Battery health determination method, controller and power supply device

Abstract

The invention discloses a battery health determination method, a controller and a power supply device, and the method comprises the steps: obtaining battery sampling information which comprises the voltage value of each battery cell; determining a battery cell maximum electric quantity value and a battery cell minimum electric quantity value in the plurality of battery cells according to the plurality of voltage values; determining a compensation factor based on the maximum electric quantity value and the minimum electric quantity value of the battery cell; and updating the last acquired battery health value based on the compensation factor. According to the method, the problem of how to improve SOH estimation accuracy is solved.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method for determining battery health, a controller, and a power supply device. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the State of Health (SOH) of the power battery, as the core energy unit, has a crucial impact on the vehicle's range, safety performance, and energy management strategies. SOH estimation is one of the important functions of the Battery Management System (BMS), used to assess the battery capacity and performance degradation level to guide adjustments to vehicle control strategies and user energy usage.

[0003] Current power batteries typically consist of several cells forming a battery pack. The cells are electrically connected in an "S×P" configuration, meaning that S cells are connected in series, and then multiple series branches are connected in parallel via P circuits to achieve the required voltage and capacity levels. Most mainstream SOH estimation strategies are based on experimental calibration methods. This involves using standard test data for each cell to establish a capacity decay curve for that cell at a specific number of cycles, assuming that all cells age uniformly, and then applying this curve to estimate the SOH of the entire vehicle battery pack.

[0004] However, existing estimation strategies, which rely solely on nominal average capacity, cannot accurately reflect the true usable energy of the battery pack, easily leading to an overestimation of the vehicle's State of Charge (SOH). In actual use, this error is amplified at low SOC levels, causing a sharp drop in the vehicle's actual usable energy, inaccurate judgment by the BMS system, and in extreme scenarios, even sudden power outages, posing significant safety hazards.

[0005] The information disclosed in this background section is included only to enhance the understanding of the context of this disclosure, and therefore may contain information that does not constitute relevant technology currently known to those skilled in the art. Summary of the Invention

[0006] This application provides a battery health determination method, controller, and power supply device to address the problem of how to improve the accuracy of SOH estimation.

[0007] The technical solution adopted in this application is as follows.

[0008] Firstly, this application provides a method for determining battery health, including: Obtain battery sampling information, which includes the voltage value of each cell; The maximum and minimum charge values ​​of a battery cell are determined based on multiple voltage values. The compensation factor is determined based on the maximum and minimum battery cell capacity. The battery health value is updated based on the compensation factor. The battery health determination method provided in this application determines the maximum and minimum charge values ​​of each cell by collecting voltage values ​​from multiple cells, thereby reflecting the capacity differences and the dispersion of health status among the cells. Furthermore, this method constructs a compensation factor based on the charge differences between cells, and dynamically corrects the current battery health value (SOH) using this compensation factor, so that the calculated SOH can reflect the actual aging distribution of the cells.

[0009] Related technologies estimate State of Health (SOH) based solely on the average nominal capacity of individual cells, neglecting aging differences between cells. In contrast, this application effectively considers the impact of SOH dispersion among different cells on battery health, unlike methods that calculate the SOH of the entire battery pack based solely on the capacity of a single nominal cell. When cell capacity differences exist, traditional averaging strategies easily lead to an overestimation of the overall vehicle SOH. This application, by introducing a quantitative compensation mechanism for cell differences, incorporates distributional information into the SOH estimation, allowing the updated SOH to more accurately reflect the overall battery pack's health status. This is especially true in the later stages of cycle aging, when cell differences gradually intensify, maintaining high estimation accuracy and helping to avoid sudden power outages caused by underestimating or overestimating risks.

[0010] Furthermore, this application adopts a real-time correction method based on the most recent sampling results, which does not rely on complex aging modeling and the accumulation of a large amount of historical data. This improves the lightweightness and adaptability of the algorithm deployment, making it suitable for various battery configurations and application scenarios. It significantly improves the accuracy of energy availability judgment for the whole vehicle in the low SOC stage, and helps to avoid sudden power outage problems caused by underestimating or overestimating risks.

[0011] In conjunction with the first aspect, in one possible implementation, the battery sampling information includes fault codes, and the method further includes: Before determining the maximum and minimum charge values ​​of multiple battery cells based on multiple voltage values, the following condition must be met: the fault code indicates that the battery is not faulty.

[0012] The battery health determination method provided in this application introduces fault code judgment conditions before calculating the maximum and minimum charge values ​​of the battery cells. This ensures that SOH estimation and compensation updates are only performed when the battery is in normal operating condition, thereby enhancing the reliability and stability of the SOH estimation results. Specifically, by collecting fault codes from battery sampling information and determining whether they indicate a battery fault, it can effectively avoid deviations in the SOH estimation results due to abnormal voltage data when there are serious abnormalities in the battery cells or sensor failures, thus improving the fault tolerance and data validity of the SOH estimation.

