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

Abstract

The embodiment of the application relates to the battery management technical field, in particular to a kind of estimation method of battery system health state value, battery management system, battery system and storage medium.The embodiment of the application calculates the real-time health state value of battery system according to the nominal capacity, health state value, state of charge value and temperature of battery pack, comprehensively considers the various state data of battery pack, truly reflects the available capacity of battery system, can estimate the real-time health state value of battery system more accurately, under the condition of meeting the correction condition, according to the special working condition data of battery pack, the correction health state value of battery system is calculated, finally the real-time health state value and correction health state value are weighted and fused, obtain the final health state value of accurate battery system, improve the accuracy of battery system health state estimation, can guarantee the accuracy of control strategy, reduce the risk of overcharge and overdischarge, improve the energy utilization efficiency of battery system.

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 health of a battery system, a battery management system, a battery system, and a storage medium. Background Technology

[0002] In electric vehicles, energy storage power stations, secondary battery packs, and uninterruptible power supplies, multiple battery packs are connected in series to form a battery system to meet system voltage and capacity requirements. However, due to differences in manufacturing processes, operating environments, and aging levels, the State of Health (SOH) of each battery pack varies significantly. Furthermore, during system startup, charging / discharging interruptions, or insufficient balancing, the initial State of Charge (SOC) of each battery pack will also be inconsistent.

[0003] The State of Health (SOH) of a battery system is one of the core parameters of a Battery Management System (BMS). It directly determines the energy utilization efficiency, operational safety, and accuracy of lifespan prediction of the battery system, as well as affecting the accuracy of SOC estimation and the effectiveness of SOP control strategies. Most methods for estimating the SOH of a battery system use either the average SOH of each battery pack or the minimum SOH of each battery pack. This approach has at least the following drawbacks: 1) Using the average SOH overestimates the health of the battery system, leading to malfunctions in control strategies and increasing the risk of overcharging and over-discharging; 2) Using the minimum SOH only considers the aging degree of the battery packs and does not take into account the differences in the initial SOC of the battery packs, severely underestimating the health of the battery system and reducing energy utilization efficiency. 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 health of a battery system, a battery management system, a battery system, and a storage medium, so as to improve the low accuracy of battery system state of health estimation in related technologies.

[0005] In a first aspect, embodiments of this application provide a method for estimating the state of health (SQH) value of a battery system. The battery system includes multiple battery packs connected in series. The estimation method includes: acquiring first state data, which includes the nominal capacity, SQH value, state of charge (SBC) value, and temperature of the battery packs; calculating the remaining usable capacity and rechargeable capacity of the battery packs based on the nominal capacity, SQH value, SBC value, and temperature; determining the system's dischargeable capacity and rechargeable capacity based on the remaining usable capacity and rechargeable capacity of the battery packs, where the system's dischargeable capacity is the dischargeable capacity of the battery system and the system's rechargeable capacity is the rechargeable capacity of the battery system; calculating the real-time SQH value of the battery system based on the system's nominal capacity, system's dischargeable capacity, and system's rechargeable capacity, where the system's nominal capacity is the minimum of the nominal capacities of all battery packs; responding to the battery system meeting a correction condition, calculating a corrected SQH value of the battery system based on second state data, where the correction condition is used to indicate the correction of the real-time SQH value, and the second state data is operating condition data related to the battery packs; and weightedly fusing the real-time SQH value and the corrected SQH value to obtain the final SQH value of the battery system.

[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 health state 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, and a battery pack includes multiple individual batteries.

[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 battery system health state estimation method provided in the first aspect.

[0009] The embodiments of this application have the following beneficial effects: Unlike related technologies, the embodiments of this application calculate the real-time health status value of the battery system based on the nominal capacity, health status value, state of charge value, and temperature of the battery pack. By comprehensively considering various state data of all battery packs, the available capacity of the battery system is truly reflected, and a more accurate real-time health status value of the battery system can be estimated. Under the condition of meeting the correction, the corrected health status value of the battery system is calculated based on the special operating condition data of the battery pack. Finally, the real-time health status value and the corrected health status value are weighted and fused to obtain the accurate final health status value of the battery system. This significantly improves the accuracy of the battery system health status estimation, ensures the accuracy of the control strategy, reduces the risk of overcharging and over-discharging, and improves the energy utilization efficiency of the battery system. 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 application scenarios for estimating the health status 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 3 This is a flowchart illustrating a method for estimating the state of health of a battery system according to some embodiments of this application; Figure 4A yes Figure 3 A schematic diagram of a sub-process of step S33 in the battery system health status estimation method shown in the embodiment; Figure 4B This is a schematic diagram of the OCV-SOC curve of the battery pack of the battery system in some embodiments of this application; Figure 4C This is a schematic diagram of the OCV-SOC curves of individual cells in the battery pack in some embodiments of this application; Figure 5 yes Figure 3 A schematic diagram of a sub-process of step S34 in the battery system health status estimation method 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] In electric vehicles, energy storage power stations, secondary battery packs, and uninterruptible power supplies, multiple battery packs are connected in series to form a battery system to meet system voltage and capacity requirements. However, due to differences in manufacturing processes, operating environments, and aging levels, the State of Health (SOH) of each battery pack varies significantly. Furthermore, during system startup, charging / discharging interruptions, or insufficient balancing, the initial State of Charge (SOC) of each battery pack will also be inconsistent.

[0016] Battery system state of health (SOH) is one of the core parameters of the battery management system (BMS). It directly determines the energy utilization efficiency, operational safety, and accuracy of life prediction of the battery system, as well as affecting the accuracy of battery system state of charge (SOC) estimation and the effectiveness of SOP (State of Power) control strategies. Most methods for estimating the State of Harmony (SOH) of a battery system use either the average SOH of each battery pack or the minimum SOH of each battery pack. These methods fail to consider the impact of the initial State of Charge (SOC) of the battery pack on the available capacity of the battery system. This approach has at least the following drawbacks: 1) Using the average SOH: This ignores the "weakest link" effect in the battery system, meaning the available capacity is constrained by the worst-performing battery pack. The average SOH overestimates the battery system's health, leading to malfunctions in control strategies and increasing the risk of overcharging and over-discharging. 2) Using the minimum SOH: This only considers the aging of the battery packs and ignores the differences in their initial SOC. In some cases, even if a battery pack has a low SOH, its high initial SOC may result in a higher remaining available capacity than other battery packs with high SOH but extremely low initial SOC. In such cases, using the minimum SOH severely underestimates the battery system's health and reduces energy utilization efficiency. 3) Lack of distinction between charging and discharging scenarios: Failing to differentiate between the SOH of the battery system in the discharging and charging states fails to comprehensively characterize the battery system's health under different operating scenarios, making it difficult to meet the real-time control requirements of the Battery Management System (BMS).

[0017] In view of this, embodiments of this application provide a method for estimating the state of health (SQH) of a battery system. The method calculates the real-time SQH of the battery system based on the nominal capacity, SQH value, state of charge (SQC) value, and temperature of the battery pack. By comprehensively considering various state data from all battery packs, the method accurately reflects the available capacity of the battery system and can estimate a more accurate real-time SQH value. Under corrective conditions, a corrected SQH value is calculated based on the specific operating conditions of the battery pack. Finally, the real-time SQH value and the corrected SQH value are weighted and fused to obtain an accurate final SQH value for the battery system. This significantly improves the accuracy of battery system SQH estimation, ensures the accuracy of control strategies, reduces the risk of overcharging and over-discharging, and improves the energy utilization efficiency of the battery system.

[0018] Please refer to the following: Figure 1A and Figure 1B , Figure 1A This illustration schematically depicts application scenarios for estimating battery system health status values ​​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 1BAs shown, this application scenario includes a battery system 100. The battery system 100 includes a battery management system 110 and multiple ( ) connected in series. A battery pack 120 comprises multiple battery packs 120, and a battery management system 110 is connected to each battery pack 120. A battery pack 120 includes multiple battery packs 120. 121 individual cells. Among them, and All are integers greater than or equal to 2. and They can be the same or different.

[0020] The battery system 100 can be used as any suitable form / type of equipment or device, such as an energy storage power station, a cascaded battery pack, or an uninterruptible power supply. The individual battery cell 121 can be any suitable type of battery, such as a storage battery or a lithium battery. It is understood that, according to actual needs, multiple individual batteries 121 in a 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 / individual battery cell 121. For example, each battery pack 120 is equipped with current sensors, voltage sensors, and temperature sensors, etc., so as to detect and collect current, voltage, and temperature of the battery pack 120 / individual battery cell 121.

