Battery cell health state estimation method, device and system and readable storage medium

Abstract

The invention provides a cell health state estimation method, device and system and a readable storage medium, and the method relates to the technical field of battery management, and the method comprises the steps: obtaining the temperature and current parameters during the operation of a cell in real time; accessing a preset life mapping model, wherein the model represents the acceleration relationship of the aging rate of the battery cell at different temperatures and multiplying powers relative to the reference working condition; according to the real-time parameter query model, determining a real-time damage coefficient at the current moment; carrying out weighted calculation on the sampling time interval based on the coefficient to obtain a single-step damage amount and carrying out historical accumulation to obtain a total accumulated damage factor; and finally, calculating a health state value according to the relationship between the accumulated damage factor and the end-of-life threshold. According to the method, the real-time damage coefficient is introduced, the physical operation time under the dynamic working condition is converted into the equivalent electrochemical damage time, the problem that temperature and multiplying power nonlinear influences are ignored in a traditional method is effectively solved, and the health state estimation precision under the complex working condition is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a method, apparatus, system, and readable storage medium for estimating the health status of battery cells. Background Technology

[0002] As the core energy carrier of electric vehicles and energy storage systems, the accurate assessment of the State of Health (SOH) of lithium-ion batteries is crucial for ensuring safe system operation and extending service life. Existing SOH estimation techniques typically employ lookup table methods or ampere-hour integration methods based on standard cycle life tests. This involves measuring the battery's capacity decay curve under standard laboratory conditions (such as constant temperature and fixed charge / discharge rates). In practical applications, the battery management system calculates the current state of health by statistically analyzing the battery's cumulative charge / discharge cycles or energy throughput and directly consulting a pre-stored static correspondence table.

[0003] However, in real-world applications, batteries face extremely complex and highly dynamic operating conditions. In particular, fluctuations in ambient temperature and charge / discharge rate have a significant nonlinear accelerating effect on the degradation of internal electrochemical activity. Existing static estimation methods often ignore the damage differences between high-stress conditions (such as high temperature, low temperature, or high rate) and standard conditions, treating unit operating time or unit charge throughput under different environments as having the same impact on lifespan. As a result, under complex and variable actual operating conditions, the estimated state of harm (SOH) cannot accurately reflect the accelerated aging of the battery caused by accumulated environmental stress. As operating time increases, the accumulated error gradually increases, making it difficult to meet the requirements of high-precision battery management. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, system, and readable storage medium for estimating the state of health (SOH) of a battery cell, in order to solve the technical problem mentioned in the background art that the existing static estimation method cannot accurately reflect the accelerated aging of the battery caused by the accumulation of environmental stress under complex and variable actual operating conditions, and the accumulated error gradually increases with the increase of operating time, making it difficult to meet the requirements of high-precision battery management.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for estimating the health status of a battery cell is provided, the method comprising:

[0006] Obtain the operating parameters of the battery cell at the current sampling moment, wherein the operating parameters include at least the current temperature and the current current;

[0007] The current charge / discharge rate is determined based on the current current and the rated capacity of the battery cell;

[0008] Access the preset lifetime mapping model, and determine the real-time damage coefficient of the cell at the current sampling time based on the current temperature and the current charge / discharge rate; wherein, the lifetime mapping model characterizes the accelerated relationship between the aging rate of the cell at different temperatures and different charge / discharge rates and the aging rate under the reference operating conditions.

[0009] The sampling time interval is weighted based on the real-time damage coefficient to obtain the single-step damage amount at the current sampling time.

[0010] Accumulate the single-step damage amount at historical moments and update the current accumulated damage factor of the cell; generate and output the current health status value of the cell based on the correspondence between the current accumulated damage factor and the preset life end threshold.

[0011] In one possible implementation, the step of determining the real-time damage coefficient of the battery cell at the current sampling time specifically includes:

[0012] The current operating cycle life corresponding to the current temperature and the current charge / discharge rate is queried or calculated from the preset life mapping model.

[0013] Obtain the standard cycle life under pre-stored baseline operating conditions;

[0014] Calculate the ratio of the standard cycle life to the current operating condition cycle life, and determine this ratio as the real-time damage coefficient.

[0015] In one possible implementation, the step of querying or calculating the current operating cycle life corresponding to the current temperature and the current charge / discharge rate from the preset life mapping model specifically includes:

[0016] Determine whether the current temperature and the current charge / discharge rate are located at discrete data points recorded in the lifetime mapping model;

[0017] If not, multidimensional interpolation is performed using the cycle life data corresponding to multiple discrete data points adjacent to the current temperature and the current charge / discharge rate to obtain the current operating condition cycle life.

