A method for early detection of internal short circuit of power battery based on series-parallel topology constraint

Abstract

The application relates to the technical field of battery internal short circuit detection, in particular to a power battery internal short circuit early detection method based on series-parallel connection topology constraints, which comprises the following steps: two adjacent monitoring time points are combined into a time pair, the discharge relative voltage abnormal value of each battery group at each time pair is obtained; the discharge relative current abnormal value of each battery unit at each time pair is obtained; a temperature-discharge efficiency fitting curve is constructed; the discharge relative current abnormal value and the discharge relative voltage abnormal value of one battery unit and the battery group to which the battery unit belongs at the time pair are corrected and fused to obtain the discharge comprehensive abnormal value of the battery unit at the time pair; the discharge relative abnormal index of one battery unit at the current time pair is obtained, and the charging relative abnormal index is obtained in the same way; the discharge relative abnormal index and the charging relative abnormal index of each battery unit at the current time pair are used for short circuit early warning. The application can effectively detect the short circuit in the power battery.

Description

Technical Field

[0001] This application relates to the field of battery internal short circuit detection technology, specifically to an early detection method for internal short circuits in power batteries based on series-parallel topology constraints. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the power battery, as the core component for vehicle energy storage and power output, directly affects the overall vehicle performance, safety, and lifespan. During the actual operation of new energy vehicles, power batteries are constantly affected by factors such as charge-discharge cycles, rate changes, ambient temperature fluctuations, and material aging. This causes gradual changes in the internal electrode materials, interface structure, and charge transport characteristics, leading to performance differences between different battery cells. When abnormal states such as localized short circuits, electrode damage, and active material decay occur within the power battery, they typically cause changes in internal impedance, increased self-discharge, and decreased energy conversion capacity. However, because the abnormality is relatively low in the early stages of an internal short circuit, changes in external voltage and current responses are usually not obvious, making accurate identification difficult using a single detection parameter. Currently, new energy vehicle power batteries typically use multiple battery cells combined in series and parallel to form a battery pack. The series structure is used to increase the overall output voltage, while the parallel structure is used to improve output capacity and discharge capability. Due to the specific electrical topology between the battery cells within the power battery, when the performance of a particular battery cell changes, its abnormal state will manifest as changes in the voltage response in the series structure and changes in the current distribution in the parallel structure. Therefore, how to utilize the response constraint relationships inherent in the series-parallel topology of the power battery itself to mine information from externally measurable operating data that can reflect internal abnormal states and achieve early perception of internal short-circuit risks has become an important research direction in the field of power battery safety monitoring.

[0003] In the operation of power batteries for new energy vehicles, existing internal short-circuit detection methods typically rely on characteristics such as changes in internal resistance, differences in individual cell voltages, or abnormal temperatures for state assessment. While internal resistance detection can reflect changes in the battery's internal performance, it usually requires specific detection conditions or additional testing processes, making it difficult to meet the real-time online monitoring needs during vehicle operation. Detection methods based on individual cell voltage deviations or temperature changes suffer from weak abnormal signals generated in the early stages of internal short circuits and are easily affected by factors such as differences in state of charge (SOC), ambient temperature changes, and external load fluctuations, resulting in unclear abnormal characteristics and insufficient detection accuracy. Furthermore, existing methods usually analyze individual battery cells independently, failing to fully consider the impact of the series-parallel topology of the power battery on the transmission of abnormal information. In actual power battery packs, series-connected battery groups share the same current operating conditions, and internal abnormalities can lead to differentiated voltage responses between different battery groups. Parallel-connected battery cells, under common voltage constraints, will further cause branch current redistribution due to performance changes. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide an early detection method for internal short circuits in power batteries based on series-parallel topology constraints. The specific technical solution adopted is as follows:

[0005] One embodiment of this application provides a method for early detection of internal short circuits in power batteries based on series-parallel topology constraints. The method includes:

[0006] Two adjacent monitoring times are combined into a time pair. The relative voltage anomaly of the discharge voltage of the battery pack in that time pair is obtained by using the terminal voltage of the two monitoring times in a time pair of the discharge process of one battery pack and other battery packs in the battery pack.

[0007] The relative discharge current anomaly value of a battery cell in a battery pack is obtained based on the discharge current of a battery cell and other battery cells in a time pair within a corresponding time period.

[0008] The temperature-discharge efficiency fitting curve is constructed. The average temperature of a battery cell in a time pair is combined with the discharge relative current anomaly value and discharge relative voltage anomaly value of the battery cell and the battery pack to which the battery cell belongs in that time pair. The results are then fused to obtain the comprehensive discharge anomaly value of the battery cell in that time pair.

[0009] A reference discharge anomaly value sequence for a given time pair of a battery cell is formed by collecting the comprehensive discharge anomaly values ​​of a predetermined number of time pairs. The relative discharge anomaly index of the current time pair of a battery cell is obtained using the comprehensive discharge anomaly value of the current time pair and the reference discharge anomaly value sequence. Similarly, the relative charging anomaly index of the current time pair of the battery cell is obtained. Short circuit warning is then performed using the relative discharge anomaly index and the relative charging anomaly index of the current time pair of each battery cell.

[0010] Preferably, the relative voltage anomaly of the battery pack during the discharge process of one battery pack and other battery packs is obtained by using the terminal voltage at two monitoring moments in a time pair during the discharge process of the battery pack, including:

[0011] During the discharge process, the absolute value of the difference between the terminal voltage at the later monitoring moment and the earlier monitoring moment in a time pair of battery packs is recorded as the terminal voltage change of that battery pack in that time pair. The ratio of the terminal voltage change of that battery pack in that time pair to the terminal voltage changes of all other battery packs in that time pair is calculated, and the average is taken to obtain the abnormal voltage change characteristics of that battery pack in that time pair. The absolute value of the difference between the terminal voltage at the later monitoring moment in that time pair of battery packs and the terminal voltage at the later monitoring moment in the same time pair of all other battery packs is calculated, and the average is taken to obtain the abnormal voltage change characteristics of that battery pack in that time pair. The average difference at the next monitoring moment in the battery pack time pair is obtained by analogy. The average difference at the previous monitoring moment in the battery pack time pair is obtained by subtracting the normalized value of the average difference at the next monitoring moment in the battery pack time pair from the first preset value and multiplying it by the normalized value of the average difference at the previous monitoring moment in the battery pack time pair. The voltage consistency degradation characteristic of the battery pack time pair is obtained by multiplying the normalized value of the voltage change anomaly characteristic of the battery pack time pair with the voltage consistency degradation characteristic of the battery pack time pair. The discharge relative voltage anomaly value of the battery pack time pair is obtained by multiplying the normalized value of the voltage change anomaly characteristic of the battery pack time pair with the voltage consistency degradation characteristic of the battery pack time pair.

