A traffic energy storage control method and system

Abstract

This invention relates to the technical field of energy storage control in transportation, and provides a method and system for energy storage control in transportation. The method includes: acquiring real-time status information of an electric vehicle; when the real-time status information indicates a low-load operating state or a charging state, applying a current pulse to the battery cell and measuring the voltage response; calculating the internal resistance and available power based on the voltage response and storing it as a performance record; acquiring future operating plans and analyzing the charge and discharge load of the battery cell in future operating cycles based on the future operating plans; obtaining risk analysis results based on the performance record and charge and discharge load; setting personalized balancing targets for the battery cell based on the risk analysis results; and pre-allocating energy to the battery cell according to the personalized balancing targets when the real-time status information indicates a low-load operating state or a charging state. This invention improves the overall performance of energy storage control in transportation.

Description

Technical Field

[0001] This invention relates to the technical field of energy storage control in transportation, and specifically to a method and system for energy storage control in transportation. Background Technology

[0002] In modern electric vehicles, energy storage systems typically consist of a large number of independent battery cells, controlled by a battery management system. However, in actual operation, due to the varying operating pressures of electric vehicles under different conditions and environments, as well as the continuous vibrations generated during vehicle operation, the individual battery cells within the energy storage system experience non-uniform capacity decay and increased internal resistance. For example, the actual usable capacity of some battery cells may be lower than the system's prediction, while others may maintain relatively good performance. This performance difference between individual battery cells gradually widens with increasing operating time, directly challenging the uniform aging assumption upon which initial state-of-charge estimation methods rely.

[0003] Transportation operators have discovered that their electric vehicle fleets, after being in use for a period of time, are generally experiencing problems such as actual driving range falling short of the advertised range, sluggish power response, and reduced charging efficiency. These problems cannot be quickly located and resolved through conventional fault diagnosis and maintenance methods because all parameters reported by the control system are within the "normal" range, and no serious fault alarms are triggered. However, the actual operating performance of the vehicles continues to deteriorate, leading to increased operating costs, such as the need for more frequent charging, reduced vehicle availability, and user dissatisfaction with vehicle performance. This situation creates a difficult-to-trace and repair "sub-healthy" state, making it difficult for operators to effectively manage and optimize the performance of their electric vehicle fleet.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] This application discloses a transportation energy storage control method and system, which aims to solve the "sub-health" problem of existing electric vehicle energy storage systems during long-term operation, which is caused by differences in battery cell performance and uneven aging, resulting in inaccurate state of charge estimation, low active balancing efficiency, and a decline in the actual driving range and power response performance of the vehicle.

[0006] The technical solution of this application is as follows:

[0007] In a first aspect, this application discloses a method for controlling energy storage in transportation systems, including:

[0008] Obtain real-time status information of electric vehicles;

[0009] When the real-time status information indicates that the electric vehicle is in a low-load operating state or charging state, a current pulse is applied to each battery cell on the electric vehicle, and the voltage response of each battery cell is measured.

[0010] Based on the voltage response, the internal resistance and available capacity of the battery cell are calculated, and the internal resistance and available capacity are stored as the performance record of the battery cell.

[0011] Obtain the future operation plan of electric vehicles, and based on the future operation plan, analyze the charging and discharging load that each battery cell will bear during the future operation cycle;

[0012] Based on performance records and charge / discharge loads, the risk of each battery cell reaching its charge / discharge limit in future operating cycles is analyzed, and the risk analysis results are obtained.

[0013] Based on the risk analysis results, a personalized balancing target is set for each battery cell. When the real-time status information indicates that the electric vehicle is in a low-load operating state or charging state, energy is pre-allocated to the battery cells according to the personalized balancing target to achieve energy storage control.

[0014] This technical solution enables real-time performance evaluation and future load prediction of battery cells in electric vehicles, and allows for personalized energy pre-allocation. This effectively solves the problems of uneven battery cell aging leading to deviations in state of charge estimation and low balancing efficiency, thereby improving the overall performance and lifespan of the energy storage system.

[0015] Furthermore, according to the aforementioned transportation energy storage control method, the steps for analyzing the risk of each battery cell reaching its charge / discharge limit within future operating cycles, based on performance records and charge / discharge loads, and obtaining the risk analysis results, include:

[0016] Continuously collect temperature data on the surface of each battery cell and analyze the fluctuation and trend of the temperature data within a preset short time window;

[0017] Based on fluctuations and trends, the local temperature rise characteristics of the battery cell are identified; when the local temperature rise characteristic indicates that the temperature data of the battery cell rises by a certain amount within a preset short period of time, it is recorded as a local temperature rise event.

[0018] Cross-correlation analysis was performed between local temperature rise events and the corresponding battery cell's charging and discharging current, voltage changes, and internal resistance to obtain the cross-correlation analysis results.

[0019] When the cross-correlation analysis results indicate that a local temperature rise event is established, the temperature growth condition is met, and the internal resistance growth condition is met, it is determined that the local temperature rise event is caused by the acceleration of internal side reactions.

[0020] For battery cells undergoing nonlinear accelerated degradation caused by localized temperature rise events, the available capacity and internal resistance of the battery cells are corrected.

[0021] Based on the corrected available power and internal resistance, as well as the current operating load of the electric vehicle, the charging and discharging strategy of the battery cell is adjusted. The steps for adjusting the charging and discharging strategy of the battery cell include: prioritizing the transfer of energy from other battery cells to the battery cell; and reducing the upper limit of the instantaneous charging and discharging current of the battery cell based on the corrected internal resistance.

[0022] This technical solution enables the accurate identification of nonlinear accelerated degradation of battery cells through multi-dimensional data cross-correlation analysis, timely correction of their performance parameters, and adjustment of charging and discharging strategies. This effectively avoids local overcharging and over-discharging, slows down the degradation process, and improves the safety and reliability of the battery system.

[0023] More specifically, in some implementations, the steps for correcting the available capacity and internal resistance of a battery cell undergoing nonlinear accelerated degradation caused by localized temperature rise events include:

[0024] Correct the available power and internal resistance of the battery cell;

[0025] Within a preset time period after the correction is completed, the charging and discharging voltage, current and temperature change rate of the corresponding battery cell are continuously monitored to obtain actual monitoring data;

[0026] Based on the corrected available power and internal resistance, the theoretical voltage response, theoretical current change rate and expected temperature change rate of the battery cell under the current are calculated to obtain the theoretical expected data.

[0027] Determine whether there is a difference between the actual monitoring data and the theoretical expected data that exceeds a preset threshold, and obtain the data difference judgment result;

[0028] When the data difference judgment result indicates that a difference exists, the parameters of the deterioration model or the correction coefficient used for correction are adjusted according to the direction and magnitude of the difference.

[0029] Based on the adjusted degradation model parameters or correction coefficients, the available capacity and internal resistance of the battery cell are corrected a second time.

[0030] This technical solution introduces a dynamic verification mechanism for the corrected performance parameters. By comparing actual monitoring data with theoretically expected data, the degradation model parameters or correction coefficients can be adaptively adjusted, thereby ensuring more accurate correction of the battery cell's available power and internal resistance, and further enhancing the system's ability to perceive the battery's true state.

[0031] Preferably, according to the above-mentioned transportation energy storage control method, the step of adjusting the charging and discharging strategy of the battery cell based on the corrected available power and internal resistance, as well as the current operating load of the electric vehicle, includes:

[0032] Continuously monitor the instantaneous power demand and instantaneous power demand change rate of electric vehicles;

[0033] When the instantaneous power demand change rate exceeds the preset instantaneous threshold, the fast response mode is activated;

[0034] In fast response mode, the instantaneous charge and discharge capacity of each battery cell is calculated based on the corrected available power and internal resistance, as well as the current instantaneous power demand.

[0035] Based on instantaneous charge and discharge capabilities, prioritize adjusting the charge and discharge strategies of battery cells in a healthy state;

[0036] Maintain the charging and discharging current of the battery cells that are deteriorating rapidly within the corrected safety range;

[0037] Adjust the charge and discharge cutoff voltage of the battery cells that accelerate deterioration based on the corrected internal resistance;

[0038] Continuously monitor the voltage, current, and temperature change rate of each battery cell;

[0039] The charging and discharging strategy is fine-tuned based on the voltage, current, and temperature change rate of the battery cells.

[0040] This technical solution enables the activation of a rapid response mode to address instantaneous power demand changes in electric vehicles. It also dynamically adjusts the charging and discharging strategy based on the health status of the battery cells and the corrected performance parameters, prioritizing the use of healthy battery cells while protecting degraded ones. This effectively extends battery life while ensuring power performance.

[0041] Based on the above, this application further proposes that the steps for continuously monitoring the voltage, current, and temperature change rate of each battery cell include:

[0042] Continuously collect the voltage and current of each battery cell;

[0043] Continuously collect temperature data from the surface of each battery cell and the internal ambient temperature data of the battery pack;

[0044] Analyze the fluctuations and trends of internal ambient temperature data, identify and quantify the impact of ambient temperature fluctuations on the temperature data of the battery cell surface, and obtain quantitative results.

[0045] Based on the quantification results, external thermal interference is separated from the temperature data on the surface of the battery cell, and the temperature change rate reflecting the true thermal state inside the battery cell is obtained.

