BMS battery health monitoring method and system

Abstract

The invention relates to the technical field of battery health monitoring, and discloses a BMS battery health monitoring method and system. The method comprises the following steps: counting switching event times and total cycle duration of a battery in a complete charge-discharge cycle, and determining a sampling period according to the switching event times and the total cycle duration; synchronously acquiring a current value and a temperature value at each sampling moment of the sampling period, calculating a temperature variation between adjacent sampling moments, and calculating a capacity increment based on the temperature variation and the current value; accumulating and summing the capacity increment to obtain the initial cycle capacity of the ith round, calculating a temperature peak deviation ratio and a temperature integral area deviation ratio, and obtaining the corrected cycle capacity of the ith round through iterative correction; the evaluation capacity is solved, the battery health state index is calculated according to the evaluation capacity and the rated capacity, and i is larger than or equal to 5. According to the method, accidental errors of single measurement are effectively eliminated, so that the final evaluation capacity is integrated with temperature response characteristics and capacity attenuation rules of multiple rounds of circulation.

Description

Technical Field

[0001] This invention relates to the field of battery health monitoring technology, and in particular to a battery health monitoring method and system for a battery management system (BMS). Background Technology

[0002] Battery management system (BMS) health monitoring is a core technology for ensuring the safe operation of power batteries. Traditional BMS uses coulometric measurement to assess battery capacity by integrating and accumulating the charge and discharge current at fixed time intervals, but this method has significant drawbacks.

[0003] In practical applications, batteries frequently undergo charge-discharge state transitions. A fixed sampling step size leads to an unreasonable distribution of sampling points during these transitions, making it impossible to accurately capture the impact of state transitions on battery performance. Furthermore, existing methods treat temperature only as a static compensation parameter, failing to couple the dynamic temperature change process into capacity calculations. This results in decreased assessment accuracy under high-rate operating conditions with rapid temperature changes. In addition, single-cycle measurement results are susceptible to instantaneous operating condition fluctuations, exhibiting significant random errors and failing to reflect the battery's degradation trend over multiple cycles, leading to insufficient reliability in health status assessments. Summary of the Invention

[0004] The main objective of this invention is to provide a battery health monitoring method and system (BMS). This invention effectively eliminates the random errors of a single measurement, enabling the final capacity assessment to integrate the temperature response characteristics and capacity decay patterns of multiple cycles.

[0005] To achieve the above objectives, the present invention provides a BMS battery health monitoring method, comprising the following steps: The number of switching events and the total cycle duration of the battery during a complete charge-discharge cycle are counted, and the sampling period is determined based on the number of switching events and the total cycle duration. At each sampling moment of the sampling period, current and temperature values ​​are collected synchronously, the temperature change between adjacent sampling moments is calculated, and the capacity increment is calculated based on the temperature change and the current value. In the i-th cycle, the capacity increments are accumulated and summed to obtain the initial cycle capacity of the i-th cycle. The ratio of the temperature peak deviation and the ratio of the temperature integral area deviation between the i-th and i-1-th cycles are calculated, and the corrected cycle capacity of the i-th cycle is obtained through iterative correction. The evaluated capacity is obtained by calculating the arithmetic mean of the capacity of the i-th round of correction cycle. The battery health status index is calculated based on the evaluated capacity and the rated capacity, where i ≥ 5.

[0006] Optionally, in a first implementation of the first aspect of the present invention, counting the number of switching events and the total cycle time of the battery during a complete charge-discharge cycle, and determining the sampling period based on the number of switching events and the total cycle time, includes: Monitor the battery's charge and discharge status, mark the moment when the charging state switches to the discharging state or the moment when the discharging state switches to the charging state as a switching event, and count the number of switching events in a complete charge and discharge cycle. Obtain the total loop duration, divide the number of switching events by the total loop duration to obtain the switching frequency, and divide the total loop duration by the number of switching events to obtain the sampling period.

[0007] Optionally, in a second implementation of the first aspect of the present invention, monitoring the charge / discharge state of the battery, marking the instant of switching from the charging state to the discharging state or from the discharging state to the charging state as a switching event, and counting the number of switching events in the complete charge / discharge cycle, includes: The direction of the battery current is collected in real time, and the battery is determined to be in a charging or discharging state based on the direction of the current. By comparing the charging and discharging states at adjacent times, when it is detected that the charging state changes to the discharging state or the discharging state changes to the charging state, the switching event counter is incremented to obtain the number of switching events.