[0012] Preferably, obtaining the relative discharge current anomaly value of a battery cell in a given time pair based on the discharge current at each sampling time within a time period corresponding to a time pair between a battery cell and other battery cells in a battery pack includes:

[0013] The sum of the discharge currents of the main circuit of a battery pack at each sampling time within a time period corresponding to a time pair is recorded as the total discharge current of the main circuit of the battery pack for that time pair. The theoretical total discharge current of the battery cells for that time pair is obtained by dividing the total discharge current of the main circuit of the battery pack for that time pair by the total number of parallel battery cells in the battery pack.

[0014] The discharge current sequence of a battery cell in the battery pack is formed by collecting the discharge current at each sampling moment within the time period corresponding to the time pair. The mean square error of the discharge current sequence of the battery cell in the battery pack and that of the other battery cells in the same time pair is calculated and the mean value is obtained to obtain the discharge current difference characteristic of the battery cell in the battery pack for that time pair. The sum of the discharge current of the battery cell at each sampling moment within the time period corresponding to the time pair is obtained and recorded as the total discharge current of the battery cell in the time pair. The normalized value of the discharge current difference characteristic of the battery cell in the time pair is added to the normalized value of the ratio of the theoretical total discharge current of the battery cell in the time pair to the total discharge current of the battery cell in the time pair, and the average value is obtained to obtain the relative discharge current anomaly value of the battery cell in the time pair.

[0015] Preferably, the temperature-discharge efficiency fitting curve is constructed, and the average temperature of a battery cell within a time period is used to correct the relative discharge current anomalies and relative discharge voltage anomalies of the battery cell and the battery pack to which the battery cell belongs for that time period. These corrections are then fused to obtain the comprehensive discharge anomaly value of the battery cell for that time period, including:

[0016] The average temperature of a battery cell within a given time period is input into the temperature-discharge efficiency fitting curve to obtain the discharge efficiency of that battery cell for that time period. The discharge efficiency of that battery cell for that time period is multiplied by the relative discharge voltage anomaly of the battery pack to which that battery cell belongs for that time period to obtain the corrected relative discharge voltage anomaly value of that battery cell for that time period. The discharge efficiency of that battery cell for that time period is multiplied by the relative discharge current anomaly value of that battery cell for that time period to obtain the corrected relative discharge current anomaly value of that battery cell for that time period. The corrected relative discharge voltage anomaly value and the corrected relative discharge current anomaly value of that battery cell for that time period are multiplied and the square root is taken to obtain the comprehensive discharge anomaly value of that battery cell for that time period.

[0017] Preferably, a reference discharge anomaly value sequence for a battery cell at a given time pair is formed by acquiring the comprehensive discharge anomaly values ​​of a predetermined number of time pairs prior to the acquisition of a time pair, including:

[0018] The sum of the number of discharges and charges of the battery pack within a set time period before a monitoring time is normalized to obtain a normalized value. The difference between the maximum and minimum number of time pairs is multiplied by the normalized value to obtain an adjustment range. The minimum number of time pairs is added to the adjustment range and rounded up to obtain the set number corresponding to the monitoring time. The comprehensive discharge anomaly value of the time pair of the previous battery cell with the time pair of the next monitoring time is obtained to form a reference comprehensive discharge anomaly value sequence for the battery cell at that time pair.

[0019] Preferably, the relative discharge anomaly index of the battery cell for the current time pair is obtained using the comprehensive discharge anomaly value of the current time pair and a reference comprehensive discharge anomaly value sequence, including:

[0020] The distance between a time pair in the reference discharge anomaly value sequence and the current time pair of a battery cell is obtained and compared with the sum of the distances between each time pair in the reference discharge anomaly value sequence and the current time pair to obtain the distance ratio of the time pair. The time weight of the time pair is obtained by subtracting the distance ratio of the time pair from a first preset value. The discharge anomaly value of the current time pair of the battery cell is obtained by comparing the discharge anomaly value of the current time pair of the battery cell with the discharge anomaly value of a time pair of the same battery cell in the reference discharge anomaly value sequence. The discharge anomaly value ratio of the current time pair of the battery cell is obtained by weighted summing the discharge anomaly value ratios of each time pair corresponding to the current time pair using the time weights of each time pair in the reference discharge anomaly value sequence. The comparative anomaly feature of the current time pair of the battery cell is obtained by weighted summing the comparative anomaly feature of the current time pair of the battery cell with the discharge anomaly value of the current time pair of the battery cell to obtain the discharge relative anomaly index of the current time pair of the battery cell.

[0021] Preferably, short-circuit warning is performed using the relative abnormal discharge indicators and relative abnormal charge indicators of each battery cell at the current time, including:

[0022] The upper quartile boundary of discharge is obtained by processing the relative anomaly index of discharge for each battery cell in the current time pair using the box plot method. Similarly, the upper quartile boundary of charging is obtained. If the relative anomaly index of discharge for at least one battery cell in the current time pair is greater than the upper quartile boundary of discharge or the relative anomaly index of charging is greater than the relative anomaly index of charging, a short circuit warning is issued.

