaining capacity of the battery system and is prone to overcharging and over-discharging risks; 3) The SOC estimation model has poor adaptability: The single ampere-hour integration method or open-circuit voltage method cannot adapt to battery packs with different degrees of SOH degradation. The OCV-SOC curve of severely degraded battery packs is significantly deviated, and the ampere-hour integration error accumulates faster, leading to distortion in the battery pack SOC estimation and thus reducing the accuracy of the battery system SOC estimation.

[0017] In view of this, embodiments of this application provide a method for estimating the state of charge (SOC) value of a battery system. Based on the health status value, SOC value, and internal resistance of the battery pack, a first weight value and a second weight value of the battery pack are determined. Combined with the voltage of the battery system, an accurate target SOC value of the battery system can be estimated, improving the accuracy of the SOC estimation and ensuring the accuracy of the control strategy.

[0018] Please refer to the following: Figure 1A and Figure 1B , Figure 1A The illustrations depict application scenarios for estimating the state of charge (SOC) of a battery system in some embodiments of this application. Figure 1B A schematic diagram of the structure of a battery system provided in some embodiments of this application is shown.

[0019] like Figure 1A and Figure 1B As shown, this application scenario includes a battery system 100. The battery system 100 includes a battery management system 110 and multiple ( ) connected in series. Each battery pack 120 is connected to a battery management system 110. It is an integer greater than or equal to 2.

[0020] The battery system 100 can be used as an energy storage power station, a secondary battery pack, an uninterruptible power supply, or any other suitable form / type of equipment or device. The battery pack 120 includes one or more individual batteries of any suitable type, such as a storage battery or a lithium battery. It is understood that, according to actual needs, one or more individual batteries in the battery pack 120 can be connected in series, parallel, or a combination thereof (including series and parallel connections).

[0021] In some embodiments, each battery pack 120 is equipped with multiple types of sensors to detect and collect various types of operating data of the battery pack 120. For example, each battery pack 120 is equipped with current sensors, voltage sensors, and temperature sensors, etc., so as to detect and collect the current, voltage, and temperature of the battery pack 120.

[0022] The battery management system 110 communicates with various types of sensors and acquires operational data (such as current, voltage, and temperature) of the battery pack 120 from these sensors. Based on this data, it calculates and obtains the state data (such as state of charge and state of health) of the battery pack 120. The battery management system 110 then performs an operation to estimate the state of charge of the battery system, implementing the method for estimating the state of charge of the battery system provided below. The battery management system 110 can be any suitable type of component or device, such as a microcontroller, FPGA chip, or microcontroller; this application embodiment does not impose any limitations on this.

[0023] See Figure 1A As shown, this embodiment of the application performs the following operation to estimate the state of charge (SOC) value of the battery system: First, first state data and second state data are acquired. The first state data includes the health state value, SOC value, and internal resistance of the battery pack 120, and the second state data includes the voltage of the battery system 100. Next, based on the health state value and internal resistance of the battery pack 120, a first weight value for the battery pack 120 is determined. Then, the SOC deviation value of the battery pack 120 is multiplied by the health state difference value to obtain the deviation product of the battery pack 120. The SOC deviation value is the difference between the SOC value of the battery pack 120 and the mean SOC value. The mean SOC value is the average value of the SOC values ​​of all battery packs 120, and the health state difference value is the difference between the standard health state value and the health state value of the battery pack 120. Furthermore, a correction amount is subtracted from the preset standard weight value to obtain the second weight value of the battery pack 120. The correction amount is the product of a preset deviation correction coefficient and the deviation product. Finally, based on the voltage of the battery system 100, the state of charge of the battery pack 120, the first weight value, and the second weight value, the target state of charge of the battery system 100 is calculated.

[0024] By using the above methods, the accurate target state of charge (SOC) value of the battery system can be estimated, significantly improving the accuracy of SOC estimation and ensuring the accuracy of the control strategy.

[0025] It should be understood that, Figure 1A The illustrated embodiments are merely illustrative of one scenario for estimating the state of charge (SOC) value of the battery system 100 in some embodiments of this application, and do not limit the structure, type, or quantity of the battery system or battery management system in other embodiments. For example, in some other embodiments, the battery system may also include a... Figure 1B The structure shown includes more or fewer components, or has the same... Figure 1B The diagram shows different configurations of the structure.

[0026] To facilitate understanding of the method for estimating the state of charge (SOC) of a battery system provided in the embodiments of this application, the battery management system provided in the embodiments of this application will first be described in detail.

[0027] Please see Figure 2 , Figure 2 The schematic diagram illustrates the structure of a battery management system provided in some embodiments of this application.

[0028] See Figure 2 As shown, the battery management system 110 includes at least one processor 111 and at least one memory 112 connected in communication, wherein, Figure 2 Taking a bus system 113, a processor 111, and a memory 112 as an example, the various components of the battery management system 110 are coupled together through the bus system 113, which is used to realize the connection and communication between the various components. It is easy to understand that the bus system 113 may include, in addition to the data bus, a power bus, a control bus, and a status signal bus, etc., but for the sake of clarity and brevity, these will not be discussed further. Figure 2 In this designation, all buses are labeled as Bus System 113. Understandably, Figure 2 The structures shown in the embodiments are merely illustrative and do not limit the structure of the battery management system described above. For example, the battery management system may also include... Figure 2 The structure shown has more or fewer components, or has the same as Figure 2 The diagram shows different configurations of the structure.