[0013] Compared to related technologies that directly estimate SOH based on voltage sampling and lack an effective data filtering mechanism, this application makes the determination process of the SOH compensation factor more rigorous by setting the necessary condition of "no battery fault." This avoids the problem of misidentifying and incorrectly including extreme cell charge values ​​in SOH correction under fault conditions, fundamentally reducing the probability of misestimation. Furthermore, this method relies only on existing fault code signals, has simple logic, low deployment cost, and can be embedded into existing BMS architectures. While improving the robustness of the SOH algorithm, it does not increase system hardware complexity and has good engineering feasibility.

[0014] In conjunction with the first aspect, in one possible implementation, the fault codes include hardware fault codes, acquisition fault codes, and differential pressure fault codes. Determining whether the battery is faulty based on the fault codes includes: Based on the hardware fault code, determine whether the battery controller is faulty. If the result is negative, then based on the acquisition fault code, determine whether the battery acquisition sensor is faulty. If the result is negative, then based on the differential pressure fault code, determine whether the battery cell is damaged.

[0015] The battery health determination method provided in this application refines fault codes into hardware fault codes, acquisition fault codes, and differential pressure fault codes, and judges them in order of priority. This effectively reduces the risk of SOH estimation error under abnormal operating conditions, avoids decision-making bias caused by the system estimating health status based on invalid or distorted data, and further ensures the safety of vehicle operation and the rationality of energy management strategies.

[0016] Specifically, the system first uses hardware fault codes to determine if there are logical abnormalities or functional failures in the controller (such as the main control unit or BMS motherboard), which can prevent incorrect SOH calculations under system-level control faults. If the controller is not faulty, the system continues to use the acquired fault codes to determine if there are any abnormalities in the sensors in the acquisition link, such as voltage, current, and temperature, to prevent interference from abnormal sensor signals from affecting the correct judgment of the compensation factor. If the acquisition function is normal, the system finally uses the differential voltage fault codes to determine whether the voltage difference between cells exceeds the set threshold, which is used to identify whether there is abnormal degradation or internal short circuit in a single cell, thereby avoiding the use of distorted voltage values ​​in the determination of maximum / minimum charge values.

[0017] In conjunction with the first aspect, in one possible implementation, the battery sampling information includes multiple temperature values ​​corresponding to multiple regions inside the battery, and the method further includes: The maximum temperature difference inside the battery is determined based on multiple temperature values; Before determining the maximum and minimum charge values ​​of multiple battery cells based on multiple voltage values, the following condition must also be met: the maximum temperature difference must not exceed the temperature difference threshold.

[0018] In practical applications, thermal coupling exists between battery cells. When an abnormal temperature rise occurs in a local area (e.g., due to increased internal resistance or precursors to thermal runaway), it can cause abnormal voltage changes in that area, thus affecting the determination of the maximum / minimum capacity of the cell based on the voltage value. If SOH estimation is performed directly under these conditions, it may lead to misjudgment of the aging degree of individual cells, thereby introducing a large error. Therefore, this application sets a temperature difference threshold, and only calculates the compensation factor and updates the SOH when the internal temperature distribution of the battery is relatively uniform (i.e., the maximum temperature difference is not greater than the threshold), in order to avoid estimation deviations under uneven temperature conditions.

[0019] This application further enhances the effectiveness and accuracy of compensation factor calculation and SOH update by introducing a judgment condition for the maximum internal temperature difference of the battery into the SOH update process. Specifically, by acquiring temperature sampling values ​​from multiple regions inside the battery and determining the maximum temperature difference value based on these values, the uniformity of the current temperature field can be evaluated, effectively identifying state distortion caused by local overheating or abnormal heat distribution.

[0020] In conjunction with the first aspect, in one possible implementation, the battery sampling information includes the battery current value, and the method includes: Before determining the maximum and minimum charge values ​​of multiple battery cells based on multiple voltage values, the following condition must be met: the absolute value of the battery current value is not greater than the current threshold.

[0021] In practical applications, when a battery is under high current charging and discharging, instantaneous fluctuations in cell voltage can occur due to phenomena such as polarization and electrochemical dynamic hysteresis. These fluctuations do not accurately reflect the static charge level of the cell. If these fluctuations are used directly to estimate the maximum / minimum charge value of the cell under these conditions, it will lead to misjudgment of the compensation factor, thereby affecting the accuracy of the SOH estimation result. This error is particularly significant under high-rate operating conditions or during frequent charge-discharge switching.

[0022] Therefore, this application sets a current threshold, allowing the SOH update process based on voltage differences only when the battery current value is in a relatively stable state (i.e., the absolute value of the current is not greater than the preset threshold) or when the battery is in a static state. This significantly reduces the estimation deviation caused by transient interference. This strategy effectively enhances the reliability and adaptability of battery health estimation in dynamic operating scenarios, and improves the control accuracy and safety stability of the vehicle BMS system under complex operating conditions.