[0018] In one possible implementation, the step of obtaining the single-step damage amount at the current sampling time specifically includes:

[0019] The weighted time increment is obtained by multiplying the real-time damage coefficient by the sampling time interval.

[0020] Calculate the ratio of the weighted time increment to the standard time required to complete a single full charge-discharge cycle under the baseline operating condition, and use this ratio as the single-step damage amount.

[0021] In one possible implementation, the method for estimating the cell health status further includes:

[0022] Detect whether the current temperature or the current charge / discharge rate exceeds the preset safe operating range;

[0023] If the safe operating range is exceeded, a preset penalty weighting logic is invoked to apply a penalty factor greater than 1 to the real-time damage coefficient, thereby increasing the single-step damage amount.

[0024] In one possible implementation, the method for estimating the cell health status further includes:

[0025] In response to the detection that the cell meets the preset calibration trigger conditions, the current measured capacity data of the cell is obtained;

[0026] The measured health status value is determined based on the measured capacity data.

[0027] The measured health status value is used to update the current output health status value, and the value of the current accumulated damage factor is corrected in reverse according to the updated health status value; wherein, the calibration triggering condition includes the cell being in a fully charged and idle state or the cell completing a preset number of charge and discharge cycles.

[0028] In one possible implementation, the data stored in the lifetime mapping model is obtained by offline testing of the same type of battery cell. The data includes the number of cycles or energy throughput of the battery cell when it reaches the end-of-life condition, measured under a combination of multiple discrete temperature points and multiple discrete charge / discharge rate points.

[0029] According to another aspect of the present invention, a cell health status estimation device is provided, comprising:

[0030] The data acquisition module is configured to acquire real-time temperature and current data of the battery cells;

[0031] The storage module is configured as a storage lifetime mapping model, which includes lifetime data under baseline operating conditions and aging acceleration relationship data under multi-dimensional operating stress.

[0032] The calculation module is configured to determine the real-time damage coefficient based on the real-time temperature and current data and the lifetime mapping model, and to calculate the cumulative damage factor based on the real-time damage coefficient, thereby mapping to obtain a health status value.

[0033] An output module is configured to output the health status value; wherein the calculation module executes the cell health status estimation method as described in any of the possible implementations above.

[0034] According to another aspect of the present invention, a battery management system is provided, comprising:

[0035] Sensor components are used to collect voltage, current, and temperature signals from the cells in the battery pack;

[0036] The cell health status estimation device described in the above implementation is communicatively connected to the sensor assembly and is used to receive the collected signals and estimate the cell health status.

[0037] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the cell health state estimation method as described in any of the above possible implementations.

[0038] The above-described one or more technical solutions in the embodiments of this application have at least one or more of the following technical effects:

[0039] This invention provides a method and system for estimating the state of health (SOH) of a battery cell. By pre-setting a lifespan mapping model containing multi-dimensional stress relationships within the battery management system (BMS) and acquiring real-time temperature and charge / discharge rate data of the cell, it innovatively introduces a real-time damage coefficient as an intermediate variable. This dynamically maps the aging rate of the cell under complex and variable actual operating conditions to a normalized acceleration factor relative to a baseline operating condition. This method can convert physical running time into an equivalent electrochemical damage time for accumulation, effectively solving the estimation distortion problem caused by neglecting the nonlinear influence of temperature and charge / discharge rate on the aging rate in traditional methods. Regardless of whether the cell is in a high-temperature accelerated decay zone or a high-rate mechanical fatigue zone, the system can adaptively adjust the single-step damage amount, significantly improving the accuracy and robustness of SOH estimation under dynamic and complex operating conditions. Furthermore, this solution only requires the existing voltage, current, and temperature acquisition functions of the BMS, without the need for additional expensive electrochemical detection hardware or complex mechanism modeling calculations. It has advantages such as clear algorithm logic, low computational load, and ease of engineering deployment in embedded systems, providing reliable data support for the safety management and residual value assessment of the battery throughout its entire life cycle.

[0040] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0041] Figure 1 This is a schematic flowchart of a method for estimating the health status of a battery cell according to an exemplary embodiment.

[0042] Figure 2 This is a schematic diagram of the modular structure of a battery management system according to an exemplary embodiment.

[0043] Explanation of reference numerals in the attached figures: 100, data acquisition module; 200, storage module; 300, calculation module; 400, output module; 500, sensor assembly. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0045] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of systems and methods consistent with some aspects of the invention as detailed in the appended claims.