[0046] This technical solution can effectively separate external thermal interference by quantifying the impact of external environmental temperature fluctuations on the surface temperature of battery cells, thereby obtaining a more accurate temperature change rate that reflects the true thermal state inside the battery cell, and providing more reliable data support for subsequent fine-tuning of charging and discharging strategies.

[0047] In some preferred embodiments, according to the above-described transportation energy storage control method, the step of fine-tuning the charging and discharging strategy based on the voltage, current, and temperature change rate of the battery cell includes:

[0048] Continuously identify the current operating conditions of electric vehicles;

[0049] Adjust the response speed and adjustment range of the fine-tuning operation according to the operating conditions;

[0050] Continuously assess the degree of degradation of each battery cell;

[0051] Based on the degree of degradation, a corresponding set of fine-tuning parameters is set for each battery cell;

[0052] After the fine-tuning operation is performed, the voltage, current and temperature change rate of the battery cell are continuously monitored, and the effect judgment result is obtained based on the voltage, current and temperature change rate of the battery cell.

[0053] If the effect judgment result indicates that the expected effect has not been achieved, then based on the deviation between the actual effect and the expected effect, the response speed, adjustment range and fine-tuning parameter set of the fine-tuning operation are iteratively optimized.

[0054] This technical solution enables dynamic adjustment of fine-tuning parameters based on the operating conditions of electric vehicles and the degree of battery cell degradation. It also introduces an effect judgment and iterative optimization mechanism to ensure that the fine-tuning of the charging and discharging strategy can continuously adapt to changes in battery status and operating requirements, thereby achieving more refined energy management.

[0055] Furthermore, the steps for continuously assessing the degree of degradation of each battery cell include:

[0056] Continuously monitor the current, voltage, and temperature of each battery cell;

[0057] Adjust the weighting parameters of the deterioration assessment according to the operating conditions;

[0058] The real-time degradation index of each battery cell is calculated based on the current, voltage, temperature, and adjusted weighting parameters of the battery cell.

[0059] Continuously track the growth rate of internal resistance and the decay rate of available power of each battery cell, and perform correlation analysis with the real-time degradation index;

[0060] When the analysis results indicate that the real-time degradation index, the growth rate of internal resistance, and the decay rate of available power all meet their respective preset conditions, the degree of degradation of the battery cell is determined.

[0061] This technical solution enables a more comprehensive and accurate assessment of the degradation level of battery cells through multi-parameter comprehensive evaluation and dynamic weight adjustment, providing a more reliable basis for subsequent fine-tuning strategies.

[0062] As an optional solution, according to the above-mentioned transportation energy storage control method, after the step of setting a corresponding fine-tuning parameter set for each battery cell based on the degree of degradation, the method further includes:

[0063] Continuously monitor the operational attitude data of electric vehicles;

[0064] Identify whether there are abnormal impact characteristics in the running posture data that are consistent with road bumps or minor collisions;

[0065] After identifying abnormal impact characteristics, a rapid diagnostic process for the impacted battery cell is initiated. The rapid diagnostic process includes charging and discharging the impacted battery cell with a preset pulse current and acquiring the corresponding voltage response and internal resistance change rate at high frequency.

[0066] The voltage response and internal resistance change rate collected at high frequency are compared with the corresponding baseline data before the impact to identify whether there is a voltage fluctuation pattern or a sudden increase in internal resistance.

[0067] When a voltage fluctuation pattern or a sudden increase in internal resistance is detected, it is determined that there is hidden damage to the battery cell.

[0068] For battery cells with hidden damage, when adjusting the fine-tuning parameter set, the upper limit of the maximum charge and discharge current should be reduced first, and the safety margin of the charge and discharge cutoff voltage should be increased.

[0069] This technical solution enables the monitoring of the operating posture of electric vehicles, timely identification of potential hidden damage to battery cells, and targeted adjustment of charging and discharging strategies, thereby effectively preventing potential safety risks and improving the overall safety of the battery system.

[0070] To improve the solution, the steps for continuously monitoring the operational attitude data of electric vehicles include:

[0071] Identify the types of electric vehicles;

[0072] Select the appropriate attitude data acquisition frequency, sensor sensitivity, and impact recognition threshold based on the type of electric vehicle.

[0073] Adjust the parameters of the attitude data filtering algorithm, the selected attitude data acquisition frequency, and the sensor sensitivity according to the type of electric vehicle;

[0074] Based on the parameters of the adjusted attitude data filtering algorithm, the selected attitude data acquisition frequency, and the sensor sensitivity, the operating attitude data of the electric vehicle is monitored. The monitored operating attitude data is then compared with the selected impact identification threshold in real time to determine whether there are any abnormal impact characteristics.

[0075] This technical solution can adaptively adjust the attitude data acquisition and processing parameters according to the type of electric vehicle, thereby improving the accuracy and robustness of abnormal impact feature identification and ensuring the timely detection of hidden damage to battery cells.

[0076] Secondly, this application also discloses a transportation energy storage control system for performing transportation energy storage control, including:

[0077] The real-time status acquisition module is used to acquire real-time status information of electric vehicles.

[0078] The voltage response measurement module is used to apply current pulses to each battery cell of the electric vehicle and measure the voltage response of each battery cell when real-time status information indicates that the electric vehicle is in a low-load operating state or charging state.

[0079] The performance record storage module is used to calculate the internal resistance and available capacity of the battery cell based on the voltage response, and store the internal resistance and available capacity as the performance record of the battery cell.

[0080] The future load analysis module is used to obtain the future operation plan of electric vehicles and, based on the future operation plan, analyze the charging and discharging load that each battery cell will bear during the future operation cycle.

[0081] The risk analysis and processing module is used to analyze the risk of each battery cell reaching its charge and discharge limit in future operating cycles based on performance records and charge and discharge loads, and obtain the risk analysis results.

[0082] The energy control execution module is used to set personalized balancing targets for each battery cell based on the risk analysis results, and to pre-allocate energy to the battery cells according to the personalized balancing targets when the real-time status information indicates that the electric vehicle is in a low-load operation state or charging state, so as to achieve energy storage control.

[0083] This technical solution provides a system for implementing the aforementioned transportation energy storage control method. Through modular design, it enables real-time status acquisition, performance measurement, future load analysis, risk assessment, and energy pre-allocation of battery cells, thereby effectively improving the intelligent management level of electric vehicle energy storage systems.

[0084] Beneficial effects

[0085] The energy storage control method for transportation disclosed in this application acquires real-time status information of an electric vehicle and applies current pulses to battery cells and measures voltage response during low-load operation or charging. This allows for the calculation and storage of the battery cell's internal resistance and available capacity as performance records. Based on this, and considering the electric vehicle's future operating plan, the method analyzes the charge and discharge loads each battery cell will experience during future operating cycles. Furthermore, based on the performance records and charge / discharge loads, the method analyzes the risk of the battery cell reaching its charge / discharge limits. Finally, based on the risk analysis results, a personalized balancing target is set, and energy is pre-allocated to the battery cells during low-load operation or charging to achieve energy storage control.

[0086] Through the above technical solutions, this application effectively solves the problems of inaccurate state of charge estimation, low active balancing efficiency, and decreased vehicle performance caused by uneven aging of battery cells in existing technologies. Specifically, by acquiring the internal resistance and available capacity of battery cells in real time, this application can more accurately reflect the true aging state and actual available capacity of each battery cell, overcoming the limitations of traditional methods that rely on the assumption of uniform aging. By predicting future operating loads and conducting risk analysis, this application can identify the risk of battery cells reaching their charge and discharge limits in advance, avoiding local overcharging or over-discharging, thereby effectively suppressing the acceleration of internal side reactions and reducing the generation of additional heat. In addition, by setting personalized balancing targets and pre-allocating energy, this application can achieve more precise and efficient energy management, avoiding the ineffective or harmful energy transfer caused by information deviations in traditional balancing systems, thereby significantly improving the overall performance of the energy storage system, extending battery life, and improving the driving range and power response of electric vehicles, solving the problem of the "sub-healthy" state of vehicles. Attached Figure Description

[0087] Figure 1 This is a flowchart of a transportation energy storage control method according to one embodiment of the present invention;

[0088] Figure 2 This is a flowchart of a transportation energy storage control method according to another embodiment of the present invention;

[0089] Figure 3 This is a system block diagram of a transportation energy storage control system according to another embodiment of the present invention;

[0090] Explanation of reference numerals in the attached figures:

[0091] 1. Transportation energy storage control system; 11. Real-time status acquisition module; 12. Voltage response measurement module; 13. Performance recording and storage module; 14. Future load analysis module; 15. Risk analysis and processing module; 16. Energy control execution module. Detailed Implementation

[0092] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0093] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0094] This application proposes a transportation energy storage control method, combining... Figure 1 As shown, it includes:

[0095] S1, obtain real-time status information of electric vehicles;

[0096] S2, when the real-time status information indicates that the electric vehicle is in a low-load operating state or charging state, apply a current pulse to each battery cell on the electric vehicle and measure the voltage response of each battery cell;

[0097] S3, based on the voltage response, calculate the internal resistance and available capacity of the battery cell, and store the internal resistance and available capacity as the battery cell's performance record;

[0098] S4, obtain the future operation plan of the electric vehicle, and analyze the charging and discharging load that each battery cell will bear in the future operation cycle based on the future operation plan;

[0099] S5, based on performance records and charge / discharge load, analyzes the risk of each battery cell reaching its charge / discharge limit in future operating cycles and obtains the risk analysis results;

[0100] S6 sets a personalized balancing target for each battery cell based on the risk analysis results. When the real-time status information indicates that the electric vehicle is in a low-load operating state or charging state, it pre-allocates energy to the battery cells according to the personalized balancing target to achieve energy storage control.

