Charging management method and charging device

Abstract

This application provides a charging management method and a charging device. The method includes: during battery charging, in response to determining that the current excitation condition is met, performing a current reduction excitation operation on the battery, and resuming after a target time corresponding to the duration of the current reduction excitation operation; acquiring voltage response data of the battery at different stages of the current reduction excitation operation, wherein the different stages include a current reduction stage and a current holding stage; determining battery health detection information based on the voltage response data at different stages, and performing charging management operations on the battery based on the health detection information, wherein the health detection information includes lithium plating risk assessment information and / or battery aging risk assessment information, and the charging management operations include at least one of adjusting subsequent charging parameters, generating early warning prompts, and updating the battery health record. This method enables timely online health detection during normal battery charging, effectively improving the safety and reliability of battery use.

Description

1. Description of Charging Management Method and Charging Equipment Technical Field This application relates to the field of charging management technology, and more particularly to a charging management method and charging equipment. Background Technology With the rapid development of the new energy vehicle industry, the power battery, as the core energy storage component of electric vehicles, has its health status and safety performance directly related to the vehicle's service life and driving safety. During the daily use of electric vehicles, the power battery will inevitably age, affecting the vehicle's range and power output. Furthermore, more seriously, under some extreme conditions, lithium plating may occur inside the battery, which not only accelerates battery performance degradation but may also cause internal short circuits or even thermal runaway, seriously threatening personal and property safety. Therefore, timely and accurate detection of the power battery's health status is of great significance. Currently, most methods for detecting the health status of power batteries rely on offline testing. Taking electric vehicle batteries as an example, common offline testing methods include: sending the vehicle to a repair shop and using specialized equipment to perform charge and discharge tests on the battery pack to obtain parameters such as capacity and internal resistance; or disassembling the battery pack and conducting detailed analysis in a laboratory environment using professional instruments. While these offline detection methods can reflect the battery's condition to some extent, the detection process requires interrupting normal vehicle use, has a low detection frequency, and is difficult to achieve continuous tracking. This results in the inability to promptly detect performance degradation and safety hazards that occur during battery use, failing to meet the needs of full lifecycle battery health management. This application provides a charging management method, charging management device, charging equipment, computer-readable storage medium, and computer program product that can detect the battery's health status online during charging, thereby promptly identifying lithium plating or aging risks, meeting the needs of online health management throughout the battery's lifecycle, and improving the safety and reliability of battery use. Firstly, a charging management method is provided, comprising: during battery charging, in response to determining that the current excitation condition is met, performing a current reduction excitation operation on the battery, and resuming after a target time corresponding to the duration of the current reduction excitation operation; acquiring voltage response data of the battery at different stages of the current reduction excitation operation, wherein the different stages include a current decrease stage and a current holding stage, the current decrease stage being the stage where the charging current decreases from the pre-excitation current to the target excitation current, and the current holding stage being the stage where the charging current is maintained at the target excitation current; determining health detection information of the battery based on the voltage response data of different stages, and performing charging management operations on the HK 30138784 A 2 battery based on the health detection information, wherein the health detection information includes lithium plating risk assessment information and / or aging risk assessment information of the battery, and the charging management operations include at least one of adjusting subsequent charging parameters, generating early warning prompts, and updating the battery health record.Secondly, a charging management device is provided, comprising: a current-reducing excitation module, configured to, during battery charging, perform a current-reducing excitation operation on the battery in response to determining that the current excitation condition is met, and resume operation after a target duration corresponding to the current-reducing excitation operation; an acquisition module, configured to acquire voltage response data of the battery at different stages of the current-reducing excitation operation, wherein the different stages include a current-decreasing stage and a current-holding stage, the current-decreasing stage being the stage where the charging current decreases from the pre-excitation current to the target excitation current, and the current-holding stage being the stage where the charging current is maintained at the target excitation current; and a health detection module, configured to determine health detection information of the battery based on the voltage response data at different stages, and to perform charging management operations on the battery based on the health detection information, wherein the health detection information includes lithium plating risk assessment information and / or aging risk assessment information of the battery, and the charging management operations include at least one of adjusting subsequent charging parameters, generating early warning prompts, and updating the battery health record. Thirdly, a charging device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the charging management method of the first aspect described above. Fourthly, a computer-readable storage medium is provided, storing a computer program that, when executed by a processor, causes the processor to perform the charging management method of the first aspect. Fifthly, a computer program product is provided, comprising a computer program that, when executed by a processor, causes the processor to perform the charging management method of the first aspect. The charging management method provided in the first aspect of this application, firstly, during battery charging, in response to determining that the current excitation condition is met, performs a current-reducing excitation operation on the battery, and resumes operation after a target duration corresponding to the current-reducing excitation operation. Then, by acquiring voltage response data of the battery at different stages of the current-reducing excitation operation, and based on the voltage response data at different stages, timely and accurately determines the battery's health detection information, and then performs corresponding charging management operations based on the health detection information. On the one hand, this charging management scheme eliminates the need to remove the battery from the vehicle for offline testing, enabling online health detection to be completed directly during normal battery charging. This does not interrupt normal battery use and can promptly identify lithium plating risks and aging risks, meeting the online health management needs throughout the battery's entire lifecycle and effectively improving the safety and reliability of battery use. On the other hand, by analyzing the voltage response data during the current decrease and current holding phases, it is possible to distinguish between the transient and quasi-steady-state responses of the battery. This helps to extract characteristic parameters reflecting different physical processes of the battery from a single excitation process, improving the accuracy of subsequent battery health problem detection, such as lithium plating risk and aging risk assessment.Based on this, health detection information determined from voltage response data at different stages can more comprehensively reflect the internal state of the battery, reducing judgment biases that may result from relying solely on data from a single stage, thereby further improving the accuracy and reliability of battery health detection. Furthermore, by performing charging management operations such as adjusting subsequent charging parameters, generating early warning prompts, or updating battery health records based on this health detection information, proactive battery management and risk warnings can be achieved, extending battery life while ensuring charging safety. It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect above, and will not be repeated here. Figure 1 illustrates a flowchart of a charging management method according to an embodiment of this application; Figure 2 illustrates the changes in charging current, battery pack voltage, and individual cell voltage over time during the current reduction excitation process; Figure 3 illustrates the changes in battery pack internal resistance and individual cell internal resistance with SOC during the current decrease and current recovery phases when current reduction excitation is applied at different SOC detection points during the charging process; Figure 4 illustrates a flowchart of a charging management method according to another embodiment of this application; Figure 5 illustrates a structural schematic diagram of a charging management device according to an embodiment of this application; Figure 6 illustrates a structural schematic diagram of a charging device according to an embodiment of this application; Figure 7 illustrates a structural schematic diagram of a charging pile for electric vehicles according to an embodiment of this application. Detailed Description The technical solutions in the embodiments of this application will be described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; "and / or" in this text is merely a description of the relationship between related objects, indicating that three relationships can exist, for example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, in the description of the embodiments of this application, "a plurality of" refers to two or more. Hereinafter, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include one or more of that feature. Specific details, such as particular system structures and techniques, are set forth for illustrative purposes rather than for limiting purposes, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted to avoid unnecessary detail that could hinder the description of this application.As mentioned earlier, existing offline methods for detecting battery health status cannot promptly detect performance degradation and safety hazards that occur during battery use, making it difficult to meet the needs of full life-cycle battery health management. Furthermore, research has revealed that while some attempts exist to perform battery testing at the charging station side, they still face numerous technical bottlenecks. First, existing solutions mostly calculate equivalent internal resistance based on a single step or simple step current, making it difficult to separate the instantaneous ohmic response, polarization response, and recovery response. With limited sampling accuracy at the charging station side, insufficient total voltage resolution, or delays in battery management system messages, the single equivalent internal resistance is easily affected by noise, temperature, state of charge, and contact impedance changes, leading to insufficient stability of the detection results. Second, existing solutions only provide health warnings based on increased internal resistance or historical trend changes, making it difficult to distinguish between lithium risk and normal aging. Especially under high state of charge or low-temperature charging conditions, lithium plating may manifest as an abnormal plateauing, decline, or abnormal recovery process in the impedance curve, while normal aging tends to show a continuous rise in the impedance baseline across multiple state of charge intervals. Without a multi-timescale feature extraction and decoupling judgment mechanism for the excitation response process, misjudgments or omissions are easily caused. Firstly, existing solutions often use preset amplitudes and durations for the excitation signals, making it difficult to adapt to different vehicles, battery states, state of charge / temperature conditions, current limiting strategies of different battery management systems, and the output capabilities of different charging piles. When the excitation amplitude is too small, the voltage response signal-to-noise ratio is insufficient, and impedance calculation errors are large; when the excitation amplitude is too large or the duration is too long, it may trigger low-current protection, affecting charging continuity or reducing user experience. To at least partially solve the above technical problems, this application first provides a charging management method. The charging management method of this application can be applied to various fields, including but not limited to: charging management of new energy vehicle power batteries, battery health management of energy storage power stations, and daily maintenance of household energy storage batteries, etc., scenarios requiring online health monitoring of batteries. Optionally, each step of the charging management method provided in this application can be executed by a processor (or a control unit inside the charging equipment) deployed on the charging equipment. Alternatively, the steps of the charging management method provided in this application embodiment can also be executed by a control device (such as an edge computing gateway, industrial control computer, cloud server, etc.) that communicates with the charging device. The charging device can be an on-board charger for an electric vehicle, a DC charging pile for an electric vehicle, an AC charging pile, or a battery management controller for an energy storage system, and is not specifically limited here. For simplicity, the following description uses an electric vehicle charging pile as an example. As shown in Figure 1, the charging management method provided in this application embodiment includes the following steps S110, S120, and S130.Step S110: During battery charging, in response to determining that the current excitation condition is met, a current-reducing excitation operation is performed on the battery, and resumes after the target time corresponding to the duration of the current-reducing excitation operation. In this embodiment, the battery can be various types of rechargeable batteries. For example, it can be a rechargeable battery used in electric vehicles, energy storage systems, or other devices requiring charging, such as a lithium-ion battery pack. During charging, the internal state of the battery changes, requiring effective management to ensure its safety and performance. In this embodiment, the current-reducing excitation operation can be an operation that applies a specific current disturbance to the battery by actively reducing the charging current during battery charging. This operation aims to cause a change in the battery voltage response, facilitating the acquisition and analysis of the battery's internal characteristics. In this embodiment, the target time can be a preset duration of the current-reducing excitation operation. During this duration, the charging current is maintained at a low level to ensure that the battery's voltage response can be fully acquired. In this step, various suitable methods can be used to first determine whether the current excitation condition is met. The determination of the incentive condition can be based on preset charging time points, such as performing an incentive operation once every hour of charging; or based on the battery's state of charge (SOC) reaching a specific percentage, such as performing incentives when the SOC reaches 30%, 60%, and 90%. After determining that the incentive condition is met, a current reduction incentive operation can be triggered. For example, after the electric vehicle connects to the charging pile and completes the charging handshake protocol and enters the formal charging stage, the charging pile's control unit monitors the charging status in real time and determines whether to perform a current reduction incentive operation based on preset incentive conditions. The incentive conditions include three types: initial incentive conditions, periodic incentive conditions, and event incentive conditions. First, when the charging pile and the vehicle battery management