Vehicle battery aging detection method and device, new energy vehicle and readable storage medium

Abstract

The application relates to a vehicle battery aging detection method and device, a new energy automobile and a readable storage medium. The method comprises the following steps: acquiring a current parameter of a vehicle battery under a target charging mode; the target charging mode is a charging mode in which the vehicle gradually switches the amplitude of the charging current according to the state of the battery; identifying a current step event in a target state of charge interval according to the change of the current parameter; extracting battery steady-state data of the vehicle battery before and after the current step event; the battery steady-state data comprises voltage data and current data of the vehicle battery in a current stable time period; and determining a target impedance characteristic of the vehicle battery corresponding to the current step event according to the voltage data and the current data. The method can improve the accuracy of vehicle battery aging state detection under the working condition of real vehicle multi-stage constant current charging.

Description

Technical Field

[0001] This application relates to the field of new energy vehicle battery management technology, and in particular to a method, apparatus, new energy vehicle, and readable storage medium for detecting vehicle battery aging. Background Technology

[0002] Electric vehicle batteries continuously degrade over time, undergoing irreversible electrochemical reactions such as thickening of the solid electrolyte interface film, loss of active lithium, and damage to electrode material structures, leading to capacity decay and increased internal resistance. Accurately assessing the battery's degradation state is crucial for ensuring the safe operation and lifespan of vehicles. A prerequisite for achieving this goal is the ability to extract characteristic parameters reflecting the physical degradation process of the battery from actual vehicle operation.

[0003] Currently, the extraction techniques for battery aging characteristics are mainly divided into two categories: one is the active excitation testing method, such as electrochemical impedance spectroscopy, which measures the complex impedance spectrum by applying a broadband sinusoidal AC signal to the battery, and hybrid pulse power characteristic testing, which calculates the DC internal resistance by applying a strictly defined pulse current sequence; the other is the passive charging curve analysis method, such as incremental capacity analysis and differential voltage analysis, which uses voltage and current data during long-term continuous constant current charging to convert the voltage plateau into peak characteristics through differential geometry methods to identify the internal degradation process of the battery.

[0004] However, in practical electric vehicle applications, battery management systems generally employ a multi-stage constant current charging strategy, which involves progressively switching the charging current amplitude based on the battery state to balance charging speed and safety. Active excitation testing methods rely on external testing equipment and specific offline testing conditions, making them unsuitable for direct application in real-world vehicle online operation scenarios. Passive charging curve analysis methods, on the other hand, heavily depend on long-term, continuous, single-current charging data. The discontinuous current conditions in multi-stage constant current charging scenarios disrupt the data continuity prerequisites required by traditional methods, making it difficult to extract effective aging characteristics and thus accurately determine the aging state of the vehicle battery. Therefore, improving the accuracy of vehicle battery aging state detection under real-world multi-stage constant current charging conditions remains a pressing technical challenge. Summary of the Invention

[0005] Based on this, this application addresses the aforementioned technical problems by providing a vehicle battery aging detection method, apparatus, new energy vehicle, and readable storage medium that can improve the accuracy of vehicle battery aging status detection under multi-stage constant current charging conditions in real vehicles.

[0006] In a first aspect, this application provides a method for detecting the aging of a vehicle battery, including:

[0007] Obtain the current parameters of the vehicle battery in the target charging mode; the target charging mode is a charging mode in which the vehicle switches the charging current amplitude step by step according to the battery status.

[0008] Based on the changes in the current parameters, current step events are identified within the target state of charge range, where the target state of charge range is the effective feature extraction range determined based on the historical charging records of the vehicle battery; the current step events include events that perform a step-by-step switching operation on the charging current amplitude.

[0009] Extract the steady-state data of the vehicle battery before and after the current step event occurs; the steady-state data includes the voltage data and current data of the vehicle battery during the current stable period; the current stable period is the time period during which the change in the current of the vehicle battery is less than a preset change threshold.

[0010] Based on the voltage data and the current data, the target impedance characteristics of the vehicle battery corresponding to the current step event are determined.

[0011] The technical solution of this application embodiment obtains the current parameters of the vehicle battery in a charging mode where the charging current amplitude switches stepwise according to the battery state, and identifies current step events from them. This allows the inherent current amplitude switching during the actual vehicle charging process to be used as a usable excitation source. Without adding additional hardware or interrupting the charging process, feature data for aging detection can be obtained under actual vehicle online operating conditions, reducing reliance on offline testing conditions. Then, based on the changes in current parameters, current step events are identified within the target state of charge range, enabling feature extraction within the valid state of charge range verified by historical charging records, thus improving battery aging characteristics. The effectiveness of the extraction is further demonstrated by extracting steady-state battery data before and after a current step event. Based on the voltage and current data in the steady-state battery data, the target impedance characteristics of the vehicle battery are determined. Since the steady-state battery data is extracted when the vehicle battery is in a stable state where the current change is less than a preset threshold, the interference of transient fluctuations on the feature calculation is reduced. Moreover, the impedance characteristics are directly related to the ohmic impedance and polarization characteristics inside the vehicle battery, exhibiting strong physical interpretability and robustness under operating conditions. This allows for the effective extraction of features that accurately reflect the battery aging state, thereby improving the accuracy of vehicle battery aging state detection under real-world multi-stage constant current charging conditions.

[0012] In an alternative embodiment of the first aspect, before identifying a current step event within a target state of charge range based on changes in the current parameters, the method further includes:

[0013] Obtain historical charging records corresponding to the vehicle battery in multiple historical charging processes, and each historical charging record includes the state of charge range of the vehicle battery in each historical charging process;

[0014] Based on the historical charging records, the charging frequency of the vehicle battery in each state of charge interval and the number of current step events in each state of charge interval are statistically analyzed; the charging frequency is used to characterize the frequency at which the vehicle battery is in the state of charge interval during each historical charging process.

[0015] The target state of charge interval is determined from each of the state of charge intervals based on the charging frequency and the number of occurrences.

[0016] The technical solution of this embodiment determines the state of charge interval by statistically analyzing the charging frequency and the number of step occurrences in each state of charge interval from historical charging records. This allows the feature extraction range to be based on actual charging behavior data, rather than relying on manually set or fixed experience values. This improves the matching degree between the target state of charge interval and the actual charging behavior of the vehicle, and helps to obtain more sufficient and effective battery aging feature data within the target state of charge interval.

[0017] In an optional embodiment of the first aspect, determining the target state of charge interval from each of the state of charge intervals based on the charging frequency and the number of occurrences includes:

[0018] The state of charge interval where the charging frequency is higher than a first threshold is defined as the first target interval, and the state of charge interval where the number of occurrences is higher than a second threshold is defined as the second target interval.

[0019] The target state of charge range is determined based on the intersection of the first target range and the second target range.

[0020] The technical solution of this embodiment, by taking the intersection of the first target interval and the second target interval, can achieve a balance between sufficient charging coverage and richness of step events, making the finally determined target state of charge interval more suitable for the identification of current step events and subsequent feature extraction.

[0021] In an optional embodiment of the first aspect, the current stabilization time period includes a first time period before the current step event occurs and a second time period after the current step event occurs; the voltage data includes first voltage data of the first time period and second voltage data of the second time period; the current data includes first current data of the first time period and second current data of the second time period; determining the target impedance characteristics of the vehicle battery corresponding to the current step event based on the voltage data and the current data includes:

[0022] The voltage change is determined based on the difference between the average voltage value of the first voltage data and the average voltage value of the second voltage data;

[0023] The change in current is determined based on the difference between the average current value of the first current data and the average current value of the second current data;

[0024] The target impedance characteristics of the vehicle battery corresponding to the current step event are determined based on the ratio of the voltage change to the current change.

[0025] The technical solution of this embodiment uses the difference between the mean values ​​of steady-state data before and after the step to calculate the changes in voltage and current, suppressing the interference of fluctuations at a single sampling point on the impedance calculation, making the determined impedance characteristics more stable; the target impedance characteristics are determined by the ratio of voltage change to current change, which matches the actual physical response process of the battery under current step, and can accurately reflect the equivalent impedance level of the battery under the current change condition, thereby improving the accuracy of battery aging detection.