[0101] To better understand the transportation energy storage control method proposed in this application, the key terms involved will be explained first.

[0102] "Electric vehicles" refers to all types of vehicles that use electricity as their primary power source, such as electric cars, electric buses, and electric trucks.

[0103] A "battery cell" refers to the basic electrochemical energy storage unit that makes up the energy storage system of an electric vehicle. Usually, multiple battery cells are combined to form a battery pack.

[0104] "Real-time status information" refers to the operational data of electric vehicles acquired at a specific point in time, such as vehicle speed, motor power, battery pack voltage, current, and temperature.

[0105] "Low-load operation" refers to the state in which the power demand of an electric vehicle is far lower than its maximum power output capacity during operation, such as when the vehicle is driving at a constant speed on a flat road, idling, or coasting.

[0106] "Charging status" refers to the state in which the battery pack of an electric vehicle is drawing power from an external power source.

[0107] A "current pulse" refers to a current signal with a specific waveform and amplitude applied to a battery cell for a short period of time, used to detect the electrochemical characteristics of the battery cell.

[0108] "Voltage response" refers to the curve or value of the voltage across a battery cell changing over time after a current pulse is applied.

[0109] "Internal resistance" refers to the obstruction to current flow within a battery cell and is a key parameter for measuring the health and power performance of a battery cell. Internal resistance typically includes ohmic resistance and electrochemical polarization resistance.

[0110] "Available power" refers to the amount of electricity that a battery cell can actually release or store under its current health condition, usually expressed in ampere-hours (Ah) or watt-hours (Wh).

[0111] "Performance records" refer to the historical data stored on key performance parameters of battery cells, such as internal resistance and available capacity.

[0112] "Future operation plan" refers to the expected operating mode and tasks of electric vehicles in the future, such as the planned driving route, load, speed curve, etc.

[0113] "Charge and discharge load" refers to the magnitude and duration of the charging and discharging current that a battery cell withstands under specific operating conditions.

[0114] "Charge and discharge limits" refer to the maximum or minimum voltage, current, temperature, and other parameters that a battery cell can withstand during the charging and discharging process. Exceeding these limits may damage the battery cell or shorten its lifespan.

[0115] "Risk analysis results" refers to the output that assesses the likelihood and severity of a battery cell reaching its charge / discharge limits during future operating cycles.

[0116] "Personalized balance target" refers to a customized energy balance strategy target set for each battery cell based on its actual performance and risk analysis results.

[0117] "Energy pre-allocation" refers to the process of adjusting and distributing energy among battery cells in advance, based on individual balance goals, when an electric vehicle is operating under low load or charging conditions, in order to optimize its performance and extend its lifespan.

[0118] The energy storage control method for transportation energy in this application achieves refined management of battery cells in electric vehicles through a series of steps.

[0119] First, it is necessary to obtain real-time status information of the electric vehicle. This can be achieved through an onboard sensor network, for example, by acquiring operating data output from the vehicle controller via the CAN bus, or by collecting parameters such as the voltage, current, and temperature of the battery pack through individual sensors. For instance, a data acquisition unit can be configured, connected to the various sensors in the vehicle, and continuously reading data at a preset sampling frequency (e.g., once every 100 milliseconds). This data can include the vehicle's speed, acceleration, motor speed, total voltage and current of the battery pack, and the voltage and temperature of each battery cell.

[0120] Next, when real-time status information indicates that the electric vehicle is in a low-load operating state or a charging state, a current pulse is applied to each battery cell in the electric vehicle, and the voltage response of each battery cell is measured. In low-load operating states, such as when the vehicle is traveling at a constant speed on a flat road, or in a charging state, the internal electrochemical reactions of the battery cells are relatively stable, allowing for a more accurate voltage response when current pulses are applied. The application of current pulses can be achieved through a pulse generator in the battery management system (BMS), which can generate current pulses of specific amplitude and duration, for example, a charging pulse lasting 1 second with an amplitude of 0.1C (C being the battery capacity), followed by a discharging pulse lasting 1 second with an amplitude of 0.1C. Simultaneously with the pulse application, high-precision voltage sensors measure the voltage change of each battery cell at a higher sampling frequency (e.g., once every 1 millisecond).

[0121] Then, based on the voltage response, the internal resistance and available capacity of the battery cell are calculated and stored as a performance record for the battery cell. Voltage response data can be analyzed using Ohm's law and electrochemical models. For example, the Ohmic resistance of the battery cell can be calculated by analyzing the voltage drop or rise at the moment a current pulse is applied. Electrochemical polarization resistance can be estimated by analyzing the voltage recovery curve after the pulse ends. The calculation of available capacity can be based on the relationship between open-circuit voltage and state of charge (SOC), with corrections made for the influence of internal resistance on voltage. These calculation results—the internal resistance and available capacity of each battery cell—are stored in an onboard storage device or a cloud database, forming a performance record for the battery cell. These records may include a timestamp, battery cell ID, internal resistance value, available capacity value, and environmental conditions at the time of calculation.

[0122] Subsequently, the future operating plan of the electric vehicle is obtained, and based on this plan, the charge and discharge loads that each battery cell will experience during its future operating cycle are analyzed. The future operating plan can be obtained from the vehicle's navigation system, dispatch system, or user input, such as the planned route, expected load, and expected parking time. Based on these plans, vehicle dynamics models and battery models can be used to predict the charge and discharge current, voltage, and temperature profiles that each battery cell may experience during its future operating cycle. For example, if the future plan includes an uphill section, it can be predicted that the battery cell will experience a higher discharge load; if the plan includes a long period of parking, it can be predicted that the battery cell will experience a charging or low-load state.

[0123] Next, based on performance records and charge / discharge loads, the risk of each battery cell reaching its charge / discharge limits during future operating cycles is analyzed, yielding risk analysis results. Risk analysis can employ various methods; for example, based on historical performance records, it can predict the growth trend of internal resistance and the decay trend of usable capacity within the battery cell. By combining future charge / discharge load predictions, the voltage, current, and temperature changes of the battery cell during future operating cycles can be simulated and compared with preset charge / discharge limits (e.g., maximum charging voltage, minimum discharging voltage, maximum operating temperature, etc.). For example, if it is predicted that the voltage of a battery cell may fall below the minimum discharging voltage or the temperature may exceed the maximum operating temperature at some future point, it is marked as high-risk. The risk analysis result can be a risk level, a risk probability value, or a detailed risk report.

[0124] Finally, based on the risk analysis results, a personalized balancing target is set for each battery cell. When real-time status information indicates that the electric vehicle is in a low-load operating state or charging state, energy is pre-allocated to the battery cells according to the personalized balancing target to achieve energy storage control. The personalized balancing target can be dynamically adjusted based on the risk analysis results. For example, for battery cells identified as high-risk, the balancing target may include prioritizing their charging or discharging to prevent them from reaching their charge / discharge limits. Energy pre-allocation can be achieved through the active balancing module in the battery management system. This module can transfer energy from battery cells with higher state of charge (SOC) to those with lower SOC during low-load operating or charging states, or perform targeted energy transfer to specific battery cells based on the personalized balancing target. For example, if a battery cell is predicted to face over-discharge risk in the future, energy can be transferred from other healthy battery cells to it during the current low-load state to improve its SOC.

[0125] Optional, combined Figure 2 As shown, S5 analyzes the risk of each battery cell reaching its charge / discharge limit within future operating cycles based on performance records and charge / discharge loads. The steps to obtain the risk analysis results include:

[0126] S51 continuously collects temperature data on the surface of each battery cell and analyzes the fluctuation and trend of temperature data within a preset short time window.

[0127] S52, based on fluctuations and trends, identifies local temperature rise characteristics of battery cells; when a local temperature rise characteristic indicates that the temperature data of a battery cell increases by a specific amount within a preset short period of time, it is recorded as a local temperature rise event.

[0128] S53, cross-correlation analysis is performed on the local temperature rise event and the corresponding battery cell's charging and discharging current, voltage changes and internal resistance to obtain the cross-correlation analysis results;

[0129] S54. When the cross-correlation analysis results indicate that the local temperature rise event is established, the temperature growth condition is met, and the internal resistance growth condition is met, it is determined that the local temperature rise event is caused by the acceleration of internal side reactions.

[0130] S55 corrects the available capacity and internal resistance of battery cells that are undergoing nonlinear accelerated degradation caused by local temperature rise events.

[0131] S56, based on the corrected available power and internal resistance, and the current operating load of the electric vehicle, adjust the charging and discharging strategy of the battery cell; the steps of adjusting the charging and discharging strategy of the battery cell include: preferentially transferring energy from other battery cells to the battery cell; and reducing the upper limit of the instantaneous charging and discharging current of the battery cell based on the corrected internal resistance.

[0132] Specifically, continuously collecting temperature data from the surface of each battery cell refers to continuously acquiring temperature readings at a preset sampling frequency (e.g., multiple times per second) by deploying high-precision temperature sensors on or near each battery cell surface. Analyzing the fluctuations and trends of these temperature data within a preset short time window (e.g., several seconds to tens of seconds) aims to capture subtle changes in the internal thermal state of the battery cell, which are often early signals of abnormal internal activity.