[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 / individual cells 121 from these sensors. Based on this data, it calculates and obtains the state data of the battery pack 120 (such as state of charge and state of health). The battery management system 110 then performs an operation to estimate the state of health of the battery system, implementing the method for estimating the state of health of the battery system provided below. It is understood that 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 specific limitations on this.

[0023] See Figure 1AAs shown, this embodiment of the application performs the following operations to estimate the state of health (SHS) value of the battery system: First, first SHS data and second SHS data are acquired. The first SHS data includes the nominal capacity, SHS value, state of charge (SBC) value, and temperature of the battery pack 120. The second SHS data is operating condition data of the battery pack 120, such as the current and voltage of the battery pack 120. Next, based on the nominal capacity, SHS value, SBC value, and temperature of the battery pack 120, the remaining usable capacity and rechargeable capacity of the battery pack 120 are calculated. Then, based on the remaining usable capacity and rechargeable capacity of the battery pack 120, the system discharge capacity and system rechargeable capacity are determined. Furthermore, based on the system discharge capacity and system rechargeable capacity, the real-time SHS value of the battery system 100 is calculated. Finally, it is determined whether the battery system 100 meets the correction conditions, which are used to indicate the correction of the real-time SHS value. When the battery system 100 meets the correction conditions, the corrected SHS value of the battery system 100 is calculated based on the second SHS data. The final health status value of the battery system 100 is obtained by weighted fusion of the real-time health status value and the corrected health status value.

[0024] By using the above methods, the accurate final state of health value of the battery system can be estimated, which significantly improves the accuracy of the battery system's state of health estimation, ensures the accuracy of the control strategy, reduces the risk of overcharging and over-discharging of the battery system, and improves the energy utilization efficiency of the battery system.

[0025] It should be understood that, Figure 1A The illustrated embodiments are merely illustrative of one scenario for estimating the health status value of the battery system 100 in some embodiments of this application, and do not impose any specific limitations on 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 has more or fewer components, or has the same as Figure 1B The diagram shows different configurations of the structure.

[0026] To facilitate understanding of the battery system health status estimation method 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 2Taking 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 health state value estimation method provided in this application embodiment, or in executing the steps in any possible implementation of the battery system health state value 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 health state value 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, so as to implement the battery system health state value estimation method provided in the embodiments of this application, or execute the steps in any possible implementation of the battery system health state value 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 through 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 health state 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 health state 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 health of a battery system provided in this application, with reference to exemplary applications and implementations of the battery system provided in the embodiments of this application.

[0033] To facilitate the description of the battery system health status estimation method provided in the embodiments of this application, the relevant identifiers and their meanings are first explained below, as shown in Table 1.

[0034] Table 1:

[0035] The following, in conjunction with Table 1 above, details the method for estimating the state of health of the battery system provided in the embodiments of this application.

[0036] See Figure 3 As shown, the method for estimating the state of health of a battery system provided in this application includes steps S31 to S34 to estimate the state of health of the battery system.

[0037] Step S31: Obtain the first state data.

[0038] In this embodiment, the first state data includes the battery pack's nominal capacity, state of health value, state of charge value, and temperature.

[0039] 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 rated voltage of the battery pack.

[0040] For any given battery pack, this application embodiment detects and collects the operating status information of the battery pack in real time. The operating status information includes the health status value, state of charge value, and temperature of the battery pack.

[0041] 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.

[0042] For example, for any battery pack, this application embodiment summarizes the nominal capacity, state of health value, state of charge value and temperature of the battery pack to obtain the first state data corresponding to the battery pack.

[0043] Step S32A: Calculate the remaining usable capacity and rechargeable capacity of the battery pack based on its nominal capacity, state of health value, state of charge value and temperature.

[0044] Step S32B: Based on the remaining available capacity and rechargeable capacity of the battery pack, determine the system's discharge capacity and rechargeable capacity. The system's discharge capacity is the discharge capacity of the battery system, and the system's rechargeable capacity is the rechargeable capacity of the battery system.

[0045] Step S32C: Calculate the real-time health status value of the battery system based on the system's nominal capacity, system's discharge capacity, and system's rechargeable capacity.

[0046] 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 nominal capacity Calculate the first Maximum usable capacity / actual capacity of each battery pack ,Right now: .in, Maximum available capacity / actual capacity is used to characterize the current degree of degradation (i.e., the current health status value). The maximum amount of electricity that the battery pack can store or release. Understandably, the higher the battery pack's health status value, the greater its maximum usable capacity; conversely, the lower the battery pack's health status value, the smaller its maximum usable capacity.

[0047] For example, according to the first Maximum usable capacity of each battery pack and real-time state of charge value (The initial state of charge value at system startup) ), calculate the first The original remaining available capacity of the battery pack ,Right now, (When the battery system starts up) The original remaining available capacity represents the maximum amount of electricity that the battery pack can release at the current moment. Understandably, the remaining available capacity will differ between different battery packs because their state of charge (SOC) values ​​may vary.

[0048] For example, according to the first Temperature of the battery pack Determine the first The battery pack is at a temperature Temperature compensation coefficient at time For example, to obtain the preset first The temperature-capacity mapping curve of the first battery pack represents the temperature-capacity mapping curve of the first battery pack. The relationship between the temperature and compensation coefficient of the first battery pack is determined in the temperature-capacity mapping curve. Temperature of the battery pack Corresponding compensation coefficient For the first The battery pack is at a temperature Temperature compensation coefficient at time Using temperature compensation coefficient For the The original remaining available capacity of the battery pack Compensation calculations are performed to obtain the first... The remaining available capacity of the battery pack For example, the temperature compensation coefficient With the The original remaining available capacity of the battery pack Multiply to get the first The remaining available capacity of the battery pack ,Right now: (When the battery system starts up) The remaining available capacity can more accurately reflect the maximum amount of electricity that the battery pack can release at the current moment and under the current operating conditions.

[0049] For example, the rechargeable capacity of each battery pack is calculated based on its nominal capacity, state of health value, state of charge value, and temperature compensation coefficient. Specifically, the nominal capacity and state of health value of each battery pack are multiplied by the charge difference to obtain the effective rechargeable capacity of each battery pack. The charge difference is the difference between the state of charge value 1 corresponding to full charge and the state of charge value of each battery pack. The effective rechargeable capacity of each battery pack is multiplied by the temperature compensation coefficient of each battery pack to obtain the rechargeable capacity of each battery pack.

[0050] It is understandable that, since multiple battery packs are connected in series, when the remaining usable capacity of any one battery pack is exhausted (i.e., that battery pack has no remaining charge to release), the entire battery system will stop discharging. Therefore, the minimum remaining usable capacity among all the remaining usable capacities of the battery packs is determined as the system's dischargeable capacity divided by the system's usable capacity. (That is, the discharge capacity / usable capacity of the battery system), i.e.: .

[0051] In this embodiment of the application, the system's rechargeable capacity This is the minimum rechargeable capacity among all battery packs, i.e.: ,in , ... These represent the rechargeable capacity of the first battery pack, the rechargeable capacity of the second battery pack, ..., the rechargeable capacity of the third battery pack. The rechargeable capacity of each battery pack.

[0052] Understandably, when charging a battery system with multiple battery packs connected in series, the charging current of each battery pack is the same. Therefore, the total amount of electricity that the battery system can charge is constrained by the battery pack with the smallest rechargeable capacity. If the battery system continues to charge, the battery pack with the smallest rechargeable capacity will reach its charging limit first, leading to overcharging, damaging the battery, and causing safety hazards. Thus, the system's rechargeable capacity... The value is the minimum rechargeable capacity of all battery packs in the battery system.

[0053] For example, in this application embodiment, the nominal capacity of the system is obtained. (i.e., the nominal capacity / rated capacity of the battery system), for example, determining the minimum nominal capacity from all the nominal capacities of the battery packs as the system's nominal capacity. ,Right now: and the system's discharge capacity With system nominal capacity The ratio is determined as the real-time health status value of the battery system. The real-time health status value reflects the overall health of the battery system at present.

[0054] Step S33: In response that the battery system meets the correction conditions, calculate the corrected health state value of the battery system based on the second state data.