[0026] In an optional embodiment of the first aspect, determining the target impedance characteristic of the vehicle battery corresponding to the current step event based on the ratio of the voltage change to the current change includes:

[0027] The initial impedance characteristics of the vehicle battery are determined based on the ratio of the voltage change to the current change.

[0028] Obtain the actual temperature value of the vehicle battery at the time the current step event occurs;

[0029] By using a preset temperature correction model, the initial impedance characteristics at the actual temperature value are mapped to the corrected impedance characteristics at the reference temperature value.

[0030] The corrected impedance characteristics are smoothed to obtain the target impedance characteristics of the vehicle battery.

[0031] The technical solution of this embodiment maps the impedance extracted at different temperatures to a reference temperature using a temperature correction model, reducing the interference of temperature differences on impedance characteristics and making the impedance characteristics extracted under different temperature conditions comparable. The smoothing process suppresses random noise in single feature extraction, alleviates high-frequency fluctuations of random measurement noise, and improves the stability of the target impedance characteristics.

[0032] In an optional embodiment of the first aspect, smoothing the corrected impedance characteristics to obtain the target impedance characteristics of the vehicle battery includes:

[0033] Obtain multiple historical impedance characteristics of the vehicle battery within a preset time window;

[0034] Based on the statistical information of each of the historical impedance characteristics, the reference impedance characteristics are determined;

[0035] Determine the characteristic differences between the corrected impedance characteristics and the reference impedance characteristics;

[0036] If the difference in characteristics is less than or equal to a preset difference threshold, the target impedance characteristics of the vehicle battery are determined based on the average value of the corrected impedance characteristics and each of the historical impedance characteristics.

[0037] The technical solution of this embodiment introduces historical impedance characteristics as a benchmark reference, which helps to identify whether there is an abnormal deviation in the current extraction result. Under the premise that the difference is controllable, mean fusion is performed so that the target impedance characteristics are integrated with the historical trend information within the time window. This helps to alleviate the random deviation caused by the fluctuation of operating conditions in a single extraction and improves the continuity and reliability of impedance characteristic output.

[0038] In an optional embodiment of the first aspect, determining the target impedance characteristics of the vehicle battery based on the average of the corrected impedance characteristics and each of the historical impedance characteristics includes:

[0039] Based on the average value of the corrected impedance characteristics and each of the historical impedance characteristics, the candidate impedance characteristics of the vehicle battery are determined.

[0040] In the case that the current charging process of the vehicle battery includes multiple current step events, the candidate impedance characteristics corresponding to each current step event and the actual state of charge of the vehicle battery at the time of each current step event are obtained.

[0041] A battery aging feature mapping model is constructed based on each candidate impedance characteristic and each actual state of charge; the battery aging feature mapping model is used to characterize the mapping relationship between the impedance characteristics and the state of charge of the vehicle battery.

[0042] The preset state of charge corresponding to each current step event is input into the battery aging feature mapping model to obtain the target impedance characteristics of the vehicle battery corresponding to each current step event.

[0043] The technical solution of this embodiment constructs a mapping model and takes a preset state of charge as input, so that the characteristics of different charging trips can be output at a unified state of charge node. This achieves the alignment of impedance characteristics in the state of charge dimension, solves the problem that the impedance characteristics are difficult to compare directly when the current step occurs during the actual vehicle charging process due to the random drift of the state of charge position, and improves the comparability of battery aging characteristics between different charging processes.

[0044] Secondly, this application also provides a vehicle battery aging detection device, comprising:

[0045] The acquisition module is used to acquire the current parameters of the vehicle battery in the target charging mode; the target charging mode is a charging mode in which the vehicle switches the charging current amplitude step by step according to the battery status.

[0046] The identification module is used to identify current step events within a target state of charge range based on the changes in the current parameters. The target state of charge range is an effective feature extraction range determined based on the historical charging records of the vehicle battery. The current step events include events that perform a step-by-step switching operation on the charging current amplitude.

[0047] An extraction module is used to extract the steady-state data of the vehicle battery before and after the current step event occurs; the steady-state data includes voltage data and current data of the vehicle battery during a current stable period; the current stable period is the time period during which the change in current of the vehicle battery is less than a preset change threshold.

[0048] The determination module is used to determine the target impedance characteristics of the vehicle battery corresponding to the current step event based on the voltage data and the current data.

[0049] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0050] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0051] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the above aspects.

[0052] Regarding the beneficial effects of any of the technical solutions in the second to fifth aspects mentioned above, refer to the beneficial effects of the corresponding technical solutions in the first aspect; repeated examples will not be listed here. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a schematic diagram illustrating an optional application environment for a vehicle battery aging detection method in one embodiment.

[0055] Figure 2 This is a schematic diagram illustrating the relationship between impedance characteristics and battery capacity in one embodiment.

[0056] Figure 3 This is a logic diagram of a vehicle battery aging detection method in one embodiment;

[0057] Figure 4 This is a schematic diagram of an optional process for a vehicle battery aging detection method in another embodiment;

[0058] Figure 5 This is a schematic diagram of an optional structure of a vehicle battery aging detection device in one embodiment;

[0059] Figure 6 This is a schematic diagram of an optional internal structure of a new energy vehicle in one embodiment. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0061] The terms "first," "second," etc., used in this application may be used to describe various elements, but these elements are not limited by these terms. These terms are used only to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0062] The vehicle battery aging detection method provided in this application can be applied to new energy vehicles. These vehicles may include a Battery Management System (BMS), which monitors and controls the charging process of the vehicle battery, including collecting parameters such as current, voltage, temperature, and state of charge during charging. The processor of the new energy vehicle can obtain these parameters from the BMS and execute the vehicle battery aging detection method of this application based on these parameters.

[0063] In practical electric vehicle applications, to balance charging speed, thermal management, and safety, Battery Management Systems (BMS) generally employ a multi-stage constant current charging strategy. This means that during charging, the BMS gradually reduces the charging current amplitude in steps, based on factors such as the battery's state of charge (SOC), temperature, and individual cell voltage. This strategy results in the charging current no longer being a single constant value, but rather composed of multiple current steps with varying amplitudes.

[0064] However, existing technologies, when processing multi-stage constant current charging data, typically treat the step-like switching of current as interference or noise to the constant current process, often employing segmented truncation or smoothing filtering methods, ignoring the excitation value inherent in the current jumps themselves. Traditional methods have failed to establish signal processing logic for calculating battery dynamic impedance using the voltage transient response induced by current jumps, thus failing to effectively obtain high signal-to-noise ratio physical parameters under passive operating conditions. On the other hand, existing statistical features (such as the average voltage of charging segments and segment charging duration) essentially record the system-level behavior of the battery and BMS together, rather than the intrinsic properties of individual battery cells. Under multi-stage constant current charging conditions, the charging duration and voltage curve trends depend not only on the battery's health state but also on the BMS's real-time current regulation strategy based on temperature and voltage boundaries. Due to the lack of temperature correction and operating condition normalization methods based on electrochemical mechanisms, the extracted features cannot be separated from the influence of ambient temperature and BMS control logic, making it difficult to characterize the internal electrochemical degradation of the battery as an independent physical quantity.

[0065] The discontinuous current conditions in multi-stage constant current charging scenarios disrupt the data continuity prerequisite required by passive charging curve analysis methods, and active excitation testing methods, which rely on external testing equipment, cannot be applied to real-vehicle online scenarios. Therefore, the vehicle battery aging detection method in this application aims to solve the problem of how to improve the accuracy of vehicle battery aging state detection under multi-stage constant current charging conditions in real vehicles.

[0066] In one exemplary embodiment, such as Figure 1 As shown, a method for detecting vehicle battery aging is provided. Taking the application of this method to new energy vehicles as an example, the method includes the following steps:

[0067] Step 101: Obtain the current parameters of the vehicle battery in the target charging mode.

[0068] The target charging mode is a charging mode in which the vehicle switches the charging current amplitude step by step according to the battery status.

[0069] The target charging mode is a charging mode in which the vehicle gradually switches the charging current amplitude based on the battery status. Specifically, in target charging mode, the BMS determines whether to adjust the charging current amplitude based on the current state of the battery (including state of charge, temperature, and individual cell voltage). When the battery's state of charge reaches a preset switching threshold, or the battery temperature or individual cell voltage reaches a preset safety boundary, the BMS switches the current charging current amplitude to the next level, usually reducing the charging current from the current amplitude to a lower amplitude, in order to continue charging while ensuring charging safety. This switching operation results in a stepped change in the current throughout the charging process; that is, the charging current remains constant at a certain amplitude for a period of time, then changes at the switching point and remains constant at the new amplitude, and so on step by step until charging is completed.

