A battery fault diagnosis method, diagnosis device, electronic equipment and medium

Abstract

The application provides a battery fault diagnosis method, a diagnosis device, an electronic device and a medium, comprising: collecting the operation parameters of the battery monomer, and extracting the health characteristic parameters representing the aging degree of the battery; based on the health characteristic parameters, the current aging stage of the battery is evaluated; according to the aging stage and the real-time working condition, the corresponding basic threshold set is retrieved from the pre-constructed multi-dimensional dynamic threshold atlas; based on the operation parameters, the voltage deviation change rate and the temperature change rate are calculated, the basic threshold set is dynamically corrected by using the voltage deviation change rate and the temperature change rate, and the adaptive fault judgment threshold is generated; the operation parameters are compared with the adaptive fault judgment threshold, when the operation parameters exceed the adaptive fault judgment threshold range and the duration exceeds the preset time window, it is judged that the electrical performance fault occurs, and the hierarchical alarm is triggered. Through the application, the diagnosis accuracy is improved, and the false alarm caused by aging or working condition fluctuation is reduced.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more specifically, to a battery fault diagnosis method, diagnostic device, electronic device, and medium. Background Technology

[0002] Currently, during the long-term operation of electric vehicle power batteries, batteries gradually age, and the actual operating conditions are complex and variable, posing a challenge to the accurate diagnosis of electrical performance faults. The commonly used fixed threshold judgment method uses unchanging voltage, temperature, and other limits for fault monitoring throughout the entire lifespan, without considering the increase in internal resistance and capacity decay caused by battery aging. As usage time increases, normal aging behaviors can easily trigger false alarms; at the same time, fixed thresholds are difficult to adapt to instantaneous parameter fluctuations caused by dynamic operating conditions such as sudden current changes, leading to frequent false alarms. Another method, the single dynamic threshold judgment method, although introducing some adaptive mechanisms, usually relies on a single parameter such as health status or temperature for threshold adjustment, lacking comprehensive consideration of multi-source heterogeneous information such as voltage, temperature, voltage difference, and aging degradation. This results in insufficient ability to identify early minor faults and delayed fault warning time.

[0003] Due to the inherent limitations of each of the aforementioned methods, existing technologies consistently face the problem of insufficient diagnostic accuracy in practical applications: either they suffer from excessively high false alarm rates due to ignoring aging factors, or they miss early faults due to a single diagnostic dimension. This contradiction is particularly pronounced throughout the battery's entire lifespan, as it is impossible to maintain high sensitivity in the early stages of aging while effectively suppressing false alarms caused by normal degradation in the later stages of aging. Consequently, the robustness and reliability of the overall diagnostic system are constrained. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a battery fault diagnosis method, diagnostic device, electronic device, and medium to overcome at least one of the above-mentioned defects.

[0005] In a first aspect, embodiments of this application provide a battery fault diagnosis method, the method comprising: collecting operating parameters of individual battery cells and extracting health characteristic parameters characterizing the degree of battery aging; assessing the current aging stage of the battery based on the health characteristic parameters; retrieving a corresponding basic threshold set from a pre-constructed multidimensional dynamic threshold map according to the aging stage and real-time operating conditions; calculating the voltage deviation change rate and temperature change rate based on the operating parameters, dynamically correcting the basic threshold set using the voltage deviation change rate and temperature change rate to generate an adaptive fault determination threshold; comparing the operating parameters with the adaptive fault determination threshold, and determining that an electrical performance fault has occurred and triggering a graded alarm when the operating parameters exceed the range of the adaptive fault determination threshold and the duration exceeds a preset time window.

[0006] In one optional embodiment of this application, the health characteristic parameters include at least three of the following: the DC internal resistance growth rate of the battery cell, the capacity decay rate, the coulombic efficiency deviation, and the charge / discharge voltage plateau offset.

[0007] In one optional embodiment of this application, when the health characteristic parameters include the DC internal resistance growth rate and the capacity decay rate, the DC internal resistance growth rate and the capacity decay rate are obtained by: calculating the percentage increase of the current measured DC internal resistance relative to the initial DC internal resistance at the factory to obtain the DC internal resistance growth rate; and calculating the percentage decrease of the nominal capacity at the factory relative to the current measured capacity to obtain the capacity decay rate.

[0008] In one optional embodiment of this application, the current aging stage of the battery is assessed by: setting a scoring function for each health characteristic parameter and mapping the value of each health characteristic parameter to an aging score value; determining the weight coefficient of each health characteristic parameter based on its correlation with the battery failure risk; calculating the weighted sum of each aging score value to obtain a comprehensive aging score value; and determining the aging stage of the battery by comparing the comprehensive aging score value with a preset aging stage threshold, wherein the aging stage includes at least a healthy stage, a mild aging stage, and a moderate aging stage.

[0009] In one optional embodiment of this application, the multidimensional dynamic threshold map is constructed using three dimensions: aging stage, temperature range, and state of charge range. The temperature range is at least divided into a low-temperature range, a normal-temperature range, and a high-temperature range, and the state of charge range is at least divided into a low state of charge range, a medium state of charge range, and a high state of charge range. Each grid point stores a set of basic thresholds for the corresponding combination. The set of basic thresholds includes an upper voltage threshold, a lower voltage threshold, an upper temperature threshold, an upper temperature rise rate threshold, and a maximum permissible voltage difference between individual cells.

[0010] In one optional embodiment of this application, the preset time window is set differently according to the fault type, wherein voltage faults correspond to the first time window, temperature faults correspond to the second time window, and differential pressure faults correspond to the third time window.