system complete the charging handshake protocol, enter the formal charging stage, and the charging current rises to the charging pile's required current and tends to stabilize, it is determined that the initial incentive condition is met. The control unit immediately performs the first current reduction incentive detection. The purpose of this first detection is to quickly screen for abnormalities in the battery at the beginning of charging, and to prevent abnormal vehicles from entering the normal charging process and causing charging safety hazards. Next, the control unit can determine whether the periodic excitation conditions are met according to preset periodic detection rules, and periodically trigger the current reduction excitation operation. The periodic detection rules include triggering based on state of charge nodes, charging time windows, cumulative charging capacity, charging stages, or combinations thereof. As a preferred implementation, the detection node can be triggered once every preset step (e.g., every 5% increase in state of charge), thereby achieving rolling monitoring throughout the charging process and acquiring battery response data at different states of charge. Furthermore, when an abnormal event is detected during charging, it can be determined that the event excitation conditions are met. The control unit immediately triggers the current reduction excitation operation.Abnormal events include, but are not limited to: the battery being in a high state of charge, low temperature conditions, abnormal fluctuations in the voltage curve, abnormal fluctuations in the charging current, abnormal increases or decreases in impedance trends, abnormal temperature rises, abnormal voltage plateaus, or insufficient confidence in the previous detection. The event triggering mechanism enables timely detection when abnormal changes occur in the battery state, thereby improving the response capability to sudden risks. For example, when it is determined that any of the above excitation conditions are met, the control unit can perform a current-reduction excitation operation on the charging battery. In one example, the current-reduction excitation operation can be simply implemented by stepping down the charging current from its current value to a preset fixed low current value. For example, reducing the charging current from 50A to 10A. This current-reduction operation will last for a preset fixed duration, such as 10 seconds, after which the charging current will return to the level before the excitation operation. Thus, in another example, the control unit can dynamically determine the excitation parameters for the current reduction excitation operation based on the real-time operating parameters of the current battery (including state of charge, temperature, health status, minimum charging current allowed by the battery management system, etc.) to perform a dynamic current reduction excitation operation on the battery, causing the charging current to drop from the pre-excitation current to the target excitation current (dynamic value), and then maintain the current for the corresponding target time (dynamic value) before returning to the pre-excitation current. Step S120 involves acquiring voltage response data of the battery at different stages of the current reduction excitation operation. These different stages include a current decrease stage and a current holding stage. The current decrease stage is the stage where the charging current drops from the pre-excitation current to the target excitation current, and the current holding stage is the stage where the charging current is maintained at the target excitation current. In this embodiment, voltage response data refers to the measured value of the battery terminal voltage changing over time during the current reduction excitation operation. These data reflect the electrochemical behavior of the battery under different current conditions. Battery terminal voltage may include, but is not limited to: total battery pack voltage, module voltage (the voltage of multiple individual cells), and individual cell voltage (such as a single cell). In addition to acquiring voltage response data, HK 30138784 A 6 can also acquire battery temperature and state of charge (SOC) in this step. In this embodiment, the current decrease phase refers to the transition phase where the charging current decreases from the normal charging current level before the excitation operation to the target excitation current level. During this phase, the battery voltage responds rapidly to the sudden change in current. In this embodiment, the current holding phase refers to the phase where the charging current is maintained at the target excitation current level. In this embodiment, the target excitation current refers to a specific current value that is reduced and maintained during the current-reducing excitation operation, lower than the normal charging current before the excitation operation. In this step, during the current-reducing excitation operation, voltage response data can be acquired at a certain sampling frequency. Optionally, the sampling frequency can be the same in the current decrease phase and the current holding phase.For example, the total battery voltage data can be recorded every 100 milliseconds. Alternatively, different sampling frequencies can be set according to the response characteristics of the two stages. A higher sampling frequency, such as once every 10 milliseconds, can be used during the current-decreasing stage because the voltage response changes rapidly, while a lower sampling frequency, such as once every 100 milliseconds, can be used during the current-holding stage because the voltage response changes gradually. In this way, unnecessary computing power and storage consumption are reduced while fully acquiring the voltage response characteristics. As shown in Figure 2, during the charging of an electric vehicle using a charging pile, before the current-reducing excitation is applied, the charging current is maintained at a normal high level, and the battery pack voltage and individual cell voltage rise steadily accordingly. When the control unit determines that the excitation triggering condition is met and issues a current-reducing command, the charging current responds rapidly and drops stepwise to the target excitation current, entering the current-holding stage. During this period, due to the sudden decrease in current, the total battery pack voltage and the voltage of each individual cell experience an instantaneous step drop; subsequently, under continuous low-current stimulation, the voltage continues to decrease slowly and gradually tends to level off. After maintaining the predetermined target time, the control unit issues a recovery command, and the charging current quickly rises back to the high level before excitation. The battery pack voltage and individual cell voltage recover accordingly and reach a stable state again after a brief relaxation. For example, after the voltage response data acquisition is completed, the acquired raw data can be filtered and denoised to remove high-frequency noise such as current ripple and electromagnetic interference introduced during sampling, improving the accuracy of subsequent feature extraction and state judgment. Step S130: Based on the voltage response data at different stages, determine the battery's health detection information. Based on the health detection information, perform charging management operations on the battery. The health detection information includes lithium plating risk assessment information and / or battery aging risk assessment information. The charging management operations include at least one of adjusting subsequent charging parameters, generating early warning prompts, and updating the battery health record. In this embodiment, health detection information refers to the assessment result of the battery's current health status obtained by analyzing the battery's voltage response data. This information is used to guide subsequent charging management. In this embodiment, lithium plating risk assessment information refers to the assessment of the risk level of lithium ion deposition on the negative electrode surface (i.e., lithium plating) that may occur inside the battery. Lithium plating is a significant factor leading to battery capacity decay and safety hazards. In this embodiment, aging risk assessment information refers to the assessment of the risk level of battery performance degradation (i.e., aging) due to long-term use or improper operation. For example, aging specifically manifests as capacity decay, increased internal resistance, etc. In this embodiment, charging management operation refers to the adjustment and control of the battery charging process based on health detection information. Its purpose is to optimize charging efficiency, extend battery life, and ensure charging safety. In this embodiment, subsequent charging parameters refer to charging-related parameters that can be adjusted during charging management operation, such as charging current, charging voltage, and charging cutoff conditions.In this embodiment, the early warning notification refers to the warning message issued by the system to the user or manager when a potential risk to the battery is detected, to remind them to take appropriate measures. In this embodiment, the battery health record refers to a document or database that records various health indicators and historical data of the battery from production to use. By updating this record, long-term tracking and management of the battery's health status can be achieved. In this embodiment, a single current reduction excitation operation can include at least a current reduction phase, a current holding phase, and a current recovery phase. Furthermore, for cases where a composite current reduction waveform is used for excitation, a single current reduction excitation operation can include a pre-steady-state segment, a first current reduction segment (corresponding to the current reduction phase), a first holding segment (corresponding to the current holding phase), a second disturbance segment, and a recovery segment (corresponding to the current recovery phase). For example, the pre-steady-state segment is used to confirm that the voltage and current fluctuations before excitation are less than a stable threshold; the first current reduction segment can be used to extract the instantaneous ohmic response; the first holding segment can be used to extract the polarization response; the second disturbance segment can be used to enhance the reliability of impedance fitting; and the recovery segment can be used to extract the recovery time constant and recovery slope. In this step, various suitable methods can be used to determine the battery's health detection information based on voltage response data from different stages. In one example, response features at different time scales can be extracted based on the different variation patterns of voltage response data during the current decrease and current holding phases. For instance, the ohmic response features of the battery can be extracted based on the instantaneous voltage change pattern during the current decrease phase, the polarization response features can be extracted based on the gradual voltage change pattern during the current holding phase, and the recovery response features can be extracted for the voltage recovery process after the current recovers to the original charging current. Based on the extracted multi-time-scale response features, the ohmic impedance, activation polarization impedance, and concentration polarization impedance of the battery are calculated respectively, completing the decoupling and separation of different response components. This reduces the problem of poor stability of results caused by multiple interference factors affecting a single equivalent internal resistance. After decoupling and obtaining different types of impedance components, the current temperature and state of charge of the battery can be further combined to compare the current impedance components with the healthy baseline of the battery under the same operating conditions, and the health status of the battery can be judged based on the change pattern of the impedance components. For example, if all impedance components show a sustained and consistent increase across multiple state-of-charge (SOC) ranges, the battery is deemed to have a normal aging risk, and corresponding aging risk assessment information is generated. If only in the high SOC or low temperature range does the polarization impedance exhibit an abnormal plateau change, or if the impedance curve shows an abnormal decrease and the recovery process deviates from the normal trend, the battery is deemed to have a lithium plating risk, and corresponding lithium plating risk assessment information is generated. In another example, a deep learning-based battery health detection model can be pre-trained.This health monitoring model can learn the mapping relationship between voltage response data at different stages and battery health status (such as lithium plating and aging). After inputting the collected voltage response data at different stages into the model, it can directly output the lithium plating risk assessment results and aging risk assessment results without the need for manual feature extraction and impedance calculation, adapting to the rapid detection needs in complex scenarios. For example, after determining the battery's health monitoring information, corresponding charging management operations can be performed based on this information. For instance, for batteries with a slight aging risk, the maximum current of subsequent charging can be appropriately reduced, and the charging time can be extended to ensure charging safety; for batteries with a high lithium plating risk, the current charging can be directly terminated, and a safety warning can be pushed to the user's vehicle's battery management system and the charging pile client; at the same time, the impedance data and risk assessment results obtained in this detection are updated and stored in the battery's full life cycle health record, providing a baseline reference for subsequent detection and judgment. In a specific example, after the electric vehicle is connected to the charging pile and starts charging, the control unit performs a current reduction excitation operation when the excitation conditions are met. The current reduction excitation operation employs a composite current reduction waveform, including a pre-steady-state segment, a first current reduction segment, a first holding segment, a second disturbance segment, and a recovery segment. During excitation execution, the control unit synchronously acquires data on the total battery pack voltage, individual cell voltages, temperature, and state of charge (SOC), obtaining voltage response data before, during, and in the recovery phase. After data acquisition, the control unit extracts multi-timescale features based on different stages of the composite current excitation waveform. Specifically, it extracts the pack-level ohmic impedance and individual cell ohmic impedance from the voltage response of the first current reduction segment to characterize the battery's ohmic response characteristics; it extracts the polarization impedance from the voltage response of the first holding segment to characterize the battery's polarization response characteristics; and it extracts the recovery time constant and recovery slope from the voltage response of the recovery segment to characterize the battery's voltage relaxation characteristics after current recovery. Furthermore, the control unit calculates the rate of change of impedance with SOC, as well as the non-monotonic exponent of impedance in the high SOC range, and analyzes the consistency between the pack-level impedance and the individual cell or module impedance. In addition to the impedance characteristics mentioned above, the control unit can also extract statistical and waveform characteristics during the charging process, including voltage curve fluctuation amplitude, voltage recovery curve shape, and time constant distribution. After extracting the above characteristic parameters, the control unit monitors the extracted characteristic parameters in real time before, during, and after excitation. The characteristic parameters include at least one of the following: equivalent impedance, ohmic impedance, polarization impedance, recovery time constant, recovery slope, impedance-state-of-charge rate of change, impedance residual after temperature compensation, and voltage curve fluctuation characteristics.The real-time monitoring includes: whether the impedance shows abnormal increases or decreases, whether the voltage curve shows abnormal fluctuations, whether the recovery process is abnormally slow, whether the impedance residual after temperature compensation is abnormal, whether the individual cell response dispersion increases, and whether the impedance change in the high-charge state range shows abnormal reactions indicating lithium plating risk. Based on the above real-time monitoring characteristics, the control unit calculates the lithium plating risk index and the aging risk index. The lithium plating risk index is used to assess whether the battery currently has a lithium plating risk, and the aging risk index is used to assess