[0070] Among them, current parameters refer to current-related parameters of the vehicle battery during charging in the target charging mode, including real-time sampled values ​​of the charging current and current sampling timestamps. New energy vehicles can read the battery current sampling data during the charging process from the BMS as current parameters.

[0071] Optionally, in addition to acquiring current parameters, voltage, temperature, and state of charge (SOC) parameters of the vehicle battery during the charging process can also be acquired simultaneously. Voltage parameters refer to the real-time sampled values ​​of the battery terminal voltage; temperature parameters refer to the real-time sampled values ​​of the battery temperature during charging; and SOC parameters refer to the real-time estimated value of the percentage of the battery's current remaining charge relative to its full capacity. These parameters can also be read from the BMS and used for identifying current step events and determining target impedance characteristics in subsequent steps. For example, when the battery is detected to be in a multi-stage constant current charging mode, a preset current switching range can be locked in the BMS. For instance, current switching may occur near specific SOC nodes, such as 50%, 60%, 70%, and 80% SOC.

[0072] Step 102: Identify current step events within the target state of charge range based on the changes in current parameters.

[0073] The target state of charge (SOC) range is the effective feature extraction range determined based on the vehicle battery's historical charging records. The purpose of this target SOC range is to limit the identification of current step events to SOC ranges validated by historical data, avoiding indiscriminate searching across the entire charging range. Since different SOC ranges are covered at varying frequencies during actual charging, and the frequency of current step events varies within different SOC ranges, limiting the identification range to the target SOC range allows for identification within SOC ranges frequently covered by actual charging behavior and where current step events occur more frequently, thereby improving the effectiveness of feature extraction.

[0074] Among them, current step events include events that perform a step-by-step switching operation on the amplitude of the charging current. That is, when the BMS switches the charging current from the current amplitude to another amplitude during the charging process, this switching operation constitutes a current step event. In terms of current parameters, a current step event is characterized by a significant change in the amplitude of the current sample value between adjacent sampling times, that is, the current value jumps from one relatively stable level to another relatively stable level.

[0075] In specific implementations, identifying current step events based on changes in current parameters can include: calculating the current difference between adjacent sampling times; when this current difference exceeds a preset threshold, a potential current step event is preliminarily determined to have been detected at that sampling time. The threshold can be set based on the typical current change that occurs when the BMS switches current amplitude in actual charging scenarios. Its purpose is to filter out minor fluctuations caused by current ripple or sensor noise, ensuring that the detected signal is a BMS-triggered level switching signal. For example, when the BMS switches the charging current from 100A to 80A, the current difference is 20A. If the threshold is set to 15A, this difference exceeds the threshold, and a potential current step event is preliminarily determined to have been detected.

[0076] In practice, identifying current step events within the target state of charge (SOC) range means that the aforementioned current step event detection operation is only performed when the battery's current SOC is within the target SOC range. If the battery's current SOC is not within the target SOC range, the current step event detection may not be performed, or even if the detected current difference exceeds the jump threshold, it may not be identified as a current step event.

[0077] Step 103: Extract the steady-state data of the vehicle battery before and after the current step event.

[0078] The battery steady-state data includes voltage and current data of the vehicle battery during a period of stable current. The stable current period is the time during which the change in the vehicle battery current is less than a preset threshold. In other words, during the stable current period, the battery charging current remains relatively constant within a certain range, with very small fluctuations and no significant jumps.

[0079] The preset change threshold is a criterion used to determine whether the current is in a stable state. It can be set according to the actual sampling accuracy and the normal fluctuation range of the current in the charging scenario. When the difference between the maximum and minimum current values ​​within a certain period is less than the preset change threshold, that period can be determined as a stable current period.

[0080] The purpose of extracting steady-state battery data before and after a current step event is to obtain voltage and current information of the battery in a stable charging state before and after the current step event, so as to calculate impedance characteristics based on this steady-state data. Data from the current stabilization period is needed because during the transient process immediately following the current jump, the battery voltage is affected by polarization effects and experiences transient fluctuations. The voltage value at this time cannot accurately reflect the true terminal voltage of the battery under stable current. Only after the current stabilizes at the new amplitude for a period of time does the voltage tend to stabilize, and the recorded voltage data at this time can be used to accurately calculate the battery's equivalent impedance.

[0081] In specific implementation, the current stabilization period includes a first time period before the current step event and a second time period after the current step event. Voltage data includes the first voltage data of the first time period and the second voltage data of the second time period, and current data includes the first current data of the first time period and the second current data of the second time period. Specifically, the time of the current step event can be used as a boundary, extracting a segment of sampled data before that time as the first time period and a segment of sampled data after that time as the second time period. The lengths of the first and second time periods can be set according to the actual sampling frequency and the time required for current stabilization; for example, they can be set to the time periods corresponding to the 20 sampling points before and after the current step event.

[0082] Then, a bidirectional steady-state verification can be performed on the data within the first and second time periods. For example, the difference between the maximum and minimum current values ​​(i.e., the current range) within the first time period and the difference between the maximum and minimum current values ​​within the second time period are calculated respectively. If the current range of both the first and second time periods is less than a preset change threshold, then both the first and second time periods are determined to be current stable periods, and the current step event is a valid event. The voltage and current data within these two time periods can then be extracted as battery steady-state data. If the current range of at least one time period is greater than or equal to the preset change threshold, then the current within that time period is determined to have not reached a stable state, and the current step event may have been affected by transient interference. Therefore, the battery steady-state data corresponding to this event does not need to be extracted. The above bidirectional steady-state verification mechanism performs steady-state checks on the data before and after the jump point, which can eliminate one-time transient ripple interference and ensure the physical validity of passively extracted features.

[0083] For example, the preset change threshold can be set to 2 amperes (A). The first time period can be selected from the 20 sampling points before the occurrence of the current step event, and the second time period can be selected from the 20 sampling points after the occurrence of the current step event. The current range in the two time periods can be calculated respectively. If the current range in both time periods is less than 2A, the current step event is determined to be valid, and the corresponding battery steady-state data is extracted.

[0084] Alternatively, other methods can be used to determine whether the current is in a stable state. For example, the standard deviation of the current sample values ​​over a certain time period can be calculated, and the current is determined to be in a stable state when the standard deviation is less than a preset standard deviation threshold. Another example is to calculate the absolute value of the current difference between adjacent sampling points over a certain time period, and the current is determined to be in a stable state when the absolute value of all adjacent differences is less than a preset difference threshold.

[0085] Step 104: Based on the voltage and current data, determine the target impedance characteristics of the vehicle battery corresponding to the current step event.

[0086] The target impedance characteristic is used to reflect the aging status of the vehicle battery. In practice, the equivalent impedance of the vehicle battery at a current step event can be calculated based on the voltage and current data in the battery's steady-state data, and this equivalent impedance is used as the target impedance characteristic.

[0087] Since the equivalent impedance of a battery is directly related to its internal ohmic resistance and polarization characteristics, as the battery ages, the thickening of the solid electrolyte interface film and the degradation of the electrode material structure will lead to an increase in the equivalent impedance. Therefore, the target impedance characteristics can reflect the aging status of the battery.

[0088] In the aforementioned vehicle battery aging detection method, by acquiring the current parameters of the vehicle battery under a charging mode where the charging current amplitude switches stepwise according to the battery state, and identifying current step events, the inherent current amplitude switching during the actual vehicle charging process can be used as a usable excitation source. This allows for the acquisition of characteristic data for aging detection under actual vehicle online operating conditions without the need for additional hardware or interruption of the charging process, reducing reliance on offline testing conditions. Then, based on the changes in current parameters, current step events are identified within the target state of charge range, enabling feature extraction within valid state of charge ranges verified through historical charging records, thus improving the accuracy of battery aging detection. The effectiveness of feature extraction is further enhanced by extracting steady-state battery data before and after a current step event. Based on the voltage and current data in the steady-state battery data, the target impedance characteristics of the vehicle battery are determined. Since the steady-state battery data is extracted when the vehicle battery is in a stable state where the current change is less than a preset threshold, the interference of transient fluctuations on feature calculation is reduced. Moreover, the impedance characteristics are directly related to the ohmic impedance and polarization characteristics inside the vehicle battery, exhibiting strong physical interpretability and robustness under operating conditions. This allows for the effective extraction of features that accurately reflect the battery aging state, thereby improving the accuracy of vehicle battery aging state detection under real-world multi-stage constant current charging conditions.