[0011] In one optional embodiment of this application, the hierarchical alarm is implemented in the following manner: when the monitoring data exceeds the adaptive fault judgment threshold but the duration has not reached the preset time window, a first-level alarm is triggered and a prompt message is issued; when the duration reaches the preset time window, a second-level alarm is triggered and the battery charging and discharging power is limited; when the duration reaches a preset multiple of the preset time window, a third-level alarm is triggered and the battery main circuit relay is disconnected.

[0012] Secondly, embodiments of this application also provide a battery fault diagnosis device, the device comprising: a health feature parameter extraction module, used to collect operating parameters of individual battery cells and extract health feature parameters characterizing the degree of battery aging; a battery current aging stage assessment module, used to assess the current aging stage of the battery based on the health feature parameters; a basic threshold set retrieval module, used to retrieve the corresponding basic threshold set from a pre-constructed multi-dimensional dynamic threshold map according to the aging stage and real-time operating conditions; an adaptive fault judgment threshold generation module, used to calculate the voltage deviation change rate and temperature change rate based on the operating parameters, and dynamically correct the basic threshold set using the voltage deviation change rate and temperature change rate to generate an adaptive fault judgment threshold; and a graded alarm triggering module, used to compare the operating parameters with the adaptive fault judgment threshold, and when the operating parameters exceed the range of the adaptive fault judgment threshold and the duration exceeds a preset time window, determine that an electrical performance fault has occurred and trigger a graded alarm.

[0013] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps of the method described above are performed.

[0014] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the method described above.

[0015] The battery fault diagnosis method, diagnostic device, electronic device, and medium provided in this application collect the operating parameters of individual battery cells and extract health characteristic parameters characterizing the degree of battery aging. Based on the health characteristic parameters, the current aging stage of the battery is assessed. According to the aging stage and real-time operating conditions, the corresponding basic threshold set is retrieved from a pre-constructed multi-dimensional dynamic threshold map. The voltage deviation change rate and temperature change rate are calculated based on the operating parameters, and the basic threshold set is dynamically corrected using the voltage deviation change rate and temperature change rate to generate an adaptive fault judgment threshold. The operating parameters are compared with the adaptive fault judgment threshold. When the operating parameters exceed the range of the adaptive fault judgment threshold and the duration exceeds a preset time window, an electrical performance fault is determined to have occurred, and a graded alarm is triggered. This application improves the diagnostic accuracy and reduces false alarms caused by aging or fluctuations in operating conditions.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a battery fault diagnosis method provided in an embodiment of this application; Figure 2 A flowchart illustrating the DC internal resistance growth rate and capacity decay rate provided in an embodiment of this application; Figure 3 A flowchart for evaluating the current aging stage of a battery, provided as an embodiment of this application; Figure 4 This is a flowchart illustrating the implementation of a tiered alarm system provided in an embodiment of this application; Figure 5 This is a schematic diagram of the battery fault diagnosis device provided in the embodiments of this application; Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, 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 some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0020] First, the applicable scenarios for this application will be introduced. This application can be applied to the field of vehicle technology.

[0021] Research has revealed that existing fixed threshold diagnostic methods fail to account for normal parameter shifts caused by battery aging, resulting in a persistently high false alarm rate for aging batteries. While single dynamic threshold methods partially incorporate adaptive mechanisms, their threshold adjustments rely solely on a single parameter, failing to comprehensively reflect the coupled impact of battery aging and real-time operating conditions, and exhibiting a significant lag in early warning of minor faults.

[0022] Based on this, embodiments of this application provide a battery fault diagnosis method, diagnostic device, electronic device, and medium, which, through multi-dimensional parameter fusion, enables the fault judgment threshold to be adaptively adjusted in coordination with the battery aging state and real-time operating conditions, thereby reducing false alarms and improving the speed of early fault identification.

[0023] Please see Figure 1 , Figure 1 This is a flowchart illustrating a battery fault diagnosis method provided in an embodiment of this application. Figure 1 As shown in the embodiments of this application, the battery fault diagnosis method includes: S101. Collect the operating parameters of individual battery cells and extract health characteristic parameters that characterize the degree of battery aging.

[0024] Health characteristics parameters include at least three of the following: DC internal resistance growth rate of individual cells, capacity decay rate, coulombic efficiency deviation, and charge / discharge voltage plateau offset.

[0025] The battery management system (BMS) uses voltage, temperature, and current sensors to collect real-time data on the terminal voltage, surface temperature, and charge / discharge current of each battery cell at a preset sampling frequency. An algorithm combining ampere-hour integration and open-circuit voltage correction is then used to estimate the battery's state of charge (SOC) online. These terminal voltage, temperature, current, and SOC data constitute the basic dataset of operating parameters, stored in the BMS's memory buffer for subsequent processing stages.

[0026] For further details, please refer to Figure 2 , Figure 2 This is a flowchart illustrating the process of obtaining the DC internal resistance growth rate and capacity decay rate, provided as an embodiment of this application. Figure 2 As shown, when the health characteristic parameters include the DC internal resistance growth rate and the capacity decay rate, the DC internal resistance growth rate and the capacity decay rate are obtained in the following manner: S201. Calculate the percentage increase in the current measured DC internal resistance relative to the initial DC internal resistance at the factory, and obtain the DC internal resistance growth rate.