[0055] In this embodiment, the correction condition is used to indicate the correction of the real-time health status value. The second status data is the operating condition data of the battery pack, which includes the current and voltage of all battery packs and the current and voltage of the battery system.

[0056] For example, in this application embodiment, it is determined whether the battery system meets the correction conditions. The correction conditions include whether the battery system is in a preset stable operating condition, whether the temperature change of the battery system during a preset detection period is less than a temperature threshold, whether the current fluctuation of the battery system during a preset detection period is less than a current fluctuation threshold, and whether the corresponding state of charge change of the battery system is greater than a preset state of charge change threshold. When any of the above conditions are met, it is determined that the battery system meets the correction conditions, and the real-time health status value of the battery system is corrected.

[0057] In response to the battery system meeting the correction conditions, embodiments of this application acquire second state data within the operating cycle. The capacity integral is calculated based on the current and time data within the operating cycle to obtain the actual discharge capacity within the operating cycle. The actual discharge capacity characterizes the actual amount of electricity that the battery system can discharge under the current operating conditions. The actual discharge capacity is then compared with the system's nominal capacity. The ratio is determined as the corrected state of health value of the battery system. .

[0058] Step S34: Weight and fuse the real-time health status value and the corrected health status value to obtain the final health status value of the battery system.

[0059] Understandably, real-time health status values ​​reflect the real-time health of the battery system under normal charge and discharge conditions. They rely on the BMS's online monitoring of dynamic data such as voltage, current, and temperature, and continuous estimation using algorithmic models (e.g., Kalman filtering, ampere-hour integration). While offering high update frequency and immediate response, they are susceptible to planned effects and accumulated errors from ampere-hour integration, leading to long-term drift risks. Corrected health status values, on the other hand, are designed for full charge / discharge conditions or prolonged periods of inactivity. They utilize the absolute SOC reference points (0% and 100%) provided by full charge / discharge to forcibly eliminate accumulated ampere-hour errors, or calibrate the SOC starting point using the high-precision open-circuit voltage (OCV) obtained after polarization decay during inactivity. Furthermore, they directly "measure" the current capacity by recording the actual total charge and discharge volume of a complete cycle, thus obtaining the corrected health status value. This value is highly accurate and free from accumulated drift, but it can only be obtained under specific conditions, resulting in sparse data updates.

[0060] By weighted fusion of real-time health status values ​​and corrected health status values—dynamically allocating weights based on the data reliability under current operating conditions (such as time since last calibration, resting time, current fluctuation amplitude, polarization degree, etc.): In daily dynamic operation, real-time values ​​have a higher weight to ensure continuous tracking capability; while when an effective corrected value is obtained (i.e., the conditions for full charge / discharge or resting are met), the corrected value is given a higher weight, using its benchmark accuracy to "pull" the final estimation result back to near the true value, suppressing long-term drift of the real-time value. The weighted output of the two is the final health status value of the battery system. This fusion strategy retains the real-time and continuous nature of dynamic estimation, and achieves error correction through periodic benchmark injection, thus balancing the accuracy, reliability, and timeliness of SOH assessment throughout the entire life cycle and under all operating conditions.

[0061] This application's embodiments calculate the real-time health status value of the battery system based on the battery pack's nominal capacity, health status value, state of charge value, and temperature. By comprehensively considering various state data from all battery packs, it accurately reflects the available capacity of the battery system and can estimate a more accurate real-time health status value. Under corrective conditions, a corrected health status value of the battery system is calculated based on the battery pack's specific operating condition data. Finally, the real-time health status value and the corrected health status value are weighted and fused to obtain an accurate final health status value of the battery system. This improves the accuracy of the battery system health status estimation, ensures the accuracy of the control strategy, reduces the risk of overcharging and over-discharging, and improves the energy utilization efficiency of the battery system.

[0062] In some embodiments, the present application embodiments, through steps S32A1 to S32A3, calculate the remaining usable capacity and rechargeable capacity of the battery pack based on the nominal capacity, state of health value, state of charge value and temperature of the battery pack.

[0063] Step S32A1: Determine the temperature compensation coefficient of the battery pack based on its temperature.

[0064] Step S32A2: Calculate the remaining usable capacity of the battery pack based on its nominal capacity, state of health value, state of charge value, and temperature compensation coefficient.

[0065] Step S32A3: Calculate the rechargeable capacity of the battery pack based on its nominal capacity, state of health value, state of charge value, and temperature compensation coefficient.

[0066] In step S32A1, because the performance of the battery pack varies at different temperatures, the capacity of the battery pack increases at higher temperatures and decreases at lower temperatures. The battery pack is at a temperature Temperature compensation coefficient at time From the preset first The temperature-capacity curve (i.e., temperature-capacity mapping curve) of the first battery pack can be obtained by looking up a table. Alternatively, in some embodiments, the temperature-capacity mapping curve of the first battery pack can be obtained by looking up a table. Temperature of the battery pack Substitute into the following formula: Calculate the first The battery pack is at a temperature Temperature compensation coefficient at time .in, , , As calibration coefficients, embodiments of this application pre-fit temperature compensation coefficients with battery pack data (such as battery pack temperature, capacity, etc.) to obtain calibration coefficients. , , .

[0067] In step S32A2, the first The nominal capacity of each battery pack Health status value State of charge (Initial state of charge value) and temperature compensation coefficient Multiply to get the first The remaining available capacity of the battery pack ,Right now: (When the battery system starts up) ).

[0068] Understandably, when the battery system starts up, the first step used to estimate the real-time health status of the battery system is... The state of charge (SOC) of each battery pack is the initial SOC value. After the battery system starts up, the first time the real-time health status value of the battery system is estimated, the first time the battery system is used. The state of charge (SOC) of each battery pack is .

[0069] In step S32A3, for the first The battery pack, subtract the state of charge value 1 corresponding to full charge from the first battery pack. State of charge (SOC) of each battery pack (or initial state of charge value) ), to obtain the first The charge difference of each battery pack ,Right now: ,or . Indicates the first The percentage of remaining charging capacity for each battery pack. With the The nominal capacity of each battery pack Health status value Temperature compensation coefficient The product of is the first. The maximum amount of electricity that a battery pack can be charged (i.e., its rechargeable capacity).

[0070] For example, the embodiments of this application will use the first... The nominal capacity of each battery pack Health status value Charge difference and temperature compensation coefficient Multiply to get the first The rechargeable capacity of each battery pack ,Right now: .

[0071] In some embodiments, the present application implements step S32C1 to calculate the real-time health status value of the battery system based on the system nominal capacity, the system discharge capacity, and the system rechargeable capacity.

[0072] Step S32C1: Determine the ratio of the system's dischargeable capacity to the system's nominal capacity as the battery system's dischargeable health state.

[0073] In this embodiment, the system nominal capacity This is the minimum nominal capacity among all battery packs, i.e.: , , ... These are the nominal capacities of the first battery pack, the second battery pack, ..., and so on. The nominal capacity of each battery pack. System discharge capacity. This is the minimum remaining usable capacity among all battery packs, i.e.: ,in, , ... These represent the remaining available capacity of the first battery pack, the remaining available capacity of the second battery pack, ..., the remaining available capacity of the third battery pack. The remaining available capacity of each battery pack.

[0074] In a battery system with multiple battery packs connected in series, the charging or discharging current of each battery pack is exactly the same. Therefore, the system's discharge capacity is constrained by the battery pack with the smallest remaining usable capacity. If the battery system continues to discharge, the battery pack with the smallest remaining usable capacity will reach its discharge limit first, leading to over-discharge, damaging the battery, and causing safety hazards. Thus, the system's discharge capacity is limited. This value is the minimum remaining usable capacity of all battery packs in the battery system. Similarly, for a battery system with multiple battery packs connected in series, the system's nominal capacity... This value is the minimum of the nominal capacities of all battery packs in the battery system. It should be understood that if all battery packs connected in series have the same nominal capacity, then the system's nominal capacity is... With the nominal capacity of any battery pack (e.g., the first) The nominal capacity of each battery pack The same, that is: .

[0075] In step S32C1, the discharge capacity of the system in this embodiment of the application is calculated. With system nominal capacity The ratio of the two values ​​is used to obtain the first capacity ratio, and the first capacity ratio is determined as the usable discharge health state of the battery system. ,Right now: Discharge can be performed in a healthy state. Real-time health status value .