[0008] Optionally, in a third implementation of the first aspect of the present invention, current and temperature values ​​are synchronously acquired at each sampling moment of the sampling period, the temperature change between adjacent sampling moments is calculated, and the capacity increment is calculated based on the temperature change and the current value, including: The current and temperature values ​​of the battery are collected synchronously at each sampling time in the sampling period, and the difference in temperature values ​​between adjacent sampling times is calculated to obtain the temperature change. The ratio of the temperature change to the normalized reference temperature difference is increased by 1 to obtain the temperature correction coefficient. The current value, the sampling period, and the temperature correction coefficient are then multiplied to obtain the capacity increment.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, the ratio of the temperature change to the normalized reference temperature difference is added by 1 to obtain a temperature correction coefficient, and the current value, the sampling period, and the temperature correction coefficient are multiplied to obtain the capacity increment, including: Divide the temperature change by the normalized reference temperature difference to obtain the temperature deviation ratio, and add 1 to the temperature deviation ratio to obtain the temperature correction coefficient. Multiply the current value by the sampling period to obtain a preliminary capacity value, and multiply the preliminary capacity value by the temperature correction coefficient to obtain the capacity increment.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the capacity increment is accumulated and summed in the i-th cycle to obtain the initial cycle capacity of the i-th cycle, and the ratio of the temperature peak deviation and the ratio of the temperature integral area deviation between the i-th and (i-1)-th cycles are calculated. The corrected cycle capacity of the i-th cycle is obtained through iterative correction, including: The capacity increments in the i-th cycle are summed to obtain the initial cycle capacity of the i-th cycle. The maximum temperature value is extracted from the temperature value sequence of the i-th cycle as the temperature peak value of the i-th cycle. The temperature integral area of ​​the i-th cycle is obtained by multiplying each temperature value by the sampling period and summing them. Divide the difference between the temperature peak value of the i-th round and the temperature peak value of the (i-1)-th round by the temperature peak value of the (i-1)-th round to obtain the temperature peak deviation ratio; divide the difference between the temperature integral area of ​​the i-th round and the temperature integral area of ​​the (i-1)-th round by the temperature integral area of ​​the (i-1)-th round to obtain the temperature integral area deviation ratio. The average of the temperature peak deviation ratio and the temperature integral area deviation ratio is calculated to obtain the comprehensive deviation ratio. The decreasing weight coefficient is determined based on the distance between each historical round and the round of round i. The cycle capacity of each historical round is corrected by multiplying the difference between 1 and the product of the decreasing weight coefficient and the comprehensive deviation ratio. The corrected cycle capacity of round i is obtained by multiplying the initial cycle capacity of round i by the difference between 1 and the comprehensive deviation ratio.

[0011] Optionally, in a sixth implementation of the first aspect of the present invention, dividing the difference between the temperature peak value of the i-th round and the temperature peak value of the (i-1)-th round by the temperature peak value of the (i-1)-th round to obtain a temperature peak deviation ratio, and dividing the difference between the temperature integral area of ​​the i-th round and the temperature integral area of ​​the (i-1)-th round by the temperature integral area of ​​the (i-1)-th round to obtain a temperature integral area deviation ratio, includes: The difference between the temperature peak value in the i-th round and the temperature peak value in the (i-1)-th round is used to obtain the temperature peak value difference. The temperature peak value difference is then divided by the temperature peak value in the (i-1)-th round to obtain the temperature peak value deviation ratio. The temperature integral area difference is obtained by subtracting the temperature integral area of ​​the i-th round from the temperature integral area of ​​the (i-1)-th round, and the temperature integral area deviation ratio is obtained by dividing the temperature integral area difference by the temperature integral area of ​​the (i-1)-th round. The comprehensive deviation ratio is obtained by adding the peak temperature deviation ratio to the integral area deviation ratio and then dividing by 2.

[0012] Optionally, in the seventh implementation of the first aspect of the present invention, the decreasing weight coefficient is determined based on the distance between each historical round and the i-th round, and the cycle capacity of each historical round is corrected by multiplying the difference between 1 and the product of the decreasing weight coefficient and the comprehensive deviation ratio, and the corrected cycle capacity of the i-th round is obtained by multiplying the initial cycle capacity of the i-th round by the difference between 1 and the comprehensive deviation ratio, including: The round distance is obtained by calculating the difference between the round number of the i-th round and each historical round, and the decreasing weight coefficient corresponding to each historical round is calculated based on the round distance and the preset attenuation coefficient. The historical correction factor is obtained by multiplying the decreasing weight coefficient by the comprehensive deviation ratio and then subtracting it from 1. The corrected cycle capacity of each historical cycle is obtained by multiplying the cycle capacity of each historical cycle by the corresponding historical correction factor. The current correction factor is obtained by subtracting the comprehensive deviation ratio from 1, and the correction cycle capacity of the i-th round is obtained by multiplying the initial cycle capacity of the i-th round by the current correction factor.