[0089] In an exemplary embodiment, before identifying current step events within the target state of charge interval based on changes in current parameters, the method further includes: acquiring historical charging records corresponding to the vehicle battery in multiple historical charging processes; based on each historical charging record, calculating the charging frequency of the vehicle battery in each state of charge interval and the number of current step events occurring in each state of charge interval; and determining the target state of charge interval from each state of charge interval based on the charging frequency and the number of occurrences.

[0090] Each historical charging record includes the state of charge (SOC) range of the vehicle battery during each historical charging process. Specifically, it collects full charging data for the vehicle battery throughout the historical period, extracting the initial and final SOC of each charging cycle. In other words, for each charging cycle completed by the vehicle battery in the past, the range of SOC change during that charging cycle can be recorded. For example, if a charging cycle starts at 30% SOC and ends at 85% SOC, then the corresponding SOC range for that charging cycle is 30% to 85%.

[0091] The charging frequency is used to characterize the frequency with which the vehicle battery is in a state of charge (SOC) interval during various historical charging processes. Specifically, the entire SOC range can be divided into multiple equally spaced SOC intervals. Then, for each SOC interval, the proportion of historical charging processes that occurred within that interval to the total number of historical charging processes is counted, or the number of historical charging processes that occurred within that interval is counted. This count is used as the charging frequency corresponding to that SOC interval. Optionally, a probability density distribution function for charging trips can be established based on each historical charging record, and the coverage frequency of different SOC intervals across all charging trips can be counted. This probability density distribution function describes the probability distribution of each SOC interval being covered by charging processes.

[0092] The higher the charging frequency, the more times the state of charge range is covered in the actual charging process. Users' daily charging behavior is usually concentrated in this state of charge range. Feature extraction in this range can obtain more effective and richer aging feature data.

[0093] The number of current step events occurring in each state of charge (SOC) interval refers to the total number of current step events that occurred during all historical charging processes within each SOC interval. Specifically, historical charging records can be traversed, and for each SOC interval, the cumulative number of current step events detected within that interval can be used as the occurrence count for that interval. Optionally, the spatial density distribution of current step events can be calculated, i.e., the number of current jumps occurring within a unit SOC change range. This spatial density distribution reflects the frequency at which the BMS triggers current switching operations at different SOC stages.

[0094] The higher the number of occurrences, the higher the frequency of BMS triggering current switching operations within the state of charge range, and the greater the possibility of identifying current step events within this range.

[0095] In practical applications, the target state of charge range can be determined by filtering each state of charge range based on two dimensions: charging frequency and the number of occurrences.

[0096] In one embodiment, a target state of charge interval is determined from each state of charge interval based on the charging frequency and the number of occurrences, including: determining the state of charge interval with a charging frequency higher than a first threshold as a first target interval, and determining the state of charge interval with a number of occurrences higher than a second threshold as a second target interval; and determining the target state of charge interval based on the intersection of the first target interval and the second target interval.

[0097] The first and second thresholds can be set based on the actual data distribution. The first threshold can be set to select the intervals where the charging frequency ranks among the top certain percentages. For example, the first threshold can be set to the 90th quantile of the charging frequency, i.e., based on the probability density distribution model of charging trips, the smallest SOC union interval that can cover the vast majority of charging behaviors within a 90% confidence level is calculated, and this interval is determined as the first target interval. The second threshold can be set to select the intervals where the occurrence frequency ranks among the top certain percentages. Subsequently, the target state of charge interval is determined based on the intersection of the first and second target intervals. For example, if the state of charge intervals where the charging frequency is higher than the first threshold include 30%~40%, 40%~50%, and 50%~60%, then the first target interval is 30%~60%; if the state of charge intervals where the occurrence frequency is higher than the second threshold include 40%~50% and 50%~60%, then the second target interval is 40%~60%. Therefore, the intersection of 30%~60% and 40%~60% is taken as 40%~60%.

[0098] The above determination method is a data-driven interval optimization mechanism. By jointly analyzing the coverage distribution of historical charging behavior and the spatial density of current steps, the optimal state of charge window for feature extraction is determined, so as to balance the richness of features and the success rate of extraction.

[0099] The intersection operation described above ensures that the target state of charge (SOC) interval simultaneously satisfies two conditions: firstly, the interval is covered sufficiently frequently during actual charging, guaranteeing ample charging data for feature extraction; secondly, the interval experiences a sufficient number of current step events, ensuring frequent identification of these events. Selecting an interval solely based on charging frequency might result in selecting intervals frequently covered by charging behavior but with few current step events, making it difficult to identify such events. Conversely, selecting an interval solely based on the number of occurrences might result in selecting intervals with frequent current step events but limited coverage during actual charging, leading to a lack of charging data for feature extraction despite the presence of step events. By taking the intersection, a balance is struck between sufficient charging coverage and abundant step events, making the final target SOC interval more suitable for current step event identification and subsequent feature extraction.

[0100] For example, based on statistical results from a large amount of historical real-vehicle data, users' daily charging behavior is concentrated in the 40% to 90% SOC range, meaning the charging frequency in this range is higher than a first threshold, and is therefore identified as the first target range. Simultaneously, the multi-stage constant current charging strategy exhibits the highest current step density in the 40% to 95% SOC range, meaning the frequency of occurrence in this range is higher than a second threshold, and is therefore identified as the second target range. The intersection of these two ranges, 40% to 90% SOC, is identified as the target state of charge range. This target state of charge range ensures that it represents both the range of electricity most frequently used by users, guaranteeing the continuity of feature extraction, and the region where BMS current regulation is most active. Sufficient excitation signals provide favorable conditions for passive impedance feature extraction.

[0101] In other embodiments, the target state of charge (SOC) interval can also be determined based on the charging frequency and the number of occurrences using other methods. For example, a weighted score of the charging frequency and the number of occurrences can be calculated for each SOC interval, and the SOC interval with the weighted score higher than a preset score threshold can be determined as the target SOC interval. Alternatively, the charging frequency and the number of occurrences can be normalized separately and then summed, and the SOC interval with the highest normalized sum can be determined as the target SOC interval.

[0102] In an exemplary embodiment, the current stabilization period includes a first time period before the current step event and a second time period after the current step event; the voltage data includes first voltage data for the first time period and second voltage data for the second time period; the current data includes first current data for the first time period and second current data for the second time period; based on the voltage data and current data, the target impedance characteristics of the vehicle battery corresponding to the current step event are determined, including:

[0103] The voltage change is determined based on the difference between the average voltage value of the first voltage data and the average voltage value of the second voltage data; the current change is determined based on the difference between the average current value of the first current data and the average current value of the second current data; and the target impedance characteristics of the vehicle battery corresponding to the current step event are determined based on the ratio of the voltage change to the current change.

[0104] In the specific implementation, the average voltage of the first voltage data within the first time period and the average voltage of the second voltage data within the second time period are calculated respectively. The average voltage refers to the arithmetic mean of all voltage samples within that time period. For example, if the first time period contains N sampling points, the average voltage of the first voltage data is the sum of the voltage values ​​of those N sampling points divided by N; if the second time period contains M sampling points, the average voltage of the second voltage data is the sum of the voltage values ​​of those M sampling points divided by M.

[0105] Subsequently, the difference between the average voltage of the second voltage data and the average voltage of the first voltage data is calculated, i.e., the voltage change. This voltage change reflects the magnitude of the battery voltage change before and after the current step event. Using the average value to calculate the voltage change can suppress the interference of fluctuations at individual sampling points on the calculation results, making the determined voltage change more accurate.

[0106] Similarly, the average current value of the first current data within the first time period and the average current value of the second current data within the second time period are calculated respectively. The average current value refers to the arithmetic mean of all current samples within that time period. Subsequently, the difference between the average current value of the first current data and the average current value of the second current data is calculated, i.e., the current change. This current change reflects the magnitude of the change in the charging current amplitude before and after the current step event.