[0076] When all series-connected battery packs have the same nominal capacity (the most common scenario in practical applications), the system's nominal capacity... At this point, the battery system's discharge is in a healthy state. It can be simplified to: The simplified formula requires minimal computation, eliminating the need for additional calculations of remaining available capacity and total system discharge capacity. It can be directly embedded into the BMS for real-time operation, significantly reducing hardware computing power requirements.

[0077] In some embodiments, the present application implements steps S32C2 to S32C3 to calculate the real-time health status value of the battery system based on the system nominal capacity, the system discharge capacity, and the system rechargeable capacity.

[0078] Step S32C2: Sum the system's discharge capacity and rechargeable capacity to obtain the system's fully charged usable capacity.

[0079] For example, determining the system's discharge capacity and system rechargeable capacity The sum of these values ​​gives the system's full usable capacity. ,Right now: .

[0080] Step S32C3: Determine the ratio of the system's fully charged usable capacity to the system's nominal capacity as the battery system's fully charged health status.

[0081] For example, the computing system in this application embodiment is fully utilized. With system nominal capacity The ratio of the two values ​​is used to obtain the second capacity ratio, and the second capacity ratio is determined as the full-charge health state of the battery system. ,Right now: Among them, a fully charged healthy state Real-time health status value .

[0082] In some embodiments, considering the changes in battery system health status caused by battery equalization, and to improve the accuracy and real-time performance of health status estimation, this application embodiment collects data such as voltage and current of each battery pack in real time, and uses relevant algorithms (such as extended Kalman filter algorithm, ampere-hour integral, neural network algorithm, etc.) to dynamically calculate and update the state of charge and health status values ​​of each battery pack. Then, based on the updated state of charge and health status values ​​of each battery pack, the usable discharge health status of the battery system is re-estimated. and full of health .

[0083] In some embodiments, to avoid errors in health status estimation due to real-time sampling errors or assignment errors, a data anomaly detection algorithm is used to detect the health status value of the battery system. When an abnormal health status value of the battery system is detected, it is removed, and the current estimation result (i.e., the current estimated health status value of the battery system) directly inherits the previous estimation result (i.e., the previous estimated health status value of the battery system). For example, embodiments of this application use a sliding window and... The hybrid detection algorithm has the following steps 1 to 9.

[0084] Step 1. Initialize and set the sliding window size Initial threshold Smoothing factor Parameters such as the sliding window size. This is to retain only the data within the sliding window (e.g., the most recent data). The system implements a "forgetting mechanism" to effectively address data distribution drift (e.g., the health status value of each battery system). For example, the sliding window size... The data is updated every 0.1 seconds, covering the real-time health status of the battery system over a 10-second period. An initial score threshold is set. ,For example Set the smoothing factor. Smoothing factor Used for smooth updating of score thresholds ,in, ,For example .

[0085] Step 2. Fill the sliding window with the estimated state of health values ​​of the battery system, and calculate the mean of the state of health values ​​of the battery system within the sliding window. and standard deviation The mean value of the battery system's health status within the sliding window is calculated. and standard deviation This is a standard method and will not be elaborated upon here.

[0086] Step 3. Obtain the estimated state of health value of the battery system. .

[0087] Step 4. Average health status value of the battery system within the sliding window. and standard deviation The health status value of the battery system is calculated. corresponding ,Right now: .

[0088] Step 5. Judgment Is it greater than the initial threshold? ,if Greater than the initial threshold Determine the estimated state of health value of the battery system. If it is an outlier, proceed to step 6; if Greater than or equal to the initial threshold Then the estimated health status value of the battery system is determined. If the value is normal, proceed to step 7.

[0089] Step 6. Determine the previously estimated battery system health status value as the current estimated battery system health status value (i.e., the current estimation result directly inherits the previous estimation result), and proceed to Step 8.

[0090] Step 7. Retain the estimated state of health values ​​of the battery system. Proceed to step 8.

[0091] Step 8. Update the sliding window to display the estimated battery system health status value. Add it to the sliding window and remove the health status value of the oldest battery system in the sliding window.

[0092] Step 9. Output the estimated health status value of the battery system in real time.

[0093] Understandably, besides using sliding windows and... In addition to using hybrid detection algorithms to detect anomalies in the health status values ​​of the battery system, statistical moving window algorithms, exponentially weighted moving average algorithms, incremental learning-based isolated forest algorithms, and streaming data anomaly detection algorithms can also be used to detect anomalies in the health status values ​​of the battery system. This application does not limit these methods in any way.

[0094] Steps S32A to S32C and their detailed steps, sliding window and Hybrid detection algorithms estimate the real-time state of health (SOH) of a battery system under normal charge-discharge conditions by eliminating outliers. However, under normal charge-discharge conditions, polarization effects caused by current and temperature severely interfere with the accuracy of the terminal voltage, and the ampere-hour integration method generates cumulative errors that cannot be eliminated over time, limiting the accuracy of SOH estimation. Full charge and discharge provide the BMS with two absolute physical reference points: SOC=0% and 100%. This allows for the immediate elimination of all accumulated measurement errors, while prolonged rest allows the internal polarization of the battery to completely dissipate, restoring the terminal voltage to a stable open-circuit voltage (OCV) that has a strictly monotonically corresponding relationship with SOC. The combined effect of these two methods allows the BMS to directly "measure" the current capacity by recording the actual total charge and discharge volume of a complete cycle, rather than relying on noisy data and complex algorithms. This transforms SOH assessment from an uncertain "mathematical estimation" to a highly reliable "physical measurement," fundamentally ensuring the accuracy of the calculation results.

[0095] See Figure 4A As shown, in this embodiment of the application, steps S331 to S333 are used to realize the response battery system meeting the correction conditions and calculate the corrected health state value of the battery system based on the second state data.

[0096] Step S331: Determine the system charging cutoff voltage and the system discharging cutoff voltage based on the battery pack's charging cutoff voltage and discharging cutoff voltage.

[0097] In this embodiment, the second state data includes the battery pack's charging cut-off voltage, discharging cut-off voltage, and current. The system charging cut-off voltage and system discharging cut-off voltage are the charging cut-off voltage and discharging cut-off voltage of the battery system, respectively.

[0098] In some embodiments, the charging cut-off voltage and discharging cut-off voltage of the battery pack can be configured in the parameter configuration table of the battery pack. In this embodiment, the charging cut-off voltage and discharging cut-off voltage of the battery pack are read from the parameter configuration table of the battery pack.

[0099] In some embodiments, the battery pack is equipped with a current sensor, which is used to detect and collect the current of the battery pack. This application embodiment obtains the current of the battery pack collected by the current sensor.

[0100] Due to differences in battery pack parameters or manufacturing processes, the OCV-SOC curves (which characterize the relationship between the open circuit voltage (OCV) and the state of charge (SOC) of battery packs of different specifications) will not be identical. Therefore, the charging cut-off voltage and discharging cut-off voltage of battery packs of different specifications may be the same or different. To ensure that a battery system with multiple battery packs connected in series can be charged and discharged within safe limits and is compatible with the safety parameter standards of each battery pack, it is necessary to determine the charging cut-off voltage and discharging cut-off voltage of the battery system (i.e., the system charging cut-off voltage and the system discharging cut-off voltage).

[0101] In some embodiments, the present application embodiments achieve the determination of the system charging cut-off voltage and the system discharging cut-off voltage based on the charging cut-off voltage and the discharging cut-off voltage of the battery pack through steps S3311 to S3312.

[0102] Step S3311: Determine the minimum value of the charging cutoff voltage of all battery packs as the system charging cutoff voltage.

[0103] Step S3312: Determine the maximum value of the discharge cutoff voltage of all battery packs as the system discharge cutoff voltage.

[0104] It is understandable that the battery system's charging cutoff voltage (i.e., the system's charging cutoff voltage) The system charging cutoff voltage is determined by the charging cutoff voltage of each battery pack in the battery system. The value is the minimum among the charging cutoff voltages of all battery packs in the battery system, that is: ,in, , ... These are the charging cutoff voltages for the first battery pack, the second battery pack, ..., the... The charging cutoff voltage of each battery pack. The system charging cutoff voltage is constrained by the battery pack with the lowest charging cutoff voltage, which effectively prevents overcharging of the battery pack. If the charging cutoff voltages of all battery packs connected in series are the same, then the system charging cutoff voltage is the same as the charging cutoff voltage of any single battery pack, that is: , For the first The charging cutoff voltage of each battery pack.