[0029] Specifically, the processor 111 is configured to provide computational and control capabilities to support the battery management system 110 in executing corresponding business logic and functions. For example, it supports the battery management system 110 in executing the battery system state-of-charge estimation method provided in this application embodiment, or in executing the steps in any possible implementation of the battery system state-of-charge estimation method provided in this application embodiment. It is understood that the processor 111 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., or it can be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0030] The memory 112, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, instructions, and modules, such as the program, instructions, and modules corresponding to the battery system state-of-charge (SOC) estimation method in the embodiments of this application. In some embodiments, the memory 112 may include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, and the data storage area may store data created according to the use of the processor 111. The processor 111 executes various functional applications and data processing of the battery management system 110 by running the non-transitory software programs, instructions, and modules stored in the memory 112, to implement the battery system SOC estimation method provided in the embodiments of this application, or to execute the steps in any possible implementation of the battery system SOC estimation method provided in the embodiments of this application. In some embodiments, the memory 112 may include high-speed random access memory and may also include non-transitory memory. For example, at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 112 may also include memory remotely located relative to the processor 111, and these remotely located memories may be connected to the processor 111 via a communication network. It is understood that examples of the aforementioned communication networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0031] As can be understood from the above, the entity implementing the battery system state-of-charge estimation method provided in this application embodiment can be any suitable type of battery management system with certain computational and control capabilities, such as the battery management system 110 described above. In some feasible implementations, the battery system state-of-charge estimation method provided in this application embodiment can be implemented by a processor executing computer program instructions stored in memory.

[0032] The following will describe in detail the method for estimating the state of charge (SOC) of a battery system provided in this application, using exemplary applications and implementations of the battery system provided in the embodiments of this application.

[0033] Please see Figure 3A As shown, the method for estimating the state of charge (SOC) of a battery system provided in this application includes steps S31 to S34 to estimate the SOC of the battery system.

[0034] Step S31: Obtain the first state data and the second state data.

[0035] The first state data includes the battery pack's health status, state of charge, and internal resistance, while the second state data includes the battery system's voltage.

[0036] For example, for any battery pack, the embodiments of this application can detect and collect the operating status information of the battery pack in real time, wherein the operating status information includes the current, voltage, temperature, health status value, state of charge value and internal resistance of the battery pack.

[0037] In some embodiments, the state of charge (SOC) value of the battery pack is determined based on the current integration result and the open-circuit voltage correction result. The SOC value of the battery pack includes the initial SOC value of the battery pack (i.e., the SOC value of the battery pack when the battery system starts up). In this embodiment, the temperature of the battery pack is obtained from a temperature sensor disposed inside the battery pack, and the internal resistance of the battery pack is determined using a DC internal resistance test method, a pulse test method, or an equivalent model identification method.

[0038] In some embodiments, when there is abnormal data in the current, voltage, temperature, state of health value, state of charge value, etc. of the battery pack, the abnormal data is removed and the current, voltage, temperature, state of health value, or state of charge value of the battery pack is reacquired.

[0039] For example, for any battery pack, this application embodiment summarizes the current, voltage, temperature, health status value, and state of charge value of the battery pack to obtain the first state data corresponding to the battery pack.

[0040] For example, in this application embodiment, the total voltage at the output terminal of the battery system is collected as the voltage of the battery system.

[0041] Step S32: Determine the first weight value of the battery pack based on the health status value and internal resistance of the battery pack.

[0042] For example, in this application embodiment, first state data corresponding to all battery packs is obtained, for the first... The battery pack, according to the first Health status of each battery pack With internal resistance Determine the first The first weight value of the battery pack, that is: when the first... Health status of each battery pack When the value is greater than or equal to the first health threshold, it falls within the preset weight value range. One of the values ​​is selected as the first weight value, and the internal resistance is followed. The smaller the value, the closer the first weight is to 1.1; when the first weight is smaller... Health status of each battery pack When the value is greater than or equal to the second health threshold and less than the first health threshold, it falls within the preset weight value range. One of the values ​​is selected as the first weight value, and the internal resistance is followed. The larger the value, the closer the first weight value is to 0.8; when the first weight value is larger... Health status of each battery pack When the value is below the second health threshold, it falls within the preset weight range. One of the values ​​is selected as the first weight value, and the internal resistance is followed. The larger the value, the closer the first weight value is to 0.6. The average first weight value for all battery packs is 1.

[0043] It is understood that engineers can customize the first health threshold and the second health threshold based on experience data and actual needs, and the first health threshold must be greater than the second health threshold. This application embodiment does not impose any limitations on this.

[0044] Step S33A: Multiply the state of charge deviation value of the battery pack by the health status difference value to obtain the deviation product of the battery pack.

[0045] Step S33B: Subtract the correction amount from the preset standard weight value to obtain the second weight value of the battery pack.

[0046] In this embodiment, the state of charge deviation is the difference between the state of charge value of the battery pack and the mean state of charge, where the mean state of charge is the average of the state of charge values ​​of all battery packs. The health status difference is the difference between the standard health status value and the health status value of the battery pack. The correction amount is the product of a preset deviation correction coefficient and the deviation product. The standard health status value... It is 1, that is: The standard weight value is 1, that is: .

[0047] For example, in this application embodiment, the average value of the state of charge (SOC) of all battery packs is calculated to obtain the average SOC, i.e.: ,in For the first The state of charge (SOC) value of each battery pack. The average charge value, This is the sum of the state of charge values ​​of all battery packs.

[0048] For example, for the first The battery pack, in this embodiment of the application, calculates the first... State of charge (SOC) of each battery pack With the mean charge The difference is used to obtain the first... State of charge deviation of each battery pack ,Right now: , This represents the deviation from the state of charge. Formula: Used to retrieve the first State of charge (SOC) of each battery pack With the mean charge The absolute value of the difference (i.e. the first) State of charge deviation of each battery pack ).

[0049] For example, according to the first State of charge deviation of each battery pack Calculate the first The second weight value of the battery pack, namely: the standard health status value 1 and the first weight value 2. Health status of each battery pack The difference is used to obtain the first... Health status difference of individual battery packs ,Right now ; calculate the first Health status difference of individual battery packs With the State of charge deviation of each battery pack The product of , we get the first The deviation product of each battery pack ,Right now ; Calculate the standard weight value 1 and the first The deviation product of each battery pack The difference is used to obtain the first... The second weighting value of the battery pack ,Right now .