[0046] Figure 1 This is a flowchart of a method for estimating the state of health of a battery cell according to an exemplary embodiment, such as... Figure 1 As shown, the method includes:

[0047] In step S100, the operating parameters of the battery cell at the current sampling moment are acquired. These operating parameters include at least the current temperature and current current. Specifically, this step is performed by the data acquisition unit in the battery management system (BMS). The system periodically samples the state of the battery cell at a preset fixed frequency, for example, once every 100ms. The current temperature is obtained through a temperature sensor attached to the surface of the battery cell or a key heat dissipation point of the module to reflect the real-time thermal state of the battery cell; the current current is obtained through a high-precision current sensor in the main circuit, such as a shunt or Hall sensor. Preferably, after acquiring the raw data, the system filters the signal to eliminate noise caused by electromagnetic interference, ensuring the accuracy of subsequent calculation inputs.

[0048] In step S200, the current charge / discharge rate is determined based on the current current and the rated capacity of the battery cell. The system reads the pre-stored rated capacity parameters of the battery cell from the memory and converts the collected current current value into a normalized charge / discharge rate. This processing method can eliminate the differences in absolute current values ​​between battery cells of different capacity specifications, making subsequent aging damage calculations have universal calibration significance. For example, if the current current is 200A and the rated capacity of the battery cell is 100Ah, then the current charge / discharge rate is 2C.

[0049] In step S300, a preset lifetime mapping model is accessed, and the real-time damage coefficient of the battery cell at the current sampling time is determined based on the current temperature and the current charge / discharge rate. The lifetime mapping model characterizes the acceleration relationship between the aging rate of the battery cell at different temperatures and charge / discharge rates and the aging rate under baseline conditions. The lifetime mapping model is a multi-dimensional database pre-built and stored in the BMS. Specifically, this step includes:

[0050] The model defines a baseline operating condition (e.g., 25°C, 1C charge / discharge) and the damage coefficient under this condition. Defined as 1.

[0051] When the collected current temperature and current charge / discharge rate are located between preset discrete nodes in the model, the system uses bilinear interpolation for calculation. Specifically, the system selects four standard operating points adjacent to the current operating point. Using the standard loop count corresponding to these four points Calculate the equivalent standard cycle number under the current operating conditions. Thus, the real-time damage coefficient is obtained. This step ensures that the damage coefficient can be accurately solved under any temperature-magnification combination, avoiding the discrete errors of the lookup table method.

[0052] In step S400, the sampling time interval is weighted based on the real-time damage coefficient to obtain the single-step damage amount at the current sampling moment; this step aims to convert the physical running time into an equivalent electrochemical damage time. The single-step damage amount (or single-point equivalent damage weight) is calculated according to the formula:

[0053]

[0054] in, The real-time damage coefficient determined in step S300; The sampling time interval (e.g., 0.1 seconds); This refers to the time required to complete one full cycle under baseline operating conditions, such as under the 4C baseline. The time is 900 seconds. This calculation shows that if the battery cell is under high stress conditions such as high temperature or high rate of operation (…), then… The damage per step will be significantly greater than the proportion of physical time, thus reflecting the physical fact of accelerated aging.

[0055] In step S500, the single-step damage amount at historical moments is accumulated, and the current accumulated damage factor of the cell is updated. Based on the correspondence between the current accumulated damage factor and a preset lifespan termination threshold, the current health status value of the cell is generated and output. The system continuously accumulates the calculated single-step damage amount throughout the entire lifespan to obtain the total weighted accumulated damage factor. Based on this accumulated value, the system outputs the current SOH value using a preset mapping relationship. Preferably, this mapping relationship is represented by a linear function:

[0056]

[0057] in, This represents the total lifecycle cycle count under baseline operating conditions. This logic indicates that when... At that time, SOH was 100%; when cumulative damage Reaching the baseline number of cycles At that time, SOH decays to the end-of-life threshold (e.g., 80%).

[0058] By pre-setting a lifespan mapping model in the battery management system (BMS) that incorporates multi-dimensional stress relationships under various operating conditions, and acquiring real-time temperature and charge / discharge rate data of the battery cells, this method innovatively introduces a real-time damage coefficient as an intermediate variable. This dynamically maps the aging rate of the battery cells under complex and variable actual operating conditions to a normalized acceleration factor relative to a baseline operating condition. This method can convert physical runtime into an equivalent electrochemical damage time for accumulation, effectively solving the estimation distortion problem caused by traditional methods that ignore the nonlinear effects of temperature and charge / discharge rate on the aging rate. Regardless of whether the battery cell is in the high-temperature accelerated decay zone or the high-rate mechanical fatigue zone, the system can adaptively adjust the single-step damage amount, significantly improving the accuracy and robustness of SOH estimation under dynamic and complex operating conditions. Furthermore, this solution only requires the existing voltage, current, and temperature acquisition functions of the BMS, without the need for additional expensive electrochemical detection hardware or complex mechanism modeling calculations. It has advantages such as clear algorithm logic, low computational load, and ease of engineering deployment in embedded systems, providing reliable data support for the safety management and residual value assessment of the entire battery lifecycle.