[0133] Based on fluctuations and trends, the system identifies localized temperature rise characteristics of battery cells. Specifically, this involves real-time processing of collected temperature data using algorithms, such as calculating the rate of temperature change, predicting trends, or comparing it to a baseline temperature. When the temperature rises by a specific amount (e.g., more than 2 degrees Celsius) within a preset time period (e.g., 10 seconds), and this rise is not caused by changes in the external ambient temperature, it is recorded as a localized temperature rise event. The purpose is to accurately locate battery cells that may be experiencing abnormal overheating.

[0134] In practical applications, localized temperature rise events are cross-correlated with the corresponding battery cell's charging / discharging current, voltage changes, and internal resistance. For example, machine learning algorithms or statistical methods can be used to comprehensively analyze temperature data during the localized temperature rise event, along with concurrent charging / discharging current and voltage fluctuations, and internal resistance changes obtained through impulse response methods. The aim is to verify whether the localized temperature rise event is related to abnormal electrochemical behavior of the battery cell, thereby eliminating the possibility of heat generated by external interference or normal operation.

[0135] Furthermore, when the cross-correlation analysis results indicate that a localized temperature rise event is established, the temperature growth condition is met, and the internal resistance growth condition is met, the localized temperature rise event is determined to be caused by the acceleration of internal side reactions. This typically means that when a localized temperature rise event is confirmed, and the rate or magnitude of temperature increase exceeds a preset threshold, while the internal resistance of the battery cell also shows an abnormal growth trend, the system can diagnose that the temperature rise is caused by the acceleration of irreversible side reactions inside the battery (such as SEI film rupture, lithium dendrite growth, etc.), which lead to nonlinear accelerated degradation of battery performance.

[0136] For battery cells undergoing nonlinear accelerated degradation caused by localized temperature rise events, the available capacity and internal resistance of the battery cells are corrected. The aim is to update the actual state parameters of the battery cells to more accurately reflect their current health status. The correction process can be based on a pre-established battery degradation model, combined with real-time monitoring data and diagnostic results, to adjust the available capacity (e.g., corrected using coulomb counting or open-circuit voltage methods) and internal resistance (e.g., corrected using AC impedance spectroscopy or impulse response methods).

[0137] Therefore, based on the revised available power and internal resistance, as well as the current operating load of the electric vehicle, the charging and discharging strategy of the battery cells is adjusted. This adjustment aims to optimize the energy management of the battery cells to slow down the degradation process and ensure operational safety. Specifically, the steps to adjust the charging and discharging strategy of the battery cells include: prioritizing the transfer of energy from other battery cells to the battery cells, which means that within the battery pack, energy is transferred from healthier battery cells to deteriorating battery cells through a balancing management system to reduce their charging and discharging burden. Simultaneously, based on the revised internal resistance, the upper limit of the instantaneous charging and discharging current of the battery cells is reduced to avoid further accelerated degradation due to thermal effects and electrochemical stress caused by high current.

[0138] Optionally, for battery cells undergoing nonlinear accelerated degradation caused by localized temperature rise events, the steps to correct the available capacity and internal resistance of the battery cell include:

[0139] Correct the available power and internal resistance of the battery cell;

[0140] Within a preset time period after the correction is completed, the charging and discharging voltage, current and temperature change rate of the corresponding battery cell are continuously monitored to obtain actual monitoring data;

[0141] Based on the corrected available power and internal resistance, the theoretical voltage response, theoretical current change rate and expected temperature change rate of the battery cell under the current are calculated to obtain the theoretical expected data.

[0142] Determine whether there is a difference between the actual monitoring data and the theoretical expected data that exceeds a preset threshold, and obtain the data difference judgment result;

[0143] When the data difference judgment result indicates that a difference exists, the parameters of the deterioration model or the correction coefficient used for correction are adjusted according to the direction and magnitude of the difference.

[0144] Based on the adjusted degradation model parameters or correction coefficients, the available capacity and internal resistance of the battery cell are corrected a second time.

[0145] Specifically, correcting the available capacity and internal resistance of a battery cell refers to making a preliminary assessment and adjustment of the battery performance degradation caused by localized temperature rise events, based on existing battery degradation models or empirical data. For example, the current available capacity and internal resistance of the battery cell can be preliminarily estimated and corrected based on the severity and duration of the localized temperature rise event, combined with a preset degradation curve.

[0146] Within a preset timeframe after correction, the system continuously monitors the charge / discharge voltage, current, and temperature change rates of the corresponding battery cells to obtain actual monitoring data. The purpose is to obtain the actual operating performance of the corrected battery cells. The preset timeframe can be flexibly configured according to battery type, degradation level, and application scenario; for example, it can be several minutes, several hours, or several charge / discharge cycles. Monitoring is achieved through high-precision sensors integrated into the battery management system (BMS), which collects real-time data on the battery cell's voltage, current, and surface temperature and calculates their rate of change.

[0147] Furthermore, based on the corrected available charge and internal resistance, the theoretical voltage response, theoretical current change rate, and expected temperature change rate of the battery cell under the current current are calculated to obtain theoretical prediction data. This typically involves a battery electrochemical model or equivalent circuit model that can predict the battery's response under ideal conditions based on the battery's current state (corrected available charge and internal resistance) and external excitation (current current). For example, state estimation algorithms such as Kalman filtering, extended Kalman filtering, or unscented Kalman filtering can be used in conjunction with the battery model to predict the battery's theoretical behavior.

[0148] In practical applications, determining whether the actual monitoring data differs from the theoretically expected data by more than a preset threshold is crucial for assessing the accuracy of the initial correction. The preset thresholds can be set based on the system's required correction accuracy, battery characteristics, and environmental noise. For example, thresholds can be set for voltage response difference, current rate of change difference, and temperature rate of change difference. A significant difference is considered to exist when any difference exceeds its corresponding threshold.

[0149] When the data discrepancy assessment indicates its existence, the parameters or correction coefficients of the degradation model used for correction are adjusted according to the direction and magnitude of the discrepancy. This is an adaptive learning mechanism used to correct biases in the initial correction. For example, if the actual voltage response is lower than theoretically expected, it may mean that available power is overestimated or internal resistance is underestimated. In this case, the parameters related to capacity decay or impedance growth in the degradation model can be adjusted accordingly, or the correction coefficients can be adjusted directly. Adjustment methods can employ gradient descent, least squares, or machine learning-based optimization algorithms.

[0150] Therefore, based on the adjusted degradation model parameters or correction coefficients, the usable capacity and internal resistance of the battery cell are corrected a second time. This correction is an optimization based on the first correction and feedback from actual operating data, aiming to make the correction results closer to the actual degradation state of the battery.

[0151] Optionally, adjusting the battery cell charge / discharge strategy based on the corrected available power and internal resistance, as well as the current operating load of the electric vehicle, includes the following steps:

[0152] Continuously monitor the instantaneous power demand and instantaneous power demand change rate of electric vehicles;

[0153] When the instantaneous power demand change rate exceeds the preset instantaneous threshold, the fast response mode is activated;

[0154] In fast response mode, the instantaneous charge and discharge capacity of each battery cell is calculated based on the corrected available power and internal resistance, as well as the current instantaneous power demand.

[0155] Based on instantaneous charge and discharge capabilities, prioritize adjusting the charge and discharge strategies of battery cells in a healthy state;

[0156] Maintain the charging and discharging current of the battery cells that are deteriorating rapidly within the corrected safety range;

[0157] Adjust the charge and discharge cutoff voltage of the battery cells that accelerate deterioration based on the corrected internal resistance;

[0158] Continuously monitor the voltage, current, and temperature change rate of each battery cell;

[0159] The charging and discharging strategy is fine-tuned based on the voltage, current, and temperature change rate of the battery cells.

[0160] Specifically, continuously monitoring the instantaneous power demand and instantaneous power demand change rate of electric vehicles refers to acquiring the vehicle's current power output or input demand in real time through onboard sensors or powertrain controllers, and calculating its rate of change over a very short period of time. The purpose is to grasp the dynamic energy demand of the vehicle in real time, providing accurate data for subsequent rapid response.

[0161] When the instantaneous rate of change in power demand exceeds a preset instantaneous threshold, the fast response mode is activated. This preset instantaneous threshold can be set based on vehicle type, battery system characteristics, and safety requirements. For example, a fast response is triggered when the power demand changes by more than 20% within 100 milliseconds. The fast response mode is designed to handle sudden high power demands or energy recovery, preventing delays that could impact vehicle performance or battery life.

[0162] In fast response mode, the instantaneous charge / discharge capacity of each battery cell is calculated based on the corrected available capacity and internal resistance, as well as the current instantaneous power demand. Instantaneous charge / discharge capacity refers to the maximum charge / discharge power that the battery cell can provide at the current moment without damaging it. This calculation comprehensively considers the actual health condition of the battery cell (corrected available capacity and internal resistance) and the current external demand to ensure the rationality of energy allocation.

[0163] Based on instantaneous charge and discharge capabilities, the charge and discharge strategies of healthy battery cells are prioritized. This means that, while meeting the total power demand, more charge and discharge load is allocated to healthy battery cells to reduce the burden on rapidly deteriorating cells and slow down their degradation process. For example, this can be achieved by adjusting the charge and discharge rate or duration of healthy battery cells.