[0023] The embodiments of this application have at least the following beneficial effects: This application utilizes the terminal voltage at two monitoring moments during a time pair of the discharge process of one battery pack and other battery packs to obtain the relative voltage anomaly value of the battery pack in that time pair, and performs preliminary characterization of the voltage anomaly; further, for battery cells connected in parallel within the battery pack, it utilizes the current redistribution characteristics under terminal voltage constraints in the parallel structure, combined with the theoretical discharge current deviation, to obtain the relative current anomaly value of the battery cell in that time pair based on the discharge current at each sampling moment within a time period corresponding to one battery cell and other battery cells in a battery pack; subsequently, considering the influence of temperature differences caused by different installation positions and heat dissipation conditions on battery charge and discharge performance, a theoretical mapping relationship between temperature and battery charge and discharge efficiency is established, constructing... A temperature-discharge efficiency fitting curve is constructed to correct the relative discharge current anomalies and relative discharge voltage anomalies, and then the results are fused to obtain the comprehensive discharge anomaly value for each battery cell at each time pair, thereby reducing misjudgments caused by temperature factors. Finally, a reference comprehensive discharge anomaly value sequence for each time pair is obtained, and then combined with the comprehensive discharge anomaly value for each time pair, the relative discharge anomaly index and relative charging anomaly index for each battery cell at the current time pair are obtained. Short circuit warning is then performed using the relative discharge anomaly index and relative charging anomaly index for each battery cell at the current time pair, enabling early identification of progressive internal short circuit faults. This application does not require an additional internal resistance detection device; it can achieve anomaly detection using only the existing data collected by the battery management system, thereby improving the accuracy of power battery safety monitoring and its adaptability to engineering applications. Detailed Implementation

[0024] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with preferred embodiments, details the specific implementation, structure, features, and effects of a power battery internal short circuit early detection method based on series-parallel topological constraints proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0026] The following details the specific scheme of the early detection method for internal short circuits in power batteries based on series and parallel topology constraints provided in this application.

[0027] In this embodiment, the main application scenario of this application is as follows: Based on the series and parallel topological constraints of power batteries, this application establishes an analysis method from battery pack-level anomaly screening to single-cell-level anomaly location, and combines the dynamic change trend during operation to achieve accurate detection of progressive internal short-circuit anomalies.

[0028] This application mainly includes the following steps:

[0029] Step S1: Combine two adjacent monitoring times into a time pair, and use the terminal voltages of the two monitoring times in a time pair of the discharge process of one battery pack and other battery packs in the battery pack to obtain the relative voltage anomaly value of the discharge of the battery pack in that time pair.

[0030] First, multi-dimensional state data of the power battery during operation is acquired through the Battery Management System (BMS) of the new energy vehicle. For battery packs formed by multiple battery cells connected in parallel, voltage and current data of each parallel cell are collected during charging and discharging. For power battery packs formed by multiple battery cells connected in series, terminal voltage of each series battery cell and current data of the entire series circuit are collected during discharging and charging. At the same time, temperature data of each battery cell is also collected to eliminate performance response deviations caused by differences in battery installation location, heat dissipation conditions, and ambient temperature. To ensure the accuracy of subsequent anomaly feature analysis, the collected voltage, current, and temperature data are time-synchronized so that the data of each dimension corresponds to the same operating time. In this application, the data is collected once every 0.01 seconds, and the time of data collection is called the sampling time. The time of anomaly monitoring is defined as the monitoring time once per second. Subsequent analysis is based on the monitoring time, and there are multiple sampling times between two adjacent monitoring times.

[0031] During actual operation, the power batteries of new energy vehicles typically undergo different working states, including charging, discharging, and resting. In the early stages, potential internal short-circuit faults within the battery usually do not manifest as obvious fault signals. Instead, they cause changes in the battery's internal equivalent impedance, increased local self-discharge, and decreased charge storage capacity, causing the voltage and current responses during charging and discharging to gradually deviate from normal battery behavior. Therefore, by analyzing the relative response differences between different battery cells based on the operating data such as individual cell voltage, current, and temperature collected by the battery management system (BMS) of the new energy vehicle, it is possible to uncover the abnormal characteristics of potential internal short circuits.

[0032] Since the power batteries of new energy vehicles are typically composed of multiple cells (battery units) connected in parallel and series, multiple cells connected in parallel form a battery pack to increase output capacity and discharge capability. Multiple battery packs are further connected in series to form a power battery pack to meet the voltage level required for vehicle operation. Therefore, during long-term operation of the power battery, due to factors such as cell material aging, decreased electrode activity, changes in electrolyte state, and internal micro-short circuits, performance differences may occur between different battery packs, causing the overall voltage response of some battery packs to deviate from the normal state.

[0033] In this application, multiple battery packs connected in series are first analyzed. By analyzing the relative voltage change characteristics between each series battery pack, the abnormality of the abnormal battery pack is initially quantified. Then, the multiple battery cells connected in parallel within the battery pack are further analyzed to accurately locate the abnormal individual battery cells.

[0034] Taking a series-connected battery pack structure as an example, according to the characteristics of a series circuit, the current flowing through each series-connected battery pack remains consistent during operation. Therefore, during discharge, when an abnormal battery cell exists within a series-connected battery pack, the abnormal cell causes an increase in the equivalent internal resistance, a decrease in output capacity, and a reduction in charge release capacity. Under the same discharge current, the overall terminal voltage drop rate of this battery pack will differ from that of a normal battery pack. This results in abnormal voltage variation amplitudes between adjacent monitoring times and abnormal voltage consistency with other series-connected battery packs. Consequently, two adjacent monitoring times are paired into a time pair, which is then used for subsequent analysis.

[0035] The relative voltage anomaly of the discharge of a battery pack is obtained by using the terminal voltage at two monitoring moments during a time pair of the discharge process of one battery pack and other battery packs.

[0036] Specifically, during the discharge process, the absolute value of the difference between the terminal voltage at the later monitoring moment and the earlier monitoring moment in a time pair of battery packs is recorded as the terminal voltage change of that battery pack in that time pair. The ratio of the terminal voltage change of that battery pack in that time pair to the terminal voltage changes of all other battery packs in that time pair is calculated, and the average is taken to obtain the abnormal voltage change characteristics of that battery pack in that time pair. The absolute value of the difference between the terminal voltage at the later monitoring moment in that time pair of battery packs and the terminal voltage at the later monitoring moment in the same time pair of all other battery packs is calculated, and the average is taken to obtain... The average difference of the battery pack at the next monitoring moment in the time pair is obtained by analogy with the average difference of the battery pack at the previous monitoring moment in the time pair. The voltage consistency degradation characteristic of the battery pack at the time pair is obtained by subtracting the normalized value of the average difference of the battery pack at the next monitoring moment in the time pair from the first preset value and multiplying it by the normalized value of the average difference of the battery pack at the previous monitoring moment in the time pair. The normalized value of the voltage change anomaly characteristic of the battery pack at the time pair is multiplied by the voltage consistency degradation characteristic of the battery pack at the time pair to obtain the discharge relative voltage anomaly value of the battery pack at the time pair.