the current degree of aging or power degradation of the battery. Based on the calculated lithium plating risk index and aging risk index, the control unit determines the battery's health detection information, which includes lithium plating risk assessment information and / or aging risk assessment information. Then, based on this health detection information, the control unit performs corresponding charging management operations, including adjusting subsequent charging parameters, generating early warning prompts, or updating at least one of the following in the battery health record: In addition, the control unit of the charging equipment can also upload the voltage response data, extracted feature parameters, and preliminary health detection information obtained during the current charging process to the cloud server. Based on the battery's historical charging records, reference sample data of batteries of the same model, and normal distribution data of vehicles of the same specifications, the cloud server uses machine learning models or deep learning models to assist in the correction, trend prediction, and abnormal sample screening of the lithium plating risk index and aging risk index, and outputs health scores, lithium plating risk levels, aging risk levels, and warning suggestions. The control unit receives the corrected health detection information fed back from the cloud server and outputs it as the final health detection information, thereby realizing a closed-loop diagnosis combining pile-end detection and cloud analysis. HK 30138784 A 9 The charging management method of this application realizes online non-destructive detection of battery health status during charging by actively applying current reduction excitation during the charging process and analyzing the voltage response of the battery at different stages of the excitation process. It can timely identify battery health problems such as lithium plating risk and aging risk, without the need for additional detection hardware or offline detection by stopping the vehicle. It improves charging safety and extends battery life without significantly affecting the normal charging progress. Specifically, firstly, by performing a current-reducing excitation operation in response to the determination that the excitation condition is met during the charging process, the detection process can be integrated with the actual charging state of the battery, rather than being performed blindly. Integrating excitation detection into the charging process enables online, real-time detection capabilities, reducing problems such as insufficient signal-to-noise ratio or protection triggering caused by improper excitation. Furthermore, by acquiring voltage response data during the current-decreasing and current-holding phases of the current-reducing excitation operation, it is possible to distinguish between the transient and quasi-steady-state responses of the battery.Compared to the methods in the background that calculate a single equivalent internal resistance based solely on a single step or simple step current, this approach provides richer data dimensions, laying the foundation for more refined impedance separation and characteristic analysis, and helping to improve the accuracy and stability of battery health monitoring results. Furthermore, by analyzing the characteristics of the voltage response at different stages, it helps to distinguish the overall impedance rise caused by battery aging and identify specific polarization anomalies caused by lithium plating, facilitating accurate assessment of battery lithium plating and aging risks. This proactive risk assessment and management mechanism significantly improves the safety and reliability of the battery charging process, helping to reduce problems such as accelerated capacity decay, power performance degradation, and even thermal runaway caused by the failure to identify abnormal states in a timely manner. In one embodiment, step S130 determines the battery health detection information based on voltage response data at different stages, including the following steps: Step S131, fitting a first equivalent impedance of the battery based on the voltage response data during the current decrease stage, wherein the first equivalent impedance is used to characterize the ohmic response characteristics of the battery; Step S132, fitting a second equivalent impedance of the battery based on the voltage response data during the current holding stage, wherein the second equivalent impedance is used to characterize the polarization response characteristics of the battery; Step S133, determining the battery health detection information based on the first and second equivalent impedances. In this embodiment, the first equivalent impedance is used to characterize the ohmic response characteristics of the battery, mainly reflecting the instantaneous resistance to electron and ion transport inside the battery. Step S131 can be implemented using various suitable methods. For example, during the current decrease stage, the impedance value can be calculated using Ohm's law by analyzing the instantaneous voltage jump caused by the instantaneous change in current. Alternatively, by performing time-domain or frequency-domain analysis on the voltage response data of this stage and combining it with equivalent circuit model fitting, the impedance component related to the high-frequency response can be extracted. In this embodiment, the second equivalent impedance is used to characterize the polarization response characteristics of the battery, mainly reflecting the resistance to the electrochemical reaction kinetics, charge transport, and mass diffusion processes inside the battery. Step S132 can be implemented using various suitable methods. For example, during the current holding phase, the polarization impedance component is obtained by analyzing the relaxation process of the voltage changing slowly over time and fitting it with an equivalent circuit model (e.g., an RC parallel circuit including charge transfer resistance and double-layer capacitance). Alternatively, by performing time-domain analysis on the voltage response data of this phase, the impedance component related to the mid-to-low frequency response can be extracted to characterize the polarization effect. It can be understood that the purpose of step S133 is to decompose the overall health status of the battery into more specific ohmic and polarization responses, thereby providing more refined and accurate health detection information. Step S133 can be implemented using various suitable methods.For example, the fitted first and second equivalent impedances can be used as feature inputs and fed into a pre-trained diagnostic model (e.g., a model based on machine learning algorithms). This model can output lithium plating risk assessment information and / or aging risk assessment information of the battery based on these impedance values. Alternatively, by setting preset thresholds or rate-of-change rules, for example, when the first or second equivalent impedance exceeds a specific threshold, or shows a significant increase relative to historical data, the battery is determined to have a corresponding health risk. In the above scheme, a current-reducing excitation operation is performed on the battery, and voltage response data is acquired during the current-decreasing and current-holding phases. During the current-decreasing phase, due to the rapid change in current, the battery's voltage response is mainly manifested as an instantaneous ohmic voltage drop. Therefore, by analyzing the voltage response data of this phase, a first equivalent impedance characterizing the battery's ohmic response characteristics can be fitted. Subsequently, during the current-holding phase, the current stabilizes at the target excitation current. At this time, the battery's voltage response is mainly caused by electrochemical polarization effects. By analyzing the voltage response data of this phase, a second equivalent impedance characterizing the battery's polarization response characteristics can be fitted. In this way, the overall impedance response of the battery is decomposed into two independent components with clear physical meaning: ohmic impedance and polarization impedance. This decomposition allows for a more in-depth and accurate analysis of the battery's internal degradation mechanisms. For example, an abnormal increase in ohmic impedance may indicate problems such as loose internal connections, current collector corrosion, or decreased electrolyte conductivity; while an abnormal increase in polarization impedance may indicate problems such as electrode surface passivation, SEI film thickening, loss of active material, or obstructed lithium-ion diffusion. By comprehensively analyzing these two equivalent impedances, the lithium plating risk and aging risk of the battery can be more accurately identified and quantified, thereby providing a more targeted and effective basis for subsequent charging management operations. In one embodiment, the charging management method of this application further includes the following steps: Step S101, when the current time is an excitation time, determine that the current excitation condition is met, wherein the excitation time is the time when the battery's state of charge is equal to a preset state of charge, and the number of preset states of charge is greater than 2; Step S133, determine the battery's health detection information based on the first equivalent impedance and the second equivalent impedance, including the following steps S1331 and / or S1332.Step S1331: If the second equivalent impedance fitted under different states of charge after the first excitation time does not increase with the increase of state of charge, it is determined that the battery has a risk of lithium plating. The first excitation time is the moment when the battery's state of charge equals a first preset state of charge, which is greater than a state of charge threshold. Step S1332: If multiple first equivalent impedances and multiple second equivalent impedances fitted during this charging process are significantly higher than their corresponding historical equivalent impedances, it is determined that the battery has an aging risk. The historical equivalent impedance is the equivalent impedance fitted by the battery at the corresponding state of charge within a historical time period. In this embodiment, the excitation time can be the moment when the battery's state of charge (SOC) equals a preset state of charge, and the number of preset states of charge is greater than 2. That is, the current reduction excitation operation is not randomly triggered, but is performed at multiple specific state of charge points during the battery charging process. For example, excitation can be set at multiple points such as 20%, 50%, and 80% battery SOC to obtain battery response data at different charging stages. This multi-point SOC-based excitation strategy provides more comprehensive battery impedance characteristic information, laying the foundation for subsequent health diagnosis. For example, a car's battery management system can continuously monitor the battery's real-time state of charge and send it to the charging pile's control unit. When the real-time state of charge matches any of the pre-stored preset state of charge values, an excitation operation is triggered. Alternatively, by using a lookup table or a preset SOC range, when the battery's SOC enters or reaches a certain preset range or specific point, a current-reducing excitation operation is automatically initiated. In this embodiment, the first excitation moment can be the moment when the battery's state of charge equals a first preset state of charge, and this first preset state of charge is greater than a state of charge threshold. That is, the battery is at a specific point in a relatively high state of charge. In other words, after the first excitation moment, the battery's state of charge is in a high SOC range and continuously increases. The second equivalent impedance fitted after the first excitation moment is always fitted in the high SOC range. The second equivalent impedance characterizes the battery's polarization response characteristics. Under normal circumstances, as the battery's state of charge increases, the battery's polarization impedance will show an increasing trend. In step S1331, if the second equivalent impedance fails to increase with increasing state of charge after the first excitation moment (i.e., in the high SOC region), this may indicate an anomaly inside the battery, such as lithium ions depositing on the negative electrode surface to form metallic lithium, i.e., lithium plating. This suggests a risk of lithium plating in the battery. Metallic lithium deposition consumes recyclable lithium ions in the electrolyte and alters the polarization characteristics of the negative electrode interface, causing the polarization impedance, which should increase with increasing SOC, to exhibit abnormal changes. Therefore, this characteristic can serve as an effective basis for determining the risk of lithium plating.Optionally, the battery can be excited at multiple preset state of charge (SOC) points and fitted with a second equivalent impedance. Then, the values ​​of the second equivalent impedance after the first excitation time (e.g., from 80% SOC to 95% SOC) can be compared to determine if they exhibit the expected increasing trend. Alternatively, curve fitting or trend analysis can be performed on the fitted second equivalent impedance values ​​after the first excitation time to calculate its slope or rate of change. If the slope is not positive or less than a certain preset threshold, it is considered that it has not increased with the increase of SOC. Historical equivalent impedance refers to the first and second equivalent impedances fitted at a past point in time under the same SOC state. This historical data can serve as a benchmark for the battery's health status. During normal use, the battery's internal impedance gradually increases, which is an important indicator of aging. In step S1332, if the ohmic impedance and polarization impedance fitted at multiple SOC points during the current charging process are significantly higher than their historical benchmark values, it indicates an overall increase in the battery's internal resistance, which is usually a sign of battery aging, thus indicating an aging risk. Specifically, historical first and second equivalent impedance data of the battery at different SOCs can be stored, and after each charge, the fitted impedance value is compared with the historical data to calculate the degree of deviation. Alternatively, an ohmic ratio threshold (greater than 1) and a polarization ratio threshold (greater than 1) can be set. If the ratio of the actual ohmic impedance fitted at multiple SOC points to the historical ohmic impedance fitted at the corresponding SOC point is greater than the ohmic ratio threshold, and the ratio of the actual polarization impedance fitted at multiple SOC points to the historical polarization impedance fitted at the corresponding SOC point is greater than the polarization ratio threshold, then the battery is considered to have an aging risk, or the battery aging risk index is considered to have increased. In the above scheme, during the battery charging process, current reduction excitation is triggered at multiple preset state of charge points, and the voltage response data of the battery in the current reduction phase and current holding phase are obtained, thereby fitting the battery's first equivalent impedance HK 30138784 A 12 and second equivalent impedance. This method of excitation and data acquisition at multiple states of charge (SOCs) helps obtain impedance characteristic curves of the battery at different SOCs, rather than just the impedance value at a single SOC. After acquiring this multi-point impedance data, this solution further introduces specific diagnostic rules. For determining the risk of lithium plating, the system pays particular attention to the trend of the second equivalent impedance changing with the state of charge after the first excitation moment (i.e., when the battery is at a higher SOC). Since lithium plating leads to the formation of metallic lithium on the surface of the battery's negative electrode, affecting the lithium-ion insertion and extraction process, and thus altering the battery's polarization behavior, the second equivalent impedance may no longer show a normal increasing trend in the high SOC region. By monitoring this abnormal change, this solution can effectively identify potential lithium plating risks inside the battery.To determine aging risk, this approach focuses on changes in the overall battery impedance level. Battery aging typically manifests as a general increase in internal resistance, including ohmic and polarization impedances. Therefore, multiple first and second equivalent impedances fitted at various states of charge (SOCs) during the current charging process are compared with historical impedance data of the battery at the same SOC. If the current impedance values ​​are significantly higher than historical benchmarks, it indicates that the battery has undergone significant aging. Through this mechanism, this approach combines the dynamic impedance response data of the battery at different SOCs with specific health diagnostic rules to assess