[0164] At the same time, the charging and discharging current of the battery cells, which are prone to accelerated degradation, is maintained within the corrected safety range. This is to prevent further degradation due to overcharging and discharging, ensuring that they operate within safe boundaries. The corrected safety range is calculated based on their corrected internal resistance and available capacity.

[0165] Furthermore, based on the corrected internal resistance, the charge / discharge cutoff voltage of the battery cells that accelerate degradation is adjusted. Increased internal resistance leads to a larger voltage drop in the battery cells at the same current, therefore the cutoff voltage needs to be adjusted accordingly to avoid overcharging or over-discharging and further protect the degraded battery cells.

[0166] To achieve more precise control, this application also proposes continuous monitoring of the voltage, current, and temperature change rates of each battery cell. These parameters are the most direct reflection of the battery cell's operating status, and their change rates can reveal the internal dynamics of the battery cell in a timely manner.

[0167] The charging and discharging strategy is fine-tuned based on the voltage, current, and temperature change rate of the battery cells. This fine-tuning is a precise adjustment based on real-time feedback. For example, when the temperature change rate of a battery cell rises abnormally, its charging and discharging current can be slightly reduced to prevent overheating; or when voltage fluctuations are large, its balancing strategy can be adjusted. The goal is to achieve more stable, efficient, and safer energy management with rapid response.

[0168] Optionally, the steps for continuously monitoring the rate of change of voltage, current, and temperature for each battery cell include:

[0169] Continuously collect the voltage and current of each battery cell;

[0170] Continuously collect temperature data from the surface of each battery cell and the internal ambient temperature data of the battery pack;

[0171] Analyze the fluctuations and trends of internal ambient temperature data, identify and quantify the impact of ambient temperature fluctuations on the temperature data of the battery cell surface, and obtain quantitative results.

[0172] Based on the quantification results, external thermal interference is separated from the temperature data on the surface of the battery cell, and the temperature change rate reflecting the true thermal state inside the battery cell is obtained.

[0173] Specifically, continuously collecting voltage and current data for each battery cell is fundamental data for obtaining the battery cell's electrical state, used to assess its instantaneous operating point and energy flow. Simultaneously, continuously collecting temperature data from the surface of each battery cell and the internal ambient temperature data of the battery pack aims to provide more comprehensive thermal environment information. The surface temperature data of the battery cell directly reflects its external thermal performance, while the internal ambient temperature data provides background information on the macroscopic thermal environment in which the battery cells are located. By analyzing the fluctuations and trends in the internal ambient temperature data, the impact of ambient temperature fluctuations on the surface temperature data of the battery cells can be identified and quantified, thus obtaining a quantitative result. This quantitative result characterizes the contribution of external thermal disturbances to the surface temperature of the battery cells. Furthermore, based on the quantitative result, external thermal disturbances can be separated from the surface temperature data of the battery cells, thereby obtaining the temperature change rate reflecting the true internal thermal state of the battery cells. This separation mechanism ensures that the obtained temperature change rate more accurately reflects the heat generation or absorption within the battery cells, rather than being simply driven by changes in ambient temperature.

[0174] In some preferred embodiments, it is assumed that during the operation of an electric vehicle, the internal ambient temperature of its battery pack fluctuates due to changes in the external environment (e.g., the vehicle entering or exiting a tunnel, passing through different climate zones) or adjustments by the internal air conditioning system. If the temperature change rate is calculated solely based on the temperature data of the battery cell surface, these fluctuations in external ambient temperature may be misinterpreted as changes in the thermal state inside the battery cell, leading to incorrect adjustments to the charging and discharging strategy. For example, when the ambient temperature suddenly rises, the surface temperature of the battery cell may also rise. If this is not distinguished, the system may incorrectly assume that heat generation inside the battery cell has increased, and thus adopt conservative strategies such as reducing the charging and discharging current, affecting vehicle performance. However, with the solution of this application, the system simultaneously collects the internal ambient temperature data of the battery pack and analyzes its fluctuations. For example, if the ambient temperature rises by 5°C, and quantitative analysis reveals that this 5°C increase contributes 3°C to the surface temperature of the battery cell, then the system can subtract this 3°C external thermal interference from the total temperature change of the battery cell surface, thereby obtaining a more accurate temperature change rate that reflects the true thermal state inside the battery cell. In this way, even if the ambient temperature fluctuates, the system can accurately determine whether there is a real risk of internal overheating in the battery cell, thereby avoiding unnecessary strategy adjustments or making timely and effective interventions when truly needed.

[0175] Optionally, the steps of fine-tuning the charge / discharge strategy based on the voltage, current, and temperature change rate of the battery cells include:

[0176] Continuously identify the current operating conditions of electric vehicles;

[0177] Adjust the response speed and adjustment range of the fine-tuning operation according to the operating conditions;

[0178] Continuously assess the degree of degradation of each battery cell;

[0179] Based on the degree of degradation, a corresponding set of fine-tuning parameters is set for each battery cell;

[0180] After the fine-tuning operation is performed, the voltage, current and temperature change rate of the battery cell are continuously monitored, and the effect judgment result is obtained based on the voltage, current and temperature change rate of the battery cell.

[0181] If the effect judgment result indicates that the expected effect has not been achieved, then based on the deviation between the actual effect and the expected effect, the response speed, adjustment range and fine-tuning parameter set of the fine-tuning operation are iteratively optimized.

[0182] Specifically, continuously identifying the current operating condition of electric vehicles refers to determining the vehicle's operating status in real time by collecting information such as vehicle speed, acceleration, motor torque, GPS data, and driver operation. This status could include accelerating, decelerating, maintaining a constant speed, climbing, descending, or being heavily or lightly loaded. The identification of operating conditions can be achieved using machine learning algorithms or rule-based expert systems.

[0183] Adjusting the response speed and adjustment range of fine-tuning operations based on operating conditions means that the execution method of the fine-tuning strategy will differ under different operating scenarios. For example, in operating conditions where electric vehicles require rapid response (such as emergency acceleration or braking), the response speed of fine-tuning operations will be increased, and the adjustment range may also be increased accordingly to ensure that the system can quickly adapt to changes; while in stable driving conditions, a smoother response speed and a smaller adjustment range can be used to avoid unnecessary fluctuations.

[0184] In practical applications, continuously assessing the degradation level of each battery cell involves analyzing data such as the rate of increase in internal resistance, the rate of decrease in usable capacity, the number of cycles, and historical temperature, combined with a pre-defined degradation model, to calculate and update the health status or degradation index of each battery cell in real time. The purpose is to provide an accurate basis for subsequent personalized fine-tuning.

[0185] Furthermore, based on the degree of degradation, a corresponding set of fine-tuning parameters is set for each battery cell. This means that the system will allocate different charging and discharging parameters for battery cells with different degrees of degradation. For example, for battery cells with a high degree of degradation, the upper limit of their maximum charging and discharging current may be appropriately reduced, and the safety margin of the charging and discharging cutoff voltage may be expanded to protect the battery and delay its further degradation; while for battery cells in good health, they can be allowed to charge and discharge within a wider range to fully utilize their performance.

[0186] Furthermore, based on the continuous monitoring of the voltage, current, and temperature change rates of the battery cells after the fine-tuning operation is performed, and the determination of whether the fine-tuning has achieved the expected effect based on the voltage, current, and temperature change rates of the battery cells, a key feedback mechanism is formed. The system compares the actual monitored battery response (such as voltage stability, current distribution uniformity, and whether the temperature change rate is within the safe range) with the preset expected target in real time. The purpose is to verify the effectiveness of the current fine-tuning strategy.

[0187] If the effect assessment indicates that the expected results have not been achieved, the system iteratively optimizes the response speed, adjustment range, and fine-tuning parameter set of the fine-tuning operation based on the deviation between the actual and expected results. This means that when the fine-tuning effect is unsatisfactory, the system will automatically adjust and optimize various parameters of the fine-tuning strategy according to the direction and magnitude of the actual deviation. For example, it may speed up the response speed, increase or decrease the adjustment range, or modify the fine-tuning parameter set of a specific battery cell, thereby achieving adaptive learning and continuous improvement.

[0188] As a specific implementation method, a concrete example is given below. Assume an electric bus operating in a city, its battery pack consisting of multiple battery cells. During a certain operation, the bus needs to climb a long hill, and the system identifies the current operating condition as "high-load hill climbing." Simultaneously, the system continuously assesses and finds that battery cell A has a higher degree of degradation, with a slight increase in internal resistance, while battery cell B is in a healthy state. Based on this, the system sets a relatively conservative set of fine-tuning parameters for battery cell A, such as slightly lowering its instantaneous charge / discharge current limit and increasing its charge / discharge cutoff voltage safety margin; while setting a relatively aggressive set of parameters for battery cell B, allowing it to provide greater power output within a safe range. After the fine-tuning operation is executed, the system continuously monitors the voltage, current, and temperature change rates of battery cells A and B. If the monitoring results show that the temperature rise rate of battery cell A is too fast, exceeding the expected safe range, the effect judgment result is "the expected effect has not been achieved." At this point, the system iteratively optimizes the fine-tuning parameter set of battery cell A based on the actual deviation. For example, it may further reduce the current contribution ratio of battery cell A under high-load ramp-up conditions or adjust its charge / discharge cutoff voltage to ensure that it operates within a safe range. In this way, the system can dynamically adjust the charge / discharge strategy according to real-time operating conditions and individual differences of battery cells, achieving refined and adaptive energy storage control.