[0023] In conjunction with the first aspect, in one possible implementation, a compensation factor is determined based on the maximum and minimum battery capacity of the cell, including: The compensation factor is calculated based on the maximum and minimum battery capacity of the battery cell. If the calculated compensation factor is greater than the most recently determined compensation factor, the most recently determined compensation factor is updated based on the calculated compensation factor.

[0024] In practical applications, as battery usage time increases, the differences in the health status of individual cells become increasingly significant. The compensation factor reflects the degree of dispersion in charge levels among cells; a higher value indicates a more significant difference in health status. Therefore, under normal circumstances, the compensation factor increases with battery usage time. In this application, the compensation factor is not updated unconditionally. Instead, it is updated by comparing the current calculated value with the most recently determined compensation factor. Only when the currently calculated compensation factor is greater than the historical compensation factor is it updated. This avoids fluctuations in SOH estimation caused by abnormal sampling or instantaneous disturbances, ensuring that the SOH value remains a stable and reliable reference value even in the later stages of cell aging.

[0025] In conjunction with the first aspect, one possible implementation involves updating the battery health value based on a compensation factor, including: Subtract the compensation factor from the most recently obtained battery health value to obtain the updated battery health value.

[0026] In practical applications, if some battery cells are severely aged but the State of Health (SOH) is still overestimated based on the average capacity, the system may incorrectly determine that the battery still has a lot of remaining capacity, potentially leading to deep discharge or even a complete vehicle power outage. This solution introduces a compensation factor for subtraction correction, making the SOH estimate more conservative and closer to the weakest cell, thus improving the safety of the estimation results.

[0027] In conjunction with the first aspect, in one possible implementation, a compensation factor is determined based on the maximum and minimum battery capacity of the cell, including: The compensation factor is obtained by subtracting the minimum charge value of the battery cell from the maximum charge value of the battery cell.

[0028] By taking the difference between the maximum and minimum charge values, the dispersion of the current cell capacity within the battery pack can be accurately captured. The larger the difference, the more significant the inconsistency in aging between the cells, and the more likely the usable capacity of the entire battery pack is limited by the weakest cell, reflecting the risk that the current State of Health (SOH) may be overestimated.

[0029] In conjunction with the first aspect, in one possible implementation, determining the maximum and minimum charge values ​​of a battery cell among multiple battery cells based on multiple voltage values ​​includes: The maximum capacity of the battery cell is determined based on the highest voltage value among multiple voltage values. The minimum charge value of the battery cell is determined based on the minimum voltage value among multiple voltage values.

[0030] There is a close functional relationship between cell voltage and charge, especially under static or low current conditions, voltage fluctuations can serve as an effective indicator of charge changes. This application uses the maximum and minimum voltage values ​​as measurement standards, which can intuitively reflect the differences in capacity and health status among cells in the battery pack, and accurately identify weak cells.

[0031] In conjunction with the first aspect, in one possible implementation, the battery sampling information includes multiple temperature values ​​corresponding to multiple regions inside the battery, and determining the maximum and minimum charge values ​​of the multiple battery cells based on the multiple voltage values ​​includes: The maximum charge capacity of the battery cell is determined based on the maximum temperature value among multiple temperature values ​​and the maximum voltage value among multiple voltage values. The minimum charge value of the battery cell is determined based on the minimum temperature value among multiple temperature values ​​and the minimum voltage value among multiple voltage values.

[0032] A temperature gradient exists within the battery pack, resulting in differences in cell performance and degradation rates across different regions. Battery voltage also varies significantly under different temperature conditions. Simply relying on voltage values ​​to determine cell capacity can lead to inaccuracies. By combining the maximum temperature and maximum voltage values ​​for a given region to determine the maximum cell capacity, and by combining the minimum temperature and minimum voltage values ​​to determine the minimum cell capacity, the impact of temperature on voltage is effectively corrected, improving the accuracy of the capacity values.

[0033] By considering both temperature and voltage simultaneously, the actual health status of the cells can be more comprehensively reflected, avoiding misjudgments caused by ignoring the influence of temperature when relying solely on voltage indicators.

[0034] In conjunction with the first aspect, in one possible implementation, the maximum charge capacity of the battery cell is determined based on the maximum temperature value among multiple temperature values ​​and the maximum voltage value among multiple voltage values, including: Based on the first mapping relationship, determine the maximum battery capacity value corresponding to the maximum temperature value and the maximum voltage value; The minimum charge value of the battery cell is determined based on the minimum temperature value among multiple temperature values ​​and the minimum voltage value among multiple voltage values, including: Based on the first mapping relationship, the minimum charge value of the battery cell corresponding to the minimum temperature value and the minimum voltage value is determined.

[0035] The first mapping relationship in this application can be obtained through a three-dimensional calibration experiment of temperature-voltage-charge in actual battery cells, and has good engineering feasibility. The first mapping relationship can be in the form of interpolation lookup table or fitting function.