the battery's health status from multiple dimensions. This method not only provides more refined battery health information but also distinguishes between lithium plating and aging, two different failure modes, thus providing a more accurate basis for subsequent charging management operations. Compared to methods based solely on a single impedance value or simple trend judgment, this approach significantly improves the accuracy and reliability of battery health detection by stimulating multiple preset SOCs and comparing them with historical data, resulting in a more comprehensive and in-depth assessment of the battery's health status. For example, during battery charging, the charging station's control unit can preset three excitation times, such as when the battery's state of charge (SOC) reaches 30%, 60%, and 90%, respectively. When the SOC reaches these preset values, the control unit triggers a current-reduction excitation operation, for example, reducing the charging current from C / 2 to C / 10, maintaining this position for a period, and then resuming. During this process, the control unit continuously collects the battery's voltage response data. Specifically, during the current reduction phase, the control unit records voltage and current data at a high sampling rate and uses this data to fit the battery's first equivalent impedance, which reflects the battery's ohmic response characteristics. Subsequently, during the current holding phase, the control unit continues to record voltage and current data and fits the battery's second equivalent impedance, which reflects the battery's polarization response characteristics. This process is repeated at the three excitation times of 30%, 60%, and 90% SOC. Assuming the first excitation time is set to when the SOC reaches 80%, during this charging process, the control unit fitted the second equivalent impedance values ​​at SOCs of 80%, 85%, 90%, and 95%, respectively. If it is observed that these second equivalent impedance values ​​do not show a gradually increasing trend from 80% SOC to 95% SOC, for example, they remain stable or even slightly decrease, the control unit will determine that the battery has a risk of lithium plating. At the same time, the control unit will store the average or reference value of the first and second equivalent impedances corresponding to 30%, 60%, and 90% SOC points during the battery's historical charging process.After the current charge is completed, the control unit compares the six impedance values ​​(three first equivalent impedances and three second equivalent impedances) obtained at the 30%, 60%, and 90% SOC points with the corresponding historical benchmark values. If all six impedance values ​​are found to be generally higher than the historical benchmark values, for example, more than 15% higher, the control unit determines that the battery has an aging risk. Based on this health detection information, the control unit can further perform charging management operations. For example, if a lithium plating risk is detected, subsequent charging parameters can be adjusted, such as lowering the charging cutoff voltage or limiting the charging current; if an aging risk is detected, a warning prompt can be generated, suggesting that the user check the battery and update the aging information in the battery health record. Through the above technical solution, this application can overcome the problem of the lack of accurate diagnostic basis for battery health detection in the prior art. By performing current reduction excitation operations at multiple preset state of charge points, and combining the changing trends of the first and second equivalent impedances under different states of charge and comparing them with historical data, this solution can provide more refined and accurate battery health detection information. Specifically, by monitoring abnormal changes in the second equivalent impedance under high charge states, the risk of lithium plating in the battery can be effectively identified, which is of great significance for early warning and prevention of battery thermal runaway. Simultaneously, by comparing the first and second equivalent impedances under multiple charge states during the current charging process with historical data, the overall aging degree of the battery can be accurately assessed, thus providing a reliable basis for battery life management and performance optimization. This multi-dimensional diagnostic method based on dynamic impedance characteristics significantly improves the accuracy and reliability of battery health detection, enabling more intelligent and personalized charging management operations, thereby extending battery life and improving the safety and reliability of the battery system. In one embodiment, determining the risk of lithium plating in step S1331 includes the following steps: Step S1331.1: If the second equivalent impedance fitted under different charge states after the first excitation time does not increase with the increase of the charge state, and the changing trends of the fitted first and second equivalent impedances after the first excitation time are inconsistent, then the battery is determined to have a risk of lithium plating. Specifically, if the fitted second equivalent impedance does not increase with increasing state of charge (SOC) after the first excitation moment, this indicates an anomaly in the relationship between the battery's polarization response characteristics and SOC at a specific charging stage. Normally, the battery's polarization impedance exhibits a certain trend as the SOC increases. If the second equivalent impedance does not show the expected increasing trend with increasing SOC, this may suggest an anomaly within the battery, such as lithium ion deposition on the negative electrode surface, leading to abnormal polarization behavior. This phenomenon can be identified by continuously monitoring the rate or trend of change of the second equivalent impedance under different SOCs.For example, a threshold can be set, and if the growth rate of the second equivalent impedance is lower than this threshold, it is considered that it "does not increase with the increase of the state of charge"; or the second equivalent impedance under the current state of charge is compared with the second equivalent impedance under the previous state of charge, and if the former is not greater than the latter, the condition is met. Simultaneously, a correlation condition between the first equivalent impedance (characterizing ohmic response characteristics) and the second equivalent impedance (characterizing polarization response characteristics) is introduced to improve the accuracy of lithium plating determination. In normal batteries, the changing trends of ohmic impedance and polarization impedance are, to some extent, interrelated. When lithium plating occurs in the battery, lithium ions deposit on the negative electrode surface, which not only affects the battery's polarization behavior but may also affect the ohmic impedance. However, the mechanisms and degrees by which they are affected may differ, leading to inconsistencies in their changing trends. This inconsistency can manifest as: one impedance value increasing while the other decreases, or one impedance value changing significantly while the other changes insignificantly, or both changing in the same direction but with vastly different magnitudes. For example, the consistency of trends can be determined by calculating the rate of change of two impedances with a gradually increasing state of charge and comparing whether the difference between these two rates of change exceeds a threshold; or by fitting curves of the two impedances changing with the state of charge and comparing whether the slopes or curvatures of the fitted curves are significantly different. When both of these conditions are met simultaneously, the battery is determined to have a risk of lithium plating. HK 30138784 A 14 Through the above technical solution, when determining the risk of lithium plating in a battery, not only is the change of the second equivalent impedance with the state of charge considered, but the consistency of the changing trends of the first and second equivalent impedances is also introduced as an auxiliary judgment criterion. This comprehensive judgment method can more accurately identify lithium plating phenomena inside the battery. The lithium plating process will have different degrees of impact on the ohmic response and polarization response of the battery; observing the polarization response alone may not be able to fully capture the complex characteristics of lithium plating. By combining the changing trend of the ohmic response, the physicochemical changes inside the battery can be reflected more comprehensively, thereby significantly improving the accuracy and reliability of lithium plating risk assessment, reducing the possibility of missed or false alarms that may be caused by a single indicator, providing a more solid data foundation for subsequent charging management operations, and helping to extend battery life and improve safety.In one embodiment, the battery includes multiple cell units, and different stages further include a current recovery stage, which is the stage where the charging current recovers from the target excitation current to the pre-excitation current. Step S130 determines the battery health detection information based on the voltage response data of different stages, and further includes the following steps: Step S134, performing feature extraction on the voltage response data of the current recovery stage to obtain the voltage relaxation feature data of the battery in the current recovery stage, wherein the voltage relaxation feature data includes the recovery time constant and / or recovery slope; Step S135, fitting the third equivalent impedance of each cell unit according to the voltage response data components of each cell unit in multiple stages, wherein the third equivalent impedance is used to characterize the comprehensive impedance characteristics of the cell unit; Step S136, determining the single-cell impedance dispersion of the battery based on the differences between the third equivalent impedances of each cell unit; Step S1331.1, determining that the battery has a risk of lithium plating, includes the following steps: Step S1331.1a: If the second equivalent impedance fitted under different states of charge after the first excitation time does not increase with the increase of the state of charge, and the changing trends of the first equivalent impedance and the second equivalent impedance fitted after the first excitation time are inconsistent, and the voltage relaxation characteristic data shows anomalies and / or the single-cell impedance dispersion is not less than the dispersion threshold, then the battery is determined to have a risk of lithium plating. Step S1332: Determining the battery to have an aging risk includes the following steps: Step S1332a: If multiple first equivalent impedances and multiple second equivalent impedances fitted during this charging process are significantly higher than the corresponding historical equivalent impedances, and the single-cell impedance dispersion is less than the dispersion threshold, then the battery is determined to have an aging risk. In this embodiment, the battery is composed of multiple independent electrochemical units (i.e., cell units) connected in series or parallel to meet specific voltage and capacity requirements. These cell units may have performance differences during manufacturing and use, or may experience inconsistent degradation during long-term operation. For example, a battery pack can consist of dozens or even hundreds of cylindrical, prismatic, or pouch cells, which are connected and managed via busbars and a battery management system (BMS). Another approach is to use a modular design, with each module containing several cells, and multiple modules combined to form a complete battery. The current recovery phase refers to the transition period after the current reduction excitation operation ends, during which the charging current gradually or rapidly recovers from the lower target excitation current to the normal charging current level before the excitation operation. During this phase, the electrochemical reactions and charge distribution inside the battery undergo a relaxation process, and its voltage response contains rich information about the battery's internal kinetic characteristics. Voltage relaxation characteristic data (HK 30138784 A 15) is a quantitative description of the battery's voltage change behavior during the current recovery phase, reflecting the dynamic response characteristics of the battery's internal electrochemical processes.The recovery time constant characterizes the time scale required for the voltage to recover to a steady state, while the recovery slope describes the rate of change of voltage in the initial stage of recovery. These characteristics are closely related to parameters such as the battery's internal impedance, charge transfer impedance, and diffusion impedance, and can serve as indicators for assessing the battery's health status. For example, in step S134, the recovery time constant can be extracted by mathematically fitting the voltage-time curve of the current recovery phase (e.g., by an exponential function fitting); and the recovery slope can be obtained by calculating the ratio of the voltage change within a specific time window to the length of the time window. The third equivalent impedance is an impedance parameter obtained by modeling and fitting the voltage response data of a single cell in a specific excitation phase (e.g., the current recovery phase or a combination of multiple phases). It can more precisely reflect the internal characteristics of a single cell and help identify anomalies in a single cell. For example, the battery management system (BMS) can monitor the voltage of each cell in real time during charging. At each stage of the current reduction excitation operation (current decrease, current hold, current recovery), the charging pile's control unit can acquire the voltage component data of each cell. Then, in step S135, the voltage response data components of each cell can be fitted using an electrochemical impedance spectroscopy (EIS) model or an equivalent circuit model (ECM) to obtain its third equivalent impedance. For example, an RC parallel network model or a Warburg impedance model can be used for fitting. Exemplarily, the third equivalent impedance can be approximately equal to the cell's internal resistance, i.e., it can correspond to the sum of ohmic impedance and polarization impedance. Individual cell impedance dispersion is an indicator of the consistency or difference in impedance among individual cell units within a battery pack. When cell units in a battery pack exhibit inconsistent performance, their impedance values ​​will show greater dispersion. High dispersion usually indicates the presence of unhealthy cell units within the battery pack, potentially leading to capacity decay, increased internal resistance, or safety risks. Exemplarily, in step S136, statistical measures such as the standard deviation, variance, difference between the maximum and minimum values, or ratio of the maximum to the average value of the third equivalent impedance of each cell unit can be calculated to characterize the individual cell impedance dispersion. For example, the standard deviation of the third equivalent impedance of all cell units can be calculated and compared with the average value. As shown in Figure 3, the charging pile control unit sequentially performed current-reducing excitation operations at multiple different state of charge (SOC) detection nodes during the charging process. Based on the collected voltage response data, the evolution trends of the battery pack-level internal resistance and the individual cell internal resistance as a function of SOC were fitted. As shown in the figure, both the battery pack-level internal resistance and the individual cell internal resistance exhibit a relatively stable or slow changing trend in the medium SOC range (e.g., 40% to 80%), reflecting the basic impedance characteristics of the battery during normal aging.However, in the high SOC range near full charge (e.g., above 85%), the internal resistance of the battery pack and individual cells exhibit significant non-monotonic changes, showing abnormal fluctuations or local peaks. The control unit, by monitoring these abnormal impedance changes in the high SOC range in real time, can use this as an important basis for assessing the risk of lithium plating, thereby achieving accurate online diagnosis of the battery's health status. In step S1331.1a, when determining the risk of lithium plating, two more refined indicators—voltage relaxation characteristic data and individual cell impedance dispersion—are introduced on top of the original judgment based on the overall impedance change trend. Lithium plating typically leads to changes in the internal structure of the battery, affecting charge transfer kinetics and diffusion processes, which may manifest as abnormalities in voltage relaxation behavior and potentially exacerbate inconsistencies between cell units. First, the overall impedance trend is determined based on the above conditions. Second, a normal range or change pattern for voltage relaxation characteristic data (e.g., recovery time constant or recovery slope) is set. When the actually extracted relaxation characteristic data deviates from this range or pattern, an anomaly is considered to exist. Simultaneously, a dispersion threshold is set. When the calculated individual cell impedance dispersion exceeds this threshold, it is also considered abnormal. Only when the overall impedance trend conforms to lithium plating characteristics, and the voltage relaxation characteristic data is abnormal or the individual cell impedance dispersion is too high, is a lithium plating risk ultimately determined. In step S1332a, when determining aging risk, individual cell impedance dispersion is also considered. It can be understood that battery aging usually manifests as a general increase in internal resistance, but if aging is uniform, the impedance dispersion between cell units may not increase significantly. By combining individual cell impedance dispersion, general aging and localized faults can be distinguished. First, it is determined whether the overall impedance has increased significantly based on the above conditions. Second, the individual cell impedance dispersion is calculated and compared with the preset dispersion threshold. If the overall impedance has increased significantly, but the individual cell impedance dispersion is still within an acceptable range (less than the dispersion threshold), it is more likely to be determined as a general aging risk rather than caused by a serious fault in one or a few cell units. In a specific example, when the battery pack under test is in the high SOC region, and the polarization impedance after temperature compensation does not increase, plateaus, or decreases with increasing SOC, while the recovery time constant increases, the recovery slope is abnormal, or the single-cell impedance dispersion increases, the battery lithium plating risk index is increased, and lithium plating risk is determined to exist. When the battery pack under test shows that the baseline of ohmic impedance and polarization impedance after temperature / SOC compensation continuously rises at multiple SOC detection nodes, and there is no obvious high SOC non-monotonic decrease characteristic or local single-cell abrupt change characteristic, the battery aging risk index is increased, and the battery pack is determined to have accelerated aging or power degradation problems.The proposed solution for battery charging management first performs a current-reducing excitation operation on the battery when the excitation conditions are met, and then recovers after a target time to obtain voltage response data of the battery during the current reduction phase, current holding phase, and the newly added current recovery phase. Since batteries are typically composed of multiple cell units, to more precisely assess the battery's health status, this solution further monitors the voltage of each cell unit independently, thereby obtaining voltage response data components for each cell unit in the aforementioned multiple phases. After obtaining these component data, this solution not only fits the battery's first equivalent impedance based on the voltage response data of the current reduction phase to characterize ohmic response characteristics, and fits the battery's second equivalent impedance based on the voltage response data of the current holding phase to characterize polarization response characteristics, but also extracts features from the voltage response data of the current recovery phase to obtain voltage relaxation feature data, such as recovery time constant or recovery slope. These relaxation feature data can provide dynamic information about the internal electrochemical processes of the battery, which is of great significance for identifying early failure modes such as lithium plating. Furthermore, this solution fits the third equivalent impedance of each cell unit based on its voltage response data components at multiple stages. By comparing the differences between the third equivalent impedances of these individual cell units, the individual cell impedance dispersion of the battery can be determined. Individual cell impedance dispersion directly reflects the consistency of the cell units within the battery pack and is a key indicator for assessing localized faults and uneven degradation. When determining the risk of lithium plating, this solution introduces voltage relaxation characteristic data and individual cell impedance dispersion as auxiliary criteria, building upon the existing judgment based on the changing trends of the first and second equivalent impedances. Specifically, only when the overall impedance trend conforms to the characteristics of lithium plating, and the voltage relaxation characteristic data shows anomalies or the individual cell impedance dispersion is not less than the dispersion threshold, is a final determination of the existence of lithium plating risk. This multi-dimensional and refined judgment mechanism can effectively avoid misjudgments and improve the accuracy of lithium plating risk detection, especially in the early stages. When determining the risk of aging, this solution also incorporates individual cell impedance dispersion. When both the overall first and second equivalent impedances of the battery are significantly higher than historical values, it indicates a general increase in internal resistance. However, if the impedance dispersion of individual cells is still less than the dispersion threshold, it is more likely to be classified as a uniform aging risk. This helps distinguish between general aging and overall performance degradation caused by the failure of a few cell units, thus providing a more accurate basis for subsequent charging management operations. Through the above mechanism, this solution can more comprehensively and accurately assess the health status of the battery from both the overall battery pack and individual cell unit levels, providing a more refined and reliable diagnostic method, especially for the two key risks of lithium plating and aging.This analytical approach, combining overall and local features, significantly improves the sensitivity and specificity of health detection, enabling earlier detection of potential problems and the implementation of targeted charging management measures. A concrete example illustrates this below. The battery can be an electric vehicle battery pack containing 96 series-connected pouch cells. During charging, when the charging station's control unit detects that the battery's state of charge (SOC) has reached a preset excitation point (e.g., SOC of 20%, 40%, 60%, 80%, etc.), and other excitation conditions are met, the control unit controls the charger to perform a current-reduction excitation operation. For example, the charging current is rapidly reduced from the pre-excitation current (e.g., 1C) to the target excitation current (e.g., 0.2C) and held for a period, before being restored from 0.2C to 1C. During these three phases—current reduction, current holding, and current recovery—the control unit can synchronously collect voltage data for each cell at a high sampling rate (e.g., 100Hz). For the current recovery phase, an exponential function can be fitted to the voltage relaxation curve of each cell to extract its recovery time constant and recovery slope as voltage relaxation characteristic data. For example, if the recovery time constant of a certain cell is significantly shorter than that of other cells or historical normal values, an anomaly may exist. Simultaneously, using the voltage response data components of each cell across all excitation phases, an equivalent circuit model can be fitted for each cell, such as an R0+R1CPE1+R2CPE2 model, where R0 represents ohmic impedance, and R1CPE1 and R2CPE2 represent charge transfer impedance and diffusion impedance, respectively. The weighted sum of the fitted parameters R0, R1, and R2 is used as the third equivalent impedance of the cell. Subsequently, the standard deviation of the third equivalent impedance of these 96 cells is calculated and compared with the average value to determine the individual cell impedance dispersion. For example, if the ratio of the standard deviation to the average value exceeds a preset dispersion threshold (e.g., 5%), the individual cell impedance dispersion is considered too high. When assessing the risk of lithium plating, if the changing trends of the battery's overall first and second equivalent impedances have preliminarily indicated a potential for lithium plating, and further anomalies are found in the voltage relaxation characteristic data of one or more cell units (e.g., abnormal deviation of the recovery time constant), or the individual cell impedance dispersion is not less than the dispersion threshold, then the risk of lithium plating can be assessed with greater certainty. When assessing the risk of aging, if both the battery's overall first and second equivalent impedances are significantly higher than historical data, indicating a general increase in internal resistance, but the calculated individual cell impedance dispersion is less than the dispersion threshold, then the battery can be determined to be in a uniform aging state. In this case, the control unit can adjust subsequent charging parameters, such as appropriately reducing the maximum charging current or limiting the upper limit of the charging voltage, to extend battery life.The above-described scheme (HK 30138784 A 18) introduces independent monitoring and analysis of multiple battery cells, combined with voltage relaxation characteristic data during the current recovery phase. This scheme can capture the changes in the electrochemical state inside the battery more comprehensively and precisely. Specifically, by extracting voltage relaxation characteristic data, early lithium plating phenomena inside the battery can be identified more sensitively, as lithium plating often affects the charge transfer kinetics and diffusion process of the battery, thus exhibiting unique anomalies in voltage recovery behavior. Simultaneously, by calculating the impedance dispersion of individual cells, this scheme can effectively assess the consistency of battery cells within the battery pack, promptly identify cells with localized performance degradation or failure, and avoid affecting the performance and safety of the entire battery pack due to problems with individual cells. Regarding lithium plating risk assessment, combining abnormal voltage relaxation characteristic data and / or a single-cell impedance dispersion not less than a dispersion threshold as auxiliary criteria significantly improves the accuracy and reliability of lithium plating risk detection, reduces the false positive rate, and enables the battery management system to issue early warnings and take intervention measures earlier. In terms of aging risk assessment, by combining the individual cell impedance dispersion, it is possible to distinguish between general aging and localized faults, providing a clearer basis for precise battery management and maintenance. Therefore, this solution can provide more accurate battery health detection information, especially for assessing lithium plating risk and aging risk, thereby supporting more effective charging management operations, extending battery life, and improving the safety and reliability of the battery system. In one embodiment, step S110 performs a current reduction excitation operation on the battery, including the following steps: Step S111, dynamically determine the excitation parameters of the current reduction excitation operation based on the real-time operating parameters of the battery, wherein the real-time operating parameters include at least one of the following: current charging current, state of charge, temperature, health status, minimum charging current allowed by the battery management system, minimum stable output current of the charging device, and low current protection threshold; the excitation parameters include at least one of the following: excitation type, excitation amplitude, and target time; Step S112, perform a current reduction excitation operation on the battery based on the excitation parameters. In this embodiment, the real-time operating parameters can be various physical quantities or state quantities that can reflect the current operating state of the battery in real time during the charging process. These parameters are dynamically changing. For example, the battery's current state of charge (hereinafter referred to as real-time state of charge) can be estimated using coulomb counting or open-circuit voltage methods; the battery's current temperature (hereinafter referred to as real-time temperature) can be measured using a built-in temperature sensor; and the battery's health status can be obtained based on historical data or previous diagnostic results. The minimum charging current allowed by the battery management system can be a limit set by the battery management system according to battery safety and performance requirements. The minimum stable output current of the charging device can be the lowest output current determined by the charging device's own capabilities. If the output current is lower than this value, the charging device may not be able to operate stably and may even trigger a protective shutdown.The low current protection threshold is a safety threshold set by the system to prevent excessively low charging current. For example, in step S111, the charging pile control unit can use various suitable methods to adjust the specific parameters (excitation parameters) of the current reduction excitation operation based on these real-time changing operating parameters. These include, but are not limited to, rule-based methods and machine learning models. In this embodiment, the excitation parameters can be adjustable variables used to define the characteristics of the current reduction excitation operation. The excitation type can refer to the specific form of the current reduction excitation, such as a simple current step drop or a composite waveform containing multiple drop or rise phases. The excitation amplitude refers to the magnitude of the charging current drop, i.e., the difference between the current before excitation and the target excitation current. The target time (or duration) refers to the total duration of the current reduction excitation operation from start to finish, or specifically the time the current is maintained at the target excitation current. By dynamically determining the excitation parameters, each current reduction excitation operation can effectively stimulate the electrochemical response of the battery while reducing unnecessary stress or damage to the battery. This application's solution introduces a dynamic adjustment mechanism for the current-reduction excitation operation parameters, enabling the excitation process to better adapt to the battery's real-time operating conditions. Specifically, before performing the current-reduction excitation operation on the battery, the system continuously monitors and acquires key operating condition parameters such as the battery's real-time state of charge, real-time temperature, current battery health status, and the minimum charging current allowed by the battery management system. This real-time data is input into the decision module, which comprehensively analyzes these operating condition parameters based on preset strategies or algorithms to intelligently determine the most suitable excitation parameters for the current battery state. For example, based on the battery's real-time state of charge and temperature, the system can adjust the amplitude and duration of the excitation to ensure effective stimulation of the battery's internal response under different conditions. Simultaneously, the system also considers the minimum charging current allowed by the battery management system to ensure that the determined target excitation current does not fall below a safety threshold. Once the excitation parameters are dynamically determined, the charging controller precisely performs the current-reduction excitation operation on the battery based on these parameters. This dynamic adjustment mechanism improves the effectiveness and safety of the current-reduction excitation operation, allowing the acquired voltage response data to more accurately reflect the battery's true health status, thus providing a high-quality data foundation for subsequent health monitoring information. In one implementation, step S111 dynamically determines the excitation parameters for the current reduction excitation operation based on the real-time operating parameters of the battery, including steps S1111 to S1113. Step S1111 determines the excitation type based on the battery's current state of charge range and / or detected abnormal events. The excitation type includes at least one of the following: current reduction step, segmented current reduction signal, positive and negative pulse signals, and small-amplitude periodic disturbance signal. In this embodiment, the excitation type refers to the current waveform shape used in the current reduction excitation operation.Different types of excitation waveforms can elicit different timescale response characteristics in the battery, suitable for various detection purposes and operating conditions. Common excitation types include: current-dropping step, where the charging current drops from its current