[0189] Optionally, steps for continuously assessing the degree of degradation of each battery cell include:

[0190] Continuously monitor the current, voltage, and temperature of each battery cell;

[0191] Adjust the weighting parameters of the deterioration assessment according to the operating conditions;

[0192] The real-time degradation index of each battery cell is calculated based on the current, voltage, temperature, and adjusted weighting parameters of the battery cell.

[0193] Continuously track the growth rate of internal resistance and the decay rate of available power of each battery cell, and perform correlation analysis with the real-time degradation index;

[0194] When the analysis results indicate that the real-time degradation index, the growth rate of internal resistance, and the decay rate of available power all meet their respective preset conditions, the degree of degradation of the battery cell is determined.

[0195] Specifically, continuous monitoring of the current, voltage, and temperature of each battery cell aims to acquire fundamental electrochemical and thermal data during actual operation. This data is crucial input for assessing battery health. Operating conditions can be understood as the operating modes of an electric vehicle within a specific time period, such as high-speed driving, urban congestion, and charging while parked. Different operating conditions have varying impacts on the battery degradation process. Therefore, adjusting the weighting parameters of the degradation assessment based on operating conditions aims to enable the degradation assessment model to dynamically adapt to different operating scenarios and more accurately reflect the main drivers of battery degradation under current conditions. For example, under high-temperature and high-load conditions, the weights of temperature- and current-related degradation indicators can be increased.

[0196] Furthermore, based on the current, voltage, temperature, and adjusted weighting parameters of each battery cell, a real-time degradation index is calculated for each cell. This real-time degradation index is a comprehensive indicator used to quantify the current degree of degradation of the battery cell. Its calculation can be based on a preset degradation model that combines multiple degradation mechanisms (such as SEI film growth, active material loss, and increased internal resistance) and their performance under different operating conditions. In addition, the growth rate of internal resistance and the rate of decrease in usable capacity of each battery cell are continuously tracked and correlated with the real-time degradation index. The increase in internal resistance and the decrease in usable capacity are direct manifestations of battery aging. By correlating them with the real-time degradation index, the accuracy of the index can be verified and calibrated, thereby improving the reliability of degradation assessment. The degree of degradation of the battery cell is determined when the analysis results indicate that the growth rate of the real-time degradation index, the rate of increase in internal resistance, and the rate of decrease in usable capacity simultaneously meet their respective preset conditions. This means that the degradation state of the battery cell is only finally confirmed when multiple key indicators point to degradation, avoiding misjudgments that may result from a single indicator.

[0197] In some preferred embodiments, a specific example is given below. Assume an electric vehicle is operating in urban congestion conditions, where the system continuously monitors the current, voltage, and temperature of each battery cell. Because urban congestion typically involves frequent start-stop cycles and low-speed operation, battery cells may experience more shallow charge-discharge cycles and lower heat dissipation efficiency. Therefore, the weighting parameters for degradation assessment are adjusted according to the operating conditions; for example, the rate of temperature rise and voltage fluctuations at low currents may be given higher weights.

[0198] Based on the adjusted weighting parameters, the system calculates a real-time degradation index for each battery cell. For example, if the real-time degradation index of a battery cell indicates a high degree of degradation, the system will further track the rate of increase in internal resistance and the rate of decrease in usable capacity of that battery cell. Suppose that after a period of monitoring, the rate of increase in internal resistance of the battery cell exceeds a preset threshold, and the rate of decrease in usable capacity also exceeds another preset threshold. When all three indicators—the real-time degradation index, the rate of increase in internal resistance, and the rate of decrease in usable capacity—simultaneously meet their respective preset conditions, the system will determine that the battery cell has significant degradation.

[0199] This multi-dimensional and dynamically adjustable evaluation method can accurately identify the degradation state of battery cells even under complex operating conditions, thus providing a reliable basis for subsequent fine-tuning of charging and discharging strategies. For example, the maximum charging and discharging current limit of the degraded battery cell can be reduced, or its charging and discharging cutoff voltage can be adjusted to slow down its degradation rate and ensure the overall operational safety of the system.

[0200] Optionally, after setting a corresponding set of fine-tuning parameters for each battery cell based on the degree of degradation, the method further includes:

[0201] Continuously monitor the operational attitude data of electric vehicles;

[0202] Identify whether there are abnormal impact characteristics in the running posture data that are consistent with road bumps or minor collisions;

[0203] After identifying abnormal impact characteristics, a rapid diagnostic process for the impacted battery cell is initiated. The rapid diagnostic process includes charging and discharging the impacted battery cell with a preset pulse current and acquiring the corresponding voltage response and internal resistance change rate at high frequency.

[0204] The voltage response and internal resistance change rate collected at high frequency are compared with the corresponding baseline data before the impact to identify whether there is a voltage fluctuation pattern or a sudden increase in internal resistance.

[0205] When a voltage fluctuation pattern or a sudden increase in internal resistance is detected, it is determined that there is hidden damage to the battery cell.

[0206] For battery cells with hidden damage, when adjusting the fine-tuning parameter set, the upper limit of the maximum charge and discharge current should be reduced first, and the safety margin of the charge and discharge cutoff voltage should be increased.

[0207] Specifically, operational attitude data can be understood as data reflecting the dynamic information of an electric vehicle in three-dimensional space, including its position, velocity, acceleration, angular velocity, and attitude (such as pitch, roll, and yaw). This data is typically collected using sensors such as inertial measurement units (IMUs), accelerometers, and gyroscopes. Its purpose is to monitor the vehicle's motion status in real time, providing fundamental data for identifying external impacts.

[0208] Abnormal impact characteristics refer to signals in the operating posture data that differ significantly from fluctuations under normal driving conditions, exhibiting instantaneous high amplitude or specific frequency characteristics. For example, an accelerometer may detect a drastic acceleration change exceeding a preset threshold within a short period, or a gyroscope may detect an abnormal change in angular velocity. These characteristics are used to determine whether the electric vehicle has encountered events that could cause mechanical stress to the battery cells, such as road bumps, pothole impacts, or minor collisions.

[0209] In practical applications, the rapid diagnostic process refers to a series of targeted detection steps that the system immediately initiates upon identifying abnormal impact characteristics, aiming to quickly assess the health status of the impacted battery cell. The "rapid" aspect of this process is its short response time, enabling it to be performed immediately after the impact to prevent further deterioration of potential damage.

[0210] Specifically, charging and discharging a battery cell subjected to an impact using a preset pulsed current refers to simulating its actual charging and discharging process by applying current pulses of specific waveforms and amplitudes to the battery cell. High-frequency acquisition of the corresponding voltage response and internal resistance change rate involves acquiring voltage change data of the battery cell at a frequency far exceeding that of conventional monitoring (e.g., every millisecond or less) while applying the pulsed current, and calculating the instantaneous change rate of its internal resistance in real time. Its purpose is to capture subtle electrochemical or structural changes that may be caused by the impact.

[0211] The baseline data refers to the voltage response and internal resistance change rate data collected under the same or similar pulse current when the battery cell is unaffected or in a healthy state. By comparing the high-frequency collected voltage response and internal resistance change rate with these baseline data, anomalies caused by the impact can be identified. Voltage fluctuation patterns refer to abnormal instantaneous peaks, drops, oscillations, or prolonged recovery times in the voltage response curve. A sudden increase in internal resistance refers to a significant and unexpected rise in internal resistance within a short period of time. These abnormal patterns or sudden increases are potential indicators of internal structural damage to the battery cell (such as separator microcracks, electrode detachment, current collector deformation, etc.).

[0212] When the above voltage fluctuation patterns or sudden increase in internal resistance are detected, the system determines that the battery cell has hidden damage. Hidden damage refers to battery cells that may not show obvious damage on the outside, but whose internal structure or electrochemical performance has been affected, which may lead to a decrease in performance, a shortened lifespan, or an increased safety risk.

[0213] For battery cells with latent damage, when adjusting the fine-tuning parameter set, prioritizing a reduction in the maximum charge / discharge current limit means that to prevent further damage, the system will limit the maximum charge / discharge current that the battery cell can withstand per unit time. Increasing the safety margin of the charge / discharge cutoff voltage means stopping charging and discharging before the battery cell reaches the charge or discharge cutoff voltage, providing the battery cell with a larger operational safety range to reduce stress under extreme conditions.

[0214] Optionally, the steps for continuously monitoring the operational attitude data of electric vehicles include:

[0215] Identify the types of electric vehicles;

[0216] Select the appropriate attitude data acquisition frequency, sensor sensitivity, and impact recognition threshold based on the type of electric vehicle.

[0217] Adjust the parameters of the attitude data filtering algorithm, the selected attitude data acquisition frequency, and the sensor sensitivity according to the type of electric vehicle;

[0218] Based on the parameters of the adjusted attitude data filtering algorithm, the selected attitude data acquisition frequency, and the sensor sensitivity, the operating attitude data of the electric vehicle is monitored. The monitored operating attitude data is then compared with the selected impact identification threshold in real time to determine whether there are any abnormal impact characteristics.

[0219] Specifically, identifying the type of electric vehicle means that before the system begins monitoring, it first determines which category the current electric vehicle belongs to, such as electric cars, electric buses, electric trucks, electric motorcycles, etc. Different types of vehicles have different masses, suspension systems, body stiffness, and typical operating vibration modes, all of which directly affect the characteristics of their attitude data.