[0105] Similarly, the discharge cutoff voltage of the battery system (i.e., the system discharge cutoff voltage) The system discharge cutoff voltage is determined by the discharge cutoff voltage of each battery pack in the battery system. The value is the maximum discharge cutoff voltage of all battery packs in the battery system, that is: ,in, , ... These are the discharge cutoff voltages of the first battery pack, the second battery pack, ..., the ... The discharge cutoff voltage of each battery pack. The system discharge cutoff voltage is constrained by the battery pack with the highest discharge cutoff voltage, which effectively prevents over-discharge of the battery pack. If the discharge cutoff voltages of all battery packs connected in series are the same, then the system discharge cutoff voltage is the same as the discharge cutoff voltage of any single battery pack, that is: , For the first The discharge cutoff voltage of each battery pack.

[0106] For example, see Figure 4B As shown, Figure 4B The OCV-SOC curves of four battery packs in the battery system are shown. The four battery packs are battery pack A1, battery pack A2, battery pack A3, and battery pack A4. The OCV-SOC curve of battery pack A1 is line 51, the OCV-SOC curve of battery pack A2 is line 52, the OCV-SOC curve of battery pack A3 is line 53, and the OCV-SOC curve of battery pack A4 is line 54. Figure 4B It can be seen that the charging cutoff voltages of battery packs A1, A2, A3, and A4 are different. The charging cutoff voltage of battery pack A2 is greater than that of battery pack A3, and the charging cutoff voltage of battery pack A3 is greater than that of battery pack A1, and the charging cutoff voltage of battery pack A1 is greater than that of battery pack A4. The charging cutoff voltage of battery pack A4 is the smallest. Therefore, the system charging cutoff voltage (e.g., ...) is the lowest. Figure 4B The value shown in line 55 is the charging cutoff voltage of battery pack A4.

[0107] The discharge cutoff voltages of battery packs A1, A2, A3, and A4 are also different. The discharge cutoff voltage of battery pack A1 is greater than that of battery pack A2, which in turn is greater than that of battery pack A4, which is greater than that of battery pack A3. Battery pack A1 has the highest discharge cutoff voltage. Therefore, the system discharge cutoff voltage (e.g., ...) is the highest. Figure 4B The value shown in line 56 is the discharge cutoff voltage of battery pack A1.

[0108] Step S332: In response to the battery system voltage charging from the system discharge cutoff voltage to equal the system charge cutoff voltage or the battery system voltage discharging from the system charge cutoff voltage to equal the system discharge cutoff voltage, the first change capacity is calculated based on the battery pack current using an ampere-hour integration algorithm.

[0109] The first change in capacity is the increase in capacity of the battery system during the process of charging from the system discharge cutoff voltage to the system charge cutoff voltage, or the decrease in capacity of the battery system during the process of discharging from the system charge cutoff voltage to the system discharge cutoff voltage.

[0110] Here, the charging of the battery system from the system discharge cutoff voltage to the system charging cutoff voltage means that the battery system has undergone a full charge process. In this case, the first change in capacity is the increase in capacity of the battery system during the process of charging from the system discharge cutoff voltage to the system charging cutoff voltage (that is, the increase in capacity of the battery system during a full charge process).

[0111] Similarly, the discharge of the battery system from the system charging cutoff voltage to the system discharging cutoff voltage means that the battery system has undergone a full discharge process. In this case, the first change in capacity is the capacity that the battery system loses during the discharge process from the system charging cutoff voltage to the system discharging cutoff voltage (that is, the capacity that the battery system loses during a full discharge process).

[0112] For example, embodiments of this application implement weak current charging / discharging control of the SOP (State of Power) in the charging and discharging end regions of the battery system (i.e., the current of the battery system is less than or equal to the current corresponding to the SOP of each battery pack in the charging and discharging end regions), reducing the voltage error of the battery system and making the voltage of the battery system reach the system charging cutoff voltage / system discharging cutoff voltage, thereby calculating the ampere-hour integral change of the current (i.e., the first change capacity) of the battery system during a full charge or full discharge process. It can be understood that the charging end region of the battery system is the charging end region of the battery pack with the smallest charging cutoff voltage, and the discharging end region of the battery system is the discharging end region of the battery pack with the largest discharging cutoff voltage.

[0113] For example, the following formula can be used: The first change in capacity is calculated using an ampere-hour integration algorithm based on the current of the battery pack. For the first variable capacity, This refers to the start time of the battery system's full charge or full discharge process. This refers to the end time of the battery system's full charge or full discharge process. For the first Each battery pack at time The current (including discharge current and charging current), and at all times During the duration of the battery system's full charge or full discharge process.

[0114] In practical applications, the BMS defines the ampere-hour integral as increasing (+) during charging and decreasing (-) during discharging. The calculation can start from a specific moment and accumulate over time. The first change in capacity is obtained by subtracting historically stored ampere-hour integral data. Specifically, the first change in capacity is calculated by subtracting the ampere-hour integral value at the moment the battery system voltage reaches the system charging cutoff voltage (i.e., the start of full discharge) from the ampere-hour integral value at the moment the battery system voltage reaches the system discharging cutoff voltage. For example, the following formula can be used: The first variable capacity is calculated using an ampere-hour integration algorithm based on the current of the battery pack. To achieve the integral ampere-hour value at the system charging cutoff voltage, The integral value of ampere-hours when the system discharge cutoff voltage is reached.

[0115] Step S333: Determine the ratio of the first variable capacity to the system nominal capacity as the corrected health status value of the battery system.

[0116] For example, embodiments of this application calculate the first variable capacity. With system nominal capacity The ratio of the first capacity ratio to the second capacity ratio is used to obtain the third capacity ratio, and the third capacity ratio is determined as the corrected state of health value of the battery system. ,Right now: .

[0117] In the above manner, when the battery system is fully charged or fully discharged, the amount of charge during the full charging process or the amount of discharge during the full discharging process (i.e., the first change capacity) is estimated, and then the corrected state of health value of the battery system is calculated based on the first change capacity.

[0118] In practical engineering applications, battery systems rarely operate under full charge or full discharge conditions. To prevent excessive errors in estimating the battery system's state of health, voltage samples are taken from individual cells in a static state. The minimum single-cell voltage (i.e., the minimum value among all sampled voltages of individual cells) and the maximum single-cell voltage (i.e., the maximum value among all sampled voltages of individual cells) in each static state are recorded. By comparing the integral change in the charge-discharge ampere-hour of the non-plateau region voltage of the battery system in the two most recent static states, the corrected state of health value of the battery system can be obtained.

[0119] In some implementations, the embodiments of this application realize the corrected health state value of the battery system based on the second state data by responding to the battery system meeting the correction conditions through steps S33A to S33H.

[0120] Step S33A: Determine the charging end region and discharging end region of the individual cell based on the voltage-charge curve of the individual cell.

[0121] Step S33B: When the battery system is in a static state, the voltage of a single cell is collected once to obtain the sampled voltage of the single cell collected this time.

[0122] The voltage-charge curve is used to characterize the correspondence between the open circuit voltage (OCV) and the state of charge (SOC) of a single cell; that is, the voltage-charge curve is an OCV-SOC curve. In this embodiment, a battery pack includes multiple single cells, and the second state data also includes the sampled voltage of the single cells when the battery system is in a resting state.

[0123] For example, when the battery system is in a static state (e.g., when the battery system current is 0, the battery system reaches a specified static time (e.g., 1 hour), or the voltage of a single cell remains stable and unchanged within a specified waiting time (e.g., 30 seconds), this embodiment of the application uses a voltage sampling circuit to collect the voltage of all single cells once, and obtains the sampled voltage of all single cells collected in this static state of the battery system.

[0124] For any battery pack, based on the voltage-charge curve of each individual cell in the battery pack, the charging end region and discharging end region of each individual cell are determined. Specifically, based on the voltage-charge curve of each individual cell, the region with a voltage lower than a preset discharging end voltage is defined as the discharging end region of each individual cell; the region with a voltage higher than a preset charging end voltage is defined as the charging end region of each individual cell; and the region with a voltage greater than or equal to the discharging end voltage and less than or equal to the charging end voltage is defined as the plateau region of each individual cell.

[0125] For example, for each individual cell, the sampling voltage of that individual cell is determined to belong to the charging end region, discharging end region, or plateau region of that individual cell. That is, when the sampling voltage of that individual cell is less than the discharging end voltage, the sampling voltage is determined to belong to the discharging end region; when the sampling voltage of that individual cell is greater than the charging end voltage, the sampling voltage is determined to belong to the charging end region; and when the sampling voltage of that individual cell is greater than or equal to the discharging end voltage and less than or equal to the charging end voltage, the sampling voltage is determined to belong to the plateau region.