[0027] The battery management system (BMS) applies a short-duration pulse discharge or charging current to individual battery cells under conditions where the battery is within a specific state of charge range and the temperature is relatively constant, simultaneously collecting the instantaneous change in battery terminal voltage before and after the pulse. Using Ohm's law, the voltage change is divided by the applied current value to calculate the measured DC internal resistance of the battery cell in the current state. This measured DC internal resistance includes the combined contributions of ohmic internal resistance and polarization internal resistance, and is one of the core indicators characterizing battery power performance and internal health. The initial DC internal resistance is measured by the battery manufacturer under standard conditions using the same pulse method during factory testing and is pre-written into the BMS's storage unit as reference data.

[0028] The DC internal resistance is calculated by comparing the current measured value with the initial DC internal resistance at the factory. First, the absolute increase in internal resistance is obtained by subtracting the initial value from the current measured value. Then, the absolute increase is divided by the initial value and multiplied by 100% to obtain a percentage, which represents the DC internal resistance growth rate. This percentage directly quantifies the increase in internal resistance relative to the factory condition during battery use. The fundamental reasons for the increase in internal resistance include the continuous thickening of the solid electrolyte interface film, the decrease in ionic conductivity due to electrolyte decomposition, and aging mechanisms such as increased contact resistance between electrode material particles.

[0029] The DC internal resistance growth rate, as one of the input parameters for aging assessment, is positively correlated with the degree of battery power performance degradation. A higher growth rate indicates greater internal losses and more severe heat generation during charging and discharging. Incorporating this parameter into the comprehensive aging scoring model can provide a quantitative basis for the division of aging stages from the perspective of electrochemical impedance, enabling aging assessment to cover the key degradation path of internal resistance growth and avoiding the lack of assessment dimensions caused by relying solely on capacity parameters.

[0030] S202. Calculate the percentage decrease in capacity relative to the current measured capacity from the factory nominal capacity to obtain the capacity decay rate.

[0031] The battery management system (BMS) obtains the current measured capacity by performing a complete standard charge-discharge cycle on a single battery cell. After charging to full capacity using a constant current and constant voltage method, the battery is discharged at a preset rate with a constant current. Simultaneously, the current is integrated over time to accumulate the total amount of electricity released from full charge to the discharge cutoff voltage; this accumulated value is the current measured capacity. The factory-rated capacity is the initial capacity value measured by the battery manufacturer under the same standard charge-discharge conditions during factory testing, and it is pre-stored as a rated parameter in the BMS's storage unit.

[0032] The capacity decay rate is calculated by comparing the nominal capacity at the factory gate with the current measured capacity. Subtracting the measured capacity from the nominal capacity yields the absolute capacity loss. Dividing this absolute loss by the nominal capacity and multiplying by 100% gives the percentage, which is the capacity degradation rate. This percentage directly reflects the proportion of usable energy storage space lost by the battery due to aging. The main causes of capacity decay include the irreversible consumption of active lithium ions in the positive electrode material, the structural collapse and stripping of the negative electrode active material, and the continuous consumption of migratable lithium salts in the electrolyte.

[0033] Capacity degradation rate and DC internal resistance growth rate are used in parallel in the aging assessment model, each with different weighting coefficients. They jointly characterize the aging state of the battery from two complementary dimensions: energy storage capacity and power output capacity. Incorporating capacity degradation rate into the comprehensive assessment can effectively capture specific aging patterns characterized by capacity degradation and insignificant internal resistance growth. This compensates for the insufficient sensitivity of a single internal resistance index in assessing these aging paths, making the determination of subsequent aging stages more consistent with the actual physical nature of battery degradation.

[0034] Based on the obtained operating parameters, four health characteristic parameters that can characterize the degree of battery aging from different perspectives are extracted from the dataset. The first is the DC internal resistance growth rate, calculated by comparing the current measured DC internal resistance with the initial DC internal resistance at the factory, reflecting the increase in battery internal resistance with aging. The second is the capacity decay rate, calculated by comparing the current measured capacity with the nominal capacity at the factory, reflecting the degree of loss of usable energy storage space with aging. The third is the coulombic efficiency deviation, calculated by comparing the ratio of discharge capacity to charge capacity in the same complete cycle with the factory benchmark coulombic efficiency. The fourth is the charge / discharge voltage plateau offset, calculated by extracting the segment with the most gradual voltage change during constant current charging or discharging, and calculating the absolute value of the deviation of the average voltage in this segment relative to the initial factory plateau voltage.

[0035] These four health characteristic parameters correspond to different aging mechanisms. The DC internal resistance growth rate mainly reflects the increasing trend of the battery's ohmic internal resistance and polarization internal resistance. The capacity decay rate directly measures the total loss of usable lithium ions and active materials. The coulombic efficiency deviation can capture the accumulation degree of irreversible side reactions inside the battery. The charge / discharge voltage plateau shift reflects the voltage characteristic drift caused by changes in the structure of the positive and negative electrode materials. Extracting these four dimensions of parameters provides a more comprehensive basis for the subsequent comprehensive judgment of aging stages than a single parameter evaluation. It can cover multiple aging paths such as solid electrolyte interface film thickening, active material peeling, and electrolyte decomposition, thus substantially improving the accuracy and reliability of aging state characterization.

[0036] S102. Based on health characteristic parameters, assess the current aging stage of the battery.

[0037] A weighted scoring method is used to map each health characteristic parameter to an aging score value and assign a weight coefficient to it. The comprehensive aging score value is then calculated and compared with a preset aging stage threshold to determine the current aging stage of the battery.