value to the target value in one step and remains there; segmented current-dropping signal, where the charging current decreases in multiple steps; positive and negative pulse signals, where a brief current rebound or reverse pulse is superimposed on the current-dropping signal; and small-amplitude periodic disturbance signals, where small-amplitude periodic fluctuations are superimposed on the charging current. These excitation types can also be combined as needed to form composite waveforms. For example, in this step, the control unit can first obtain the battery's current state of charge and determine its current state of charge range. For example, in the low state of charge (SOC) range (e.g., SOC below 30%), the battery polarization is relatively mild, and a simple current-reducing step can obtain sufficient signal-to-noise ratio. In the medium-to-high SOC range (e.g., SOC above 70%), battery polarization intensifies, and the risk of lithium plating increases. In this case, a composite waveform including a current-reducing step and a recovery segment can be used to simultaneously extract the ohmic response, polarization response, and recovery response. In the event of an abnormal event (e.g., abnormal fluctuations in the voltage curve are detected, or the confidence level of the previous detection is insufficient), the control unit can select positive or negative pulse signals or small-amplitude periodic disturbance signals to enhance the sensitivity to specific abnormal states. Step S1112: Based on the minimum charging current, minimum stable output current, low current protection threshold, and excitation amplitude threshold, determine the excitation amplitude so that the estimated voltage change caused by the current-reducing excitation operation is greater than the preset signal-to-noise ratio threshold, and the target excitation current is not lower than the minimum charging current, minimum stable output current, and low current protection threshold, and not higher than the excitation amplitude threshold. In this embodiment, the excitation amplitude refers to the change in charging current from the pre-excitation current to the target excitation current during the current reduction excitation operation. The signal-to-noise ratio threshold is the ratio of the minimum voltage response amplitude required to ensure impedance calculation accuracy to the noise level. The minimum charging current is the lower limit of the charging current allowed by the battery management system; below this value may trigger battery protection. The minimum stable output current is the lowest current value that the charging device can stably output; below this value may lead to unstable output. The low current protection threshold is the current under-current protection threshold set by the charging device or vehicle. The excitation amplitude threshold is the maximum allowable current variation set to ensure user experience.For example, in this step, the control unit needs to simultaneously satisfy the following three constraints when determining the excitation amplitude: First, the identifiability constraint requires that the estimated voltage change obtained by multiplying the excitation amplitude by the historical impedance estimate be greater than a preset signal-to-noise ratio threshold, to ensure that the voltage response signal can be effectively identified from the noise; second, the safety constraint requires that the target excitation current after current reduction is not lower than the maximum value among the minimum charging current allowed by the battery management system, the minimum stable output current of the charging device, and the low current protection threshold, to ensure that no protection mechanism is triggered; finally, the user-insensitive constraint requires that the excitation amplitude not exceed a preset excitation amplitude threshold, to avoid current surges causing user perception or affecting charging comfort. Within the intersection of the above constraints, the control unit selects the excitation amplitude that maximizes the signal-to-noise ratio, thereby achieving the best detection effect while ensuring safety and user experience. Step S1113: Determine the target time based on the recovery time constant obtained from the previous current reduction excitation operation, the battery's current temperature, and the response stability during the current reduction excitation operation. If the response stability is determined to be no lower than the stability threshold during the current holding phase, the current holding phase ends; otherwise, the current holding phase is extended based on the remaining allowable detection time. In this embodiment, the target time refers to the total duration of the current holding phase during the current reduction excitation operation. The recovery time constant is the time characteristic value required for the voltage to return to a stable value after current recovery, reflecting the speed of battery polarization dissipation. Response stability refers to the degree to which the voltage response curve tends to stabilize during the current holding phase. For example, response stability can be characterized by monitoring whether the absolute value of the voltage change rate during the current current reduction excitation process is less than a preset stability threshold. Exemplarily, in this step, the control unit first sets the initial target time for the current reduction excitation operation based on the recovery time constant extracted from the previous current reduction excitation operation and the current battery temperature. Generally, the larger the recovery time constant (slower polarization dissipation) or the lower the current temperature (slower polarization establishment), the longer the initial target time. During the current reduction excitation operation, the control unit monitors the voltage response curve during the current holding phase in real time and calculates the absolute value of the voltage change rate. If the absolute value of the voltage change rate is determined to be less than the preset stability threshold (i.e., the response has reached a stable state) during the current holding phase, the control unit ends the current holding phase early and resumes charging current to avoid unnecessary extension of charging time. Conversely, if the absolute value of the voltage change rate is still greater than the stability threshold (i.e., the response has not yet stabilized), and there is still sufficient margin between the current time and the safety boundary of the charging process (i.e., sufficient remaining allowable detection time), the control unit appropriately extends the current holding phase to obtain more data points to improve the accuracy of impedance fitting.Through this dynamic adjustment mechanism, the HK 30138784 A 21 control unit can minimize the impact time of the excitation operation on the normal charging process while ensuring detection accuracy, thus balancing detection effect and charging efficiency. In the above scheme, by dynamically selecting the appropriate excitation type based on the current battery state, determining the optimal excitation amplitude under multiple constraints, and dynamically adjusting the target duration of the current holding phase, it is possible to ensure that each current reduction excitation operation generates an accurately identifiable voltage response without affecting the normal charging process and user experience. This provides reliable and high-quality data input for subsequent health status analysis, further improving the adaptability and accuracy of battery health detection. In one embodiment, after determining the battery health detection information based on the voltage response data at different stages in step S130, the charging management method of this application embodiment further includes the following steps S151 and S152. Step S151 involves uploading the excitation data from this charging process to a cloud server. The cloud server then uses this excitation data, historical battery charging data, and charging reference data from similar batteries to perform auxiliary correction on the health detection information, resulting in corrected health detection information. The excitation data includes voltage response data, battery response characteristic data determined based on the voltage response data, and health detection information. In this embodiment, the excitation data can be a set of data related to the battery response collected during the current reduction excitation operation. Specifically, the excitation data may include: original voltage response data, battery response characteristic data obtained after preliminary processing or analysis of this voltage response data (e.g., first equivalent impedance, second equivalent impedance, third equivalent impedance, voltage relaxation characteristic data, etc. fitted based on the voltage response data), and locally preliminarily determined health detection information (such as lithium plating risk assessment information and aging risk assessment information). Uploading this excitation data to the cloud server in this step aims to transmit locally collected and preliminarily processed data to a remote computing platform with centralized storage and processing capabilities. For example, the data can be encrypted and transmitted to the cloud via wireless or wired connections such as cellular networks, Wi-Fi, or Ethernet. A cloud server can be a single computer system or a group of computer systems located in a remote data center. It possesses powerful storage, computing, and network communication capabilities, enabling it to receive data from a large number of terminal devices and perform complex computational tasks. Examples include systems built upon elastic computing and storage services provided by public cloud service providers, or private cloud platforms built by enterprises themselves. Historical charging data for a battery can be various operational data accumulated over all past charging cycles, including but not limited to historical voltage, current, temperature profiles, historical excitation operation data, and historical health monitoring results. Charging reference data for similar batteries can be the charging and health data of a large number of other batteries similar to this battery model, batch, and usage environment.Auxiliary correction can be achieved by the cloud server using broader and more comprehensive data resources and more complex algorithms to revise and optimize the locally initially determined health detection information, thereby improving its accuracy and reliability. The cloud server can implement auxiliary correction of the locally initially determined health detection information through various algorithms. In one example, the cloud server can use machine learning models (such as support vector machines, neural networks, decision trees, etc.) to analyze the uploaded stimulus data and combine it with historical data and similar battery data for training and prediction, or use statistical methods, such as Bayesian inference and Kalman filtering, to fuse and correct the local detection results. HK 30138784 A 22 The corrected health detection information refers to the battery health assessment result that is more accurate, reliable, and comprehensive than the locally initially determined health detection information after processing by the cloud server using multi-source data and advanced algorithms. Step S152: Receive and output health detection information from the cloud server. For example, the control unit of the charging device can obtain corrected health detection information from a cloud server via network communication and present it in a user-understandable or system-processable form, such as displaying it to the user through a screen, providing it to other systems through an API interface, or storing it in a local database for subsequent decision-making. In a specific example, the cloud platform can aggregate feature parameters formed from multiple charging processes and combine them with the vehicle's historical charging records, reference samples of the same battery model, and the normal distribution of vehicles of the same specifications. It can then use machine learning models or large models to assist in the correction, trend prediction, and abnormal sample identification of the lithium plating risk index and aging risk index, screening for abnormal charging characteristics, abnormal vehicles, and abnormal trends, and outputting health scores, lithium plating risk levels, aging risk levels, and warning suggestions. In the above scheme, after determining the battery's health detection information based on the battery's voltage response data at different stages of the current reduction excitation operation, the excitation data from this charging session is further uploaded to the cloud server. This excitation data includes not only the original voltage response data but also the battery's response characteristic data determined based on the voltage response data and the locally preliminarily determined health detection information. After receiving this data, the cloud server performs a comprehensive analysis, combining it with the battery's accumulated historical charging data and a large amount of charging reference data from similar batteries. Through this fusion of multi-source data and the application of deep learning or advanced statistical models, the cloud server can assist in correcting the locally determined health detection information. For example, local detection may be biased due to fluctuations in single data points, but the cloud can identify and correct such biases by comparing the battery's long-term trends with the general behavior of similar batteries, thus obtaining more accurate and reliable corrected health detection information.Ultimately, the local system receives and outputs this calibrated health detection information from the cloud server, enabling subsequent charging management operations (such as adjusting charging parameters, generating warning prompts, and updating battery health records) to be based on a more comprehensive and accurate battery health assessment, significantly improving the intelligence and safety of charging management. This working mechanism combines the sensitivity of local real-time detection with the macroscopic and accurate nature of cloud big data analysis, effectively solving the limitations that may exist in relying solely on local data for health detection. In one embodiment, the charging management method of this application further includes the following steps: Step S141, after determining the battery health detection information, determining whether the detection is reliable based on the signal-to-noise ratio of the voltage response data and the diagnostic confidence level of the health detection information; Step S142, if yes, then outputting the health detection information; Step S143, if no, then not outputting the health detection information, and adjusting the excitation parameters of the current reduction excitation operation to perform subsequent current reduction excitation operations based on the adjusted excitation parameters. In this embodiment, the signal-to-noise ratio of the voltage response data refers to the ratio of signal power to noise power, which is an important indicator for measuring data quality. In battery health monitoring, voltage response data with a high signal-to-noise ratio (SNR) can more accurately reflect the battery's true electrochemical behavior. In this embodiment, various methods can be used to determine the SNR of the voltage response data. For example, a segment of unexcited voltage data can be collected before and after excitation operation (HK 30138784 A 23), its noise variance can be calculated, and then compared with the voltage signal amplitude during excitation to estimate the SNR. Alternatively, frequency domain analysis methods such as Fourier transform can be used to separate the signal and noise, and then calculate the power ratio. In this embodiment, the diagnostic confidence level of health monitoring information represents the reliability or certainty of the determined battery health status (such as lithium plating risk or aging risk). High confidence level means the diagnostic results are more reliable. In this embodiment, various suitable methods can be used to determine the diagnostic confidence level of health monitoring information. For example, it can be determined based on the consistency of multiple characteristic parameters used for diagnosis (such as first equivalent impedance, second equivalent impedance, voltage relaxation characteristic data, etc.). Alternatively, when using a machine learning model for diagnosis, the probability value or confidence interval output by the model can be directly used as the diagnostic confidence level. It is understandable that the purpose of determining the reliability of this test is to screen out high-quality, highly reliable health test results and avoid misjudgments caused by poor data quality or high diagnostic uncertainty. For example, step S141 can be achieved by comparing the signal-to-noise ratio of the voltage response data and the diagnostic confidence of the health test information with preset thresholds. For instance, if the signal-to-noise ratio is higher than a preset signal-to-noise ratio threshold and the