[0126] Step S33C: In response to the first maximum cell voltage being located at the charging end region of the first cell and / or the first minimum cell voltage being located at the discharging end region of the second cell, obtain a first state of charge value based on the first maximum cell voltage and obtain a second state of charge value based on the first minimum cell voltage.

[0127] The first maximum single-cell voltage is the maximum value of the sampled voltages of all single-cell batteries collected in this study, and the first single-cell battery is the single-cell battery whose sampled voltage is the first maximum single-cell voltage. The first minimum single-cell voltage is the minimum value of the sampled voltages of all single-cell batteries collected in this study, and the second single-cell battery is the single-cell battery whose sampled voltage is the first minimum single-cell voltage.

[0128] For example, in this embodiment of the application, the maximum value of the sampled voltage of all individual cells collected in this study is taken as the first maximum individual cell voltage. The minimum value of the sampled voltage of all individual cells collected in this study is taken as the first minimum individual cell voltage. If the first maximum single-cell voltage is located at the end of the charging phase of the first single-cell battery, and / or the first minimum single-cell voltage is located at the end of the discharging phase of the second single-cell battery, then the first state-of-charge value is obtained based on the first maximum single-cell voltage. And obtain the second state of charge value based on the first minimum single-cell voltage. It is understandable that if the first maximum single-cell voltage is not located in the charging end region of the first single-cell battery and the first minimum single-cell voltage is not located in the discharging end region of the second single-cell battery, then the sampled voltage of the single-cell battery collected this time is invalid, and the voltage of the single-cell battery will be collected again in the next resting state.

[0129] In some embodiments, the present application implements obtaining a first state of charge value based on a first maximum single-cell voltage and obtaining a second state of charge value based on a first minimum single-cell voltage through steps S33C1 to S33C4.

[0130] Step S33C1: Determine the first maximum single cell voltage as the first open-circuit voltage of the first single cell.

[0131] Step S33C2: Determine the state of charge value corresponding to the first open-circuit voltage in the voltage-charge curve of the first single cell as the first state of charge value.

[0132] For example, in this embodiment of the application, the voltage-charge curve of the first single cell is queried, and the state of charge value corresponding to the first open-circuit voltage in the voltage-charge curve of the first single cell is determined as the first state of charge value, thereby obtaining the first maximum single cell voltage. The corresponding first state of charge value .

[0133] Step S33C3: Determine the first minimum single cell voltage as the second open-circuit voltage of the second single cell.

[0134] Step S33C4: Determine the state of charge value corresponding to the second open-circuit voltage in the voltage-charge curve of the second single cell as the second state of charge value.

[0135] For example, in this embodiment of the application, the voltage-charge curve of the second single cell is queried, and the state of charge value corresponding to the second open-circuit voltage in the voltage-charge curve of the second single cell is determined as the second state of charge value, thereby obtaining the first minimum single cell voltage. The corresponding second state of charge value .

[0136] For example, see Figure 4C As shown, Figure 4C The diagram schematically illustrates the OCV-SOC curves of four individual cells in battery pack A1, namely cell B1, cell B2, cell B3, and cell B4, which are connected in series. The OCV-SOC curves of cells B1, B2, B3, and B4 are represented by lines 501, 502, 503, and 504, respectively.

[0137] according to Figure 4C It can be seen that the charging cutoff voltages of individual cells B1, B2, B3, and B4 are different. The charging cutoff voltage of individual cell B2 is greater than that of individual cell B3, and the charging cutoff voltage of individual cell B3 is greater than that of individual cell B1, and the charging cutoff voltage of individual cell B1 is greater than that of individual cell B4. Individual cell B4 has the smallest charging cutoff voltage. Therefore, the charging cutoff voltage of battery pack A1 (e.g., ...) is the lowest. Figure 4C The value shown in line 505 is the charging cutoff voltage of the single cell B4.

[0138] The discharge cutoff voltages of individual cells B1, B2, B3, and B4 are also different. The discharge cutoff voltage of cell B1 is greater than that of cell B2, which in turn is greater than that of cell B4, which in turn is greater than that of cell B3. Cell B1 has the highest discharge cutoff voltage. Therefore, the discharge cutoff voltage of battery pack A1 (e.g., ...) is the highest. Figure 4C The value shown in line 506 is the discharge cutoff voltage of single cell B1.

[0139] The first single cell is cell B2 (i.e., among the sampled voltages of cells B1, B2, B3, and B4 collected in this study, cell B2 has the highest sampled voltage). The voltage-charge curve of cell B2 (e.g.) Figure 4C The state of charge (SOC) value corresponding to the first maximum single-cell voltage in the OCV-SOC curve shown by line 502 is determined as the first SOC value. The second single-cell is single-cell B4 (i.e., the single-cell B4 has the smallest sampling voltage among the sampling voltages of single-cell B1, single-cell B2, single-cell B3, and single-cell B4 collected in this study). The voltage-charge curve of single-cell B4 (as shown by line 502) is used to determine the first SOC value. Figure 4C The state of charge value corresponding to the first minimum single-cell voltage in the OCV-SOC curve shown by line 504 is determined as the second state of charge value.

[0140] Step S33D: When the battery system is in a resting state again, the voltage of the individual cell is collected again to obtain the sampled voltage of the individual cell.

[0141] For example, when the battery system is in a resting state again, this embodiment of the application uses a voltage sampling circuit to collect the voltage of all individual cells again, thereby obtaining the sampled voltage of all individual cells collected again when the battery system is in a resting state again.

[0142] Similarly, for each individual cell, the sampling voltage of that individual cell is determined to belong to the charging end region, discharging end region, or plateau region of that individual cell. That is, when the sampling voltage of that individual cell is less than the discharging end voltage, the sampling voltage is determined to belong to the discharging end region; when the sampling voltage of that individual cell is greater than the charging end voltage, the sampling voltage is determined to belong to the charging end region; and when the sampling voltage of that individual cell is greater than or equal to the discharging end voltage and less than or equal to the charging end voltage, the sampling voltage is determined to belong to the plateau region.

[0143] Step S33E: In response to the second maximum cell voltage being located at the charging end region of the third cell and / or the second minimum cell voltage being located at the discharging end region of the fourth cell, obtain a third state of charge value based on the second maximum cell voltage and a fourth state of charge value based on the second minimum cell voltage.

[0144] The second maximum single-cell voltage is the maximum value of the sampled voltages of all single-cell cells collected again, and the third single-cell cell is the single-cell cell whose sampled voltage is the second maximum single-cell voltage. The second minimum single-cell voltage is the minimum value of the sampled voltages of all single-cell cells collected again, and the fourth single-cell cell is the single-cell cell whose sampled voltage is the second minimum single-cell voltage.

[0145] For example, in this embodiment of the application, the maximum value of the sampled voltages of all individual cells collected again is taken as the second maximum individual cell voltage. The minimum value of the sampled voltage of all individual cells collected again is taken as the second minimum individual cell voltage. If the second maximum single-cell voltage is located at the end of the charging region of the third single-cell battery, and / or the second minimum single-cell voltage is located at the end of the discharging region of the fourth single-cell battery, then the third state-of-charge value is obtained based on the second maximum single-cell voltage. And obtain the fourth state of charge value based on the second minimum unit voltage. Understandably, if the second maximum single-cell voltage is not located in the charging end region of the third single-cell battery and the second minimum single-cell voltage is not located in the discharging end region of the fourth single-cell battery, the sampled voltage of the single-cell battery collected again is invalid, and the voltage of the single-cell battery will be collected again in the next resting state.

[0146] In some embodiments, the present application embodiments achieve, through steps S33E1 to S33E4, obtaining a third state of charge value based on the second maximum single-cell voltage and a fourth state of charge value based on the second minimum single-cell voltage.

[0147] Step S33E1: Determine the second maximum single cell voltage as the third open-circuit voltage of the third single cell.

[0148] Step S33E2: Determine the state of charge value corresponding to the first open-circuit voltage in the voltage-charge curve of the third cell as the third state of charge value.