[0050] For example, for the first A battery pack, in this embodiment of the application, is based on the first formula: Calculate the first The second weighting value of the battery pack. For the first The second weight value of each battery pack. The deviation correction coefficient is obtained through calibration using experimental test data. , The value range is [range]. Any value in the range. Where, For the first The state-of-charge deviation of each battery pack. The difference in health status. For the first The deviation product of each battery pack Indicates the first The correction amount for the first battery pack. The first formula reflects: for the first... Each battery pack, state of charge deviation value The larger the health status value The lower the value (the more severe the battery pack aging), the higher the second weighting value. The smaller, the lower the first The weighting of each battery pack in the estimation of the battery system's state of charge (SOC) reduces the estimation bias caused by uneven SOC and severe degradation of the SOC.

[0051] Step S34: Based on the battery system voltage, the battery pack state of charge value, the first weight value, and the second weight value, obtain the target state of charge value of the battery system.

[0052] For example, in this embodiment of the application, the open circuit voltage (OCV) of each battery pack is obtained, and a system correction coefficient is calculated based on the voltage of the battery system and the open circuit voltages of all battery packs. .

[0053] For example, the first The first weight value of each battery pack Second weight value With state of charge Multiply to get the first Candidate state of charge values ​​of each battery pack ,Right now: First weight value The second weight value is a weighted value related to the health status value of the battery pack. Weight values ​​associated with the state of charge (SOC) of the battery pack.

[0054] For example, embodiments of this application are based on system correction coefficients. The target state of charge (SOC) of the battery system is calculated by finding the minimum SOC among all candidate SOC values ​​of the battery packs. ,Right now: ,in, It is the minimum among all candidate state of charge values ​​for all battery packs.

[0055] The embodiments of this application determine the first weight value and the second weight value of the battery pack based on the battery pack's health status value, state of charge value and internal resistance. By comprehensively considering the battery system voltage, the target state of charge value of the battery system can be accurately estimated, which improves the accuracy of the battery system's state of charge estimation and ensures the accuracy of the control strategy.

[0056] In some implementations, the present application embodiments achieve the acquisition of the state of charge value of the battery pack through steps S311 to S315.

[0057] Step S311: Calculate the charge and discharge efficiency of the battery pack based on its temperature and health status.

[0058] For example, in this embodiment of the application, a mapping relationship between the temperature and health status values ​​of the battery pack and the charge / discharge efficiency of the battery pack is pre-fitted based on experimental test data. Based on this mapping relationship, the charge / discharge efficiency corresponding to the temperature and health status values ​​of the battery pack is determined.

[0059] In some embodiments, calculating the charge-discharge efficiency of the battery pack based on the battery pack's temperature and state of health values ​​includes: substituting the battery pack's temperature and state of health values ​​into a reference function to calculate the charge-discharge efficiency.

[0060] In this embodiment, the reference function is used to characterize the correspondence between the battery pack's temperature and health status values ​​and the battery pack's charge and discharge efficiency.

[0061] For example, the reference function is: ,in, , , For calibration coefficients, For the first Health status values ​​of each battery pack For the first The temperature of the battery pack For the first The charging and discharging efficiency of a battery pack. In this embodiment, battery pack data (such as temperature, state of health, and capacity) is used in advance to fit the charging and discharging efficiency of the battery pack to the changes in temperature and state of health, thus obtaining calibration coefficients. , , For the first One battery pack The higher the temperature The closer it is to room temperature (i.e., 20-25℃), The closer it is to 1, the smaller the error in subsequent estimations of the state of charge.

[0062] Step S312: Based on the nominal capacity, charge and discharge efficiency, and initial state of charge of the battery pack, the ampere-hour integral state of charge of the battery pack is calculated using the ampere-hour integral algorithm.

[0063] In this embodiment, the initial state of charge (SOC) value is the SOC value of the battery pack when the battery system is powered on.

[0064] For example, for any battery pack, this application embodiment reads the basic parameter information of the battery pack from the parameter configuration table of the battery pack. The basic parameter information includes the nominal capacity (also known as rated capacity) and OCV-SOC curve of the battery pack.

[0065] In some embodiments, the embodiments of this application, through steps S3121 to S3123, calculate the ampere-hour integrated state of charge value of the battery pack using an ampere-hour integral algorithm based on the nominal capacity, charge-discharge efficiency, and initial state of charge value of the battery pack.

[0066] Step S3121: Calculate the ampere-hour integral value for the current cycle based on the battery system's current, charge / discharge efficiency, and sampling time.

[0067] Step S3122: Divide the ampere-hour integral value by the nominal capacity of the battery pack to obtain the capacity consumption ratio.

[0068] Step S3123: Subtract the capacity consumption ratio from the initial state of charge value to obtain the ampere-hour integrated state of charge value of the battery pack.

[0069] In this embodiment, the second state data also includes the battery system current. It can be understood that for a battery system with multiple battery packs connected in series, the battery system current is the same as the battery pack current.

[0070] For example, for the first A battery pack, as described in this application embodiment, uses the following formula: Calculate the first The ampere-hour integrated state of charge value of each battery pack For the first The initial state of charge (SOC) of each battery pack For the battery system (i.e. the first) (each battery pack) at time The current (including discharge current and charging current), For the first The nominal capacity (or rated capacity) of each battery pack, Indicates the first The capacity consumption ratio of each battery pack Indicates the first The ampere-hour integrated state of charge value of each battery pack.

[0071] Step S313: Determine the voltage-charge curve of the battery pack based on the health status value of the battery pack.

[0072] Among them, the voltage-charge curve of the battery pack is used to characterize the correspondence between the open-circuit voltage and the state of charge value of the battery pack.

[0073] It is understood that the OCV-SOC curves are different for battery packs with different degrees of degradation (i.e., state of health values). In this application embodiment, the OCV-SOC curves of battery packs with different degrees of degradation are obtained in advance based on experimental test data.