[0036] Secondly, this application also provides a power supply device. The power supply device includes modules for performing the battery health determination method, controller, and power supply device method in the first aspect or any optional implementation of the first aspect. For example, the power supply device includes a battery and a controller. For more detailed implementation information regarding the power supply device, please refer to the description of any of the implementation methods in the first aspect above.

[0037] Thirdly, this application provides a controller. The controller includes: a memory and a processor, wherein the memory stores computer instructions; and the processor, when executing the computer instructions, implements the battery health determination method in the first aspect or any optional implementation of the first aspect.

[0038] The beneficial effects of the second and third aspects described above can be referenced to the first aspect or any possible implementation thereof, and will not be elaborated upon here. Based on the implementations provided in the above aspects, this application can also be further combined to provide more implementations.

[0039] Other advantages, objectives and features of this application will be partly apparent from the description below, and partly understood by those skilled in the art through study and practice of this application. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0041] Figure 1This is one of the flowcharts of the battery health determination method provided in the embodiments of this application; Figure 2 This is the second flowchart of the battery health determination method provided in the embodiments of this application; Figure 3 This is the third flowchart of the battery health determination method provided in the embodiments of this application; Figure 4 This is the fourth flowchart of the battery health determination method provided in the embodiments of this application; Figure 5 This is the fifth flowchart of the battery health determination method provided in the embodiments of this application. Detailed Implementation

[0042] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0043] The term "and / or" as used in this application includes any and all combinations of one or more of the associated listed items. In this application, "at least one" means one or more, and "more than one" means two or more. The purpose of using these terms is solely to distinguish one element from the others and should not be construed as indicating or implying relative importance. For example, without departing from the scope of this application, a first element may be named a second element, and similarly, a second element may be named a first element.

[0044] Each circuit or other component may be described or referred to as "for" performing one or more tasks. In this context, "for" is used to imply a structure by indicating that the circuit / component includes a structure (e.g., a circuit system) that performs one or more tasks during operation. Therefore, even when the specified circuit / component is currently inoperable (e.g., not turned on), it can still be referred to as "for performing that task." Circuits / components used with the term "for" include hardware, such as circuits that perform operations.

[0045] Before introducing the embodiments of this application, the technical terms and background technology involved in this application will be introduced first.

[0046] A battery cell refers to a single electrochemical energy storage unit in a battery, and is the most basic energy storage unit in a battery system. The series and parallel connection of multiple battery cells in a battery is expressed in the form of x series y parallel. For example, 8 series 4 parallel means that 4 battery cells are first connected in parallel to form a 4P small module, and then 8 such "small modules" are connected in series.

[0047] In related technologies, the State of Health (SOH) of a battery is usually calculated based on experimental calibration methods. This involves establishing a capacity decay curve for a cell at a specific number of cycles using standard test data of the cell, assuming that all cells age uniformly, and then applying this curve to the SOH estimation of the entire vehicle battery pack.

[0048] However, existing estimation strategies, which rely solely on nominal average capacity, cannot accurately reflect the true usable energy of the battery pack, easily leading to an overestimation of the vehicle's State of Health (SOH). In practical use, this error is amplified at low SOC levels, causing a sharp drop in the vehicle's actual usable energy, inaccurate judgment by the BMS system, and in extreme scenarios, even sudden power outages, posing significant safety hazards. To address these issues, this application provides a battery health determination method, controller, and power supply device that can improve the accuracy of battery SOH estimation and reduce the risk of sudden vehicle power outages.

[0049] The following first describes one or more exemplary operating environments to facilitate a clearer understanding of the functions and intentions of the various implementation methods in this application. A battery device can serve as the implementation environment for the battery health determination method of this application. This implementation environment includes a battery and a controller. The battery includes multiple battery cells. The controller is used to acquire and store data from the battery cells and execute the method of this embodiment based on the battery cell data. That is, the controller can serve as the execution subject of the battery health determination method, thereby improving the accuracy of battery SOH estimation and reducing the risk of sudden power outages in the vehicle.

[0050] Several embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the following embodiments can be implemented in many different forms and should not be construed as being limited to the embodiments set forth herein.

[0051] refer to Figure 1 In a first aspect, this application provides a method for determining battery health, the battery comprising multiple cells, including: S101. Obtain battery sampling information, which includes multiple voltage values ​​corresponding to multiple cells; S103. Determine the maximum and minimum charge values ​​of multiple battery cells based on multiple voltage values. The maximum battery capacity value refers to the battery capacity value of the cell with the highest capacity value among multiple battery cells.

[0052] The minimum battery cell capacity refers to the battery cell with the lowest capacity among multiple battery cells.

[0053] S105. Determine the compensation factor based on the maximum and minimum battery cell capacity. S107. Update the battery health value based on the compensation factor.