[0059] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] In an exemplary embodiment, the step of determining the real-time damage coefficient of the battery cell at the current sampling time specifically includes:

[0061] The system queries or calculates the current operating condition cycle life corresponding to the current temperature and current charge / discharge rate from the preset lifespan mapping model. The core of this step is mapping the current complex dynamic operating condition into a normalized acceleration factor relative to a baseline operating condition. The system first accesses the preset lifespan mapping model. This model is essentially a multi-dimensional database built based on a large amount of experimental data, storing the standard number of cycles a battery cell undergoes from its initial state to the end of its lifespan (e.g., SOH=80%) under different temperature and charge / discharge rate combinations. The system queries the database based on the current temperature collected by the sensor and the calculated current charge / discharge rate. If the current operating condition point happens to be located on a discrete node in the database, the corresponding cycle life value is directly read as the current operating condition cycle life. If the current operating point is located between nodes, the cycle life data of adjacent nodes are used to calculate the current operating condition cycle life using bilinear interpolation. ).

[0062] The system acquires the standard cycle life under a pre-stored reference condition. The reference condition is a predefined standard test condition, preferably set to a standard charge / discharge rate (e.g., 1C or 4C) at room temperature (25°C). This standard cycle life is stored in the system as a fixed reference constant. For example, in one embodiment, the reference condition is defined as 25°C and 4C, corresponding to a standard cycle life of 30,000 cycles.

[0063] The ratio of the standard cycle life to the current operating condition cycle life is calculated, and this ratio is determined as the real-time damage coefficient. The calculation formula is expressed as follows:

[0064]

[0065] The physical meaning of this ratio lies in quantifying how many times the aging rate of the current operating condition is compared to the baseline operating condition.

[0066] To illustrate the calculation process more clearly, let's take a specific example: Assume the system is set to a baseline operating condition of 25℃ and 4C, with a standard cycle life of 30,000 cycles. If, at the current sampling moment, the actual operating temperature of the battery cell is 10℃, and the charge / discharge rate is 4C, the system first queries the life mapping model or calculates the cell's cycle life under this low-temperature, high-rate condition using interpolation. The result is 26,667 cycles (Note: Lithium-ion activity decreases at low temperatures, and high-rate charge / discharge shortens lifespan). Next, the system performs a ratio calculation: The results show that operating at the current 10℃, 4C conditions for one unit of time causes damage to the battery cell equivalent to 1.12 unit times of damage under the baseline conditions. Through this ratio calculation, the system can uniformly convert the physical time under different environmental stresses into an equivalent baseline damage time, thereby achieving accurate cumulative estimation of SOH (State of Harm).

[0067] Furthermore, the step of querying or calculating the current operating cycle life corresponding to the current temperature and the current charge / discharge rate from the preset life mapping model specifically includes:

[0068] The system determines whether the current temperature and current charge / discharge rate fall within the discrete data points recorded in the lifetime mapping model. Considering storage space limitations, the lifetime mapping model is typically stored as discrete data in the BMS's non-volatile memory, for example, in a lookup table constructed with 5°C temperature intervals and 0.5°C rate intervals. In actual operation, the cell's operating conditions change continuously. Therefore, the system first determines whether the collected current temperature and calculated current charge / discharge rate accurately match the discrete data points recorded in the model.

[0069] If not, then multidimensional interpolation is performed using the cycle life data corresponding to multiple discrete data points adjacent to the current temperature and the current charge / discharge rate to obtain the current operating condition cycle life. Specifically, if the determination result is negative, i.e., the current operating point is suspended between grids, the system will initiate the multidimensional interpolation operation logic. In a preferred embodiment, this multidimensional interpolation operation specifically employs bilinear interpolation. The system first identifies the area surrounding the current operating point on the two-dimensional plane. Four adjacent discrete data points, denoted as , , and Subsequently, the system retrieves the standard cycle life data corresponding to these four discrete points in the model. .

[0070] Next, the system determines the temperature based on the current temperature. distance The relative distance, and the current multiplier. distance The relative distances between nodes are used to calculate the weight coefficients for each node. Finally, the four cycle life data points are weighted and summed based on these weight coefficients to accurately calculate the cycle life under the current operating condition. Through this multidimensional interpolation process, the present invention can construct a continuous lifetime prediction surface covering the entire temperature range and the entire rate range using limited discrete experimental data, effectively solving the problems of estimation jumps and insufficient accuracy caused by coarse lookup table granularity.