[0149] For example, in this application embodiment, the voltage-charge curve of the third single cell is queried, and the state of charge value corresponding to the third open-circuit voltage in the voltage-charge curve of the third single cell is determined as the third state of charge value, thereby obtaining the second maximum single cell voltage. The corresponding third state of charge value .

[0150] Step S33E3: Determine the second minimum single cell voltage as the fourth open-circuit voltage of the fourth single cell.

[0151] Step S33E4: Determine the state of charge value corresponding to the fourth open-circuit voltage in the voltage-charge curve of the fourth single cell as the fourth state of charge value.

[0152] For example, in this embodiment of the application, the voltage-charge curve of the fourth single cell is queried, and the state of charge value corresponding to the fourth open-circuit voltage in the voltage-charge curve of the fourth single cell is determined as the fourth state of charge value, thereby obtaining the second minimum single cell voltage. The corresponding fourth state of charge value .

[0153] Step S33F: In response to the first minimum single cell voltage being located at the discharge end region of the second single cell and the second maximum single cell voltage being located at the charging end region of the third single cell, and / or the first maximum single cell voltage being located at the charging end region of the first single cell and the second minimum single cell voltage being located at the discharge end region of the fourth single cell, the second variable capacity is calculated based on the current of the battery pack using an ampere-hour integration algorithm.

[0154] In this embodiment, the second variable capacity is the increase or decrease in battery system capacity when the maximum value of the sampled voltage of a single battery cell between two adjacent resting states meets the charging end region or the minimum value of the sampled voltage of a single battery cell meets the discharging end region. "The first minimum single-cell voltage is located in the discharging end region of the second single-cell battery and the second maximum single-cell voltage is located in the charging end region of the third single-cell battery" means that the first minimum single-cell voltage in the previous sampling was located in the discharging end region and the second maximum single-cell voltage in the subsequent sampling was located in the charging end region. Similarly, "The first maximum single-cell voltage is located in the charging end region of the first single-cell battery and the second minimum single-cell voltage is located in the discharging end region of the fourth single-cell battery" means that the first maximum single-cell voltage in the previous sampling was located in the charging end region and the second minimum single-cell voltage in the subsequent sampling was located in the discharging end region.

[0155] For example, the embodiments of this application adopt the following formula: The second variable capacity is calculated using an ampere-hour integration algorithm based on the current of the battery pack. This is the ampere-hour integral value of the battery system when the voltage of a single cell was collected in the previous (current) data collection. The ampere-hour integral value of the battery system is obtained when the voltage of a single cell is collected again (the last time). This represents the second variable capacity. Formula: The capacity of the battery system is used to determine the voltage of a single cell in the current (previous) data collection. The capacity of the battery system when the voltage of a single cell is collected again (the next time). The absolute value of the difference.

[0156] Step S33G: Calculate the absolute difference between the second state of charge value and the third state of charge value to obtain the state of charge difference value, or calculate the absolute difference between the first state of charge value and the fourth state of charge value to obtain the state of charge difference value.

[0157] For example, the following formula can be used: The absolute difference between the first and fourth state of charge values ​​is calculated to obtain the state of charge difference. This is the first state of charge value. This is the fourth state of charge value. This represents the difference in state of charge. Formula: Used to obtain the first state of charge value With the fourth state of charge value The absolute value of the difference.

[0158] For example, the following formula can be used: The absolute difference between the second and third state-of-charge values ​​is calculated to obtain the state-of-charge difference. This is the second state of charge value. This is the third state of charge value. This represents the difference in state of charge. Formula: Used to obtain the second state of charge value With the third state of charge value The absolute value of the difference.

[0159] Step S33H: Obtain the corrected health status value of the battery system based on the second changed capacity, the nominal capacity of the system, and the state of charge difference.

[0160] In some embodiments, the present application embodiments achieve, through steps S33H1 to S33H2, a corrected health state value of the battery system is obtained based on the second variable capacity, the nominal capacity of the system, and the state of charge difference.

[0161] Step S33H1: Multiply the nominal capacity of the system by the difference in state of charge to obtain the variable capacity of the system.

[0162] Step S33H2: Divide the second change capacity by the system change capacity to obtain the corrected health status value.

[0163] For example, embodiments of this application utilize the formula: The corrected state of health value of the battery system is calculated, that is, the second variable capacity is... System nominal capacity and the difference in state of charge Substituting into the above formula, the corrected state of health value of the battery system is calculated. .

[0164] Please see Figure 5 In this embodiment of the application, steps S341 to S343 are used to achieve weighted fusion of real-time health status value and corrected health status value to obtain the final health status value of the battery system.

[0165] Step S341: Determine the first weight value based on the first change capacity or the second change capacity.

[0166] Step S342: Determine the second weight value based on the interval between the current correction time and the previous correction time.

[0167] The current correction time is the time when the real-time health status value is corrected this time, the previous correction time is the time when the real-time health status value was corrected the last time, and the first weight value and the second weight value are both less than or equal to 1.

[0168] It is understood that since the corrected state of health value of the battery system can be calculated using the sampled voltage of the effective single cell voltage in each resting state, and the corrected state of health value of the battery system calculated each time may be the same or different, in order to reduce the estimation error, this application embodiment introduces a dynamic first weight value to perform weighted calculation on the corrected state of health value calculated each time to obtain the final state of health value of the battery system.

[0169] When the battery system experiences a full charge or full discharge condition, that is, when the amount of electricity charged or discharged by the battery system is at its maximum, the first change in capacity is the first weighted value. It is 1, that is: When the battery system has not experienced a full charge or full discharge condition, that is, the amount of electricity charged or discharged by the battery system between two adjacent resting states is the second variable capacity. In this case, the first weight value is determined based on the second variable capacity. The larger the second variable capacity, the larger the first weight value; the smaller the second variable capacity, the smaller the first weight value.

[0170] For example, in this application embodiment, the current correction time and the previous correction time are obtained, and the interval between the current correction time and the previous correction time is calculated. If the interval between the current correction time and the previous correction time is smaller, the second weight value is larger; if the interval between the current correction time and the previous correction time is larger, the second weight value is smaller.

[0171] Step S343: Obtain the final health status value based on the target weight value, real-time health status value, and corrected health status value.

[0172] In this embodiment, the target weight value is the product of the first weight value and the second weight value, that is, the first weight value... With the second weight value Multiply to obtain the target weight value. ,Right now: The reference weight value is the difference between 1 and the target weight value; that is, subtract the target weight value from 1. To obtain the reference weight value ,Right now: .

[0173] For example, the corrected health status value is weighted according to the target weight value to obtain the first weighted processing result, and the real-time health status value is weighted according to the reference weight value to obtain the second weighted processing result. The sum of the first weighted processing result and the second weighted processing result is calculated to obtain the final health status value of the battery system.

[0174] In some embodiments, the present application embodiments achieve, through steps S3431 to S3433, obtaining the final health status value based on the target weight value, the real-time health status value, and the corrected health status value.

[0175] Step S3431: Multiply the target weight value by the corrected health status value to obtain the first health status value.

[0176] Step S3432: Multiply the reference weight value by the real-time health status value to obtain the second health status value.

[0177] Step S3433: Sum the first health status value and the second health status value to obtain the final health status value.

[0178] For example, embodiments of this application are based on the following formula: Calculate the final state of health value of the battery system. That is, the target weight value Reference weight value Correct health status values and real-time health status values Substituting into the above formula, the final state of health value of the battery system can be calculated. .

[0179] In summary, the embodiments of this application calculate the real-time health status value of the battery system based on the nominal capacity, health status value, state of charge value, and temperature of the battery pack. By comprehensively considering various state data of all battery packs, it accurately reflects the available capacity of the battery system and can estimate a relatively accurate real-time health status value of the battery system. Furthermore, under the condition of meeting correction criteria, a corrected health status value of the battery system is calculated based on the specific operating conditions of the battery pack. Finally, the real-time health status value and the corrected health status value are weighted and fused to obtain an accurate final health status value of the battery system. This significantly improves the accuracy of battery system health status estimation, ensures the accuracy of control strategies, reduces the risk of overcharging and over-discharging, and improves the energy utilization efficiency of the battery system.

[0180] 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 health state estimation method provided in this application, or to perform the steps in any possible implementation of the battery system health state estimation method provided in this application.

[0181] 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.

[0182] 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.