[0054] The battery health determination method provided in this application determines the maximum and minimum charge values ​​of each cell by collecting voltage values ​​from multiple cells, thereby reflecting the capacity differences and the dispersion of health status among the cells. Furthermore, this method constructs a compensation factor based on the charge differences between cells, and dynamically corrects the current state of health (SOH) of the battery using this compensation factor, so that the calculated SOH can reflect the actual aging distribution of the cells.

[0055] Related technologies estimate State of Health (SOH) based solely on the average nominal capacity of individual cells, neglecting aging differences between cells. In contrast, this application effectively considers the impact of SOH dispersion among different cells on battery health, unlike methods that calculate the SOH of the entire battery pack based solely on the capacity of a single nominal cell. When cell capacity differences exist, traditional averaging strategies easily lead to an overestimation of the overall vehicle SOH. This application, by introducing a quantitative compensation mechanism for cell differences, incorporates distributional information into the SOH estimation, allowing the updated SOH to more accurately reflect the overall battery pack's health status. This is especially true in the later stages of cycle aging, when cell differences gradually intensify, maintaining high estimation accuracy and helping to avoid sudden power outages caused by underestimating or overestimating risks.

[0056] Furthermore, this application adopts a real-time correction method based on the most recent sampling results, which does not rely on complex aging modeling and the accumulation of a large amount of historical data. This improves the lightweightness and adaptability of the algorithm deployment, making it suitable for various battery configurations and application scenarios. It significantly improves the accuracy of energy availability judgment for the whole vehicle in the low SOC stage, and helps to avoid sudden power outage problems caused by underestimating or overestimating risks.

[0057] The following combination Figure 1-5 The steps S101 to S107 and other optional steps are described in detail.

[0058] Regarding step S103: Determine the maximum and minimum charge values ​​of the multiple battery cells based on the multiple voltage values.

[0059] In one possible implementation, step S103, determining the maximum and minimum charge values ​​of a battery cell among multiple battery cells based on multiple voltage values, includes: The maximum capacity of the battery cell is determined based on the highest voltage value among multiple voltage values. The minimum charge value of the battery cell is determined based on the minimum voltage value among multiple voltage values.

[0060] There is a close functional relationship between cell voltage and charge, especially under static or low current conditions, voltage fluctuations can serve as an effective indicator of charge changes. This application uses the maximum and minimum voltage values ​​as measurement standards, which can intuitively reflect the differences in capacity and health status among cells in the battery pack, and accurately identify weak cells.

[0061] In one possible implementation, refer to Figure 3 The battery sampling information in step S101 includes multiple temperature values ​​corresponding to multiple regions inside the battery. Step S103 determines the maximum and minimum charge values ​​of the multiple battery cells based on multiple voltage values, including: S301. Determine the maximum charge value of the battery cell based on the maximum temperature value among multiple temperature values ​​and the maximum voltage value among multiple voltage values; S302. Determine the minimum charge value of the battery cell based on the minimum temperature value among multiple temperature values ​​and the minimum voltage value among multiple voltage values.

[0062] Multiple temperature values ​​corresponding to multiple regions inside the battery refer to the fact that the battery is divided into multiple regions, each region includes multiple cells, and a temperature sensor is set in each region to obtain the temperature value of that region. In other words, the temperature value corresponds to the region inside the battery, rather than to an individual cell.

[0063] A temperature gradient exists within the battery pack, resulting in differences in cell performance and degradation rates across different regions. Battery voltage also varies significantly under different temperature conditions. Simply relying on voltage values ​​to determine cell capacity can lead to inaccuracies. By combining the maximum temperature and maximum voltage values ​​for a given region to determine the maximum cell capacity, and by combining the minimum temperature and minimum voltage values ​​to determine the minimum cell capacity, the impact of temperature on voltage is effectively corrected, improving the accuracy of the capacity values.

[0064] In one possible implementation, the maximum charge capacity of the battery cell is determined based on the maximum temperature value among multiple temperature values ​​and the maximum voltage value among multiple voltage values, including: Based on the first mapping relationship, determine the maximum battery capacity value corresponding to the maximum temperature value and the maximum voltage value; The minimum charge value of the battery cell is determined based on the minimum temperature value among multiple temperature values ​​and the minimum voltage value among multiple voltage values, including: Based on the first mapping relationship, the minimum charge value of the battery cell corresponding to the minimum temperature value and the minimum voltage value is determined.

[0065] The first mapping relationship in this application can be obtained through a three-dimensional calibration experiment of temperature-voltage-charge in actual battery cells, and has good engineering feasibility. The first mapping relationship can be in the form of interpolation lookup table or fitting function.

[0066] Regarding step S105: Determine the compensation factor based on the maximum and minimum battery cell capacity.

[0067] In one possible implementation, refer to Figure 5 Step S105 determines the compensation factor based on the maximum and minimum battery cell capacity, including: S501. Calculate the compensation factor based on the maximum and minimum battery cell capacity. S503. Determine whether the calculated compensation factor is greater than the most recently determined compensation factor; S505. If so, update the most recently determined compensation factor based on the calculated compensation factor.