[0037] The specific calculation model is as follows:

[0038] ,

[0039] ,

[0040] ,

[0041] ,

[0042] in, This represents the relative voltage anomaly value of the discharge of the a-th series-connected battery pack during the discharge process, consisting of the time pair formed by the (t-1)-th monitoring time and the t-th monitoring time. This represents the voltage change anomaly of the a-th series-connected battery pack in the time pair consisting of monitoring time t-1 and monitoring time t, where S is the number of battery packs. , These represent the terminal voltage values ​​of the a-th battery pack at monitoring time t and monitoring time t-1, respectively. Let be the change in terminal voltage of the a-th battery pack for that time period, and b represent the other battery packs. , These represent the average difference between the a-th battery pack at monitoring time t and monitoring time t-1, respectively, which is the voltage difference characteristic relative to the other battery packs connected in series. This indicates the voltage consistency degradation characteristics of the a-th battery pack for this time pair. The first preset value is 1, which indicates the voltage consistency degradation characteristics relative to other series battery packs within this time interval.

[0043] This indicates the relative proportion between the voltage change of the a-th battery pack at adjacent times and the corresponding voltage change of other battery packs. When this value is large, it indicates that the voltage change of this battery pack is higher than that of other battery packs under the same discharge conditions, and its internal performance state may be abnormal. This represents the relative voltage consistency and degradation characteristics of the a-th battery pack compared to other battery packs at monitoring times t and t-1. Since battery voltage drop typically exhibits a non-linear variation, a smaller voltage difference between the a-th battery pack and the other battery packs at monitoring time t indicates a higher consistency in its initial electromotive force and chemical energy at that time. The larger the value, the greater the difference in voltage consistency between the battery pack and the other series battery packs at the monitoring time t-1, indicating that the voltage consistency of the battery pack is relatively abnormal during the discharge process.

[0044] This represents the minimization of a linear normalization function. During normalization, the time pairs (times consisting of two adjacent monitoring times) between monitoring time t-1 and monitoring time t are obtained for each battery pack. and These are used to form two sequences, which yield the following results: and Then, the two sequences were subjected to maximum and minimum normalization to make the degree of anomaly between different battery packs comparable in the same dimension. It represents the product of the relative voltage anomaly characteristics of the a-th battery pack within the time interval, taking into account both the voltage variation amplitude and the voltage consistency. In other words, it represents the relative voltage anomaly value of the battery pack at that time.

[0045] Furthermore, during charging, since the charging current flowing through each series-connected battery pack remains consistent, if a series-connected battery pack contains an abnormal cell with reduced capacity, an internal short circuit, or decreased energy storage capacity, the effective charging capacity of that battery pack decreases. Under the same charging current, its terminal voltage rises faster than that of a normal battery pack, causing its voltage response trajectory to deviate from that of other series-connected battery packs. Therefore, the above... The analysis process can be used not only to describe the voltage anomaly characteristics during the discharge process, but also to describe the relative voltage anomaly characteristics of the battery pack during the charging process. That is, the same analysis method can be used to obtain the relative voltage anomaly values ​​of each battery pack at each time pair during the charging process.

[0046] Step S2: Based on the discharge current of a battery cell in a battery pack and other battery cells in a time pair, obtain the relative discharge current anomaly value of the battery cell in that time pair at each sampling time.

[0047] After completing the overall anomaly analysis of the series-connected battery packs within the power battery pack, further analysis of the multiple parallel-connected battery cells within the pack is needed to pinpoint the source of the anomaly. Since power battery packs typically employ a series-parallel combination structure, the series structure primarily enhances the overall output voltage, while the parallel structure mainly improves output capacity and discharge capability. Within the same battery pack, each parallel battery cell is connected to the same electrical node, and its terminal voltage is constrained by a common bus voltage. Therefore, under the same operating conditions and external load, the current distribution among the parallel battery cells is mainly determined by their equivalent impedance, capacity state, and electrochemical reactivity. Thus, analyzing the differences in current distribution among parallel battery cells can reflect changes in the internal performance state of different battery cells.

[0048] Under normal circumstances, parallel battery cells have high manufacturing consistency, resulting in similar internal material properties, equivalent impedance, capacity, and electrochemical state. During charging and discharging, the proportion of branch current borne by each cell remains relatively stable, and the current response trends of each cell show high consistency. However, when a battery cell experiences performance degradation due to factors such as internal short circuits, electrode damage, active material decay, or increased internal impedance, its internal electron transport and charge exchange capabilities change. This reduces the cell's output capacity under the same bus voltage constraint and leads to a redistribution phenomenon within the parallel network; that is, the discharge current borne by the abnormal battery cell gradually decreases compared to the normal parallel battery cells. Simultaneously, because changes in internal state affect the current response patterns of the battery cell at different discharge stages, a dynamic deviation occurs between its current change curve and that of the normal battery cell.

[0049] Therefore, based on the differences in current distribution and current change processes among the parallel battery cells within the same battery pack, abnormal features of individual battery cells are analyzed. This allows for the location of the source of the anomaly by utilizing the current redistribution characteristics in the parallel structure, resulting in more accurate anomaly detection results.

[0050] Therefore, the relative discharge current anomaly value of a battery cell in a given time pair is obtained based on the discharge current of a battery cell in a battery pack and other battery cells in a given time pair at each sampling time.

[0051] First, the sum of the discharge currents of the main circuit of a battery pack at each sampling time within a time period corresponding to a time pair is recorded as the total discharge current of the main circuit of the battery pack for that time pair. The theoretical total discharge current of the battery cells for that time pair is obtained by dividing the total discharge current of the main circuit of the battery pack for that time pair by the total number of parallel battery cells in the battery pack.