[0107] Then, the ratio of the voltage change to the current change is calculated; this ratio is the battery's equivalent impedance corresponding to a current step event. According to Ohm's law, when the current changes, the ratio of the corresponding change in battery terminal voltage to the change in current reflects the battery's equivalent impedance.

[0108] For example, with U pre U represents the average voltage value of the first voltage data. post I represents the average voltage value of the second voltage data. post I represents the average current value of the first current data. pre Let the average current value of the second current data represent the equivalent impedance R, then the equivalent impedance R can be expressed as:

[0109] .

[0110] The equivalent impedance R described above characterizes the total equivalent impedance of a battery under a specific state of charge. Its physical essence includes the battery's ohmic internal resistance and rapid polarization resistance. This impedance characteristic directly reflects the difficulty of lithium-ion insertion and extraction in the electrode material and the ionic conductivity of the electrolyte. As the battery ages (i.e., the state of health (SOH) decreases), the thickening of the solid electrolyte interfacial film and the degradation of the material structure lead to a monotonically increasing trend in this impedance characteristic value. Therefore, it is an ideal physical indicator for characterizing the battery's health. Optionally, while calculating the equivalent impedance R, the average SOC value and battery temperature corresponding to this characteristic point can also be recorded to form an original feature vector for use in subsequent steps.

[0111] In one optional implementation, the ratio of the voltage change to the current change can be directly determined as the target impedance characteristic. In another optional implementation, this ratio can be further corrected for temperature and smoothed before being determined as the target impedance characteristic, thereby improving the accuracy and stability of the target impedance characteristic.

[0112] In an exemplary embodiment, determining the target impedance characteristics of the vehicle battery corresponding to a current step event based on the ratio of voltage change to current change includes: determining the initial impedance characteristics of the vehicle battery based on the ratio of voltage change to current change; obtaining the actual temperature value of the vehicle battery when the current step event occurs; mapping the initial impedance characteristics at the actual temperature value to the corrected impedance characteristics at the reference temperature value using a preset temperature correction model; and smoothing the corrected impedance characteristics to obtain the target impedance characteristics of the vehicle battery.

[0113] The initial impedance characteristics are calculated directly from the original steady-state data without any temperature correction or smoothing. While these initial impedance characteristics already reflect the battery's equivalent impedance under this current step event, the temperature sensitivity of the battery impedance means that the extracted initial impedance characteristics may differ under different temperature conditions, thus requiring further processing.

[0114] In practice, the temperature sample value of the vehicle battery at the moment of the current step event can be obtained from the BMS as the actual temperature value. This actual temperature value reflects the temperature conditions under which the battery is located when extracting the initial impedance characteristics. Then, through a preset temperature correction model, the initial impedance characteristics at the actual temperature value are mapped to the corrected impedance characteristics at the reference temperature value.

[0115] The temperature correction model is used to characterize the mapping relationship between battery impedance and temperature. Because the internal impedance of a battery is highly sensitive to temperature, the initial impedance characteristics extracted at different ambient temperatures are not directly comparable. For example, the initial impedance characteristics extracted from the same battery in a high-temperature summer environment may be lower than those extracted in a low-temperature winter environment; this difference is caused by temperature rather than changes in aging. Therefore, the temperature correction model maps the initial impedance characteristics at the actual temperature value to a reference temperature value, obtaining corrected impedance characteristics, making the impedance characteristics extracted under different temperature conditions comparable.

[0116] The reference temperature is a pre-set fixed temperature value used as a reference temperature for impedance characteristic normalization, and its unit is Kelvin. For example, the reference temperature can be set to 298.15K (i.e., 25 degrees Celsius).

[0117] Optionally, the temperature correction model can be a model built on the principle of electrochemical kinetics, such as a correction model based on the Arrhenius law. This model uses parameters such as the battery's equivalent activation energy and Boltzmann constant to establish an exponential relationship between battery impedance and temperature, thereby mapping the impedance value at the measured temperature to the impedance value at the reference temperature.

[0118] For example, a modified model based on Arrhenius's law may include:

[0119] ;

[0120] .

[0121] Where factor is the temperature correction factor; E a is the battery's equivalent activation energy; k is Boltzmann's constant; T0 is the reference temperature, T is the actual temperature, and the unit for temperature-related parameters is Kelvin; R meas The initial impedance characteristic; R cal This is the corrected impedance characteristic. Through this correction, the initial impedance characteristics extracted under different temperature conditions are uniformly mapped to the reference temperature value, which helps to reduce impedance fluctuations caused by differences in ambient temperature, and makes the changes in the corrected impedance characteristics more accurately reflect the degree of battery aging.

[0122] The equivalent activation energy E in the above temperature correction model a The reference temperature value T0 can be determined through offline parameter optimization. Specifically, parameter optimization is performed using a historical dataset containing the initial impedance characteristics of the vehicle battery under different temperature conditions and the corresponding actual battery capacity. The equivalent activation energy E is jointly optimized with the objective of minimizing the linear regression residual between the battery capacity and the temperature-corrected impedance characteristics. a and the reference temperature value T0. That is, at different E... a Under the T0 value, the initial impedance characteristics in the historical dataset are temperature-corrected to obtain the corrected impedance characteristics. Then, the linear regression residual between the battery capacity and the corrected impedance characteristics is calculated. When this linear regression residual reaches its minimum value, the corresponding E... a T0 represents the optimized parameter values. The principle is that if the temperature correction model can effectively isolate the interference of temperature on the impedance characteristics, then the corrected impedance characteristics should only reflect the degree of battery aging, and the battery capacity should also reflect the degree of battery aging; the two should exhibit a high linear correlation. Therefore, minimizing the linear regression residual means that the linear correlation between the corrected impedance characteristics and the battery capacity is strongest, i.e., the temperature correction effect is optimal; the optimized E... a T0 is used as a fixed parameter in the temperature correction model for subsequent temperature correction calculations during online feature extraction of the actual vehicle.

[0123] Alternatively, the temperature correction model can also be constructed using a data-driven approach. For example, a multidimensional lookup table between temperature and impedance can be established, mapping the initial impedance characteristics at the actual temperature to the corrected impedance characteristics at the reference temperature. Another example is using a multinomial regression model to fit the temperature coefficient, multiplying the initial impedance characteristics at the actual temperature by the corresponding temperature coefficient and mapping them to the corrected impedance characteristics at the reference temperature. These methods also achieve the temperature correction function, only using different model forms.

[0124] Next, the corrected impedance characteristics can be smoothed to obtain the target impedance characteristics of the vehicle battery. Although the corrected impedance characteristics have been temperature-corrected, outliers caused by random noise may still exist due to limitations in the measurement accuracy of the actual vehicle sensors and transient interference during charging. Therefore, the corrected impedance characteristics can be smoothed to suppress the interference of random noise and improve the stability of the target impedance characteristics.

[0125] In one embodiment, the corrected impedance characteristics are smoothed to obtain the target impedance characteristics of the vehicle battery, including: acquiring multiple historical impedance characteristics of the vehicle battery within a preset time window; determining a reference impedance characteristic based on the statistical information of each historical impedance characteristic; determining the characteristic difference between the corrected impedance characteristics and the reference impedance characteristics; and determining the target impedance characteristics of the vehicle battery based on the average value of the corrected impedance characteristics and each historical impedance characteristic when the characteristic difference is less than or equal to a preset difference threshold.

[0126] The preset time window is a fixed period of time that traces backward from the current moment, such as 30 days. In practice, multiple historical impedance features extracted and stored within this preset time window can be retrieved from memory. These historical impedance features can be impedance features extracted and saved after multiple charging processes of the vehicle battery over a past period.

[0127] The statistical information refers to descriptive quantities obtained after statistical analysis of multiple historical impedance features, including but not limited to the arithmetic mean, median, variance, and standard deviation. For example, the arithmetic mean of multiple historical impedance features can be calculated and used as the baseline impedance feature. For example, if a preset time window contains n valid historical impedance feature data points, these historical impedance features constitute a valid feature set. The i-th historical impedance characteristic data point is denoted as R. i Then the reference impedance characteristic It can be represented as:

[0128] .