[0074] For example, for any battery pack, this application embodiment selects the voltage-charge curve corresponding to the health status value of the battery pack as the voltage-charge curve of the battery pack based on the health status value of the battery pack.

[0075] Step S314: Determine the state of charge value corresponding to the open-circuit voltage of the battery pack from the voltage-charge curve of the battery pack as the state of charge calibration value of the battery pack.

[0076] Step S315: Weightedly fuse the ampere-hour integrated state of charge value with the state of charge calibration value to obtain the state of charge value of the battery pack.

[0077] In this embodiment, the first state data also includes the open-circuit voltage of the battery pack. The open-circuit voltage of the battery pack can be collected in real time, or the battery system can be left to stand still before the open-circuit voltage of the battery pack is collected.

[0078] For example, for any battery pack, this embodiment of the application filters and determines the state of charge (SOC) value corresponding to the open-circuit voltage of the battery pack from the voltage-charge curve of the battery pack, which is then used as the SOC calibration value for the battery pack. The SOC calibration value is used to correct the error of the ampere-hour integrated SOC value calculated using the ampere-hour integration algorithm.

[0079] For example, this application embodiment uses a weighted fusion method to weight and fuse the ampere-hour integrated state of charge (SOC) value and the SOC calibration value to obtain an accurate SOC value for the battery pack. The fusion weights are... The weighting is dynamically adjusted based on the battery pack's degradation level (i.e., its state of health value). The lower the battery pack's state of health value, the lower its fusion weight. The smaller the value (i.e., reduce the weight of the ampere-hour integral state of charge value and increase the weight of the state of charge calibration value).

[0080] For example, the embodiments of this application use a weighted fusion formula: Calculate the first The state of charge (SOC) values ​​of each battery pack, where, For the first The state of charge (SOC) value of each battery pack. For the first The ampere-hour integrated state of charge value of each battery pack. For the first The state of charge calibration value of each battery pack.

[0081] In some implementations, the embodiments of this application achieve weighted fusion of the ampere-hour integrated state of charge value and the state of charge calibration value through steps S3151 to S3155 to obtain the state of charge value of the battery pack.

[0082] Step S3151: Determine the fusion weights based on the health status values ​​of the battery pack.

[0083] In some embodiments, fusion weights The adjustment rule is as follows: for battery packs with slight degradation (i.e. The battery pack will integrate weights. The value range is [range]. For any value in the range, in this case, the error of the ampere-hour integrated state of charge value is small, and the ampere-hour integrated state of charge value is the main factor; for a moderately degraded battery pack (i.e. The battery pack will integrate weights. The value range is [range]. Any value in the range, balancing the ampere-hour integral state of charge value and the state of charge calibration value; for severe attenuation (i.e. The battery pack will integrate weights. The value range is [range]. In this case, the state of charge calibration value is taken as the primary value to reduce the error of the ampere-hour integrated state of charge value.

[0084] In some implementations, the embodiments of this application determine the fusion weight based on the health status value of the battery pack through steps S31511 to S31513.

[0085] Step S31511: In response to the battery pack's health status value being greater than or equal to a preset first health status value and less than or equal to a standard health status value, select a first value range. Any value in the fusion weight is used as the fusion weight.

[0086] Step S31512: In response to the battery pack's health status value being greater than or equal to a preset second health status value and less than a first health status value, select a second value range. Any value in the fusion weight is used as the fusion weight.

[0087] Step S31513: In response to the battery pack's health status value being greater than or equal to the baseline health status value and less than the second health status value, select a third numerical range. Any value in the fusion weight is used as the fusion weight.

[0088] It is understood that engineers can customize and set the first health status value and the second health status value according to experimental data and actual needs. For example, in the embodiments of this application, the first health status value is... Set it to 0.8, that is: Second health status value Set it to 0.5, that is: Standard health status values It is 1, that is: Baseline health status values It is 0, that is: .

[0089] For example, for the first The battery pack, if the first Health status of each battery pack Greater than or equal to the first health status value And less than or equal to the standard health status value (Right now ), indicating the first If the battery pack is a slightly degraded battery pack, then the first value range is selected. Any value in the fusion weight ,For example .

[0090] For example, for the first The battery pack, if the first Health status of each battery pack Greater than or equal to the second health status value And less than the value of the first health state. (Right now ), indicating the first The battery pack is a moderately degraded battery pack, and the second value range is selected. Any value in the fusion weight ,For example .

[0091] For example, for the first The battery pack, if the first Health status of each battery pack Greater than or equal to the baseline health status value And less than the second health status value (Right now ), indicating the first The battery pack is a severely degraded battery pack, and the third value range is selected. Any value in the fusion weight ,For example .

[0092] Step S3152: Subtract the fusion weight from the preset standard weight value to obtain the weight difference.

[0093] Step S3153: Multiply the fusion weight by the ampere-hour integrated state-of-charge value to obtain the first product value.

[0094] Step S3154: Multiply the weight difference with the state of charge calibration value to obtain the second product value.

[0095] Step S3155: Sum the first product value and the second product value to obtain the state of charge value of the battery pack.

[0096] In this embodiment of the application, the standard weight value It is 1, that is: For example, for the first... A battery pack, according to the formula in this application embodiment: Calculate the first The state of charge (SOC) values ​​of each battery pack, where, To integrate weights, For the first The state of charge (SOC) value of each battery pack. For the first The ampere-hour integrated state of charge value of each battery pack For the first The state of charge calibration value of each battery pack.

[0097] Please see Figure 3B In this embodiment of the application, steps S321 to S323 are used to determine the first weight value of the battery pack based on the health status value and internal resistance of the battery pack.

[0098] Step S321: In response to the battery pack's health status value being greater than or equal to a preset first health status value and less than or equal to a standard health status value, the internal resistance of the battery pack is substituted into a preset first linear function to calculate a first reference weight value.