[0220] In this context, selecting appropriate attitude data acquisition frequencies, sensor sensitivities, and impact recognition thresholds based on the type of electric vehicle can be understood as customizing parameters to suit different vehicle characteristics. For example, a heavy electric truck may have a less sensitive response to road bumps, so a lower attitude data acquisition frequency and lower sensor sensitivity can be selected, while a higher impact recognition threshold can be set to avoid misinterpreting normal large vibrations as abnormal impacts. Conversely, a light electric car is more sensitive to road impacts, so a higher attitude data acquisition frequency and higher sensor sensitivity can be selected, while a lower impact recognition threshold can be set to ensure that subtle abnormal impacts can be detected.

[0221] In practical applications, adjusting the parameters of the attitude data filtering algorithm, the selected attitude data acquisition frequency, and the sensor sensitivity according to the type of electric vehicle refers to dynamically adjusting the filter characteristics based on the vehicle type during data acquisition and processing. For example, for vehicles with a wide vibration frequency range, a wider filtering algorithm can be used; for vehicles with significant vibration at specific frequencies, a narrowband notch filter can be used to eliminate background noise, thereby more clearly separating the impact signal. The selected attitude data acquisition frequency and sensor sensitivity are also finely adjusted at this stage to ensure that the acquired data fully reflects the vehicle's true attitude changes while effectively suppressing irrelevant noise.

[0222] Therefore, based on the adjusted parameters of the attitude data filtering algorithm, the selected attitude data acquisition frequency, and sensor sensitivity, the operating attitude data of the electric vehicle is monitored. This data is then compared in real-time with a selected impact identification threshold to determine if any abnormal impact characteristics exist. This means that the attitude data, after customized parameter configuration and optimized filtering, will be compared in real-time with an impact identification threshold that has also been matched to the vehicle type. This customized comparison can more accurately determine whether the monitored attitude data truly represents abnormal impact events such as road bumps or minor collisions, thereby improving the accuracy of abnormal impact feature identification.

[0223] In some preferred embodiments, a specific example is given below. Suppose there are two different types of electric vehicles: one is a heavy-duty electric bus, and the other is a light-duty electric car.

[0224] For heavy-duty electric buses, due to their large mass and stiff suspension system, their response to road bumps is usually relatively smooth. Therefore, when monitoring their operating attitude data, the system will identify them as "electric buses" and accordingly select a lower attitude data acquisition frequency (e.g., 50Hz), set a lower sensor sensitivity (to avoid being overly sensitive to minor vibrations), and set a higher impact recognition threshold (e.g., only acceleration peaks exceeding 5g are considered impacts). Simultaneously, the parameters of the attitude data filtering algorithm will be adjusted to handle low-frequency, high-amplitude vibrations, for example, using a low-pass filter with a low cutoff frequency to filter out high-frequency noise.

[0225] For lightweight electric cars, their lighter weight and relatively softer suspension make them more sensitive to road bumps. Once the system identifies them as "electric cars," it selects a higher attitude data acquisition frequency (e.g., 200Hz), sets a higher sensor sensitivity (to capture subtle attitude changes), and sets a lower impact recognition threshold (e.g., an acceleration peak exceeding 2g is considered an impact). The parameters of the attitude data filtering algorithm are then adjusted to handle high-frequency, small-amplitude vibrations, for example, by using a finer bandpass filter to retain key impact signals and remove interference from other frequencies.

[0226] By customizing parameters and adjusting algorithms based on vehicle type, the operating posture data of both heavy-duty buses and light-duty cars can be monitored and analyzed more accurately, thereby effectively identifying abnormal impact characteristics and providing a reliable basis for subsequent diagnosis of hidden battery damage.

[0227] This application also discloses a transportation energy storage control system for performing transportation energy storage control, combined with... Figure 3 As shown, the transportation energy storage control system 1 includes:

[0228] The real-time status acquisition module 11 is used to acquire the real-time status information of the electric vehicle.

[0229] The voltage response measurement module 12 is used to apply current pulses to each battery cell of the electric vehicle and measure the voltage response of each battery cell when the real-time status information indicates that the electric vehicle is in a low-load operating state or a charging state.

[0230] The performance record storage module 13 is used to calculate the internal resistance and available power of the battery cell based on the voltage response, and store the internal resistance and available power as the performance record of the battery cell.

[0231] The future load analysis module 14 is used to obtain the future operation plan of the electric vehicle and analyze the charging and discharging load that each battery cell will bear in the future operation cycle based on the future operation plan.

[0232] The risk analysis and processing module 15 is used to analyze the risk of each battery cell reaching its charge and discharge limit in future operating cycles based on performance records and charge and discharge load, and obtain the risk analysis results.

[0233] The energy control execution module 16 is used to set a personalized balancing target for each battery cell based on the risk analysis results, and to pre-allocate energy to the battery cells according to the personalized balancing target when the real-time status information indicates that the electric vehicle is in a low-load operation state or charging state, so as to achieve energy storage control.

[0234] Specifically, the real-time status acquisition module can be a combination of hardware interface and software driver integrated into the battery management system or vehicle control unit. For example, this module may include a CAN bus interface for receiving data streams from various vehicle sensors and controllers, such as vehicle speed, motor torque, total battery pack voltage, total current, and the voltage and temperature of each battery cell. Furthermore, it may include a data preprocessing unit to filter, calibrate, and convert the format of the acquired raw data to ensure data accuracy and consistency.

[0235] The voltage response measurement module can consist of a high-precision current source and a high-sampling-rate voltage acquisition circuit. For example, under low-load operation or charging conditions, this module can apply current pulses of preset waveforms and amplitudes to selected battery cells by controlling a power electronic switch. Simultaneously, the high-precision voltage acquisition circuit synchronously measures the voltage change of the battery cell under the pulse at a sampling frequency of milliseconds or even microseconds. To improve measurement accuracy, the module can also integrate a temperature compensation circuit to eliminate the influence of ambient temperature on the voltage response measurement.

[0236] The performance record storage module can be an embedded memory or a database server connected to the vehicle network. For example, this module receives voltage response data from the voltage response measurement module and uses a built-in electrochemical model and algorithm to calculate the internal resistance and available capacity of the battery cell. The calculation results, along with metadata such as timestamps and battery cell IDs, are stored in a structured manner to form a traceable performance record. To ensure data reliability, the module can also implement redundant data storage and error checking mechanisms.

[0237] The future load analysis module can be a prediction engine built upon vehicle dynamics and battery models. For example, this module can receive future operating plans from navigation systems, dispatch systems, or user input, such as driving routes, expected loads, and predicted ambient temperatures. Based on these inputs, the module uses pre-defined vehicle dynamics and battery charge / discharge models to simulate the power demand of the electric vehicle during future operating cycles, further decomposing it into the charge / discharge current and voltage curves that each battery cell may withstand. The module can also consider factors such as driver behavior patterns and traffic conditions to improve prediction accuracy.

[0238] The risk analysis processing module can be a decision support system based on rule engines, machine learning models, or fuzzy logic. For example, this module receives battery cell performance data from the performance record storage module and charge / discharge load predictions from the future load analysis module. This module inputs this data into a pre-trained risk assessment model, which, based on historical degradation data and charge / discharge limit thresholds, predicts the probability and severity of each battery cell reaching extreme states such as overcharge, over-discharge, or overheating within future operating cycles. The risk analysis results can be output in the form of risk level, risk index, or warning information.

[0239] The energy control execution module can be an active balancing controller and charge / discharge strategy optimizer integrated into the battery management system. For example, this module receives risk analysis results from the risk analysis processing module and dynamically sets personalized balancing targets for each battery cell based on these results. When the real-time status acquisition module indicates that the electric vehicle is in a low-load operating state or charging state, this module activates the active balancing circuit. Based on the personalized balancing targets, it transfers energy from high-charge-state battery cells to low-charge-state battery cells via an energy converter, or performs targeted energy pre-allocation for specific high-risk battery cells. This module can also adjust the charge / discharge cutoff voltage and current limit of the battery cells based on the risk analysis results to prevent them from reaching their charge / discharge limits.

[0240] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for controlling energy storage in transportation systems, characterized in that, include: Obtain real-time status information of electric vehicles; When the real-time status information indicates that the electric vehicle is in a low-load operating state or a charging state, a current pulse is applied to each battery cell on the electric vehicle, and the voltage response of each battery cell is measured. Based on the voltage response, the internal resistance and available capacity of the battery cell are calculated, and the internal resistance and available capacity are stored as a performance record of the battery cell. Obtain the future operation plan of the electric vehicle, and based on the future operation plan, analyze the charging and discharging load that each battery cell will bear during the future operation cycle; Based on the performance records and the charge / discharge load, the risk of each battery cell reaching its charge / discharge limit in future operating cycles is analyzed, and the risk analysis results are obtained. Based on the risk analysis results, a personalized balancing target is set for each battery cell. When the real-time status information indicates that the electric vehicle is in a low-load operating state or charging state, energy is pre-allocated to the battery cells according to the personalized balancing target to achieve energy storage control. The step of analyzing the risk of each battery cell reaching its charge / discharge limit in future operating cycles based on the performance records and the charge / discharge load, and obtaining the risk analysis results, includes: Continuously collect temperature data on the surface of each battery cell and analyze the fluctuation and trend of the temperature data within a preset short time window; Based on the fluctuations and trends, the local temperature rise characteristics of the battery cell are identified; when the local temperature rise characteristics indicate that the temperature data of the battery cell increases by a certain amount within a preset short period of time, it is recorded as a local temperature rise event. The local temperature rise event is cross-correlationally analyzed with the corresponding battery cell's charging and discharging current, voltage changes, and internal resistance to obtain the cross-correlation analysis results. When the cross-correlation analysis results indicate that a local temperature rise event is established, the temperature growth condition is met, and the internal resistance growth condition is met, it is determined that the local temperature rise event is caused by the acceleration of internal side reactions. For battery cells undergoing nonlinear accelerated degradation caused by the aforementioned localized temperature rise event, the available capacity and internal resistance of the battery cells are corrected. Based on the corrected available power and internal resistance, as well as the current operating load of the electric vehicle, the charging and discharging strategy of the battery cell is adjusted; the steps of adjusting the charging and discharging strategy of the battery cell include: preferentially transferring energy from other battery cells to the battery cell; and reducing the upper limit of the instantaneous charging and discharging current of the battery cell based on the corrected internal resistance.