[0183] 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 health 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, which includes the nominal capacity, state of health value, state of charge value, and temperature of the battery pack; Based on the nominal capacity, state of health value, state of charge value and temperature of the battery pack, calculate the remaining usable capacity and rechargeable capacity of the battery pack. Based on the remaining available capacity and rechargeable capacity of the battery pack, the system discharge capacity and system rechargeable capacity are determined, wherein the system discharge capacity is the discharge capacity of the battery system and the system rechargeable capacity is the rechargeable capacity of the battery system. Based on the system's nominal capacity, the system's discharge capacity, and the system's rechargeable capacity, the real-time health status value of the battery system is calculated, where the system's nominal capacity is the minimum among the nominal capacities of all the battery packs. In response to the battery system meeting the correction condition, a corrected health status value of the battery system is calculated based on the second state data. The correction condition is used to indicate the correction of the real-time health status value. The second state data is the operating condition data of the battery pack. The final health status value of the battery system is obtained by weighted fusion of the real-time health status value and the corrected health status value.

2. The estimation method according to claim 1, characterized in that, The calculation of the remaining usable capacity and rechargeable capacity of the battery pack based on its nominal capacity, state of health value, state of charge value, and temperature includes: The temperature compensation coefficient of the battery pack is determined based on the temperature of the battery pack. The remaining usable capacity of the battery pack is calculated based on its nominal capacity, state of health value, state of charge value, and temperature compensation coefficient. The rechargeable capacity of the battery pack is calculated based on its nominal capacity, state of health value, state of charge value, and temperature compensation coefficient. The calculation of the real-time health status value of the battery system based on the system's nominal capacity, the system's discharge capacity, and the system's rechargeable capacity includes: The ratio of the system's dischargeable capacity to the system's nominal capacity is determined as the dischargeable health status of the battery system. The system's dischargeable capacity is the minimum of the remaining available capacity of all the battery packs, and the dischargeable health status is the real-time health status value.

3. The estimation method according to claim 2, characterized in that, The step of calculating the real-time health status value of the battery system based on the system's nominal capacity, the system's discharge capacity, and the system's rechargeable capacity further includes: The system's discharge capacity and rechargeable capacity are summed to obtain the system's fully charged usable capacity, where the system's rechargeable capacity is the minimum among all the rechargeable capacities of the battery packs. The ratio of the system's fully charged usable capacity to the system's nominal capacity is determined as the battery system's fully charged health status, which is the real-time health status value.

4. The estimation method according to any one of claims 1 to 3, characterized in that, The second state data includes the charging cutoff voltage, discharging cutoff voltage, and current of the battery pack. The response that the battery system meets the correction conditions involves calculating the corrected health state value of the battery system based on the second state data, including: Based on the charging cutoff voltage and discharging cutoff voltage of the battery pack, the system charging cutoff voltage and the system discharging cutoff voltage are determined, wherein the system charging cutoff voltage and the system discharging cutoff voltage are the charging cutoff voltage and the discharging cutoff voltage of the battery system, respectively. In response to the battery system voltage being charged from the system discharge cutoff voltage to an amount equal to the system charge cutoff voltage or the battery system voltage being discharged from the system charge cutoff voltage to an amount equal to the system discharge cutoff voltage, a first change capacity is calculated based on the battery pack current using an ampere-hour integration algorithm. The first change capacity is the increase in capacity of the battery system during the process of charging from the system discharge cutoff voltage to the system charge cutoff voltage or the decrease in capacity of the battery system during the process of discharging from the system charge cutoff voltage to the system discharge cutoff voltage. The ratio of the first variable capacity to the system nominal capacity is determined as the corrected health state value of the battery system, where the system nominal capacity is the minimum of the nominal capacities of all the battery packs.

5. The estimation method according to claim 4, characterized in that, The step of determining the system charging cutoff voltage and the system discharging cutoff voltage based on the charging cutoff voltage and the discharging cutoff voltage of the battery pack includes: The minimum value of the charging cutoff voltage of all the battery packs is determined to be the system charging cutoff voltage; The maximum value of the discharge cutoff voltage of all the battery packs is determined to be the system discharge cutoff voltage.

6. The estimation method according to claim 4, characterized in that, The battery pack includes multiple individual cells, and the second state data also includes the sampled voltage of the individual cells when the battery system is in a quiescent state. The response that the battery system meets a correction condition involves calculating a corrected state of health value for the battery system based on the second state data, including: Based on the voltage-charge curve of the individual battery, the charging end region and the discharging end region of the individual battery are determined, wherein the voltage-charge curve is used to characterize the correspondence between the open circuit voltage and the state of charge value of the individual battery. When the battery system is in a static state, the voltage of the single cell is collected once to obtain the sampled voltage of the single cell collected this time; In response to the first maximum single-cell voltage being located at the charging end region of the first single-cell battery, and / or the first minimum single-cell voltage being located at the discharging end region of the second single-cell battery, a first state of charge value is obtained based on the first maximum single-cell voltage, and a second state of charge value is obtained based on the first minimum single-cell voltage. The first maximum single-cell voltage is the maximum value of the sampled voltages of all the single-cell batteries collected in this study. The first single-cell battery is the single-cell battery with a sampled voltage of the first maximum single-cell voltage. The first minimum single-cell voltage is the minimum value of the sampled voltages of all the single-cell batteries collected in this study. The second single-cell battery is the single-cell battery with a sampled voltage of the first minimum single-cell voltage. In response to the battery system being in the resting state again, the voltage of the individual cell is collected again to obtain the sampled voltage of the individual cell. In response to the second maximum single-cell voltage being located at the charging end region of the third single-cell battery, and / or the second minimum single-cell voltage being located at the discharging end region of the fourth single-cell battery, a third state of charge value is obtained based on the second maximum single-cell voltage, and a fourth state of charge value is obtained based on the second minimum single-cell voltage. The second maximum single-cell voltage is the maximum value of the sampled voltages of all the single-cell batteries collected again. The third single-cell battery is the single-cell battery with a sampled voltage of the second maximum single-cell voltage. The second minimum single-cell voltage is the minimum value of the sampled voltages of all the single-cell batteries collected again. The fourth single-cell battery is the single-cell battery with a sampled voltage of the second minimum single-cell voltage. In response to the first minimum single-cell voltage being located at the discharge end region of the second single-cell battery and the second maximum single-cell voltage being located at the charging end region of the third single-cell battery, and / or the first maximum single-cell voltage being located at the charging end region of the first single-cell battery and the second minimum single-cell voltage being located at the discharge end region of the fourth single-cell battery, a second variable capacity is calculated based on the current of the battery pack using an ampere-hour integration algorithm. The second variable capacity is the increase or decrease in capacity of the battery system between two adjacent resting states. The absolute difference between the second state of charge value and the third state of charge value is calculated to obtain the state of charge difference value, or the absolute difference between the first state of charge value and the fourth state of charge value is calculated to obtain the state of charge difference value. The corrected health status value of the battery system is obtained based on the second variable capacity, the nominal capacity of the system, and the state of charge difference.

7. The estimation method according to claim 6, characterized in that, The step of obtaining the corrected health state value of the battery system based on the second changed capacity, the nominal capacity of the system, and the state of charge difference includes: Multiplying the nominal capacity of the system by the difference in state of charge yields the variable capacity of the system. Divide the second change capacity by the system change capacity to obtain the corrected health status value.

8. The estimation method according to claim 6, characterized in that, The step of weightedly fusing the real-time health status value and the corrected health status value to obtain the final health status value of the battery system includes: A first weight value is determined based on either the first change capacity or the second change capacity; The second weight value is determined based on the interval between the current correction time and the previous correction time. The current correction time is the time when the real-time health status value is corrected this time, and the previous correction time is the time when the real-time health status value was corrected last time. Both the first weight value and the second weight value are less than or equal to 1. The final health status value is obtained based on the target weight value, the real-time health status value, and the corrected health status value, wherein the target weight value is the product of the first weight value and the second weight value.

9. The estimation method according to claim 8, characterized in that, The process of obtaining the final health status value based on the target weight value, the real-time health status value, and the corrected health status value includes: Multiply the target weight value by the corrected health status value to obtain the first health status value; The reference weight value is multiplied by the real-time health status value to obtain the second health status value, where the reference weight value is the difference between 1 and the target weight value. The first health status value and the second health status value are summed to obtain the final health status value.

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 battery system health state value estimation method as described in any one of claims 1 to 9.

11. A battery system, characterized in that, The battery pack 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 and a battery pack includes multiple individual cells.

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 health of a battery system as described in any one of claims 1 to 9.

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