[0129] The baseline impedance characteristic reflects the average impedance level of the vehicle battery over a recent period and can be used as a reference to judge whether the currently extracted corrected impedance characteristic is abnormal.

[0130] The difference between the corrected impedance characteristic and the reference impedance characteristic can then be calculated as the feature difference. For example, this feature difference could be the absolute value of the difference between the corrected impedance characteristic and the reference impedance characteristic. The feature difference reflects the degree to which the currently extracted corrected impedance characteristic deviates from its recent historical average.

[0131] The preset difference threshold is used to determine whether the current corrected impedance characteristic belongs to an abnormal deviation. It can be determined based on the deviation distribution statistics of historical data. When the feature difference is less than or equal to the preset difference threshold, it means that the deviation between the current corrected impedance characteristic and the recent historical average level is within an acceptable range, and the corrected impedance characteristic does not belong to an abnormal outlier. In this case, the arithmetic mean of the corrected impedance characteristic and each historical impedance characteristic can be calculated, and this average value can be determined as the target impedance characteristic of the vehicle battery. By fusing the mean of the current corrected impedance characteristic and the historical impedance characteristics, the final determined target impedance characteristic can incorporate historical trend information within the time window, mitigating the random deviation caused by operating condition fluctuations in a single extraction and improving the continuity and consistency of feature output.

[0132] When the difference in characteristics exceeds a preset threshold, it indicates that the current corrected impedance characteristic deviates significantly from the recent historical average level, potentially representing an outlier. In this case, the corrected impedance characteristic can be discarded and not used to determine the target impedance characteristic. For example, the baseline impedance characteristic (i.e., the arithmetic mean of historical impedance characteristics) can be directly determined as the target impedance characteristic, or the previously determined target impedance characteristic can be kept unchanged until a reasonable corrected impedance characteristic is extracted during the next charging process before updating.

[0133] The above smoothing process can effectively suppress high-frequency fluctuations of random measurement noise, significantly improve the linear correlation between impedance characteristics and the actual SOH of the battery, and make the characteristic trend more stable and monotonic.

[0134] In an exemplary embodiment, when the current charging process of the vehicle battery includes multiple current step events, the impedance characteristics corresponding to each current step event can be further aligned in the state of charge dimension to improve the comparability of degradation characteristics between different charging trips. Specifically, the target impedance characteristics of the vehicle battery are determined based on the average of the corrected impedance characteristics and the historical impedance characteristics, including: determining candidate impedance characteristics of the vehicle battery based on the average of the corrected impedance characteristics and the historical impedance characteristics; when the current charging process of the vehicle battery includes multiple current step events, obtaining the candidate impedance characteristics corresponding to each current step event and the actual state of charge of the vehicle battery at the time of each current step event; constructing a battery aging feature mapping model based on each candidate impedance characteristic and each actual state of charge; the battery aging feature mapping model is used to characterize the mapping relationship between the impedance characteristics and the state of charge of the vehicle battery; and inputting the preset state of charge corresponding to each current step event into the battery aging feature mapping model to obtain the target impedance characteristics of the vehicle battery corresponding to each current step event.

[0135] The candidate impedance feature is a smoothed impedance feature that has not yet been aligned along the state of charge dimension. Specifically, the average value of the corrected impedance feature and each historical impedance feature can be calculated, and this average value is used as the candidate impedance feature. For each current step event in the current charging process, a corresponding candidate impedance feature can be obtained.

[0136] During the current charging process, the BMS may perform multiple current amplitude switching operations, thus enabling the identification of multiple current step events and the determination of candidate impedance characteristics corresponding to each current step event. Simultaneously, the actual state of charge of the battery at the time each current step event occurs can also be obtained.

[0137] The actual state of charge (SOC) refers to the battery's actual SOC value at the moment a current step event occurs. Because the specific SOC position of each current step event during actual vehicle charging is affected by transient load fluctuations, the actual SOC at the time of the current step event may differ in different charging stages. This leads to inconsistencies in the SOC points corresponding to the candidate impedance features extracted between different charging stages, making direct horizontal comparison difficult. For example, when switching to constant current mode at a preset 50%, the actual capture point may be 49% or 52%, resulting in inconsistencies in the SOC points corresponding to the candidate impedance features extracted between different charging stages, making direct horizontal comparison difficult.

[0138] Therefore, a battery aging feature mapping model can be constructed based on each candidate impedance characteristic and each actual state of charge. The battery aging feature mapping model is used to characterize the mapping relationship between the impedance characteristics and the state of charge of the vehicle battery.

[0139] In practical implementation, the correspondence between multiple sets of candidate impedance features extracted during the current charging process and the actual state of charge (SOC) can be used as the data foundation to construct a continuous mapping function. This mapping function can calculate the corresponding impedance feature value based on any input SOC value within the range of the actual SOC value. Since battery impedance exhibits a continuous and slow change with SOC, a battery aging feature mapping model can be constructed using function fitting. For example, a polynomial function fitting method can be used, with candidate impedance features as the dependent variable and the actual SOC as the independent variable. The fitting coefficients are determined using the least squares method, thereby establishing a continuous mapping relationship between impedance features and SOC. For example, a cubic polynomial function can be used for fitting to construct the following impedance-SOC mapping function:

[0140] ;

[0141] Where s represents the state of charge variable, a0, a1, a2, and a3 are fitting coefficients, and R(s) is the impedance characteristic value under state of charge s. This fitting process establishes a continuous mapping relationship between candidate impedance characteristics and state of charge, fills the gaps between discrete sampling points, and initially smooths out single-point measurement noise.

[0142] Alternatively, other methods can be used to construct the battery aging characteristic mapping model. For example, piecewise linear interpolation can be used to establish a linear mapping relationship between two adjacent measured data points; or cubic spline interpolation can be used to establish a smooth curve mapping relationship between two adjacent measured data points. These methods can also establish a mapping relationship between impedance characteristics and state of charge.

[0143] Then, the preset state of charge corresponding to each current step event can be input into the battery aging feature mapping model to obtain the target impedance characteristics of the vehicle battery corresponding to each current step event.

[0144] The preset state of charge (SOC) is a pre-defined standard SOC value, which serves as the reference SOC point for impedance characteristic normalization. Specifically, the preset SOC can be used as input to a battery aging characteristic mapping model. This model calculates the impedance characteristic value corresponding to the preset SOC, and this impedance characteristic value is determined as the target impedance characteristic of the vehicle battery.

[0145] In practical implementation, a unified SOC standard feature point grid can be set, which contains multiple preset state of charge values. For example, the SOC standard feature point grid can be set to fixed SOC values ​​such as 50%, 60%, 70%, and 80%. Each preset state of charge value in the above standard feature point grid is sequentially substituted into the battery aging feature mapping model to calculate the impedance characteristic value corresponding to each preset state of charge value, which is used as the target impedance characteristic. For example, if the battery aging feature mapping model is as described above... Then, substitute the preset state of charge value s into the function. The corresponding target impedance characteristics are calculated.

[0146] Through the above method, regardless of the specific SOC position where the current step event occurs during actual vehicle charging, the target impedance feature corresponding to the preset state of charge is ultimately output. This allows the target impedance features extracted across different charging stages to be compared at a unified SOC node, achieving alignment of impedance features along the state of charge dimension and improving the comparability of degradation features across different charging stages. The final target impedance feature obtained, after temperature decoupling, historical statistical filtering, and SOC position alignment, exhibits high monotonicity and physical stability, and can be directly used as a quantitative indicator of battery SOH.

[0147] For example, during the current charging process, three current step events were identified at SOC levels of 49%, 61%, and 72%, and corresponding candidate impedance features were determined. Using these three candidate impedance features and their corresponding actual states of charge as the data basis, a battery aging feature mapping model was constructed by fitting a cubic polynomial function. Subsequently, the preset states of charge (50%, 60%, and 70%) from the SOC standard feature point grid were substituted into the model to obtain the target impedance features corresponding to 50%, 60%, and 70% SOC. In this way, even if the SOC positions of the current step events shift during different charging stages, the final output target impedance features all correspond to the same preset state of charge, allowing for direct comparison of features across different charging stages.