2. The transportation energy storage control method according to claim 1, characterized in that, The step of correcting the available capacity and internal resistance of a battery cell undergoing nonlinear accelerated degradation caused by the localized temperature rise event includes: The available power and internal resistance of the battery cell are corrected; Within a preset time period after the correction is completed, the charging and discharging voltage, current and temperature change rate of the battery cell are continuously monitored to obtain actual monitoring data; Based on the corrected available power and internal resistance, the theoretical voltage response, theoretical current change rate and expected temperature change rate of the battery cell under the current are calculated to obtain the theoretical expected data. Determine whether there is a difference between the actual monitoring data and the theoretical expected data that exceeds a preset threshold, and obtain the data difference judgment result; When the data difference judgment result indicates that a difference exists, the degradation model parameters or correction coefficients used for correction are adjusted according to the direction and magnitude of the difference. Based on the adjusted degradation model parameters or correction coefficients, the available power and internal resistance of the battery cell are corrected a second time.

3. The transportation energy storage control method according to claim 1, characterized in that, The step of adjusting the charging and discharging strategy of the battery cell based on the corrected available power and internal resistance, as well as the current operating load of the electric vehicle, includes: Continuously monitor the instantaneous power demand and instantaneous power demand change rate of electric vehicles; When the instantaneous power demand change rate exceeds a preset instantaneous threshold, a fast response mode is activated; In the fast response mode, the instantaneous charge and discharge capacity of each battery cell is calculated based on the corrected available power and internal resistance, as well as the current instantaneous power demand. Based on the instantaneous charge and discharge capability, the charge and discharge strategy of the battery cells in a healthy state is preferentially adjusted. Maintain the charging and discharging current of the battery cells that are deteriorating rapidly within the corrected safety range; Adjust the charge and discharge cutoff voltage of the battery cells that accelerate deterioration based on the corrected internal resistance; Continuously monitor the voltage, current, and temperature change rate of each battery cell; The charging and discharging strategy is fine-tuned based on the voltage, current, and temperature change rate of the battery cells.

4. The transportation energy storage control method according to claim 3, characterized in that, The steps for continuously monitoring the rate of change of voltage, current, and temperature of each battery cell include: Continuously collect the voltage and current of each battery cell; Continuously collect temperature data from the surface of each battery cell and the internal ambient temperature data of the battery pack; Analyze the fluctuations and trends of the internal ambient temperature data, identify and quantify the impact of ambient temperature fluctuations on the temperature data of the battery cell surface, and obtain the quantification results. Based on the quantification results, external thermal interference is separated from the temperature data on the surface of the battery cell to obtain the temperature change rate that reflects the true thermal state inside the battery cell.

5. The transportation energy storage control method according to claim 3, characterized in that, The step of fine-tuning the charging and discharging strategy based on the voltage, current, and temperature change rate of the battery cells includes: Continuously identify the current operating conditions of electric vehicles; Adjust the response speed and adjustment range of the fine-tuning operation according to the operating conditions described. Continuously assess the degree of degradation of each battery cell; Based on the degree of degradation, a corresponding set of fine-tuning parameters is set for each battery cell; After the fine-tuning operation is performed, the voltage, current and temperature change rate of the battery cell are continuously monitored, and the effect judgment result is obtained based on the voltage, current and temperature change rate of the battery cell. If the effect judgment result indicates that the expected effect has not been achieved, then based on the deviation between the actual effect and the expected effect, the response speed, adjustment range and fine-tuning parameter set of the fine-tuning operation are iteratively optimized.

6. The transportation energy storage control method according to claim 5, characterized in that, The steps for continuously assessing the degree of degradation of each battery cell include: Continuously monitor the current, voltage, and temperature of each battery cell; Adjust the weighting parameters of the degradation assessment based on the operating conditions described. The real-time degradation index of each battery cell is calculated based on the current, voltage, temperature, and adjusted weighting parameters of the battery cell. The rate of increase in internal resistance and the rate of decrease in available charge of each battery cell are continuously tracked and correlated with the real-time degradation index. When the analysis results indicate that the real-time degradation index, the growth rate of internal resistance, and the decay rate of available power all meet their respective preset conditions, the degree of degradation of the battery cell is determined.

7. The transportation energy storage control method according to claim 5, characterized in that, After the step of setting a corresponding fine-tuning parameter set for each battery cell based on the degree of degradation, the method further includes: Continuously monitor the operational attitude data of electric vehicles; Identify whether there are abnormal impact characteristics in the running posture data that correspond to road bumps or minor collisions; After identifying the abnormal impact characteristics, a rapid diagnostic process for the impacted battery cell is initiated; the rapid diagnostic process includes: charging and discharging the impacted battery cell with a preset pulse current, and acquiring the corresponding voltage response and internal resistance change rate at high frequency. The voltage response and internal resistance change rate collected at high frequency are compared with the corresponding baseline data before the impact to identify whether there is a voltage fluctuation pattern or a sudden increase in internal resistance. When the voltage fluctuation pattern or a sudden increase in internal resistance is detected, it is determined that the battery cell has hidden damage. For battery cells with the aforementioned latent damage, when adjusting the fine-tuning parameter set, the upper limit of the maximum charge and discharge current should be reduced first, and the safety margin of the charge and discharge cutoff voltage should be increased.

8. The transportation energy storage control method according to claim 7, characterized in that, The steps for continuously monitoring the operational attitude data of electric vehicles include: Identify the types of electric vehicles; Select the appropriate attitude data acquisition frequency, sensor sensitivity, and impact recognition threshold based on the type of electric vehicle. Adjust the parameters of the attitude data filtering algorithm, the selected attitude data acquisition frequency, and the sensor sensitivity according to the type of electric vehicle; Based on the parameters of the adjusted attitude data filtering algorithm, the selected attitude data acquisition frequency, and the sensor sensitivity, the operating attitude data of the electric vehicle is monitored. The monitored operating attitude data is then compared with the selected impact identification threshold in real time to determine whether there are any abnormal impact characteristics.

9. A transportation energy storage control system for performing transportation energy storage control, characterized in that, include: The real-time status acquisition module is used to acquire real-time status information of electric vehicles. A voltage response measurement module is used to apply current pulses to each battery cell of the electric vehicle and measure the voltage response of each battery cell when the real-time status information indicates that the electric vehicle is in a low-load operating state or a charging state. A performance record storage module is used to calculate the internal resistance and available capacity of the battery cell based on the voltage response, and store the internal resistance and available capacity as the performance record of the battery cell; The future load analysis module is used to obtain the future operation plan of the electric vehicle and, based on the future operation plan, analyze the charging and discharging load that each battery cell will bear in the future operation cycle. The risk analysis and processing module is used to analyze the risk of each battery cell reaching its charge and discharge limit in future operating cycles based on the performance records and the charge and discharge load, and obtain the risk analysis results. The energy control execution module is used to set a personalized balancing target for each battery cell based on the risk analysis results, and when the real-time status information indicates that the electric vehicle is in a low-load operating state or charging state, it pre-allocates energy to the battery cells according to the personalized balancing target to achieve energy storage control. The risk analysis and processing module is also used for: Continuously collect temperature data on the surface of each battery cell and analyze the fluctuation and trend of the temperature data within a preset short time window; Based on the fluctuations and trends, the local temperature rise characteristics of the battery cell are identified; when the local temperature rise characteristics indicate that the temperature data of the battery cell increases by a certain amount within a preset short period of time, it is recorded as a local temperature rise event. The local temperature rise event is cross-correlationally analyzed with the corresponding battery cell's charging and discharging current, voltage changes, and internal resistance to obtain the cross-correlation analysis results. When the cross-correlation analysis results indicate that a local temperature rise event is established, the temperature growth condition is met, and the internal resistance growth condition is met, it is determined that the local temperature rise event is caused by the acceleration of internal side reactions. For battery cells undergoing nonlinear accelerated degradation caused by the aforementioned localized temperature rise event, the available capacity and internal resistance of the battery cells are corrected. The charging and discharging strategy of the battery cell is adjusted based on the corrected available power and internal resistance, as well as the current operating load of the electric vehicle. The steps of adjusting the charging and discharging strategy of the battery cell include: preferentially transferring energy from other battery cells to the battery cell; Based on the corrected internal resistance, the upper limit of the instantaneous charge and discharge current of the battery cell is reduced.

Images