[0099] The first linear function characterizes the relationship between the internal resistance of the battery pack and the first reference weight value.

[0100] In this embodiment, a base weight is set based on the degree of degradation of the battery pack (i.e., health status value), and the base weight is corrected by combining the internal resistance of the battery pack to obtain a first weight value of the battery pack (including a first reference weight value, a second reference weight value, and a third reference weight value), so as to reflect the difference in contribution of battery packs with different degrees of degradation to the overall power of the battery system.

[0101] Among them, for the first The battery pack, if the first Health status of each battery pack Greater than or equal to the first health status value And less than or equal to the standard health status value (Right now ), indicating the first If the first battery pack is a slightly degraded battery pack, then the second... The first reference weight value of each battery pack The value range is [range]. any value in, and the first The internal resistance of the battery pack The smaller, the first The first reference weight value of each battery pack The closer it is to 1.1.

[0102] For example, in this embodiment of the application, the correspondence between the internal resistance of the battery pack and the first reference weight value is fitted in advance based on experimental test data to obtain a first linear function, such as: , , The first weighting coefficients obtained from the fitting are... For the first The first reference weight value for each battery pack For the first The internal resistance of each battery pack.

[0103] For example, for the first The battery pack, if the first Health status of each battery pack Greater than or equal to the first health status value And less than or equal to the standard health status value , will the The internal resistance of the battery pack Substituting into the first linear function, the first reference weight value is calculated. .

[0104] Step S322: In response to the battery pack's health status value being greater than or equal to a preset second health status value and less than a first health status value, the internal resistance of the battery pack is substituted into a preset second linear function to calculate a second reference weight value.

[0105] The second linear function characterizes the relationship between the internal resistance of the battery pack and the second reference weight value.

[0106] Among them, for the first The battery pack, if the first Health status of each battery pack Greater than or equal to the second health status value And less than the first health status value (Right now ), indicating the first If the first battery pack is a moderately degraded battery pack, then the second... The second reference weight value for each battery pack The value range is [range]. any value in, and the first The internal resistance of the battery pack The larger, the more The second reference weight value for each battery pack The closer it is to 0.8.

[0107] For example, in this embodiment of the application, the correspondence between the internal resistance of the battery pack and the second reference weight value is fitted in advance based on experimental test data to obtain a second linear function. For example, the second linear function is: , , The second weighting coefficients obtained from the fitting are... For the first The second reference weight value for each battery pack For the first The internal resistance of each battery pack.

[0108] For example, for the first The battery pack, if the first Health status of each battery pack Greater than or equal to the second health status value And less than the first health status value , will the The internal resistance of the battery pack Substituting into the second linear function, the second reference weight value is calculated. .

[0109] Step S323: In response to the battery pack's health status value being greater than or equal to the baseline health status value and less than the second health status value, the internal resistance of the battery pack is substituted into a preset third linear function to calculate the third reference weight value.

[0110] The third linear function characterizes the relationship between the internal resistance of the battery pack and the third reference weight value.

[0111] In this embodiment, the first weight value includes a first reference weight value, a second reference weight value, and a third reference weight value.

[0112] Among them, for the first The battery pack, if the first Health status of each battery pack Greater than or equal to the baseline health status value And less than the second health status value (Right now ), indicating the first If the first battery pack is a severely degraded battery pack, then the second... The third reference weight value of each battery pack The value range is [range]. any value in, and the first The internal resistance of the battery pack The larger, the more The third reference weight value of each battery pack The closer it is to 0.6.

[0113] For example, in this embodiment of the application, the correspondence between the internal resistance of the battery pack and the third reference weight value is fitted in advance based on experimental test data to obtain a third linear function. For example, the third linear function is: , , The third weighting coefficient obtained from the fitting. For the first The third reference weight value for each battery pack, For the first The internal resistance of each battery pack.

[0114] For example, for the first The battery pack, if the first Health status of each battery pack Greater than or equal to the baseline health status value And less than the second health status value , will the The internal resistance of the battery pack Substituting into the third linear function, the third reference weight value is calculated. .

[0115] It is understandable that the first weight value of all battery packs satisfies the normalization condition: That is, the average of the first weight values ​​of all battery packs is 1, to ensure the rationality of weight allocation and avoid excessive influence of a single battery pack on the estimation of the state of charge of the battery system.

[0116] See Figure 4As shown, in this embodiment of the application, steps S341 to S342 are used to obtain the target state of charge of the battery system based on the voltage of the battery system, the state of charge of the battery pack, the first weight value and the second weight value.

[0117] Step S341: Calculate the system correction coefficient based on the battery system voltage and the open-circuit voltage of the battery pack.

[0118] For example, in this application embodiment, the voltage correction factor is calibrated based on experimental test data. , The value range is [range]. Any one of the values ​​in the range. Voltage correction factor in embodiments of this application. Calculate the system correction factor based on the battery system voltage and the open-circuit voltage of the battery pack.

[0119] In some embodiments, the present application implements steps S3411 to S3412 to calculate the system correction coefficient based on the battery system voltage and the battery pack open-circuit voltage.

[0120] Step S3411: Divide the difference between the battery system voltage and the reference voltage by the battery system voltage to obtain the first ratio.

[0121] Step S3412: Sum the first preset value and the first product to obtain the system correction coefficient.

[0122] In this embodiment, the reference voltage is the sum of the open-circuit voltages of all battery packs. The first product is the product of a preset voltage correction coefficient and a first ratio. The first preset value C1 is 1, that is: .

[0123] For example, the embodiments of this application are based on the formula: Calculate the system correction coefficient. For system correction coefficients, This is the voltage correction factor. The voltage of the battery system, This is the reference voltage (i.e., the sum of the open-circuit voltages of all battery packs). When the difference between the battery system voltage and the reference voltage is large, a system correction factor is used. Correct the target state of charge value to improve estimation accuracy.