[0052] The specific calculation model is as follows:

[0053] ,

[0054] in, This represents the total theoretical discharge current of a battery cell in a battery pack from monitoring time t to monitoring time t-1 (the time pair consisting of monitoring time t and monitoring time t-1); It represents the sum of the discharge currents of the main circuit of a battery pack at each sampling time within the time period corresponding to the time pair formed by the monitoring time t and the monitoring time t-1, which is also the total discharge current of the main circuit of the battery pack at that time pair; P represents the total number of parallel battery cells in the battery pack, or it can also be expressed as the number of parallel branches.

[0055] It should be noted that the theoretical total discharge current of this battery cell is not used to predict or describe or obtain the actual output current of a single battery cell, but rather to describe the theoretical equilibrium current level of a healthy battery cell in a parallel structure, and to serve as a reference benchmark for subsequent calculations of the deviation between theoretical and actual currents.

[0056] Furthermore, the discharge current of one battery cell in the battery pack is used to form a discharge current sequence at each sampling moment within the time period corresponding to the time pair. The mean square error of the discharge current sequences of the battery cell in the battery pack and those of other battery cells in the same time pair is calculated and averaged to obtain the discharge current difference characteristics of the battery cell in the battery pack for that time pair. The sum of the discharge current of the battery cell at each sampling moment within the time period corresponding to the time pair is obtained and recorded as the total discharge current of the battery cell in that time pair. The normalized value of the discharge current difference characteristics of the battery cell in that time pair is added to the normalized value of the ratio of the theoretical total discharge current of the battery cell in that time pair to the total discharge current of the battery cell in that time pair, and the average is calculated to obtain the relative discharge current anomaly value of the battery cell in that time pair.

[0057] The specific calculation model is as follows:

[0058] ,

[0059] ,

[0060] in, This represents the relative discharge current anomaly value of the time pair consisting of the (t-1)th and (t)th monitoring times during the discharge process of the xth battery cell; The discharge current difference characteristic of the x-th battery cell for this time pair represents the difference characteristic between the discharge current segment of the x-th battery cell and the current segments of other battery cells between the (t-1)-th monitoring time and the t-th monitoring time. It is used to characterize the consistency of the dynamic current response of the battery cell under the same operating conditions.

[0061] Because internal short circuits or performance degradation in a battery can cause changes in its internal impedance, the energy conversion process and load response capability of the battery cell will change. Therefore, its current change process will deviate from that of a normal parallel cell. The larger the value, the more obvious the difference between its current response pattern during discharge and other parallel battery cells, and the higher the degree of abnormality in the internal state of the battery cell. The mean square error (MSE) function is used to calculate the degree of difference between two discharge current sequences. In this embodiment, the MSE is chosen as the metric for current response difference because, under conditions of internal short circuit or performance degradation of the power battery, its abnormal performance is mainly manifested as a shift in the output current amplitude and dynamic response pattern of the battery cell relative to the normal parallel cell under the same operating stage and external constraints, rather than a significant misalignment in the time series. Therefore, the MSE can directly measure the degree of deviation between the current response amplitudes of different battery cells and is more suitable for characterizing the characteristics of reduced output capability caused by changes in internal impedance.

[0062] , These represent the discharge current sequences of the x-th and c-th battery cells between the (t-1)-th and t-th monitoring times, respectively. This represents the mean square error between the x-th battery cell and the other parallel battery cells. The larger this value is, the more obvious the deviation of the current response change of the battery cell in the corresponding time interval from other parallel battery cells, and the worse its internal state consistency. This represents the total discharge current between the (t-1)th and tth monitoring times during the discharge process of the xth battery cell; This represents the ratio of the theoretical total discharge current of the battery cell in that time pair to the total discharge current of the battery cell in that time pair. When this value is large, it indicates that the actual discharge current undertaken by the battery cell is lower than the theoretical allocation level, that is, its output capacity is reduced compared with other parallel battery cells, and there may be abnormalities in its internal performance.

[0063] Furthermore, during the charging process, since the parallel battery cells are under the same terminal voltage condition, when the effective charging capacity of a certain battery cell decreases due to capacity reduction, internal short circuit, or reduced energy storage capacity, the charging current it needs to absorb to reach the same terminal voltage change condition will be lower than that of a normal parallel battery cell. Therefore, the above analysis of the discharge process yields... The same process can also be used to obtain abnormal values ​​of the relative charging current of the battery cell during the charging process, and the method of obtaining them is the same.

[0064] Step S3: Construct a temperature-discharge efficiency fitting curve. Combine the average temperature of a battery cell within a time period to correct the relative discharge current anomaly value and the relative discharge voltage anomaly value of the battery cell and the battery pack to which the battery cell belongs for that time period, and then fuse them to obtain the comprehensive discharge anomaly value of the battery cell for that time period.

[0065] Through the above process, the abnormal values ​​of the relative discharge current and the relative charge current of each battery cell in the power battery pack can be obtained, as well as the abnormal values ​​of the relative discharge voltage and the relative charge voltage obtained from the aforementioned series structure analysis.

[0066] However, due to the influence of installation location, heat dissipation path and thermal management status, the operating temperature of battery cells in different locations inside the power battery pack may vary. Temperature changes will further affect the internal electrochemical reaction rate, polarization degree, internal resistance and charge and discharge efficiency of the battery, causing some battery cells to produce voltage and current response deviations caused by temperature factors.

[0067] Therefore, before integrating abnormal features, it is necessary to establish a theoretical correction model for the impact of temperature factors on battery charging and discharging performance in order to reduce misjudgments of anomalies caused by temperature differences.

[0068] Furthermore, a temperature-discharge efficiency fitting curve is constructed, and the average temperature of a battery cell within a time period is combined with the discharge relative current anomaly value and discharge relative voltage anomaly value of the battery cell and the battery pack to which the battery cell belongs for that time period. After correction, the results are fused to obtain the comprehensive discharge anomaly value of the battery cell for that time period.