diagnostic confidence is higher than a preset confidence threshold, then the test is determined to be reliable; otherwise, it is determined to be unreliable.In addition, fuzzy logic or expert systems can be used to comprehensively consider multiple indicators for reliability assessment. In step S142, when the current test is determined to be reliable, health test information is output, meaning that the quality-verified battery health status data is provided to the subsequent charging management module or user. This ensures that subsequent charging management operations are based on accurate and reliable information, thereby improving the effectiveness and safety of charging management. In step S143, when the current test is determined to be unreliable, health test information is not output, aiming to avoid using uncertain or potentially erroneous diagnostic results to guide charging management. This mechanism can effectively prevent potential risks such as battery overcharging, over-discharging, and accelerated aging caused by erroneous information, protecting battery safety and extending battery life. When the test result is unreliable, adjusting the excitation parameters of the current reduction excitation operation is to optimize the subsequent excitation process in order to obtain higher quality voltage response data and more reliable health test information in the next test. Adjusting the excitation parameters can be done in various ways. For example, the excitation amplitude can be increased (i.e., the current drop amplitude can be increased) to improve the strength of the voltage response signal, thereby improving the signal-to-noise ratio. Alternatively, the excitation period or duration can be extended to ensure that the battery has enough time to respond to the excitation, thus obtaining more stable data. The excitation type can also be changed, for example, from a single current-dropping step to a composite waveform containing a current-dropping step, to obtain richer battery response characteristics. In a specific example, when the voltage response signal-to-noise ratio obtained from a single excitation is lower than the threshold, the fitting residual is higher than the threshold, the BMS message is missing, the temperature fluctuation exceeds the threshold, or the classification confidence of the lithium plating risk index and the aging risk index is insufficient, the control unit does not directly output the final risk conclusion. Instead, it adjusts the excitation current amplitude, hold time, or recovery strategy within the current allowed detection window or the next SOC detection node and performs a secondary verification excitation. In the above scheme, after determining the battery's health detection information, a mechanism for evaluating the reliability of the detection results is further introduced. This mechanism first comprehensively judges the reliability of this detection (HK 30138784 A 24) based on the signal-to-noise ratio of the voltage response data and the diagnostic confidence of the health detection information. If the determination result is reliable, the confirmed health detection information is output normally to guide subsequent charging management operations and ensure the accuracy of management decisions. Conversely, if the determination result is unreliable, the health detection information is not output, thereby reducing the use of potentially biased or uncertain information for battery management and effectively avoiding potential risks. Simultaneously, to improve the success rate and accuracy of subsequent detections, the system intelligently adjusts the excitation parameters of the current reduction excitation operation. Through this feedback adjustment mechanism, for example, the amplitude of the excitation current can be increased to enhance signal strength, or the excitation duration can be extended to obtain a more stable response, thereby obtaining higher quality voltage response data in the next excitation operation and improving the diagnostic confidence of the health detection information.This adaptive detection and adjustment strategy enables the entire charging management system to dynamically optimize its detection process, ensuring the continuous accuracy of battery health detection and the reliability of charging management operations, significantly improving the intelligence level and safety of the battery management system. Next, referring to Figure 4, the specific flow of a charging management method according to another specific embodiment of this application will be described. This charging management method can be executed by a charging pile or a control unit communicatively connected to the charging pile. As shown in Figure 4, after the electric vehicle connects to the charging pile and completes the charging handshake protocol, the charging pile begins charging the battery. During the charging process, the control unit monitors the charging condition in real time and determines whether the current excitation triggering condition (i.e., excitation condition) is met. Exemplarily, the excitation triggering condition includes three types: initial triggering, periodic triggering, and event triggering. Initial triggering is executed immediately after the charging current rises to the required value and stabilizes, used to screen for abnormalities in the battery during the initial charging phase; periodic triggering is executed according to a preset state of charge (SOC) node, for example, triggering once every 5% increase in SOC, used to achieve continuous rolling monitoring; event triggering is triggered when abnormal events such as abnormal voltage fluctuations, abnormal temperature rises, or insufficient confidence in the previous detection are detected, used to respond promptly to emergencies. When the excitation triggering conditions are determined to be met, a current-reducing excitation operation is performed on the battery. The control unit can dynamically determine the excitation parameters based on the battery's real-time operating parameters. For example, real-time operating parameters may include the current SOC, temperature, battery health status, and the minimum charging current allowed by the battery management system. Excitation parameters may include excitation type, excitation amplitude, excitation period, and duration (i.e., target time). Specifically, the control unit can determine the excitation type to be used based on the current SOC range. For example, a current-reducing step can be used in the low SOC range, and a composite waveform including a current-reducing step can be used in the high SOC range. For example, the excitation waveform is a composite current-reducing waveform, which includes a pre-steady-state section, a first current-reducing section, a first holding section, a second disturbance section, and a recovery section connected in sequence. The pre-steady-state stage is used to confirm that the voltage and current fluctuations of the battery before excitation are less than the preset stability threshold; the first current reduction stage is used to rapidly reduce the charging current to the target excitation current in order to excite the instantaneous ohmic response of the battery; the first holding stage is used to maintain the charging current at the target excitation current in order to excite the polarization response of the battery; the second disturbance stage is used to apply an additional current disturbance after the first holding stage in order to enhance the reliability of impedance fitting; the recovery stage is used to restore the charging current to the current before excitation in order to excite the recovery response of the battery.The control unit can also determine the magnitude of the excitation amplitude based on the current temperature, battery health status, and the minimum charging current allowed by the battery management system. This ensures that the voltage change caused by the current reduction is within a preset reasonable range, generating a voltage response sufficient to identify internal battery characteristics without excessive voltage fluctuations affecting charging safety or user experience. Furthermore, the target excitation current after current reduction should not be lower than the minimum charging current. Additionally, the duration of the excitation can be determined based on the rate of change of SOC and the battery's polarization time constant. After determining the excitation parameters, the control unit executes the current reduction excitation operation, reducing the charging current from the pre-excitation current to the target excitation current and maintaining this position for the corresponding target time before returning to the pre-excitation current. Throughout the current reduction excitation operation, the control unit synchronously collects battery data (including voltage response data) at different stages. The collected data includes, but is not limited to: total battery pack voltage, individual cell voltage, temperature, and SOC. For example, the control unit can perform feature extraction and impedance fitting based on the collected voltage response data. Specifically, based on the voltage response data during the current decrease phase, a first equivalent impedance of the battery can be fitted, which characterizes the ohmic response characteristics of the battery; based on the voltage response data during the current holding phase, a second equivalent impedance of the battery can be fitted, which characterizes the polarization response characteristics of the battery; based on the voltage response data during the current recovery phase, voltage relaxation feature data can be extracted, including the recovery time constant and / or recovery slope; based on the voltage response data components of each cell unit in multiple phases, a third equivalent impedance of each cell unit can be fitted, and based on the differences between the third equivalent impedances of each cell unit, the individual cell impedance dispersion of the battery can be determined. Based on the above feature parameters, the control unit can determine the battery's health monitoring information. The health monitoring information includes lithium plating risk assessment information and / or aging risk assessment information. Specifically, in terms of lithium plating risk determination, the control unit first determines whether the battery is currently in a high SOC region (e.g., SOC greater than 80%). When the battery is in the high SOC region, if the second equivalent impedance fitted under different states of charge after the first excitation time does not increase with increasing SOC, and the trends of the first and second equivalent impedances are inconsistent, while the voltage relaxation characteristic data shows anomalies (such as increased recovery time constant, abnormal recovery slope) and / or the individual cell impedance dispersion is not less than a preset dispersion threshold, then the battery is determined to have a risk of lithium plating. Regarding aging, if multiple first equivalent impedances and multiple second equivalent impedances fitted at multiple SOC nodes during this charging process are significantly higher than the corresponding historical equivalent impedances, and the individual cell impedance dispersion is less than a preset dispersion threshold, then the battery is determined to have a risk of aging.To further improve the reliability of the detection, after determining the health detection information, the control unit can also determine whether the current detection is reliable based on the signal-to-noise ratio of the voltage response data and the diagnostic confidence of the health detection information. If it is determined to be unreliable, the health detection information is not output. Instead, the excitation parameters of the current reduction excitation operation are adjusted (such as increasing the excitation amplitude, extending the holding time, etc.), and the steps of re-executing the current reduction excitation operation, acquiring voltage response data, and determining health detection information are repeated within the currently allowed detection window or at the next SOC detection node. If it is determined to be reliable, the excitation data from this charging operation can be uploaded to the cloud server. The excitation data includes voltage response data, response characteristic data determined based on the voltage response data, and health detection information. Based on the excitation data, the battery's historical charging data, and charging reference data of similar batteries, the cloud server uses machine learning models or deep learning models to perform auxiliary correction, trend prediction, and abnormal sample screening of the health detection information, and outputs the corrected health score, lithium plating risk level, aging risk level, and early warning suggestions. The control unit receives and outputs the corrected health detection information from the cloud server, thereby realizing a closed-loop diagnosis combining pile-end detection and cloud analysis. After obtaining the corrected health detection information, the control unit can perform charging management operations based on this information. These operations include adjusting subsequent charging parameters, generating early warning prompts, and updating at least one of the following: HK 30138784 A 26. For example, when a lithium plating risk is detected, the control unit can reduce the subsequent charging current or terminate charging and push an early warning prompt to the user; when an aging risk is detected, the control unit can update the aging score in the battery health record and, if necessary, recommend maintenance to the user. Through the above scheme, online, seamless, and accurate detection of battery lithium plating and aging risks is achieved during normal charging. A secondary verification mechanism ensures the reliability of the output conclusions when the detection results are unreliable, and cloud collaboration further improves the accuracy and foresight of the diagnosis. It should be understood that the above examples are intended to help those skilled in the art understand the embodiments of this application, and are not intended to limit the embodiments of this application to the specific values ​​or scenarios illustrated. Those skilled in the art can obviously make various equivalent modifications or changes based on the above examples, and such modifications or changes also fall within the scope of the embodiments of this application. The charging management method of the present application embodiments has been described in detail above with reference to Figures 1 to 4. The device embodiments of the present application will be described in detail below with reference to Figure 5. It should be understood that the charging management device in the embodiments of the present application can execute the various charging management methods described in the foregoing embodiments of the present application. That is, the specific working processes of the various products described below can be referred to the corresponding processes in the foregoing method embodiments.This application also provides a charging management device for executing various steps of the charging management method provided in any embodiment of this application. Figure 5 shows a schematic diagram of the structure of the charging management device in some embodiments of this application. As shown in Figure 5, the charging management device 500 of this application embodiment includes: a current reduction excitation module 510, used to perform a current reduction excitation operation on the battery in response to determining that the current excitation condition is met during the battery charging process, and to resume operation after a target time corresponding to the duration of the current reduction excitation operation; an acquisition module 520, used to acquire voltage response data of the battery at different stages of the current reduction excitation operation, wherein the different stages include a current decrease stage and a current holding stage, wherein the current decrease stage is the stage in which the charging current decreases from the current before excitation to the target excitation current, and the current holding stage is the stage in which the charging current is maintained at the target excitation current; and a health detection module 530, used to determine the health detection information of the battery based on the voltage response data at different stages, and to perform charging management operations on the battery based on the health detection information, wherein the health detection information includes lithium plating risk assessment information and / or aging risk assessment information of the battery, and the charging management operations include at least one of adjusting subsequent charging parameters, generating early warning prompts, and updating the battery health record. Each unit module of the charging management device 500 described above can execute the corresponding steps in the above method embodiments, so the details of each unit module will not be elaborated here. Please refer to the description of the corresponding steps above for details. It should be noted that the charging management device 500 described above is embodied in the form