[0038] This evaluation method integrates aging information from four dimensions—internal resistance, capacitance, efficiency, and voltage—into a comprehensive scoring index, avoiding the distortion caused by evaluating a single parameter under certain aging modes. The aging stage results output by the evaluation include at least a healthy stage, a mild aging stage, a moderate aging stage, and a severe aging stage, providing an index for subsequent steps to retrieve the basic threshold matching the aging degree from the multidimensional dynamic threshold map.

[0039] For details, please refer to Figure 3 , Figure 3 This is a flowchart illustrating the assessment of the current aging stage of a battery, provided as an embodiment of this application. Figure 3 As shown, the current aging stage of the battery is assessed in the following ways: S301. Set a scoring function for each health characteristic parameter and map the value of each health characteristic parameter to an aging score value.

[0040] For four health characteristic parameters—DC internal resistance growth rate, capacity decay rate, coulombic efficiency deviation, and charge / discharge voltage plateau offset—independent linear scoring functions are pre-constructed. The input to each scoring function is the measured value of that parameter, and the output is an aging score value falling within a continuous interval from zero to one. The closer the output value is to zero, the lighter the aging indicated by that parameter; the closer it is to one, the heavier the aging. The boundaries of the scoring functions are calibrated according to the battery model's manufacturer's specifications and lifespan determination criteria. The upper limit for the DC internal resistance growth rate score corresponds to twice the initial factory value; the upper limit for the capacity decay rate score corresponds to a 20% loss of nominal capacity; and the upper limits for the coulombic efficiency deviation and voltage plateau offset score are determined based on the failure statistical thresholds for the same battery model.

[0041] For a specific battery cell, the measured values ​​of the four health characteristic parameters extracted in step S101 are input into their respective scoring functions, which output four independent aging scores. These four scores map physical quantities with different dimensions and ranges of variation onto the same dimensionless scale, solving the problem of inconsistent dimensions that prevent direct weighting, such as the DC internal resistance growth rate being a percentage and the voltage plateau offset being a millivolt-level voltage value. This makes the subsequent comprehensive weighted calculation mathematically reasonable and comparable.

[0042] S302. Determine the weighting coefficient of each health characteristic parameter based on the correlation between each health characteristic parameter and the risk of battery failure.

[0043] The weighting coefficients for each health characteristic parameter are determined by analyzing the correlation strength between the parameter and electrical performance failures that occur during actual battery operation; the higher the correlation strength, the larger the weighting coefficient. The weight for DC internal resistance growth rate is 0.30, the weight for capacity decay rate is 0.25, the weight for coulombic efficiency deviation is 0.25, and the weight for charge / discharge voltage plateau offset is 0.20. The sum of these four weighting coefficients is one, ensuring that the comprehensive score always falls within the standard range of zero to one.

[0044] The DC internal resistance growth rate is given the highest weight because increased internal resistance directly leads to a sharp drop in discharge voltage and increased heat generation during charging and discharging. It is highly correlated with the probability of undervoltage and overtemperature alarms, and is the strongest indicator of safety faults. Capacity decay rate and coulombic efficiency deviation reflect the degree of aging from the perspectives of available energy loss and internal side reaction accumulation, respectively, and are given the next highest weight. Voltage plateau offset mainly reflects material structure degradation, and its direct impact on conventional electrical performance faults is relatively indirect, so its weight is set slightly lower. This weighting scheme has been validated by long-term cyclic testing data, making the correlation between the comprehensive score and the actual battery fault risk better than single-parameter evaluation, and effectively suppressing jumps in evaluation results caused by fluctuations in a single parameter.

[0045] S303. Calculate the weighted sum of each aging score to obtain the comprehensive aging score.

[0046] The four aging scores output by S301 are multiplied one by one with the four weighting coefficients determined by S302 according to their corresponding relationships: the DC internal resistance growth rate score is multiplied by 0.30, the capacity decay rate score by 0.25, the coulombic efficiency deviation score by 0.25, and the voltage plateau offset score by 0.20. These four products are then added together to obtain a single value, which is the comprehensive aging score. This value also falls within the range of zero to one, comprehensively summarizing the overall aging degree of the battery in four dimensions: internal resistance characteristics, capacity characteristics, efficiency characteristics, and voltage characteristics.

[0047] The weighted summation calculation method compresses aging information from multiple dimensions into a single scalar, facilitating subsequent aging stage division using simple threshold comparisons. When one or more parameters experience instantaneous fluctuations due to sensor noise or operating condition interference, the presence of other stable parameters can buffer the impact of such fluctuations on the overall score, making the evaluation results more robust and preventing erroneous aging stage judgments due to occasional anomalies in a single parameter.

[0048] S304. Determine the aging stage of the battery by comparing the comprehensive aging score with the preset aging stage threshold.

[0049] The preset aging stage thresholds are a set of empirical thresholds determined by accelerated aging tests and actual fleet operation data for this battery model, stored in the non-volatile storage unit of the battery management system. The judgment rules are as follows: When the comprehensive aging score is below 0.3, it is considered a healthy stage, with battery performance close to factory standards and no obvious signs of aging. When the comprehensive aging score is between 0.3 and 0.6, it is considered a mild aging stage, with internal resistance starting to rise and observable capacity degradation, but overall performance still within the normal operating range. When the comprehensive aging score is between 0.6 and 0.8, it is considered a moderate aging stage, with significant battery performance degradation requiring adjustments to usage strategies and increased monitoring frequency. When the comprehensive aging score reaches or exceeds 0.8, it is considered a severe aging stage, directly triggering a maintenance alarm and prompting battery inspection or replacement.