[0069] Specifically, a standard battery with the same model and similar parameters as the target battery can be selected. Different temperature conditions are set in a constant-temperature experimental environment. After each temperature stabilizes, the battery is adjusted to the same initial state, such as the same initial voltage, and then fixed-rate charging and discharging tests are conducted. During the test, battery terminal voltage, charging and discharging current, and temperature data are collected in real time. Based on the preset operating voltage range, the cumulative charged and discharged capacity within the corresponding voltage range is statistically analyzed. Using the theoretical capacity corresponding to the same voltage range under standard temperature conditions as a reference, the charging and discharging efficiencies under different temperature conditions are normalized to obtain the efficiency change data for each temperature condition. Subsequently, using temperature as the input variable and normalized charging and discharging efficiency as the output variable, curve fitting is performed to establish the mapping relationship model between temperature and charging efficiency and discharging efficiency, respectively. Nonlinear fitting methods such as the Levenberg-Marquardt algorithm (LM algorithm) are used to fit the mapping relationship model of the experimental data to obtain the theoretical relationship curve between temperature and battery charging and discharging efficiency, thus obtaining the temperature-discharging efficiency fitting curve and the temperature-charging efficiency fitting curve.

[0070] Furthermore, the average temperature of a battery cell within a time period corresponding to a time pair is substituted into the temperature-discharge efficiency fitting curve to obtain the discharge efficiency of the battery cell for that time pair. The discharge efficiency of the battery cell for that time pair is multiplied by the relative discharge voltage anomaly value of the battery pack to which the battery cell belongs for that time pair to obtain the corrected relative discharge voltage anomaly value of the battery cell for that time pair. The discharge efficiency of the battery cell for that time pair is multiplied by the relative discharge current anomaly value of the battery cell for that time pair to obtain the corrected relative discharge current anomaly value of the battery cell for that time pair. The corrected relative discharge voltage anomaly value and the corrected relative discharge current anomaly value of the battery cell for that time pair are multiplied and the square root is taken to obtain the comprehensive discharge anomaly value of the battery cell for that time pair.

[0071] The temperature correction process described above reduces the impact of performance differences caused by varying installation locations and heat dissipation conditions, resulting in a more concentrated reflection of anomalies caused by changes in the battery's own state, such as internal short circuits and performance degradation. After correction, anomalies of any battery pack during charging or discharging can be obtained. Since charging and discharging anomalies differ, they are classified and analyzed separately. Then, the corrected voltage and current anomaly values ​​are multiplied and the square root is taken to obtain a comprehensive anomaly value.

[0072] Subsequently, based on the charging and discharging states during battery operation, time series were established for the comprehensive anomaly characteristics obtained under different states, and subsequent trend analysis was performed for different categories. That is, the comprehensive discharge anomaly values ​​of each time pair of a battery cell were grouped into a sequence for subsequent analysis; similarly, comprehensive charging anomaly values ​​can also be obtained.

[0073] Step S4: Obtain the comprehensive discharge anomaly values ​​of a battery cell for a predetermined number of time pairs to form a reference comprehensive discharge anomaly value sequence for that battery cell's time pair; use the comprehensive discharge anomaly value of the current time pair of a battery cell and the reference comprehensive discharge anomaly value sequence to obtain the relative discharge anomaly index of the current time pair of the battery cell, and similarly obtain the relative charging anomaly index of the current time pair of the battery cell; use the relative discharge anomaly index and the relative charging anomaly index of each battery cell's current time pair to perform short circuit warning.

[0074] Furthermore, since anomalies such as internal short circuits in power batteries typically develop gradually, the degree of anomaly in a single battery cell may not be obvious in the early stages. However, as internal side reactions continue to occur, the deviations in its voltage and current responses may gradually accumulate. Therefore, judging solely based on the current anomaly characteristics can easily overlook potential anomalies in their early stages of development.

[0075] Therefore, after obtaining the sequences of comprehensive discharge anomalies and the sequences of comprehensive charging anomalies corresponding to the charging and discharging states of each battery cell, time-series analysis is performed on the sequences of comprehensive discharge anomalies and the sequences of comprehensive charging anomalies for different categories.

[0076] First, time-series filtering algorithms such as Kalman filtering can be used to smooth the sequences of discharge and charging anomalies, thereby reducing the disturbances of abnormal features caused by sensor sampling errors, current fluctuations, and transient operating condition changes, and improving the stability of subsequent trend analysis.

[0077] When a battery cell experiences an internal short circuit, capacity decay, or abnormal impedance increase, even if the corresponding abnormal characteristic value is still at a low level at the current moment, if it shows a continuous upward trend relative to its historical healthy state, it indicates that the battery cell may be in a potential abnormal development stage.

[0078] Therefore, to further consider the influence of different historical time points on the current abnormal state judgment, time weights are assigned to the historical abnormal features corresponding to any adjacent time.

[0079] First, the sum of the number of discharges and charges of the battery pack within a set time period before a monitoring time is normalized to obtain a normalized value. The difference between the maximum and minimum number of time pairs is multiplied by the normalized value to obtain an adjustment range. The minimum number of time pairs is added to the adjustment range and rounded up to obtain the set quantity corresponding to that monitoring time. The comprehensive discharge anomaly values ​​of the time pairs preceding the set quantity of the battery cell at the monitoring time are used to form a reference comprehensive discharge anomaly value sequence for that time pair of the battery cell. For example, if the set quantity corresponding to the t-th monitoring time is 10, then the comprehensive discharge anomaly values ​​of the 10 time pairs preceding the time pair (comprising the time pair consisting of the (t-1)-th and (t-2)-th monitoring times at the t-th monitoring time are used to form a reference comprehensive discharge anomaly value sequence.

[0080] The specific calculation model for the set quantity is as follows:

[0081] ,

[0082] in, This represents the set quantity corresponding to the t-th monitoring time, which is also the number of time pairs acquired. This represents the floor function; , These represent the range of selected time pairs, the minimum number of time pairs, and the maximum number of time pairs, respectively. In this example... , These settings allow you to select the number of time pairs included in the historical data for 10 days and 100 days, respectively. The specific selection can be adjusted based on the actual computing power and system performance of the vehicle's infotainment system. This represents the sum of the number of discharges and charges of the battery pack within a set time period at monitoring time t. The set time period in this application is one week. , This represents the sum of the number of discharges and charges for the battery pack, representing the historical minimum and maximum. The sum of the number of discharges and charges for the battery pack is collected once a week, and the minimum and maximum values ​​are taken. This indicates normalization; a larger value signifies more frequent battery use, leading to accelerated performance degradation and aging. More historical data is needed for analysis to improve the comparison and detection of potential internal anomalies. Conversely, less historical data is used when the battery has been used infrequently, as aging and other abnormal changes occur relatively slowly when the battery is unused or used infrequently. The smaller the value, the less historical data needs to be selected for anomaly analysis, thus reducing the computational load on the monitoring system.