[0071] In an exemplary embodiment, the step of obtaining the single-step damage amount at the current sampling time specifically includes:

[0072] The product of the real-time damage coefficient and the sampling time interval is calculated to obtain the weighted time increment; specifically, this step aims to transform the physical operation of the cell within a microscopic time segment into a substantial contribution to the total lifetime loss. The system first performs a multiplication operation to calculate the product of the real-time damage coefficient and the sampling time interval (…). The product of these factors yields an intermediate variable, the weighted time increment. The physical meaning of this weighted time increment lies in correcting the physical elapsed time to the electrochemical aging time. For example, if the current operating condition is harsh (high temperature, high rate), the real-time damage coefficient is 1.12, and the sampling interval is 0.1 seconds, then this 0.1 second physical time is equivalent to 0.112 seconds under the baseline operating condition in terms of aging. This value of 0.112 seconds is the weighted time increment.

[0073] The ratio of the weighted time increment to the standard time required to complete a single full charge-discharge cycle under the baseline operating condition is calculated, and this ratio is used as the single-step damage quantity; the system obtains the pre-stored standard time required to complete a single full charge-discharge cycle under the baseline operating condition ( The parameters are determined by the definition of the baseline operating condition. For example, if the baseline operating condition is defined as a 4C charge-discharge rate, then completing one full charge-discharge cycle theoretically takes 15 minutes, or 900 seconds. The system calculates the ratio of the weighted time increment to the standard duration and determines this dimensionless ratio as the single-step damage amount.

[0074] In mathematical terms, this step can be represented as:

[0075]

[0076] in, This represents the damage per step. The essence of this calculation logic is to convert the time segment of each discrete sampling point into the number of standard cycles consumed. For example, the calculated weighted time of 0.112 seconds, divided by the standard duration of 900 seconds, yields approximately... This means that in just 0.1 seconds of operation, the battery cell consumes the equivalent of... The lifespan of a standard cycle. Through this processing, the system successfully transforms the accumulation in the time dimension into the accumulation in the cycle count dimension, thus enabling direct comparison with the lifespan termination threshold defined by the number of cycles.

[0077] In an exemplary embodiment, the method for estimating the cell health status further includes:

[0078] The system detects whether the current temperature or the current charge / discharge rate exceeds a preset safe operating range. Specifically, considering that batteries may encounter extreme environments beyond design expectations during actual use, and that preset life mapping models typically only cover the normal test range, such as -40℃ to 60℃, table lookups or interpolation may fail or fail to accurately reflect the degree of damage for conditions exceeding this range. Therefore, the system detects in real time whether the current temperature or the current charge / discharge rate exceeds a preset safe operating range. As a specific implementation example, this safe operating range can be set as the limit threshold specified in the cell specifications, such as a temperature higher than 70℃ for more than 5 seconds.

[0079] If the system detects that the operating range has been exceeded, a preset penalty weighting logic is invoked to apply a penalty factor greater than 1 to the real-time damage coefficient, thereby increasing the single-step damage amount. Specifically, if the current operating parameters are detected to exceed the safe operating range, the system will determine that the system is in an abnormal damage zone at that moment and trigger the preset penalty weighting logic. In this logic, the system no longer relies solely on the coefficients obtained from model lookup tables, but instead applies a penalty factor greater than 1 to the real-time damage coefficient to increase the single-step damage amount. According to a preferred embodiment of the present invention, this penalty factor is set to 1.5.

[0080] The physical calculation logic of this step can be expressed as: the corrected damage coefficient. This means that under extreme over-limit conditions, even if the operating time is very short, the system will treat it as having generated 1.5 times the lifespan loss compared to normal high-load conditions. The technical effect of this approach is that it compensates for the deficiencies in the lifespan mapping model under extreme boundary conditions, preventing inflated SOH estimates due to underestimation of damage at the edge of severe thermal runaway, thus ensuring battery safety. Furthermore, when this penalty logic is triggered, the system will also simultaneously record the abnormal operating condition event in memory, providing data for subsequent cell maintenance, fault diagnosis, or warranty liability determination.

[0081] In an exemplary embodiment, the method for estimating the cell health status further includes:

[0082] In response to the detection that the cell meets the preset calibration trigger conditions, the current measured capacity data of the cell is obtained. Specifically, although the integral algorithm based on the damage coefficient can accurately capture the aging trend under dynamic operating conditions, the integral process will inevitably introduce cumulative errors as the running time goes by. In order to eliminate this long-term drift, the system is equipped with a closed-loop calibration mechanism.

[0083] First, the system monitors the cell's operating status in real time to determine whether the preset calibration trigger conditions are met. According to a preferred embodiment of the invention, the calibration trigger conditions include a combination of periodic triggering and state triggering: for example, the system counter records the cell completing a preset number of charge-discharge cycles, such as every 100 complete cycles; and it detects that the cell is in a fully charged, resting state, and the resting time reaches a preset threshold, such as resting for more than one hour, to ensure electrochemical stability and eliminate the influence of polarization voltage.