[0050] The aging stage determination result output by S304 is directly used as the index input for the aging stage dimension of the three-dimensional dynamic threshold map in step S103, enabling the retrieval of the basic threshold to automatically switch the corresponding threshold set as the battery aging process progresses. This mechanism of dividing stages based on the comprehensive aging score achieves a step-by-step adaptive adjustment of the threshold throughout the battery's entire life cycle. A tighter threshold is maintained during the healthy stage to improve fault detection sensitivity, while the threshold is moderately relaxed during the aging stage to reduce the false alarm rate caused by normal aging effects. This achieves a dynamic balance between diagnostic sensitivity and false alarm suppression that changes with the aging state.

[0051] S103. Based on the aging stage and real-time operating conditions, retrieve the corresponding set of basic thresholds from the pre-constructed multi-dimensional dynamic threshold map.

[0052] The multidimensional dynamic threshold map is a three-dimensional data table with aging stage, temperature range, and state of charge range as its three dimensions. It is pre-generated and stored offline through data collection and statistical analysis before actual deployment. The aging stage dimension includes three values: healthy stage, mild aging stage, and moderate aging stage, corresponding to the battery aging level output in S102. The temperature range dimension is divided into three segments: ambient temperature or battery temperature below 10 degrees Celsius is classified as the low-temperature zone, between 10 and 35 degrees Celsius as the normal-temperature zone, and above 35 degrees Celsius as the high-temperature zone. The state of charge range dimension is also divided into three segments: state of charge below 20% is the low-state-of-charge zone, between 20% and 80% is the medium-state-of-charge zone, and above 80% is the high-state-of-charge zone. These three dimensions combine to form twenty-seven independent grid points.

[0053] Each grid cell stores a set of basic thresholds corresponding to the given conditions, specifically including five threshold parameters: Upper voltage limit threshold, defining the highest permissible voltage of a single battery cell under this operating condition; Lower voltage limit threshold, defining the lowest permissible voltage drop of a single battery cell under this operating condition; Upper temperature limit threshold, defining the highest permissible surface temperature of a single battery cell under this operating condition; Upper temperature rise rate limit threshold, defining the maximum permissible rate of temperature rise per unit time under this operating condition; and Maximum permissible voltage difference between cells, defining the maximum permissible difference in voltage between individual cells within the battery pack at the same moment. These five basic thresholds correspond to five common electrical performance fault types, providing a unified threshold benchmark for the comprehensive diagnosis of multiple fault types.

[0054] As can be seen from the above construction method, the multidimensional dynamic threshold map is constructed using three dimensions: aging stage, temperature range, and state of charge range. The temperature range is divided into at least low temperature, normal temperature, and high temperature regions, and the state of charge range is divided into at least low state of charge, medium state of charge, and high state of charge regions. Each grid point stores the corresponding set of basic thresholds, which includes the upper voltage limit, lower voltage limit, upper temperature limit, upper temperature rise rate limit, and maximum permissible voltage difference between individual cells.

[0055] The base threshold values ​​are derived from statistical analysis of measured data from a large number of normal battery samples under different aging stages and operating conditions. Operating data of healthy battery cells covering the aforementioned twenty-seven grid points are collected to form a normal state database. The distribution of each monitored parameter within each grid point is statistically analyzed, and its mean and standard deviation are calculated. Following the normal distribution assumption, the base threshold value is determined by adding or subtracting the confidence coefficient multiple of the standard deviation from the mean, with the confidence coefficient selected between 2.5 and 4.0. The resulting graph can statistically reflect the normal fluctuation boundaries of battery parameters under different aging degrees and operating conditions. During online diagnosis, the system only needs to perform a three-dimensional lookup operation based on the current aging stage output by S102, the temperature range measured by the temperature sensor, and the range of the estimated state of charge to quickly retrieve the corresponding base threshold set. The entire process has minimal computational overhead.

[0056] S104. Calculate the voltage deviation rate of change and the temperature rate of change based on the operating parameters, and use the voltage deviation rate of change and the temperature rate of change to dynamically correct the basic threshold set to generate an adaptive fault judgment threshold.

[0057] Among the collected operating parameters, the arithmetic mean of the terminal voltages of all individual battery cells at the current moment is calculated to obtain the average voltage of the battery pack. For each individual battery cell, its terminal voltage is subtracted from the average voltage of the battery pack to obtain the voltage deviation value of that cell. Over multiple consecutive sampling periods, the difference between the voltage deviation value at the current moment and the voltage deviation value at the previous moment is divided by the sampling time interval to obtain the rate of change of the voltage deviation of that cell over time, i.e., the voltage deviation change rate. This parameter can quantify the rate of deviation of the individual cell voltage from the average level of the entire pack. The larger the absolute value of the voltage deviation change rate, the more rapidly the voltage consistency between that individual cell and the entire battery pack is deteriorating.

[0058] Among the collected operating parameters, the arithmetic mean of the temperatures of all individual battery cells at the current moment is calculated to obtain the average temperature of the battery pack. For each individual battery cell, the difference between its current temperature and the temperature at the previous moment is divided by the sampling time interval to obtain the temperature rise rate of that cell. The arithmetic mean of the temperature rise rates of all cells is then calculated to obtain the average temperature rise rate of the battery pack. The difference between the temperature rise rate of the current cell and the average temperature rise rate of the battery pack is obtained to obtain the temperature rise rate deviation of that cell. This deviation value can reflect whether there is an abnormal heat generation trend in a certain cell. When the temperature rise rate of a certain cell is significantly higher than the average level of the entire pack, even if its absolute temperature has not yet reached the upper limit, it may indicate an early fault such as an internal micro-short circuit or poor contact.