[0083] Furthermore, the relative discharge anomaly index of the battery cell for the current time pair is obtained by using the comprehensive discharge anomaly value of the current time pair and the reference comprehensive discharge anomaly value sequence.

[0084] Specifically, the distance between a time pair in the reference discharge anomaly value sequence of a battery cell's current time pair and the current time pair is obtained and compared with the sum of the distances between each time pair in the reference discharge anomaly value sequence and the current time pair to obtain the distance ratio of that time pair; the time weight of that time pair is obtained by subtracting the distance ratio of that time pair from a first preset value; the discharge anomaly value ratio of that time pair is obtained by comparing the discharge anomaly value of the current time pair of the battery cell with the discharge anomaly value of a time pair of the battery cell in the reference discharge anomaly value sequence; the discharge anomaly value ratio of each time pair is obtained by weighted summing the time weights of each time pair in the reference discharge anomaly value sequence of the current time pair of the battery cell to obtain the comparative anomaly feature of the current time pair of the battery cell; the normalized value of the comparative anomaly feature of the current time pair of the battery cell is weighted and summed with the discharge anomaly value of the current time pair of the battery cell to obtain the discharge relative anomaly index of the current time pair of the battery cell.

[0085] The specific calculation model is as follows:

[0086] ,

[0087] ,

[0088] in, This represents the time weight of the s-th time pair in the sequence of comprehensive discharge anomalies, where the t-th monitoring time of a battery cell serves as the reference time pair for the next time pair. This refers to the number of time pairs in the reference discharge anomaly value sequence, which is also the number of time pairs selected up to the t-th monitoring time. This represents the distance between the s-th time pair and the time pair consisting of the t-th monitoring time and the t-th monitoring time as the next time point, which is the interval between two time pairs in the reference discharge anomaly value sequence. For example, if the interval between the first and third time pairs is two, then the distance is 2. The distance ratio is set to a first preset value of 1;

[0089] α represents the relative abnormal discharge index of the x-th battery cell at the time pair consisting of the (t-1)-th and t-th monitoring times; α represents the weighted fusion coefficient. In this embodiment, since anomalies such as internal short circuits usually exhibit progressive accumulation characteristics, more attention is paid to the development trend of abnormal characteristics, so α is set to 0.6. The specific value can be adjusted within the range of (0,1) according to actual operating requirements. This represents the combined discharge anomaly value of the x-th battery cell in the time pair consisting of the (t-1)-th and t-th monitoring times; This represents the discharge anomaly value of the s-th time pair in the reference discharge anomaly value sequence consisting of the (t-1)-th and t-th monitoring times for the x-th battery cell. This represents the proportion of the overall discharge anomaly value corresponding to the s-th time period; This is a comparative anomaly feature of the battery cell at the (t-1)th and tth monitoring times. When this value is large, it indicates that the current anomaly feature has increased significantly compared to the historical state, indicating that the abnormal development trend of the battery cell is strengthening and its potential internal short circuit risk is higher.

[0090] Similarly, using the same method, the relative charging anomaly index for a battery cell's current time pair can be obtained. From this, the relative discharging anomaly index and the relative charging anomaly index for each battery cell's current time pair can be obtained.

[0091] Finally, to automatically detect anomalies, statistical analysis methods are used to adaptively determine the thresholds for anomaly monitoring indicators. This allows for short-circuit early warning based on the relative anomalies in discharge and charging of each battery cell at the current time pair.

[0092] Specifically, the upper quartile boundary of discharge is obtained by processing the relative anomaly index of discharge for each battery cell in the current time pair using the box plot method, and the upper quartile boundary of charging is obtained similarly. When the relative anomaly index of discharge for at least one battery cell in the current time pair is greater than the upper quartile boundary of discharge or the relative anomaly index of charging is greater than the relative anomaly index of charging, a short circuit warning is issued. This indicates that the battery cell has a significant anomaly compared to other battery cells of the same type and triggers a warning of potential internal short circuit risk.

[0093] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0094] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0095] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for early detection of internal short circuits in power batteries based on series-parallel topology constraints, characterized in that, The method includes: Two adjacent monitoring times are combined into a time pair. The relative voltage anomaly of the discharge voltage of the battery pack in that time pair is obtained by using the terminal voltage of the two monitoring times in a time pair of the discharge process of one battery pack and other battery packs in the battery pack. The relative discharge current anomaly value of a battery cell in a battery pack is obtained based on the discharge current of a battery cell and other battery cells in a time pair within a corresponding time period. The temperature-discharge efficiency fitting curve is constructed. The average temperature of a battery cell in a time pair is combined with the discharge relative current anomaly value and discharge relative voltage anomaly value of the battery cell and the battery pack to which the battery cell belongs in that time pair. The results are then fused to obtain the comprehensive discharge anomaly value of the battery cell in that time pair. A reference discharge anomaly value sequence for a given time pair of a battery cell is formed by collecting the comprehensive discharge anomaly values ​​of a predetermined number of time pairs. The relative discharge anomaly index of the current time pair of a battery cell is obtained using the comprehensive discharge anomaly value of the current time pair and the reference discharge anomaly value sequence. Similarly, the relative charging anomaly index of the current time pair of the battery cell is obtained. Short circuit warning is then performed using the relative discharge anomaly index and the relative charging anomaly index of the current time pair of each battery cell.