[0148] For the ease of understanding of those skilled in the art, Figure 2An exemplary diagram illustrating the relationship between impedance characteristics and battery capacity is provided. For example, real-vehicle verification can be performed on an electric vehicle equipped with a 114Ah ternary lithium battery. Target impedance characteristics, after temperature correction and SOC alignment, are extracted at multiple key SOC points (50%, 60%, 70%, 80%), and their correlation with the actual battery capacity calculated based on the ampere-hour integration method is analyzed. Verification results show that at each SOC extraction point, the target impedance characteristics and battery capacity exhibit a negative correlation, with the absolute value of the Pearson correlation coefficient all above 0.95. This indicates that the target impedance characteristics accurately reflect the physical degradation trend of the battery and possess excellent linearity and stability, meeting the accuracy requirements for real-vehicle online degradation assessment.

[0149] For the ease of understanding of those skilled in the art, Figure 3 An exemplary logic diagram of a vehicle battery aging detection method is provided. In its implementation, the method first monitors real-time vehicle charging data, acquiring relevant operating parameters such as current, voltage, temperature, and state of charge of the vehicle battery during the charging process. During monitoring, it continuously determines whether a current switch is detected. Specifically, it continuously identifies whether a step event in the charging current meets a preset threshold based on changes in current parameters. If no current switch is detected, the process loops back to continue monitoring real-time vehicle charging data; if a current switch is detected, the process enters a steady-state check phase.

[0150] Next, the stability of the current is checked. In this step, bidirectional steady-state verification is performed on the data before and after the current switching event to eliminate transient ripple interference. If the current is determined to be unstable, the transient data set is discarded and monitoring continues; if the current is determined to be stable (i.e., battery steady-state data with current change less than a preset change threshold is successfully obtained), the steady-state impedance characteristic is extracted based on the ratio of steady-state voltage difference to current difference, and the initial equivalent impedance corresponding to the current step event is calculated and recorded.

[0151] After feature extraction, it is further determined whether the charging process has ended. If the current charging process has not yet ended, the process returns to the real vehicle data monitoring terminal to continuously capture other current step events that may occur during the multi-stage constant current charging process; if the current charging process has ended, the feature set extracted during this charging trip is transferred to the subsequent data processing module.

[0152] In the subsequent data processing and standardization stages, a historical battery dataset is introduced, and the following feature optimization processes are performed sequentially:

[0153] Temperature-based feature calibration: By using a preset temperature correction model (such as a model based on Arrhenius's law), the impedance features extracted at the actual temperature value are uniformly mapped and calibrated to the reference temperature value, thereby achieving decoupling from the ambient temperature.

[0154] Feature cleaning based on time windows: Combine effective feature data within historical time windows to calculate local statistical benchmarks, remove outlier feature points that deviate abnormally, and smooth the remaining features to suppress random measurement noise.

[0155] Feature SOC position standardization resampling: To address the issue of random offset of the actual vehicle current switching point, a continuous battery aging feature mapping model is constructed to resample the discrete impedance features and align them to a unified standard preset state of charge grid.

[0156] Finally, the process reaches its end, outputting the battery degradation characteristics. The target impedance characteristics, after the aforementioned multiple decoupling and standardization processes, exhibit high physical stability and can be directly output for subsequent high-precision quantitative assessment of vehicle battery aging.

[0157] In another embodiment, such as Figure 4 As shown, a method for detecting vehicle battery aging is provided. Taking the application of this method to new energy vehicles as an example, the method includes the following steps:

[0158] Step S401: Obtain the current parameters of the vehicle battery in the target charging mode. The target charging mode is a charging mode in which the vehicle switches the charging current amplitude step by step according to the battery status.

[0159] Step S402: Obtain the historical charging records corresponding to the vehicle battery in multiple historical charging processes. Based on each historical charging record, count the charging frequency of the vehicle battery in each state of charge interval and the number of current step events in each state of charge interval. Based on the charging frequency and the number of occurrences, determine the target state of charge interval from each state of charge interval.

[0160] Each historical charging record includes the state of charge (SOC) range of the vehicle battery during each historical charging process; the charging frequency is used to characterize the frequency at which the vehicle battery is in the SOC range during each historical charging process.

[0161] Step S403: Identify current step events within the target state of charge range based on changes in current parameters.

[0162] The target state of charge range is the effective feature extraction range determined based on the vehicle battery's historical charging records; current step events include events that perform step-by-step switching operations on the charging current amplitude.

[0163] Step S404: Extract the steady-state data of the vehicle battery before and after the current step event.

[0164] The battery steady-state data includes voltage and current data of the vehicle battery during a period of stable current; the period of stable current is the time during which the change in current of the vehicle battery is less than a preset change threshold.

[0165] Step S405: Determine the voltage change based on the difference between the average voltage value of the first voltage data and the average voltage value of the second voltage data; determine the current change based on the difference between the average current value of the first current data and the average current value of the second current data.

[0166] Step S406: Determine the initial impedance characteristics of the vehicle battery based on the ratio of voltage change to current change; obtain the actual temperature value of the vehicle battery when a current step event occurs; and map the initial impedance characteristics at the actual temperature value to the corrected impedance characteristics at the reference temperature value using a preset temperature correction model.

[0167] Step S407: Obtain multiple historical impedance characteristics of the vehicle battery within a preset time window; determine a reference impedance characteristic based on the statistical information of each historical impedance characteristic; determine the characteristic difference between the corrected impedance characteristic and the reference impedance characteristic; if the characteristic difference is less than or equal to a preset difference threshold, determine the candidate impedance characteristics of the vehicle battery based on the average value of the corrected impedance characteristic and each of the historical impedance characteristics.

[0168] Step S408: When the current charging process of the vehicle battery includes multiple current step events, obtain the candidate impedance characteristics corresponding to each current step event and the actual state of charge of the vehicle battery at the occurrence of each current step event; construct a battery aging feature mapping model based on each candidate impedance characteristic and each actual state of charge; input the preset state of charge corresponding to each current step event into the battery aging feature mapping model to obtain the target impedance characteristics of the vehicle battery corresponding to each current step event.

[0169] Among them, the battery aging feature mapping model is used to characterize the mapping relationship between the impedance characteristics and the state of charge of a vehicle battery.

[0170] The target impedance characteristic is used to reflect the aging status of the vehicle battery.

[0171] It should be noted that the specific limitations of the above steps can be found in the specific limitations of a vehicle battery aging test method described above.

[0172] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0173] Based on the same inventive concept, this application also provides a vehicle battery aging detection device for implementing the vehicle battery aging detection method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more vehicle battery aging detection device embodiments provided below can be found in the limitations of the vehicle battery aging detection method described above, and will not be repeated here.

[0174] In one exemplary embodiment, such as Figure 5 As shown, a vehicle battery aging detection device is provided, comprising:

[0175] The acquisition module 510 is used to acquire the current parameters of the vehicle battery in the target charging mode; the target charging mode is a charging mode in which the vehicle switches the charging current amplitude step by step according to the battery status.

[0176] The identification module 520 is used to identify current step events within a target state of charge range based on the changes in the current parameters. The target state of charge range is an effective feature extraction range determined based on the historical charging records of the vehicle battery. The current step events include events that perform a step-by-step switching operation on the charging current amplitude.

[0177] Extraction module 530 is used to extract the steady-state data of the vehicle battery before and after the current step event occurs; the steady-state data of the battery includes voltage data and current data of the vehicle battery during the current stable period; the current stable period is the period during which the change in current of the vehicle battery is less than a preset change threshold.

[0178] The determination module 540 is used to determine the target impedance characteristics of the vehicle battery corresponding to the current step event based on the voltage data and the current data; the target impedance characteristics are used to reflect the aging condition of the vehicle battery.

[0179] In an exemplary embodiment, the identification module 520 is specifically used to acquire historical charging records corresponding to the vehicle battery in multiple historical charging processes, each historical charging record including the state of charge interval of the vehicle battery in each historical charging process; based on each historical charging record, to count the charging frequency of the vehicle battery in each state of charge interval, and the number of current step events occurring in each state of charge interval; the charging frequency is used to characterize the frequency at which the vehicle battery is in the state of charge interval in each historical charging process; based on the charging frequency and the number of occurrences, to determine the target state of charge interval from each state of charge interval.