[0059] The calculated voltage deviation rate and temperature rate of change are used as dynamic correction factors and superimposed on the base threshold set retrieved by S103 to generate an adaptive fault judgment threshold that changes with the battery's dynamic behavior in real time. The dynamic correction logic is that when the voltage deviation rate and temperature rate of change increase, the corresponding threshold is automatically tightened, improving the diagnostic system's sensitivity to anomalies when parameter fluctuations intensify. When the voltage deviation rate and temperature rate of change are small, the threshold remains at the base level, allowing for normal operating condition fluctuations. Through this real-time correction mechanism, the adaptive fault judgment threshold can distinguish between instantaneous parameter fluctuations caused by normal operations such as rapid acceleration and high-current charging, and abnormal parameter deviation trends caused by actual early-stage faults.

[0060] S105. Compare the operating parameters with the adaptive fault judgment threshold. When the operating parameters exceed the adaptive fault judgment threshold range and the duration exceeds the preset time window, an electrical performance fault is determined to have occurred, and a graded alarm is triggered.

[0061] The terminal voltage, temperature, voltage deviation, and temperature rise rate of each battery cell collected above are compared item by item with the corresponding upper voltage limit, lower voltage limit, upper temperature limit, upper temperature rise rate limit, and maximum allowable voltage difference between cells in the adaptive fault judgment threshold generated in S104. The comparison operation is performed once in each sampling period. When the real-time value of any operating parameter exceeds the limit range of the corresponding threshold, the system starts the timer corresponding to the fault type to accumulate the over-limit duration, instead of immediately judging it as a fault.

[0062] The preset time windows are set differently based on the fault type. Voltage faults correspond to the first time window, temperature faults to the second, and differential pressure faults to the third. For voltage faults, because voltage signals respond the fastest, the first time window is shorter, and a short-duration exceedance is considered a valid fault. For temperature faults, because temperature changes have thermal inertia, the second time window is appropriately extended; short-term fluctuations may be caused by sensor noise or environmental disturbances, requiring a longer confirmation time to filter out false alarms. The third time window for differential pressure faults is set to balance the response characteristics of the differential pressure signal with the need for continuous confirmation of consistency. If the exceedance condition recovers to the normal range within the corresponding time window, the timer is reset, and the fault event is not recorded.

[0063] When the duration of the over-limit state reaches the corresponding preset time window, the system determines that an electrical performance fault has occurred and triggers a graded alarm. The response level of the graded alarm increases with the duration of the over-limit state, gradually escalating from simply issuing a prompt message to limiting the battery charging and discharging power and even disconnecting the battery main circuit relay.

[0064] For further details, please refer to Figure 4 , Figure 4 This is a flowchart illustrating the implementation of a tiered alarm system provided in an embodiment of this application. Figure 4 As shown, tiered alarms are implemented in the following ways: S401. When the monitoring data exceeds the adaptive fault judgment threshold but the duration has not reached the preset time window, a level 1 alarm is triggered and a prompt message is issued.

[0065] In each sampling cycle, the battery management system compares the terminal voltage, temperature, differential voltage, and temperature rise rate of each cell with the adaptive fault judgment threshold generated by S104. When the real-time value of a monitored parameter first exceeds the limit range of the corresponding threshold, the fault timing module inside the battery management system immediately starts a dedicated timer for that parameter, records the start time of the over-limit state, and checks whether the parameter is still in the over-limit state in each subsequent sampling cycle. From the first sampling cycle in which the over-limit occurs until the duration has not accumulated to the preset time window, the battery management system triggers a level one alarm.

[0066] The Level 1 alarm is triggered by the battery management system sending a warning message to the vehicle controller and instrument panel via the vehicle communication bus. A yellow fault indicator light illuminates on the instrument panel, or a text message is displayed, informing the driver that the battery system is exhibiting an abnormal trend. Simultaneously, the battery management system packages key information such as the name of the out-of-limit parameter, its current value, the start time of the out-of-limit action, and the ambient temperature into a fault record frame, stores it in the built-in non-volatile memory, and uploads it to the cloud monitoring platform via the vehicle's remote communication module for remote observation by back-end maintenance personnel. The unique feature of the Level 1 alarm is that the system does not restrict the battery's charging and discharging power at this stage. The vehicle's acceleration performance, energy recovery intensity, and charging power remain at their original levels. The driver can drive the vehicle normally after receiving the warning, but has been clearly informed that they need to monitor the battery status.

[0067] If the over-limit condition disappears on its own before the timer reaches the preset time window (i.e., the real-time value of the monitored parameter falls back within the threshold range), the battery management system will reset the corresponding timer and send a signal to the instrument panel to turn off the fault indicator light. However, the previously recorded fault frames are still retained in the memory and cloud platform for subsequent offline analysis of intermittent abnormal battery behavior. This strategy of only issuing a warning without intervening in power at the initial stage of over-limit effectively filters out transient over-limits caused by instantaneous sensor spikes or brief disturbances in operating conditions, avoiding unnecessary interference to normal vehicle operation caused by frequent power limiting.

[0068] S402. When the duration reaches the preset time window, a secondary alarm is triggered and the battery charging and discharging power is limited.

[0069] When an out-of-limit condition persists for multiple consecutive sampling periods, causing the accumulated duration of the dedicated timer to reach the preset time window corresponding to the fault type, the battery management system determines that the out-of-limit event constitutes a genuine persistent fault and escalates it from a Level 1 alarm to a Level 2 alarm. The determination of a Level 2 alarm is based on the persistence of the out-of-limit condition rather than its magnitude. Even if the out-of-limit amount is small, as long as the duration reaches the set value of the time window, it indicates that this is not a random transient fluctuation, but rather that some abnormal state requiring intervention has indeed occurred within the battery.