[0068] In practical applications, as battery usage time increases, the differences in the health status of individual cells become increasingly significant. The compensation factor reflects the degree of dispersion in charge levels among cells; a higher value indicates a more significant difference in health status. Therefore, under normal circumstances, the compensation factor increases with battery usage time. In this application, the compensation factor is not updated unconditionally. Instead, it is updated by comparing the current calculated value with the most recently determined compensation factor. Only when the currently calculated compensation factor is greater than the historical compensation factor is it updated. This avoids fluctuations in SOH estimation caused by abnormal sampling or instantaneous disturbances, ensuring that the SOH value remains a stable and reliable reference value even in the later stages of cell aging.

[0069] In step S105 or step S501, the compensation factor can be calculated by subtracting the minimum charge value of the battery cell from the maximum charge value of the battery cell.

[0070] By taking the difference between the maximum and minimum charge values, the dispersion of the current cell capacity within the battery pack can be accurately captured. The larger the difference, the more significant the inconsistency in aging between the cells, and the more likely the usable capacity of the entire battery pack is limited by the weakest cell, reflecting the risk that the current State of Health (SOH) may be overestimated.

[0071] Regarding step S107: Update the battery health value based on the compensation factor.

[0072] In one possible implementation, step S107 updates the battery health value based on a compensation factor, including subtracting the minimum battery capacity value from the maximum battery capacity value to obtain the compensation factor.

[0073] In practical applications, if some battery cells are severely aged but the State of Health (SOH) is still overestimated based on the average capacity, the system may incorrectly determine that the battery still has a lot of remaining capacity, potentially leading to deep discharge or even a complete vehicle power outage. This solution introduces a compensation factor for subtraction correction, making the SOH estimate more conservative and closer to the weakest cell, thus improving the safety of the estimation results.

[0074] Considering the possibility of controller failure and other faults such as thermal runaway in the battery cells, which could lead to inaccurate battery sampling information, the following measures are taken to avoid these faults affecting the SOH estimation: Figure 2 In some implementations, in step S101, the battery sampling information includes a fault code, and after obtaining the battery sampling information in step S101, but before determining the maximum and minimum battery capacity values ​​among the multiple battery cells in step S103, the following step S203 is performed: S203: Determine if the battery is faulty based on the fault code; Fault codes are alarm messages displayed as numbers or letters by the battery management system when it detects an abnormal condition in the battery pack. These fault codes are used to indicate the abnormal state of the battery pack or the cause of the malfunction.

[0075] The battery health determination method provided in this application introduces fault code judgment conditions before calculating the maximum and minimum charge values ​​of the battery cells. This ensures that SOH estimation and compensation updates are only performed when the battery is in normal operating condition, thereby enhancing the reliability and stability of the SOH estimation results. Specifically, by collecting fault codes from battery sampling information and determining whether they indicate a battery fault, it can effectively avoid deviations in the SOH estimation results due to abnormal voltage data when there are serious abnormalities in the battery cells or sensor failures, thus improving the fault tolerance and data validity of the SOH estimation.

[0076] Compared to related technologies that directly estimate SOH based on voltage sampling and lack an effective data filtering mechanism, this application makes the determination process of the SOH compensation factor more rigorous by setting the necessary condition of "no battery fault." This avoids the problem of misidentifying and incorrectly including extreme cell charge values ​​in SOH correction under fault conditions, fundamentally reducing the probability of misestimation. Furthermore, this method relies only on existing fault code signals, has simple logic, low deployment cost, and can be embedded into existing BMS architectures. While improving the robustness of the SOH algorithm, it does not increase system hardware complexity and has good engineering feasibility.

[0077] Regarding step S203: Determine whether the battery is faulty based on the fault code.

[0078] like Figure 4As shown, in one embodiment, the fault codes include hardware fault codes, acquisition fault codes, and differential pressure fault codes. Step S203: Determine whether the battery has malfunctioned based on the fault codes, including: S401. Based on the hardware fault code, determine whether the battery controller has a fault. If the determination result is negative, proceed to the next step. S402. Based on the collected fault codes, determine whether the battery has a fault in the data acquisition sensor. If the determination result is negative, proceed to the next step. S403. Based on the differential pressure fault code, determine whether the battery cell is damaged. If the determination result is negative, proceed to the next step S103.

[0079] The battery health determination method provided in this application refines fault codes into hardware fault codes, acquisition fault codes, and differential pressure fault codes, and judges them in order of priority. This effectively reduces the risk of SOH estimation error under abnormal operating conditions, avoids decision-making bias caused by the system estimating health status based on invalid or distorted data, and further ensures the safety of vehicle operation and the rationality of energy management strategies.