[0084] Upon detecting that the above triggering conditions are simultaneously met, the system initiates the capacity self-learning process. The BMS controls the cell to perform a standard discharge test, for example, controlling the cell to discharge at a constant current of 0.3C to the cutoff voltage, and accurately calculates the cell's current measured capacity data using the ampere-hour integration method. ).

[0085] The measured health status value is determined based on the measured capacity data. Typically, this value is the difference between the measured capacity and the nominal capacity of the battery cell at the factory. The ratio of the measured health status value to the actual physical value. Then, the system uses this measured health status value to directly update the currently output health status value, forcibly pulling the estimated result back to the true physical value, thereby clearing the previous accumulated error to zero;

[0086] The measured health status value is used to update the currently output health status value, and the updated health status value is used to reverse-correct the value of the current cumulative damage factor; wherein, the calibration triggering condition includes the cell being in a fully charged and idle state or the cell completing a preset number of charge-discharge cycles; since this invention uses a cumulative damage factor As an intermediate core variable in SOH calculation (based on the formula) When the SOH output value is forcibly updated by external measured values, the system must synchronously update the internal cumulative damage factor. The system is based on the updated... The inverse function of the above mapping function is used for reverse calculation:

[0087]

[0088] The system will calculate The current cumulative damage factor variable is overwritten into memory. This step not only calibrates the current displayed value but also the aging baseline, ensuring that the integral calculation before the next calibration is based on the true physical damage baseline, thus significantly improving the estimation robustness throughout the entire lifecycle.

[0089] More specifically, the data stored in the lifetime mapping model is obtained through offline testing of the same type of battery cells. This data includes the number of cycles or energy throughput at which the battery cell reaches its end-of-life condition, measured under combined operating conditions at multiple discrete temperature points and multiple discrete charge / discharge rate points. Specifically, the data stored in the lifetime mapping model is obtained through rigorous laboratory bench testing, aiming to establish a realistic database of electrochemical aging characteristics. The experimental subjects are selected from battery cell samples of the same type used in actual applications to eliminate estimation errors caused by differences in material systems or processes.

[0090] In the offline testing experimental design, to cover the complex operating conditions that the battery cell may face throughout its entire life cycle, the test scheme sets multiple discrete temperature points and multiple discrete charge / discharge rate points. For example, the temperature points cover a wide temperature range from -40℃ to 60℃, divided in 5℃ increments; the charge / discharge rate points cover a wide load range from 0.1C to 20C, divided in 0.5C increments. By cross-combining the above temperature points and rate points, several specific sets of combined operating conditions are formed.

[0091] During the test execution and data recording phase, for each set of combined operating conditions, the battery cell is continuously subjected to charge-discharge cycle tests until it reaches a preset end-of-life condition. In a preferred embodiment, this end-of-life condition is defined as the battery cell's discharge capacity decaying to 80% of its initial capacity. At this point, the test equipment records the number of cycles (CycleNumber) or the cumulative energy throughput (EnergyThroughput) experienced by the battery cell under that specific combined operating condition.

[0092] Furthermore, preferably, after obtaining the raw test data, data cleaning and preprocessing steps are also required. Outlier data points caused by test equipment malfunctions or accidental factors, such as capacity mutations, are removed, and the remaining valid data is smoothed. Finally, these verified discrete "operating condition-lifetime" correspondence data are written into the memory of the battery management system, forming the basis of the lifetime mapping model and ensuring the high reliability of the physical benchmark for subsequent table lookup and interpolation calculations.

[0093] Please see Figure 2 This embodiment also provides a cell health status estimation device, including:

[0094] The data acquisition module 100 is configured to acquire real-time temperature and current data of the battery cells. Specifically, the data acquisition module 100 is configured to connect to the sensor network inside the battery pack to acquire real-time temperature and current data of the battery cells. In the specific hardware implementation, this module corresponds to the analog front-end (AFE) acquisition circuit and its driver program of the BMS. This module performs data acquisition tasks at a preset sampling frequency, such as once every 100ms. The real-time temperature data comes from an NTC thermistor array attached to the surface of the battery cell to capture the thermal field changes of the battery cell during operation; the real-time current data comes from a shunt or Hall sensor in the main circuit to reflect the load intensity of the battery cell. This module is also responsible for performing analog-to-digital conversion (ADC) and basic filtering on the raw analog signal to provide accurate digital signal input for subsequent calculations.