[0070] The Level 2 alarm process consists of two parts. The first part is alarm signal escalation: the battery management system (BMS) switches the yellow fault indicator light on the dashboard to a red one, triggers an audible alarm, and updates the status field of the cloud-based fault record, marking it as a confirmed fault. The second part is power limiting intervention: the BMS sends a power limiting command to the vehicle controller, forcibly reducing the battery's charging and discharging power limit to a certain percentage of the rated power. This percentage is typically set between 50% and 70%. Specifically, the BMS writes the limited power value into the current maximum allowable discharge power and maximum allowable charging power fields sent to the vehicle controller. Based on this, the vehicle controller limits the torque output and energy recovery intensity of the drive motor. The vehicle can still continue to drive, but acceleration performance is significantly reduced, and the maximum speed is limited.

[0071] The power limitation at the level two alarm stage provides drivers with the ability to limp home, meaning the vehicle can continue driving a short distance with reduced power to a safe location or the nearest repair shop, rather than completely breaking down on the road. This tiered handling strategy prioritizes personnel safety and the vehicle's controllable mobility when a fault has been confirmed but has not yet reached a critical dangerous state. Compared to the traditional approach of directly cutting off the high-voltage circuit when parameters exceed limits, this significantly reduces secondary threats to traffic safety.

[0072] S403. When the duration reaches a preset multiple of the preset time window, a level three alarm is triggered and the battery main circuit relay is disconnected.

[0073] During the Level 2 alarm period, the battery management system continues to monitor the real-time values ​​of the out-of-limit parameters. If the out-of-limit state not only fails to recover but also accumulates further, reaching a preset multiple of the preset time window, or if the real-time value of the monitored parameter deviates significantly from the threshold to a preset dangerous deviation range, the battery management system determines that the fault has escalated to a serious fault level and triggers a Level 3 alarm. The preset multiple is usually twice the preset time window. Its physical meaning is that if a fault that has been fully confirmed within the time window still fails to improve under power-limited conditions but continues to worsen, it indicates that dangerous processes such as thermal runaway or internal short circuits inside the battery may have entered an irreversible stage.

[0074] The Level 3 alarm represents the highest level of safety protection. The battery management system (BMS) directly de-energizes the main relay coil in the battery pack's high-voltage circuit via a hard-wired control signal independent of the vehicle's communication bus. The main relay contacts mechanically open within tens of milliseconds, physically isolating the battery pack from the vehicle's high-voltage bus. The hard-wired control channel is designed in parallel with the communication bus, ensuring that even in extreme cases of vehicle communication failure, the BMS can independently perform the power-off protection action. After the main relay disconnects, the battery pack's external power output and input are completely cut off, the vehicle's high-voltage circuit loses power, and the vehicle coasts to a safe stop using inertia.

[0075] Simultaneously with disconnecting the main circuit relay, the battery management system writes a complete fault record to the permanent storage area of ​​the non-volatile memory. This includes a snapshot of all operating parameters at the moment the Level 3 alarm is triggered and historical waveform data from several seconds prior to the fault. The fault data is then uploaded to the cloud platform with the highest priority via the vehicle's remote communication module, notifying emergency rescue services. The Level 3 alarm, through physical isolation at the hardware level, disconnects the faulty battery from the vehicle with minimal latency and maximum reliability, confining the risks of thermal runaway propagation and high-voltage electric shock within the battery pack, providing ultimate hardware protection for occupant safety.

[0076] Compared with existing fixed threshold judgment methods and single dynamic threshold judgment methods, the battery fault diagnosis method, diagnostic device, electronic device, and medium provided in this application integrate multi-dimensional health characteristic parameters such as DC internal resistance growth rate, capacity decay rate, coulombic efficiency deviation, and charge / discharge voltage plateau offset into a comprehensive aging score. It retrieves a basic threshold set from a pre-constructed three-dimensional dynamic threshold map based on the aging stage and real-time operating conditions, and dynamically corrects the basic thresholds in real time using voltage deviation change rate and temperature change rate. This generates a fault judgment threshold that adaptively adjusts with battery aging and operating conditions. Combined with a progressive confirmation mechanism of differentiated time windows and graded alarms, this solves the problems of high false alarm rates in existing fixed threshold methods due to ignoring aging factors, and delayed early warning times for minor faults due to the single dynamic threshold method's limited diagnostic dimensions. It achieves a comprehensive diagnostic effect by reducing false alarm rates throughout the battery's entire lifespan, advancing early fault warning times, and providing graded responses to faults of varying severity.

[0077] Based on the same inventive concept, this application also provides a battery fault diagnosis device corresponding to the battery fault diagnosis method. Since the principle of the device in this application is similar to that of the battery fault diagnosis method described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0078] Please see Figure 5 , Figure 5This is a schematic diagram of the battery fault diagnosis device provided in an embodiment of this application. Figure 5 As shown, the battery fault diagnosis device 500 includes: The health characteristic parameter extraction module 501 is used to collect the operating parameters of individual battery cells and extract health characteristic parameters that characterize the degree of battery aging. The battery current aging stage assessment module 502 is used to assess the current aging stage of the battery based on the health characteristic parameters. The basic threshold set retrieval module 503 is used to retrieve the corresponding basic threshold set from the pre-constructed multidimensional dynamic threshold map according to the aging stage and real-time operating conditions. The adaptive fault determination threshold generation module 504 is used to calculate the voltage deviation change rate and temperature change rate based on the operating parameters, and to dynamically correct the basic threshold set using the voltage deviation change rate and temperature change rate to generate an adaptive fault determination threshold. The graded alarm triggering module 505 is used to compare the operating parameters with the adaptive fault determination threshold. When the operating parameters exceed the range of the adaptive fault determination threshold and the duration exceeds a preset time window, an electrical performance fault is determined to have occurred, and a graded alarm is triggered.