[0080] Specifically, the system first uses hardware fault codes to determine if there are logical abnormalities or functional failures in the controller (such as the main control unit or BMS motherboard), which can prevent incorrect SOH calculations under system-level control faults. If the controller is not faulty, the system continues to use the acquired fault codes to determine if there are any abnormalities in the sensors in the acquisition link, such as voltage, current, and temperature, to prevent interference from abnormal sensor signals from affecting the correct judgment of the compensation factor. If the acquisition function is normal, the system finally uses the differential voltage fault codes to determine whether the voltage difference between cells exceeds the set threshold, which is used to identify whether there is abnormal degradation or internal short circuit in a single cell, thereby avoiding the use of distorted voltage values ​​in the determination of maximum / minimum charge values.

[0081] Temperature can affect the health and charge levels of a battery cell. To avoid ignoring the influence of temperature and thus improve the accuracy of estimations, in one embodiment, a reference is made... Figure 2 In step S101, the battery sampling information includes multiple temperature values ​​corresponding to multiple regions inside the battery. After obtaining the battery sampling information in step S101, and before determining the maximum and minimum charge values ​​of the multiple battery cells in step S103, the following steps S205 and S207 are executed: S205. Determine the maximum temperature difference inside the battery based on multiple temperature values; S207. Determine whether the maximum temperature difference value is not greater than the temperature difference threshold. If the maximum temperature difference value is not greater than the temperature difference threshold, proceed to the next step S103.

[0082] The battery has multiple temperature values ​​corresponding to different regions. Due to the different cooling efficiency of the battery's internal cooling system and the different heat generation capabilities of different parts of the battery cell, the temperature values ​​of different regions inside the battery are different, that is, the temperature inside the battery is distributed in a stepped manner.

[0083] The temperature threshold can be set to 5℃.

[0084] In practical applications, thermal coupling exists between battery cells. When an abnormal temperature rise occurs in a local area (e.g., due to increased internal resistance or precursors to thermal runaway), it can cause abnormal voltage changes in that area, thus affecting the determination of the maximum / minimum capacity of the cell based on the voltage value. If SOH estimation is performed directly under these conditions, it may lead to misjudgment of the aging degree of individual cells, thereby introducing a large error. Therefore, this application sets a temperature difference threshold, and only calculates the compensation factor and updates the SOH when the internal temperature distribution of the battery is relatively uniform (i.e., the maximum temperature difference is not greater than the threshold), in order to avoid estimation deviations under uneven temperature conditions.

[0085] This application further enhances the effectiveness and accuracy of compensation factor calculation and SOH update by introducing a judgment condition for the maximum internal temperature difference of the battery into the SOH update process. Specifically, by acquiring temperature sampling values ​​from multiple regions inside the battery and determining the maximum temperature difference value based on these values, the uniformity of the current temperature field can be evaluated, effectively identifying state distortion caused by local overheating or abnormal heat distribution.

[0086] The battery's current value can determine its usage status. When the battery is idle, the current value is low, there is no system resistance value interfering with the SOH estimation, and voltage fluctuations are relatively small. When the battery is in use, the load causes the battery to generate system resistance, resulting in voltage fluctuations and affecting the accuracy of the SOH estimation results. Accordingly, in one embodiment, referencing... Figure 2 In step S101, the battery sampling information includes the battery current value. After obtaining the battery sampling information in step S101 and before determining the maximum and minimum battery capacity values ​​among the multiple battery cells in step S103, the following step S209 is executed: S209. Determine whether the absolute value of the battery current is not greater than the current threshold. If the determination result is negative, proceed to the next step S103. Battery current refers to the amount of current flowing through the battery circuit during charging or discharging.

[0087] The current threshold can be the current value when the battery system is in a quiescent state, such as 3A.

[0088] In practical applications, when a battery is under high current charging and discharging, instantaneous fluctuations in cell voltage can occur due to phenomena such as polarization and electrochemical dynamic hysteresis. These fluctuations do not accurately reflect the static charge level of the cell. If these fluctuations are used directly to estimate the maximum / minimum charge value of the cell under these conditions, it will lead to misjudgment of the compensation factor, thereby affecting the accuracy of the SOH estimation result. This error is particularly significant under high-rate operating conditions or during frequent charge-discharge switching.

[0089] Therefore, this application sets a current threshold, allowing the SOH update process based on voltage differences only when the battery current value is in a relatively stable state (i.e., the absolute value of the current is not greater than the preset threshold) or when the battery is in a static state. This significantly reduces the estimation deviation caused by transient interference. This strategy effectively enhances the reliability and adaptability of battery health estimation in dynamic operating scenarios, and improves the control accuracy and safety stability of the vehicle battery management system under complex operating conditions.

[0090] Secondly, based on the same technical concept, this application also provides a power supply device. The power supply device includes modules for performing the battery health determination method, controller, and power supply device method in the first aspect or any optional implementation of the first aspect. For example, the power supply device includes a battery and a controller. For more detailed implementation information regarding the power supply device, please refer to the description of any of the implementation methods in the first aspect above.