of functional units. The term "unit" here can be implemented in software and / or hardware form, and there is no specific limitation thereto. For example, a "unit" can be a software program, hardware circuit, or a combination of both that implements the above functions. Hardware circuits may include application-specific integrated circuits (ASICs), electronic circuits, processors (e.g., shared processors, proprietary processors, or group processors, etc.) for executing one or more software or firmware programs, and memory, combined logic circuits, and / or other suitable components that support the described functions. Therefore, the units of the various examples described in the embodiments of this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of this application. This application also provides a charging device.As shown in Figure 6, the charging device 600 provided in this embodiment includes: at least one processor 610 (only one processor is shown in Figure 6), and the charging device 600 may also include a memory 620 and a computer program 630 stored in the memory 620 and executable on at least one processor 610. When the processor 610 executes the computer program 630, it implements the steps of the charging management method described above. The processor 610 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. In some embodiments, the memory 620 may be an internal storage unit of the charging device 600, such as a hard disk or memory of the charging device 600. In other embodiments, memory 620 can also be an external storage device of charging device 600, such as a pluggable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on charging device 600. Furthermore, memory 620 can include both internal storage units of charging device 600 and external storage devices. Memory 620 is used to store operating systems, applications, boot loaders, data, and other programs, such as program code for computer programs. Memory 620 can also be used to temporarily store data that has been output or will be output. The following description uses a charging pile for charging electric vehicles as an example. As shown in Figure 7, the charging pile includes a power transmission module, an output contactor, a sensing and sampling module, a control module, and a storage module. The control module can physically include one or more processors (processor 610 as shown in Figure 6), while the storage module can be composed of an independent memory (memory 620 as shown in Figure 6) connected to the processor. The processor executes the computer program stored in the memory (computer program 630 as shown in Figure 6) to realize the various functions of the control module.First, during the communication handshake phase, the control module establishes a CAN / PLC communication connection with the vehicle's BMS via the communication module, receiving the vehicle's charging request, battery status limits (such as maximum allowable charging current, SOC, temperature, etc.), and individual cell voltage data. Based on this information, the control module determines whether the charging conditions are met and establishes initial charging parameters. Subsequently, on the charging circuit side, the control module sends a current control command to the power transmission module. The power transmission module receives high-voltage power from the external power grid and performs power conversion, stably delivering the power to the vehicle's battery through the output contactor and charging interface, while maintaining a continuous connection in the charging circuit during current reduction excitation. Simultaneously, on the control and sampling side, the sensing and sampling module is connected to the output circuit between the power transmission link and the battery, collecting current, voltage, and temperature data in real time during the charging process and transmitting the collected data to the control module (HK 30138784 A 28). At the same time, the control module deeply receives and analyzes key status data such as individual cell voltage, battery pack temperature, and SOC sent by the vehicle's BMS via the communication link. The control module fuses and compares the measured data from the charging pile with the on-board data transmitted from the BMS. Through its internal waveform generation, control logic, and diagnostic logic modules, it achieves real-time control of the charging process, generation of excitation waveforms, and online diagnosis of battery status. Furthermore, a storage module is connected to the control module. The storage module may include RAM and Flash memory units. RAM is used for temporary caching of running data, while Flash memory is used for long-term storage of configuration parameters and historical records, providing support for the stable operation and data analysis of the entire charging pile system. Finally, in the remote interaction section, the control module sends the processed data and diagnostic results to the communication module. The communication module communicates remotely with the cloud server and user terminal via Ethernet, enabling data uploading, remote monitoring, and intelligent management. This application also provides a computer program product that, when executed by a processor, implements the charging management method of any of the method embodiments in this application. This computer program product can be stored in memory, for example, as a program. The program undergoes preprocessing, compilation, assembly, and linking processes to ultimately be converted into an executable object file that can be executed by a processor. This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer, implements the charging management method of any of the method embodiments of this application. The computer program may be a high-level language program or an executable object program. The computer-readable storage medium is, for example, a memory. The memory may be volatile memory or non-volatile memory, or the memory may include both volatile and non-volatile memory.The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM). In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. It should be understood that in the various embodiments of this application, the order of the above processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. HK 30138784 A 29 Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces; the indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate; components shown as units may or may not be physical units, that is, they can be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of this embodiment. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims. HK 30138784 A 1 Claim 1. A charging management method, characterized in that it includes: during the charging process of a battery, in response to determining that the current excitation condition is met, performing a current reduction excitation operation on the battery, and resuming after a target time corresponding to the duration of the current reduction excitation operation; acquiring voltage response data of the battery at different stages of the current reduction excitation operation, wherein the different stages include a current decrease stage and a current holding stage, the current decrease stage being the stage in which the charging current decreases from the pre-excitation current to the target excitation current, and the current holding stage being the stage in which the charging current is maintained at the target excitation current; determining health detection information of the battery based on the voltage response data of the different stages, and performing a charging management operation on the battery based on the health detection information, wherein the health detection information includes lithium plating risk assessment information of the battery and / or aging risk assessment information of the battery, and the charging management operation includes at least one of adjusting subsequent charging parameters, generating early warning prompts, and updating the battery health record.2. The charging management method according to claim 1, characterized in that, determining the health detection information of the battery based on the voltage response data of the different stages includes: fitting a first equivalent impedance of the battery based on the voltage response data of the current decrease stage, wherein the first equivalent impedance is used to characterize the ohmic response characteristics of the battery; fitting a second equivalent impedance of the battery based on the voltage response data of the current holding stage, wherein the second equivalent impedance is used to characterize the polarization response characteristics of the battery; and determining the health detection information of the battery based on the first equivalent impedance and the second equivalent impedance. 3. The charging management method according to claim 2, characterized in that the method further includes: when the current time is an excitation time, determining that the current excitation condition is met, wherein the excitation time is the time when the state of charge of the battery is equal to a preset state of charge, and the number of preset states of charge is greater than 2; determining the health detection information of the battery based on the first equivalent impedance and the second equivalent impedance includes: if the second equivalent impedance fitted under different states of charge after the first excitation time does not increase with the increase of the state of charge, determining that the battery has a risk of lithium plating, wherein the first excitation time is the time when the state of charge of the battery is equal to a first preset state of charge, and the first preset state of charge is greater than a state of charge threshold; and / or if multiple first equivalent impedances and multiple second equivalent impedances fitted during the current charging process are significantly higher than the corresponding historical equivalent impedances, then determining that the battery has a risk of aging, wherein the historical equivalent impedance is the corresponding equivalent impedance of the battery fitted under the corresponding state of charge within a historical time period. 4. The charging management method according to claim 3, wherein determining that the battery has a risk of lithium plating includes: if the second equivalent impedance fitted under different states of charge after the first excitation time does not increase with the increase of the state of charge, and the changing trends of the first equivalent impedance and the second equivalent impedance fitted after the first excitation time are inconsistent, then the battery is determined to have a risk of lithium plating.5. The charging management method according to claim 4, characterized in that the battery includes multiple cell units, the different stages further include a current recovery stage, the current recovery stage being the stage where the charging current recovers from the target excitation current to the pre-excitation current, and the step of determining the health detection information of the battery based on the voltage response data of the different stages further includes: performing feature extraction on the voltage response data of the current recovery stage to obtain voltage relaxation feature data of the battery in the current recovery stage, wherein the voltage relaxation feature data includes a recovery time constant and / or a recovery slope; fitting the third equivalent impedance of each cell unit according to the voltage response data components of each cell unit in the multiple stages, wherein the third equivalent impedance is used to characterize the comprehensive impedance characteristics of the cell unit; determining the single-cell impedance dispersion of the battery according to the difference between the third equivalent impedances of each cell unit; and determining that the battery has a lithium plating risk, including: If the second equivalent impedance fitted under different states of charge after the first excitation time does not increase with the increase of the state of charge, and the trends of the first equivalent impedance and the second equivalent impedance fitted after the first excitation time are inconsistent, and the voltage relaxation feature data is abnormal and / or the dispersion of the single-cell impedance is not less than the dispersion threshold, then the battery is determined to have a risk of lithium plating; the determination that the battery has a risk of aging includes: if multiple first equivalent impedances and multiple second equivalent impedances fitted during this charging process are significantly higher than the corresponding historical equivalent impedances, and the dispersion of the single-cell impedance is less than the dispersion threshold, then the battery is determined to have a risk of aging. 6. The charging management method according to any one of claims 1-5, characterized in that, the step of performing a current reduction excitation operation on the battery includes: dynamically determining the excitation parameters of the current reduction excitation operation based on the real-time operating parameters of the battery, wherein the real-time operating parameters include at least one of the following: the battery's current charging current, state of charge, temperature, health status, minimum charging current allowed by the battery management system, minimum stable output current of the charging device, and low current protection threshold; and the excitation parameters include at least one of the following: excitation type, excitation amplitude, and target time; and performing a current reduction excitation operation on the battery based on the excitation parameters.7. The charging management method according to claim 6, characterized in that, dynamically determining the excitation parameters of the current reduction excitation operation based on the real-time operating parameters of the battery includes: determining the excitation type based on the current state of charge range of the battery and / or the abnormal events detected, wherein the excitation type includes at least one of current reduction step, segmented current reduction signal, positive and negative pulse signal, and small-amplitude periodic disturbance signal; determining the excitation amplitude based on the minimum charging current, the minimum stable output current, the low current protection threshold, and the excitation amplitude threshold, so that the estimated voltage change caused by the current reduction excitation operation is greater than a preset signal-to-noise ratio threshold, and the target excitation current is not lower than the minimum charging current, the minimum stable output current, and the low current protection threshold, and is not higher than the excitation amplitude threshold; The target time is determined based on the recovery time constant obtained from the previous current reduction excitation operation, the current temperature of the battery, and the response stability during the current reduction excitation operation; wherein, if the response stability is determined to be not lower than the stability threshold during the current holding phase, the current holding phase ends; otherwise, the current holding phase is extended based on the remaining allowable detection time. 8. The charging management method according to any one of claims 1-5, characterized in that, after determining the health detection information of the battery based on the voltage response data of the different stages, the method further includes: uploading the excitation data in the current charging to a cloud server, so that the cloud server performs auxiliary correction on the health detection information based on the excitation data, the historical charging data of the battery, and the charging reference data of similar batteries, to obtain corrected health detection information, wherein the excitation data includes the voltage response data, the response characteristic data of the battery determined based on the voltage response data, and the health detection information; receiving and outputting the health detection information from the cloud server. 9. The charging management method according to any one of claims 1-5, characterized in that the method further comprises: after determining the health detection information of the battery, determining whether the current detection is reliable based on the signal-to-noise ratio of the voltage response data and the diagnostic confidence level of the health detection information; if yes, outputting the health detection information; if no, not outputting the health detection information, and adjusting the excitation parameters of the current reduction excitation operation to perform subsequent current reduction excitation operations based on the adjusted excitation parameters according to HK 30138784 A 4. 10. A charging device, characterized in that it comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the charging management method according to any one of claims 1 to 9.HK 30138784 A 1 Instruction Manual Attachments Figure 1 HK 30138784 A 2 Figure 2 HK 30138784 A 3 Figure 3 HK 30138784 A 4 Figure 4 HK 30138784 A 5 Figure 5 Figure 6 HK 30138784 A 6 Figure 7 HK 30138784 A.

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