[0079] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. Figure 6 As shown, the electronic device 300 includes a processor 310, a memory 320, and a bus 330.

[0080] The memory 320 stores machine-readable instructions executable by the processor 310. When the electronic device 300 is running, the processor 310 and the memory 320 communicate via the bus 330. When the machine-readable instructions are executed by the processor 310, they can perform the operations described above. Figure 1 The steps of the battery fault diagnosis method in the illustrated method embodiment can be found in the method embodiment for specific implementation methods, which will not be repeated here.

[0081] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The steps of the battery fault diagnosis method in the illustrated method embodiment can be found in the method embodiment for specific implementation methods, which will not be repeated here.

[0082] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0084] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0085] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0086] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0087] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A battery fault diagnosis method, characterized in that, include: Collect the operating parameters of individual battery cells and extract health characteristic parameters that characterize the degree of battery aging; Based on the aforementioned health characteristic parameters, assess the current aging stage of the battery; Based on the aging stage and real-time operating conditions, the corresponding set of basic thresholds is retrieved from the pre-constructed multi-dimensional dynamic threshold map. The voltage deviation rate of change and the temperature rate of change are calculated based on the operating parameters. The voltage deviation rate of change and the temperature rate of change are used to dynamically correct the basic threshold set to generate an adaptive fault judgment threshold. The operating parameters are compared with the adaptive fault determination threshold. When the operating parameters exceed the range of the adaptive fault determination threshold and the duration exceeds a preset time window, an electrical performance fault is determined to have occurred, and a graded alarm is triggered.

2. The method according to claim 1, characterized in that, The health characteristic parameters include at least three of the following: the DC internal resistance growth rate of the battery cell, the capacity decay rate, the coulombic efficiency deviation, and the charge / discharge voltage plateau offset.

3. The method according to claim 2, characterized in that, When the health characteristic parameters include the DC internal resistance growth rate and the capacity decay rate, the DC internal resistance growth rate and the capacity decay rate are obtained in the following manner: Calculate the percentage increase in the current measured DC internal resistance relative to the initial DC internal resistance at the factory, and obtain the DC internal resistance growth rate. Calculate the percentage decrease in capacity from the factory nominal capacity to the current measured capacity to obtain the capacity decay rate.

4. The method according to claim 1, characterized in that, Assess the current stage of battery aging using the following methods: A scoring function is set for each health characteristic parameter, and the values ​​of each health characteristic parameter are mapped to aging score values; Based on the correlation between each health characteristic parameter and the risk of battery failure, the weighting coefficient of each health characteristic parameter is determined. Calculate the weighted sum of the aging score values ​​to obtain the comprehensive aging score. The battery's aging stage is determined by comparing the comprehensive aging score with the preset aging stage threshold. The aging stage includes at least a healthy stage, a mild aging stage, and a moderate aging stage.

5. The method according to claim 1, characterized in that, The multidimensional dynamic threshold map is constructed using three dimensions: aging stage, temperature range, and state of charge range. The temperature range is divided into at least a low temperature range, a normal temperature range, and a high temperature range, and the state of charge range is divided into at least a low state of charge range, a medium state of charge range, and a high state of charge range. Each grid point stores the basic threshold set for the corresponding combination. The basic threshold set includes the upper voltage threshold, the lower voltage threshold, the upper temperature threshold, the upper temperature rise rate threshold, and the maximum allowable voltage difference between individual cells.

6. The method according to claim 1, characterized in that, The preset time window is set differently according to the fault type, wherein voltage faults correspond to the first time window, temperature faults correspond to the second time window, and differential pressure faults correspond to the third time window.

7. The method according to claim 1, characterized in that, The hierarchical alarm system can be implemented in the following ways: When the monitored data exceeds the adaptive fault determination threshold but the duration has not reached the preset time window, a level 1 alarm is triggered and a prompt message is issued. When the duration reaches the preset time window, a level 2 alarm is triggered and the battery charging and discharging power is limited; When the duration reaches a preset multiple of the preset time window, a level three alarm is triggered and the battery main circuit relay is disconnected.

8. A battery fault diagnosis device, characterized in that, include: The health characteristic parameter extraction module is used to collect the operating parameters of individual battery cells and extract health characteristic parameters that characterize the degree of battery aging. The battery current aging stage assessment module is used to assess the current aging stage of the battery based on the health characteristic parameters. The basic threshold set retrieval module is used to retrieve the corresponding basic threshold set from the pre-constructed multi-dimensional dynamic threshold map according to the aging stage and real-time operating conditions. An adaptive fault determination threshold generation module is used to calculate the voltage deviation change rate and temperature change rate based on the operating parameters, and to dynamically correct the basic threshold set using the voltage deviation change rate and temperature change rate to generate an adaptive fault determination threshold. The graded alarm triggering module is used to compare the operating parameters with the adaptive fault determination threshold. When the operating parameters exceed the range of the adaptive fault determination threshold and the duration exceeds a preset time window, an electrical performance fault is determined to have occurred, and a graded alarm is triggered.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is in operation, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method as described in any one of claims 1 to 7.

Images