2. The method for early detection of internal short circuits in a power battery based on series-parallel topology constraints according to claim 1, characterized in that, The method of obtaining the relative voltage anomaly of the battery pack during the discharge process of one battery pack and other battery packs by measuring the terminal voltage at two monitoring moments in a time pair includes: The absolute value of the difference between the terminal voltage of a battery pack at a given time point during discharge is recorded as the terminal voltage change of that battery pack at that time point. The ratio of the terminal voltage change of this battery pack at that time point to the terminal voltage changes of all other battery packs at that time point is calculated, and the average is taken to obtain the abnormal voltage change characteristics of that battery pack at that time point. The absolute value of the difference between the terminal voltage of this battery pack at a given time point and the terminal voltages of all other battery packs at the same time point are calculated, and the average is taken to obtain the abnormal voltage change characteristics of that battery pack at that time point. The average difference of the battery pack at the next monitoring moment in the time pair is obtained by analogy to the average difference of the battery pack at the previous monitoring moment in the time pair. The voltage consistency degradation characteristic of the battery pack at the time pair is obtained by subtracting the normalized value of the average difference of the battery pack at the next monitoring moment in the time pair from the first preset value and multiplying it by the normalized value of the average difference of the battery pack at the previous monitoring moment in the time pair. The normalized value of the voltage change anomaly characteristic of the battery pack at the time pair is multiplied by the voltage consistency degradation characteristic of the battery pack at the time pair to obtain the discharge relative voltage anomaly value of the battery pack at the time pair.

3. The method for early detection of internal short circuits in a power battery based on series-parallel topology constraints according to claim 1, characterized in that, The method of obtaining the relative discharge current anomaly value of a battery cell in a given time pair based on the discharge current at each sampling time within a time period corresponding to a battery cell and other battery cells in a battery pack includes: The sum of the discharge currents of the main circuit of a battery pack at each sampling time within a time period corresponding to a time pair is recorded as the total discharge current of the main circuit of the battery pack for that time pair. The theoretical total discharge current of the battery cells for that time pair is obtained by dividing the total discharge current of the main circuit of the battery pack for that time pair by the total number of parallel battery cells in the battery pack. The discharge current sequence of a battery cell in the battery pack is formed by collecting the discharge current at each sampling moment within the time period corresponding to the time pair. The mean square error of the discharge current sequence of the battery cell in the battery pack and that of the other battery cells in the same time pair is calculated and the mean value is obtained to obtain the discharge current difference characteristic of the battery cell in the battery pack for that time pair. The sum of the discharge current of the battery cell at each sampling moment within the time period corresponding to the time pair is obtained and recorded as the total discharge current of the battery cell in the time pair. The normalized value of the discharge current difference characteristic of the battery cell in the time pair is added to the normalized value of the ratio of the theoretical total discharge current of the battery cell in the time pair to the total discharge current of the battery cell in the time pair, and the average value is obtained to obtain the relative discharge current anomaly value of the battery cell in the time pair.

4. The method for early detection of internal short circuits in a power battery based on series-parallel topology constraints according to claim 1, characterized in that, The constructed temperature-discharge efficiency fitting curve, combined with the average temperature of a battery cell within a corresponding time period, corrects for the relative discharge current anomalies and relative discharge voltage anomalies of the battery cell and its associated battery pack for that time period, and then fuses them to obtain the comprehensive discharge anomaly value of the battery cell for that time period, including: The average temperature of a battery cell within a given time period is input into the temperature-discharge efficiency fitting curve to obtain the discharge efficiency of that battery cell for that time period. The discharge efficiency of that battery cell for that time period is multiplied by the relative discharge voltage anomaly of the battery pack to which that battery cell belongs for that time period to obtain the corrected relative discharge voltage anomaly value of that battery cell for that time period. The discharge efficiency of that battery cell for that time period is multiplied by the relative discharge current anomaly value of that battery cell for that time period to obtain the corrected relative discharge current anomaly value of that battery cell for that time period. The corrected relative discharge voltage anomaly value and the corrected relative discharge current anomaly value of that battery cell for that time period are multiplied and the square root is taken to obtain the comprehensive discharge anomaly value of that battery cell for that time period.

5. The method for early detection of internal short circuits in a power battery based on series-parallel topology constraints according to claim 1, characterized in that, The step of obtaining a reference discharge anomaly value sequence for a battery cell at a given time pair by collecting a predetermined number of time pairs of discharge anomaly values ​​includes: The sum of the number of discharges and charges of the battery pack within a set time period before a monitoring time is normalized to obtain a normalized value. The difference between the maximum and minimum number of time pairs is multiplied by the normalized value to obtain an adjustment range. The minimum number of time pairs is added to the adjustment range and rounded up to obtain the set number corresponding to the monitoring time. The comprehensive discharge anomaly value of the time pair of the previous battery cell with the time pair of the next monitoring time is obtained to form a reference comprehensive discharge anomaly value sequence for the battery cell at that time pair.

6. The method for early detection of internal short circuits in a power battery based on series-parallel topology constraints according to claim 1, characterized in that, The method of obtaining the relative discharge anomaly index of a battery cell for the current time pair using the comprehensive discharge anomaly value of the current time pair and the reference comprehensive discharge anomaly value sequence includes: The distance between a time pair in the reference discharge anomaly value sequence and the current time pair of a battery cell is obtained and compared with the sum of the distances between each time pair in the reference discharge anomaly value sequence and the current time pair to obtain the distance ratio of the time pair. The time weight of the time pair is obtained by subtracting the distance ratio of the time pair from a first preset value. The discharge anomaly value of the current time pair of the battery cell is obtained by comparing the discharge anomaly value of the current time pair of the battery cell with the discharge anomaly value of a time pair of the same battery cell in the reference discharge anomaly value sequence. The discharge anomaly value ratio of the current time pair of the battery cell is obtained by weighted summing the discharge anomaly value ratios of each time pair corresponding to the current time pair using the time weights of each time pair in the reference discharge anomaly value sequence. The comparative anomaly feature of the current time pair of the battery cell is obtained by weighted summing the comparative anomaly feature of the current time pair of the battery cell with the discharge anomaly value of the current time pair of the battery cell to obtain the discharge relative anomaly index of the current time pair of the battery cell.

7. The method for early detection of internal short circuits in a power battery based on series-parallel topology constraints according to claim 1, characterized in that, The method of using the relative abnormality indicators of discharge and relative abnormality indicators of each battery cell at the current time to perform short circuit warning includes: The upper quartile boundary of discharge is obtained by processing the relative abnormality index of discharge for each battery cell in the current time pair using the box plot method. Similarly, the upper quartile boundary of charging is obtained. If the relative abnormality index of discharge for at least one battery cell in the current time pair is greater than the upper quartile boundary of discharge or the relative abnormality index of charging is greater than the upper quartile boundary of charging, a short circuit warning is issued.