[0095] Storage module 200 is configured to store a lifetime mapping model, which includes lifetime data under baseline operating conditions and aging acceleration relationship data under multi-dimensional operating stress. This module corresponds in hardware to non-volatile memory on the BMS motherboard, such as Flash or EEPROM. The lifetime mapping model is a digital chart constructed based on a large amount of offline experimental data, containing lifetime data under baseline operating conditions and aging acceleration relationship data under multi-dimensional operating stress. Specifically, storage module 200 stores discrete grid data covering a wide temperature range (e.g., -40℃ to 60℃) and a wide scaling range (e.g., 0.1C to 20C), as well as a corresponding damage coefficient lookup table, enabling the device to still call upon the static model for calculations after a power outage and restart.

[0096] The calculation module 300 is configured to determine a real-time damage coefficient based on the real-time temperature and current data and the lifetime mapping model, and to calculate a cumulative damage factor based on the real-time damage coefficient, thereby mapping to obtain a health status value. In hardware, this module corresponds to a main control chip containing a microcontroller (MCU) or a digital signal processor (DSP). The calculation module 300 is programmed to perform all steps of the cell health status estimation method as described in any of the above exemplary embodiments, including: obtaining an accurate real-time damage coefficient from a lookup table in the storage module 200 using bilinear interpolation; performing a weighted integral over a time interval to update the current cumulative damage factor; and using a linear mapping formula... The final SOH percentage is calculated. Furthermore, the calculation module 300 is equipped with anomaly detection logic. When the temperature or multiplier exceeds the model's coverage range, a penalty weighting factor (e.g., 1.5 times) is automatically applied to ensure the safety of the estimation.

[0097] Output module 400 is configured to output the aforementioned health status value. In hardware implementation, this module corresponds to the communication interface circuit of the BMS, such as a CAN bus transceiver, RS485 interface, or wireless communication module. This module encapsulates the SOH value generated by calculation module 300 into a standard communication message and sends it in real time to the vehicle controller (VCU), power storage converter (PCS), or remote cloud platform, so that the upper-level system can adjust the charging and discharging strategy according to the current health status of the battery, such as reducing power limits, to achieve safe management and residual value assessment of the battery system.

[0098] This embodiment also provides a battery management system, including:

[0099] Sensor component 500 is used to collect voltage, current and temperature signals of the cells in the battery pack. In terms of hardware implementation, this component typically includes a voltage acquisition circuit, a current sensor and a temperature sensor distributed inside the battery module.

[0100] The voltage acquisition circuit is configured to monitor the terminal voltage of individual cells and battery packs in real time to prevent overcharging or over-discharging. For example, it monitors whether the ternary lithium battery has reached the 4.2V cutoff voltage and whether the lithium iron phosphate battery has reached the 3.65V cutoff voltage.

[0101] The current sensor is configured to collect the real-time current flowing through the main circuit, and the system calculates the current charge / discharge rate based on the current data and the pre-stored rated capacity.

[0102] Temperature sensors are placed at key heat-generating points in the battery cell to collect the cell's real-time operating temperature. These sensors synchronously collect signals at a preset fixed frequency and convert the analog signals into digital signals, which are then transmitted to the back-end processing unit.

[0103] The cell health status estimation device described in the above embodiment is communicatively connected to the sensor assembly 500 and is used to receive the collected signals and estimate the cell health status. In the battery management system, this cell health status estimation device is typically integrated into the microprocessor of the main control unit or battery management unit. This cell health status estimation device is communicatively connected to the sensor assembly 500 and is used to receive the collected voltage, current, and temperature signals.

[0104] During operation, the processor inside the device calls a pre-stored lifetime mapping model and calculates the cell's damage coefficient and cumulative damage factor in real time based on the received temperature and current data. It is worth noting that since the estimation method of this invention only relies on existing basic sensor data from the BMS, such as voltage, current, and temperature, the cell health status estimation device does not require expensive electrochemical impedance spectroscopy (EIS) instruments or additional dedicated detection hardware. This means that the system can be directly implemented on existing BMS hardware architecture through software algorithm upgrades, offering a significant advantage of no additional hardware costs and facilitating large-scale industrial deployment in electric vehicles and energy storage systems.

[0105] In addition, the battery management system is configured to execute safety control strategies based on the estimated State of Harm (SOH) value. When the estimated SOH value is lower than a preset retirement threshold (e.g., 80%), the system can send a maintenance alarm to the vehicle controller or energy management system via the communication interface, suggesting battery replacement or limiting power output, thereby ensuring the overall operational safety of the system.

[0106] This embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the cell health state estimation method as described in any of the exemplary embodiments above. Optionally, the storage medium is a non-transitory computer-readable storage medium, such as ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk, and optical data storage device, etc.