[0124] Step S342: Calculate the target state of charge value based on the system correction coefficient, the state of charge value of the battery pack, the first weight value, and the second weight value.

[0125] For example, based on the characteristics of "energy conservation" and "barrel effect" of the battery system (that is, the overall energy of the battery system is determined by the battery pack with the lowest energy), the target energy value of the battery system as a whole is calculated by combining the state of charge value of each battery pack, the first weight value and the second weight value.

[0126] In some embodiments, the present application implements steps S3421 to S3422 to calculate the target state of charge value based on the system correction coefficient, the state of charge value of the battery pack, the first weight value, and the second weight value.

[0127] Step S3421: Multiply the weighted product with the state of charge value of the candidate battery pack to obtain the candidate state of charge value.

[0128] Step S3422: Multiply the reference state of charge value by the system correction coefficient to obtain the target state of charge value.

[0129] The candidate battery pack is any one of multiple battery packs, and the weighted product is the product of the first weight value and the second weight value of the candidate battery pack. The reference state of charge (SOC) value is the minimum value among all candidate SOC values.

[0130] For example, the embodiments of this application are based on the following second formula: Calculate the target state of charge value of the battery system. This represents the target state of charge (SOC) value for the battery system. This refers to taking the first weight value of all battery packs. Second weight value and state of charge value The minimum value of the product of .

[0131] For example, in conjunction with the second formula, when the candidate battery pack is the first... When the first battery pack is used, the first The weighted product of the battery packs For the first The first weight value of each battery pack Second weight value The product of, that is: . No. Candidate state of charge values ​​of each battery pack For the first The weighted product of the first battery pack and the first... State of charge (SOC) of each battery pack The product of, that is: The minimum value among all candidate states of charge (SOC) values ​​for all battery packs is selected as the reference SOC value. Reference state of charge value With system correction coefficient Multiplying them together yields the target state of charge value of the battery system. .

[0132] In some embodiments, the method for estimating the state of charge of a battery system provided in this application further includes steps S41 to S44.

[0133] Step S41: Obtain the reference state of charge value of the battery system.

[0134] The reference state of charge (PSC) value is the accurate PSC value of the battery system, which can be obtained through offline calibration or measurement with high-precision equipment.

[0135] Step S42: Calculate the absolute value of the difference between the target state of charge value and the reference state of charge value to obtain the charge estimation error value.

[0136] For example, embodiments of this application are based on the following formula: Calculate the charge estimation error value. This is the charge estimation error value, used to reflect the degree of deviation of the estimation result (i.e., the degree of deviation between the target charge state value and the reference charge state value). This represents the target state of charge (SOC) value for the battery system. This is the reference state of charge value for the battery system.

[0137] Step S43: Based on the charge estimation error value, use a filtering algorithm to correct the first weight value and the second weight value of the battery pack to obtain the first correction value and the second correction value.

[0138] Step S44: Based on the battery system voltage, the battery pack state of charge value, the first correction value, and the second correction value, obtain the corrected target state of charge value of the battery system.

[0139] The first correction value is the first weight value of the corrected battery pack, and the second correction value is the second weight value of the corrected battery pack.

[0140] For example, algorithms such as the extended Kalman filter or the unscented Kalman filter are used to estimate the error value based on the charge. For observation purposes, the first and second weight values ​​of the battery pack are corrected in real time, thereby dynamically updating the target state of charge (SOC) value of the battery system and obtaining the corrected SOC value of the battery system. This suppresses the accumulation of estimation errors and ensures the accuracy of long-term SOC value estimation.

[0141] The following explanation uses the extended Kalman filter algorithm as an example.

[0142] In this embodiment, the state variable is: The observed value (i.e., the charge estimation error value) is: .

[0143] The extended Kalman filter algorithm provided in this application embodiment is as follows.

[0144] The state equation is as follows: In the formula, For a moment The system state vector, This is the state transition function. For a moment The control input vector, This represents process noise (i.e., covariance Q).

[0145] The observation equation is: In the formula, For a moment The observation vector, For the observation function, This represents the observation noise (i.e., the covariance R).

[0146] state variables and observation Substituting the values ​​into the extended Kalman filter algorithm, state prediction is performed. Based on the optimal estimate from the previous time step, the predicted state value for the current time step is calculated using a nonlinear function, i.e.: In the formula, This is for prior state estimation.

[0147] Covariance prediction: Calculate the covariance of the uncertainty of the predicted state, i.e.: In the formula, To estimate the error covariance matrix a priori, Let be the state transition Jacobian matrix.

[0148] Calculate the Kalman gain: weigh the confidence weights of predictions and observations, i.e.: In the above formula, The Kalman gain is a "weight matrix". To observe the Jacobian matrix.

[0149] State update: Combine the observed residuals to correct the predicted state and obtain the optimal estimate, i.e.: , in the formula, For posterior state estimation, in the embodiments of this application, This includes the first weight value (i.e., the first corrected value) of the corrected battery pack and the second weight value (i.e., the second corrected value) of the corrected battery pack. The observation residuals are used to correct the predicted state.

[0150] Update the error covariance, i.e.: , in the formula, For the posterior estimation of the error covariance matrix, To estimate the error covariance matrix a priori, It is an identity matrix.

[0151] As time iterates, moments Increment by 1, and repeat the above steps to update the calculation results. This is achieved by updating... This yields the corrected first weight value and the corrected second weight value.

[0152] The specific execution process of step S44 is similar to that of step S34. The only difference is that step S44 uses the corrected first weight value (i.e., the first corrected value) and the corrected second weight value (i.e., the second corrected value) to calculate the corrected target state of charge value of the battery system. For the specific execution process, please refer to the specific execution process and detailed steps of step S34, which will not be elaborated here.

[0153] In this embodiment, by using a filtering algorithm to correct the first weight value and the second weight value, adaptive weight correction is achieved, which avoids the accumulation of estimation error of the state of charge value and ensures the long-term stability of the estimation accuracy of the state of charge value.