[0180] In an exemplary embodiment, the identification module 520 is specifically configured to determine the state of charge interval where the charging frequency is higher than a first threshold as a first target interval, and to determine the state of charge interval where the occurrence frequency is higher than a second threshold as a second target interval; and to determine the target state of charge interval based on the intersection of the first target interval and the second target interval.

[0181] In an exemplary embodiment, the current stabilization time period includes a first time period before the current step event occurs and a second time period after the current step event occurs; the voltage data includes first voltage data of the first time period and second voltage data of the second time period; the current data includes first current data of the first time period and second current data of the second time period; the determining module 540 is specifically configured to determine the voltage change based on the difference between the average voltage value of the first voltage data and the average voltage value of the second voltage data; determine the current change based on the difference between the average current value of the first current data and the average current value of the second current data; and determine the target impedance characteristics of the vehicle battery corresponding to the current step event based on the ratio of the voltage change to the current change.

[0182] In an exemplary embodiment, the determining module 540 is specifically configured to: determine the initial impedance characteristics of the vehicle battery based on the ratio of the voltage change to the current change; obtain the actual temperature value of the vehicle battery when the current step event occurs; map the initial impedance characteristics at the actual temperature value to the corrected impedance characteristics at the reference temperature value using a preset temperature correction model; and smooth the corrected impedance characteristics to obtain the target impedance characteristics of the vehicle battery.

[0183] In an exemplary embodiment, the determining module 540 is specifically configured to acquire multiple historical impedance characteristics of the vehicle battery within a preset time window; determine a reference impedance characteristic based on statistical information of each of the historical impedance characteristics; determine the feature difference between the corrected impedance characteristic and the reference impedance characteristic; and, if the feature difference is less than or equal to a preset difference threshold, determine the target impedance characteristic of the vehicle battery based on the average value of the corrected impedance characteristic and each of the historical impedance characteristics.

[0184] In an exemplary embodiment, the determining module 540 is specifically configured to: determine candidate impedance characteristics of the vehicle battery based on the average value of the corrected impedance characteristics and each of the historical impedance characteristics; when the current charging process of the vehicle battery includes multiple current step events, obtain the candidate impedance characteristics corresponding to each current step event and the actual state of charge of the vehicle battery at the occurrence of each current step event; construct a battery aging feature mapping model based on each candidate impedance characteristic and each actual state of charge; the battery aging feature mapping model is used to characterize the mapping relationship between the impedance characteristics and the state of charge of the vehicle battery; and input the preset state of charge corresponding to each current step event into the battery aging feature mapping model to obtain the target impedance characteristics of the vehicle battery corresponding to each current step event.

[0185] Each module in the aforementioned vehicle battery aging detection device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0186] In one exemplary embodiment, a new energy vehicle is provided, the internal structure of which can be shown in the following diagram. Figure 6As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for detecting vehicle battery aging.

[0187] Those skilled in the art will understand that Figure 6 The structure shown is a block diagram of a partial structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0188] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0189] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0190] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0191] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0192] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program mentioned can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0193] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0194] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for detecting the aging of vehicle batteries, characterized in that, The method includes: Obtain the current parameters of the vehicle battery in the target charging mode; the target charging mode is a charging mode in which the vehicle switches the charging current amplitude step by step according to the battery status. Based on the changes in the current parameters, current step events are identified within the target state of charge range, where the target state of charge range is the effective feature extraction range determined based on the historical charging records of the vehicle battery; the current step events include events that perform a step-by-step switching operation on the charging current amplitude. Extract the steady-state data of the vehicle battery before and after the current step event occurs; the steady-state data includes the voltage data and current data of the vehicle battery during the current stable period; the current stable period is the time period during which the change in the current of the vehicle battery is less than a preset change threshold. Based on the voltage data and the current data, the target impedance characteristics of the vehicle battery corresponding to the current step event are determined.

2. The method according to claim 1, characterized in that, Before identifying current step events within the target state of charge range based on changes in the current parameters, the method further includes: Obtain historical charging records corresponding to the vehicle battery in multiple historical charging processes, and each historical charging record includes the state of charge range of the vehicle battery in each historical charging process; Based on the historical charging records, the charging frequency of the vehicle battery in each state of charge interval and the number of current step events in each state of charge interval are statistically analyzed; the charging frequency is used to characterize the frequency at which the vehicle battery is in the state of charge interval during each historical charging process. The target state of charge interval is determined from each of the state of charge intervals based on the charging frequency and the number of occurrences.

3. The method according to claim 2, characterized in that, The step of determining the target state of charge interval from each of the state of charge intervals based on the charging frequency and the number of occurrences includes: The state of charge interval where the charging frequency is higher than a first threshold is defined as the first target interval, and the state of charge interval where the number of occurrences is higher than a second threshold is defined as the second target interval. The target state of charge range is determined based on the intersection of the first target range and the second target range.

4. The method according to claim 1, characterized in that, The current stabilization period includes a first time period before the current step event occurs and a second time period after the current step event occurs; the voltage data includes first voltage data in the first time period and second voltage data in the second time period; the current data includes first current data in the first time period and second current data in the second time period. Determining the target impedance characteristics of the vehicle battery corresponding to the current step event based on the voltage data and the current data includes: The voltage change is determined based on the difference between the average voltage value of the first voltage data and the average voltage value of the second voltage data; The change in current is determined based on the difference between the average current value of the first current data and the average current value of the second current data; The target impedance characteristics of the vehicle battery corresponding to the current step event are determined based on the ratio of the voltage change to the current change.

5. The method according to claim 4, characterized in that, Determining the target impedance characteristics of the vehicle battery corresponding to the current step event based on the ratio of the voltage change to the current change includes: The initial impedance characteristics of the vehicle battery are determined based on the ratio of the voltage change to the current change. Obtain the actual temperature value of the vehicle battery at the time the current step event occurs; By using a preset temperature correction model, the initial impedance characteristics at the actual temperature value are mapped to the corrected impedance characteristics at the reference temperature value. The corrected impedance characteristics are smoothed to obtain the target impedance characteristics of the vehicle battery.

6. The method according to claim 5, characterized in that, The process of smoothing the corrected impedance characteristics to obtain the target impedance characteristics of the vehicle battery includes: Obtain multiple historical impedance characteristics of the vehicle battery within a preset time window; Based on the statistical information of each of the historical impedance characteristics, the reference impedance characteristics are determined; Determine the characteristic differences between the corrected impedance characteristics and the reference impedance characteristics; If the difference in characteristics is less than or equal to a preset difference threshold, the target impedance characteristics of the vehicle battery are determined based on the average value of the corrected impedance characteristics and each of the historical impedance characteristics.

7. The method according to claim 6, characterized in that, Determining the target impedance characteristics of the vehicle battery based on the average of the corrected impedance characteristics and the historical impedance characteristics includes: Based on the average value of the corrected impedance characteristics and each of the historical impedance characteristics, the candidate impedance characteristics of the vehicle battery are determined. In the case that the current charging process of the vehicle battery includes multiple current step events, the candidate impedance characteristics corresponding to each current step event and the actual state of charge of the vehicle battery at the time of each current step event are obtained. A battery aging feature mapping model is constructed based on each candidate impedance characteristic and each actual state of charge; the battery aging feature mapping model is used to characterize the mapping relationship between the impedance characteristics and the state of charge of the vehicle battery. The preset state of charge corresponding to each current step event is input into the battery aging feature mapping model to obtain the target impedance characteristics of the vehicle battery corresponding to each current step event.

8. A vehicle battery aging detection device, characterized in that, The device includes: The acquisition module is used to acquire the current parameters of the vehicle battery in the target charging mode; the target charging mode is a charging mode in which the vehicle switches the charging current amplitude step by step according to the battery status. The identification module is used to identify current step events within a target state of charge range based on the changes in the current parameters. The target state of charge range is an effective feature extraction range determined based on the historical charging records of the vehicle battery. The current step events include events that perform a step-by-step switching operation on the charging current amplitude. An extraction module is used to extract the steady-state data of the vehicle battery before and after the current step event occurs; the steady-state data includes voltage data and current data of the vehicle battery during a current stable period; the current stable period is the time period during which the change in current of the vehicle battery is less than a preset change threshold. The determination module is used to determine the target impedance characteristics of the vehicle battery corresponding to the current step event based on the voltage data and the current data.

9. A new energy vehicle, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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