[0107] In an exemplary embodiment, a computer program product is also provided, which includes computer program code stored in a computer-readable storage medium. A processor of a computer device reads the computer program code from the computer-readable storage medium and executes the computer program code, causing the computer device to perform the operations performed in the above-described method for estimating the health status of the battery cell.

[0108] Any aspects of this invention not described in detail are well-known to those skilled in the art.

[0109] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for estimating the health status of a battery cell, characterized in that, The method includes: Obtain the operating parameters of the battery cell at the current sampling moment, wherein the operating parameters include at least the current temperature and the current current; The current charge / discharge rate is determined based on the current current and the rated capacity of the battery cell; Access the preset lifetime mapping model, and determine the real-time damage coefficient of the cell at the current sampling time based on the current temperature and the current charge / discharge rate; wherein, the lifetime mapping model characterizes the accelerated relationship between the aging rate of the cell at different temperatures and different charge / discharge rates and the aging rate under the reference operating conditions. The sampling time interval is weighted based on the real-time damage coefficient to obtain the single-step damage amount at the current sampling time. Accumulate the single-step damage amount at historical moments and update the current accumulated damage factor of the cell; generate and output the current health status value of the cell based on the correspondence between the current accumulated damage factor and the preset life end threshold.

2. The method for estimating the health status of a battery cell according to claim 1, characterized in that, The step of determining the real-time damage coefficient of the battery cell at the current sampling time specifically includes: The current operating cycle life corresponding to the current temperature and the current charge / discharge rate is queried or calculated from the preset life mapping model. Obtain the standard cycle life under pre-stored baseline operating conditions; Calculate the ratio of the standard cycle life to the current operating condition cycle life, and determine this ratio as the real-time damage coefficient.

3. The method for estimating the health status of a battery cell according to claim 2, characterized in that, The step of querying or calculating the current operating cycle life corresponding to the current temperature and the current charge / discharge rate from the preset life mapping model specifically includes: Determine whether the current temperature and the current charge / discharge rate are located at discrete data points recorded in the lifetime mapping model; If not, multidimensional interpolation is performed using the cycle life data corresponding to multiple discrete data points adjacent to the current temperature and the current charge / discharge rate to obtain the current operating condition cycle life.

4. The method for estimating the health status of a battery cell according to claim 1, characterized in that, The steps for obtaining the single-step damage amount at the current sampling time specifically include: The weighted time increment is obtained by multiplying the real-time damage coefficient by the sampling time interval. Calculate the ratio of the weighted time increment to the standard time required to complete a single full charge-discharge cycle under the baseline operating condition, and use this ratio as the single-step damage amount.

5. The method for estimating the health status of a battery cell according to claim 1, characterized in that, The method for estimating the health status of the battery cells also includes: Detect whether the current temperature or the current charge / discharge rate exceeds the preset safe operating range; If the safe operating range is exceeded, a preset penalty weighting logic is invoked to apply a penalty factor greater than 1 to the real-time damage coefficient, thereby increasing the single-step damage amount.

6. The method for estimating the health status of a battery cell according to claim 1, characterized in that, The method for estimating the health status of the battery cells also includes: In response to the detection that the cell meets the preset calibration trigger conditions, the current measured capacity data of the cell is obtained; The measured health status value is determined based on the measured capacity data. The measured health status value is used to update the current output health status value, and the value of the current accumulated damage factor is corrected in reverse according to the updated health status value; wherein, the calibration triggering condition includes the cell being in a fully charged and idle state or the cell completing a preset number of charge and discharge cycles.

7. The method for estimating the health status of a battery cell according to claim 1, characterized in that, The data stored in the lifespan mapping model is obtained by offline testing of the same type of battery cell. The data includes the number of cycles or energy throughput of the battery cell when it reaches the end-of-life condition, measured under a combination of multiple discrete temperature points and multiple discrete charge / discharge rate points.

8. A battery cell health status estimation device, characterized in that, include: The data acquisition module is configured to acquire real-time temperature and current data of the battery cells; The storage module is configured as a storage lifetime mapping model, which includes lifetime data under baseline operating conditions and aging acceleration relationship data under multi-dimensional operating stress. The calculation module is configured to determine the real-time damage coefficient based on the real-time temperature and current data and the lifetime mapping model, and to calculate the cumulative damage factor based on the real-time damage coefficient, thereby mapping to obtain a health status value. An output module is configured to output the health status value; wherein the calculation module performs the cell health status estimation method as described in any one of claims 1 to 7.

9. A battery management system, characterized in that, include: Sensor components are used to collect voltage, current, and temperature signals from the cells in the battery pack; The cell health status estimation device as described in claim 8 is communicatively connected to the sensor assembly and is used to receive the collected signals and estimate the cell health status.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for estimating the cell health status as described in any one of claims 1 to 7.

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