[0154] Overall, the embodiments of this application have at least the following beneficial effects.

[0155] 1. Adapting to scenarios with differences in SOH and SOC among multiple battery packs: Based on the degree of SOH decay, the first weight value of the battery pack is obtained; based on the difference in SOC, the second weight value of the battery pack is obtained. This accurately adapts to battery systems with multiple battery packs connected in series with different SOH decay and SOC differences, significantly reducing the estimation deviation of the battery system's state of charge and improving the accuracy of the battery system's state of charge estimation.

[0156] 2. High accuracy in SOC estimation for a single battery pack: By integrating the ampere-hour integral method and the open-circuit voltage method, a differentiated fusion strategy is adopted for different SOH decay levels. Combined with charge and discharge efficiency correction, the error accumulation of a single estimation algorithm is reduced, thereby improving the estimation accuracy of the SOC of a single battery pack.

[0157] 3. Realistic SOC estimation of battery system: Based on the "weakest link" effect of the battery system, combined with weight correction and voltage deviation correction, it ensures that the SOC of the battery system can truly reflect the remaining capacity of the battery system, reducing the risk of overcharging and over-discharging.

[0158] 4. Combining real-time performance with long-term estimation accuracy: The introduction of a filtering algorithm corrects the weight values ​​in real time, suppresses error accumulation, and ensures the long-term stability of the accuracy of the state of charge (SOC) estimation and the continuity of SOC estimation.

[0159] 5. Integrating previous core technologies: By closely integrating the relevant technologies of battery pack SOH estimation and battery pack SOC estimation, a complete technical system of "SOH degradation assessment → accurate SOC estimation → battery system SOC estimation" has been formed, avoiding technology fragmentation and reducing the difficulty and cost of engineering implementation.

[0160] 6. Wide adaptability: The embodiments of this application are adaptable to various battery systems with multiple battery packs connected in series, such as electric vehicles, energy storage power stations, and cascaded battery packs. The SOH attenuation threshold, weight value and correction coefficient can be adjusted according to different scenarios, and the engineering feasibility is strong.

[0161] 7. Low computing power consumption: The calculation logic is simple, and the differentiated estimation strategy takes into account both accuracy and computing power. It can be directly embedded into the existing battery management system (BMS) without additional hardware costs, making it suitable for large-scale engineering applications.

[0162] This application provides a computer-readable storage medium storing processor-executable computer program instructions. When executed by a processor, the computer program instructions cause the processor to perform the battery system state-of-charge estimation method provided in this application, or to perform the steps in any possible implementation of the battery system state-of-charge estimation method provided in this application.

[0163] In some embodiments, the storage medium may be a flash memory, a hard disk, an optical disk, a register, a magnetic surface memory, a removable disk, a CD-ROM, a random access memory (RAM), a read-only memory (ROM), an electrically programmable ROM, and an electrically erasable programmable ROM, or any other form of storage medium known in the art, or various devices including one or any combination of the above storage media.

[0164] In some embodiments, computer program instructions may take the form of programs, software, software modules, scripts, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.

[0165] As an example, computer program instructions may, but do not necessarily, correspond to files in a file system, and may be stored as part of a file that holds other programs or data, for example, in one or more scripts in an HTML (Hypertext Markup Language) document, or in a single file dedicated to the program in question, or in multiple collaborative files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0166] As an example, computer program instructions can be deployed to execute on a single computing device (including devices such as smart terminals and servers), or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network. It is readily understood that all or part of the steps of the methods described in the embodiments provided above can be implemented directly using electronic hardware or processor-executable computer program instructions, or a combination of both.

[0167] Those skilled in the art will understand that the embodiments provided in this application are merely illustrative. The order in which the steps in the methods of the embodiments are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The order can be adjusted, merged, and deleted according to actual needs. Modules or sub-modules, units or sub-units in the apparatus or system of the embodiments can be merged, divided, and deleted according to actual needs. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0168] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, and of course, it can also be implemented using hardware. Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. It should be understood that the storage medium can be flash memory, hard disk, optical disk, register, magnetic surface memory, removable disk, CD-ROM, random access memory (RAM), read-only memory (ROM), electrically programmable ROM, and electrically erasable programmable ROM, etc.

[0169] It should be noted that the above embodiments are for illustrating the technical concept and features of this application, and are intended to enable those skilled in the art to understand the content of this application and implement it accordingly. They should not be construed as limiting the scope of protection of this application. Those skilled in the art can understand that all or part of the processes of the above embodiments can be implemented, modified according to the technical solutions described in the embodiments of this application, or equivalent substitutions can be made to some of the technical features. It is understood that these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should be considered as equivalent changes and modifications made based on the embodiments of this application, all of which should fall within the scope of the claims of this application.

Claims

1. A method for estimating the state of charge (SOC) of a battery system, characterized in that, The battery system includes multiple battery packs connected in series, and the estimation method includes: Acquire first state data and second state data, wherein the first state data includes the health status value, state of charge value and internal resistance of the battery pack, and the second state data includes the voltage of the battery system; Based on the health status value and internal resistance of the battery pack, a first weight value of the battery pack is determined; The deviation value of the state of charge of the battery pack is multiplied by the difference in the state of health to obtain the deviation product of the battery pack. The deviation value of the state of charge is the difference between the state of charge value of the battery pack and the mean value of the charge. The mean value of the charge is the average value of the state of charge of all the battery packs. The difference in the state of health is the difference between the standard state of health value and the state of health value of the battery pack. The second weight value of the battery pack is obtained by subtracting the correction amount from the preset standard weight value, where the correction amount is the product of the preset deviation correction coefficient and the deviation product. The target state of charge (SOC) of the battery system is obtained based on the voltage of the battery system, the state of charge (SOC) of the battery pack, the first weight value, and the second weight value.