[0091] Thirdly, based on the same technical concept, this application also provides a controller. The controller includes: a memory and a processor, wherein the memory stores computer instructions; and the processor, when executing the computer instructions, implements the battery health determination method in the first aspect or any optional implementation of the first aspect.

[0092] It should be noted that the order of description of the embodiments in this application is not intended to limit the priority of the embodiments.

[0093] Unless otherwise defined, all technical and scientific terms used in this application 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 application and in its specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0094] It should be noted that, unless otherwise specified, the term "connected" or "linked" in this application includes not only directly connecting two entities, but also indirectly connecting them through other entities that have a beneficial improvement effect. The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many forms under the guidance of this application without departing from the spirit and scope of protection of the claims. All equivalent transformations made under the inventive concept of this application using the content of this application's specification and drawings, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A method for determining battery health, the battery comprising multiple cells, characterized in that, include: Obtain battery sampling information, which includes multiple voltage values ​​corresponding to the multiple battery cells; The maximum and minimum charge values ​​of the multiple battery cells are determined based on the multiple voltage values. The compensation factor is determined based on the maximum and minimum charge values ​​of the battery cell. The battery health value is updated based on the compensation factor.

2. The battery health determination method according to claim 1, characterized in that, The determination of the compensation factor based on the maximum and minimum battery cell capacity includes: The compensation factor is calculated based on the maximum and minimum battery capacity of the battery cell. If the calculated compensation factor is greater than the most recently determined compensation factor, the most recently determined compensation factor is updated based on the calculated compensation factor.

3. The battery health determination method according to any one of claims 1-2, characterized in that, Updating the battery health value based on the compensation factor includes: The updated battery health value is obtained by subtracting the compensation factor from the most recently obtained battery health value.

4. The battery health determination method according to any one of claims 1-3, characterized in that, The determination of the compensation factor based on the maximum and minimum battery cell capacity includes: The compensation factor is obtained by subtracting the minimum charge value of the battery cell from the maximum charge value of the battery cell.

5. The battery health determination method according to any one of claims 1-4, characterized in that, The battery sampling information includes fault codes, and the method further includes: Before determining the maximum and minimum charge values ​​of the multiple battery cells based on the multiple voltage values, the following condition must also be met: the fault code indicates that the battery has not malfunctioned.

6. The battery health determination method according to claim 5, characterized in that, The fault codes include hardware fault codes, acquisition fault codes, and differential pressure fault codes; the method includes: Based on the hardware fault code, determine whether the battery has a controller fault. If the determination result is negative, then based on the acquisition fault code, determine whether the battery has an acquisition sensor fault. If the determination result is negative, then based on the differential pressure fault code, determine whether the battery has a cell damage fault.

7. The battery health determination method according to any one of claims 1-6, characterized in that, The battery sampling information includes multiple temperature values ​​corresponding to multiple regions inside the battery, and the method further includes: The maximum temperature difference inside the battery is determined based on the multiple temperature values; Before determining the maximum and minimum charge values ​​of the multiple battery cells based on the multiple voltage values, the following condition must also be met: the maximum temperature difference value is not greater than the temperature difference threshold.

8. The battery health determination method according to any one of claims 1-7, characterized in that, The battery sampling information includes the battery current value, and the method includes: Before determining the maximum and minimum battery capacity values ​​of the multiple battery cells based on the multiple voltage values, the following condition must also be met: the absolute value of the battery current value is not greater than the current threshold.

9. The battery health determination method according to any one of claims 1-8, characterized in that, The battery sampling information includes multiple temperature values ​​corresponding to multiple regions inside the battery. Determining the maximum and minimum charge values ​​of the multiple battery cells based on the multiple voltage values ​​includes: The maximum charge value of the battery cell is determined based on the maximum temperature value among the plurality of temperature values ​​and the maximum voltage value among the plurality of voltage values; The minimum charge value of the battery cell is determined based on the minimum temperature value among the plurality of temperature values ​​and the minimum voltage value among the plurality of voltage values.

10. The battery health determination method according to claim 9, characterized in that, Determining the maximum charge value of the battery cell based on the maximum temperature value among the plurality of temperature values ​​and the maximum voltage value among the plurality of voltage values ​​includes: Based on the first mapping relationship, the maximum charge value of the battery cell corresponding to the maximum temperature value and the maximum voltage value is determined; Determining the minimum charge value of the battery cell based on the minimum temperature value among the plurality of temperature values ​​and the minimum voltage value among the plurality of voltage values ​​includes: Based on the first mapping relationship, the minimum charge value of the battery cell corresponding to the minimum temperature value and the minimum voltage value is determined.

11. A controller, characterized in that, It includes a memory and a processor, the memory being used to store computer instructions; when the processor executes the computer instructions, it implements the method of any one of claims 1 to 10.

12. A power supply device, characterized in that, include: The battery and the controller as described in claim 11.

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