2. The estimation method according to claim 1, characterized in that, The step of determining the first weight value of the battery pack based on its health status value and internal resistance includes: In response to the battery pack's health status value being greater than or equal to a preset first health status value and less than or equal to a standard health status value, the internal resistance of the battery pack is substituted into a preset first linear function to calculate a first reference weight value, wherein the first linear function characterizes the correspondence between the battery pack's internal resistance and the first reference weight value. In response to the battery pack's health status value being greater than or equal to a preset second health status value and less than the first health status value, the battery pack's internal resistance is substituted into a preset second linear function to calculate a second reference weight value, wherein the second linear function characterizes the correspondence between the battery pack's internal resistance and the second reference weight value; In response to the battery pack's health status value being greater than or equal to a baseline health status value and less than a second health status value, the internal resistance of the battery pack is substituted into a preset third linear function to calculate a third reference weight value, wherein the third linear function characterizes the correspondence between the battery pack's internal resistance and the third reference weight value; The first weight value includes the first reference weight value, the second reference weight value, and the third reference weight value.

3. The estimation method according to any one of claims 1 to 2, characterized in that, The first state data also includes the open-circuit voltage of the battery pack. Obtaining the target state of charge (SOC) of the battery system based on the voltage of the battery system, the SOC of the battery pack, a first weight value, and a second weight value includes: Calculate the system correction coefficient based on the voltage of the battery system and the open-circuit voltage of the battery pack; The target state of charge (SOC) value is calculated based on the system correction coefficient, the SOC value of the battery pack, the first weight value, and the second weight value.

4. The estimation method according to claim 3, characterized in that, The step of calculating the system correction coefficient based on the voltage of the battery system and the open-circuit voltage of the battery pack includes: The difference between the voltage of the battery system and the reference voltage is divided by the voltage of the battery system to obtain a first ratio, wherein the reference voltage is the sum of the open-circuit voltages of all the battery packs; The system correction coefficient is obtained by summing the first preset value and the first product, where the first product is the product of the preset voltage correction coefficient and the first ratio.

5. The estimation method according to claim 3, characterized in that, The step of calculating the target state of charge value based on the system correction coefficient, the state of charge value of the battery pack, the first weight value, and the second weight value includes: The weight product is multiplied by the state of charge value of the candidate battery pack to obtain the candidate state of charge value. The candidate battery pack is any one of the multiple battery packs. The weight product is the product of the first weight value and the second weight value of the candidate battery pack. The target state of charge (SPO) is obtained by multiplying the reference SPO value by the system correction coefficient, where the reference SPO value is the minimum among all the candidate SPO values.

6. The estimation method according to claim 3, characterized in that, The step of obtaining the state of charge (SOC) value of the battery pack includes: The charging and discharging efficiency of the battery pack is calculated based on its temperature and health status. The ampere-hour integrated state of charge (SOC) of the battery pack is calculated using the ampere-hour integral algorithm based on the nominal capacity, charge and discharge efficiency, and initial state of charge (SOC) of the battery pack. The initial SOC is the SOC of the battery pack when the battery system is powered on. Based on the health status value of the battery pack, the voltage-charge curve of the battery pack is determined. The voltage-charge curve is used to characterize the correspondence between the open-circuit voltage and the state of charge value of the battery pack. The state of charge value corresponding to the open-circuit voltage of the battery pack in the voltage-charge curve of the battery pack is determined as the state of charge calibration value of the battery pack. The state of charge (SOC) value of the battery pack is obtained by weighted fusion of the ampere-hour integrated SOC value and the SOC calibration value.

7. The estimation method according to claim 6, characterized in that, The step of weightedly fusing the ampere-hour integrated state of charge value with the state of charge calibration value to obtain the state of charge value of the battery pack includes: The fusion weights are determined based on the health status values ​​of the battery pack; Subtract the fusion weight from the preset standard weight value to obtain the weight difference; Multiply the fusion weight by the ampere-hour integrated state of charge value to obtain the first product value; Multiply the weight difference by the state of charge calibration value to obtain the second product value; The state of charge (SOC) of the battery pack is obtained by summing the first product and the second product.

8. The estimation method according to claim 7, characterized in that, The step of determining the fusion weight based on the health status value of the battery pack includes: In response to the battery pack's health status value being greater than or equal to a preset first health status value and less than or equal to a standard health status value, a first numerical range is selected. Any one of the values ​​in is used as the fusion weight; In response to the battery pack's health status value being greater than or equal to a preset second health status value and less than the first health status value, a second value range is selected. Any one of the values ​​in is used as the fusion weight; In response to the battery pack's health status value being greater than or equal to a baseline health status value and less than a second health status value, a third numerical range is selected. Any one of the values ​​in is used as the fusion weight.

9. The estimation method according to any one of claims 1 to 2, characterized in that, The estimation method further includes: Obtain the reference state of charge value of the battery system; The absolute value of the difference between the target state of charge value and the reference state of charge value is calculated to obtain the charge estimation error value; Based on the charge estimation error value, the first weight value and the second weight value of the battery pack are corrected using a filtering algorithm to obtain a first corrected value and a second corrected value. The first corrected value is the corrected first weight value of the battery pack, and the second corrected value is the corrected second weight value of the battery pack. Based on the voltage of the battery system, the state of charge (SOC) value of the battery pack, the first correction value, and the second correction value, the corrected target SOC value of the battery system is obtained.

10. A battery management system, characterized in that, The system includes a processor and a memory, the processor being communicatively connected to the memory, the memory storing computer program instructions executable by the processor, the computer program instructions being executed by the processor to cause the battery management system to perform the method for estimating the state of charge of the battery system as described in any one of claims 1 to 9.

11. A battery system, characterized in that, It includes multiple battery packs connected in series and a battery management system as described in claim 10, wherein the battery management system is connected to each of the battery packs.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, cause the processor to perform the method for estimating the state of charge of a battery system as described in any one of claims 1 to 9.

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