[0013] Optionally, in the eighth implementation of the first aspect of the present invention, the evaluated capacity is obtained by calculating the arithmetic mean of the i-th round of corrected cycle capacity, and the battery health status index is calculated based on the evaluated capacity and the rated capacity, wherein i ≥ 5, including: The total corrected capacity is obtained by summing the corrected capacity from the first round of correction cycle capacity to the i-th round of correction cycle capacity. The total corrected capacity is then divided by i to obtain the evaluation capacity, where i ≥ 5. The rated capacity is read from the battery parameter database, the evaluated capacity is divided by the rated capacity to obtain the capacity ratio, and the capacity ratio is multiplied by 100 to obtain the battery health status index.

[0014] The present invention also provides a BMS battery health monitoring system, comprising: The statistics module is used to count the number of switching events and the total cycle time of the battery in a complete charge-discharge cycle, and to determine the sampling period based on the number of switching events and the total cycle time. The calculation module is used to synchronously collect current and temperature values ​​at each sampling moment of the sampling period, calculate the temperature change between adjacent sampling moments, and calculate the capacity increment based on the temperature change and the current value. The iterative correction module is used to accumulate and sum the capacity increment in the i-th cycle to obtain the initial cycle capacity of the i-th cycle, and calculate the ratio of the temperature peak deviation and the ratio of the temperature integral area deviation between the i-th and i-1-th cycles, and obtain the corrected cycle capacity of the i-th cycle through iterative correction. The evaluation module is used to obtain the evaluation capacity by calculating the arithmetic mean of the i-th round of corrected cycle capacity, and to calculate the battery health status index based on the evaluation capacity and the rated capacity, where i ≥ 5.

[0015] In summary, this invention controls the sampling period by adjusting the charge / discharge switching frequency, dynamically matching the sampling point density with actual operating conditions. This solves the problem of sampling loss or redundancy that occurs during state switching transients in fixed-step sampling, ensuring that the discrete data sequence accurately represents the battery's true operating state. It innovatively introduces temperature change as a weighting factor into the current integral calculation, establishing a discrete integral method for the temperature response curve. This enables real-time correction of capacity calculation based on dynamic temperature changes, allowing the capacity increment calculation for each sampling interval to truly reflect the thermal effects of the internal electrochemical reactions of the battery, significantly improving the evaluation accuracy under high-rate charge / discharge conditions. By extracting the temperature peak and temperature integral area from multiple cycles and calculating the deviation ratio of temperature characteristic parameters between adjacent cycles, an iterative correction mechanism based on temperature evolution trends is established. The capacity results of all historical cycles are dynamically adjusted with decreasing weights, effectively eliminating random errors from single measurements. This ensures that the final evaluated capacity integrates the temperature response characteristics and capacity decay patterns from multiple cycles. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the steps of a BMS battery health monitoring method in one embodiment of the present invention; Figure 2 This is a structural block diagram of a BMS battery health monitoring system according to one embodiment of the present invention.

[0017] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] Reference Figure 1 This embodiment provides a BMS battery health monitoring method, including the following steps: S1, count the number of switching events and the total cycle time of the battery in a complete charge-discharge cycle, and determine the sampling period based on the number of switching events and the total cycle time; S2, synchronously collects current and temperature values ​​at each sampling moment of the sampling period, calculates the temperature change between adjacent sampling moments, and calculates the capacity increment based on the temperature change and current value. S3, in the i-th cycle, the capacity increment is accumulated and summed to obtain the initial cycle capacity of the i-th cycle, and the ratio of the temperature peak deviation and the ratio of the temperature integral area deviation between the i-th cycle and the (i-1)-th cycle are calculated. The corrected cycle capacity of the i-th cycle is obtained through iterative correction. S4. Calculate the arithmetic mean of the i-th cycle capacity to obtain the evaluation capacity. Calculate the battery health status index based on the evaluation capacity and the rated capacity, where i ≥ 5.

[0020] In one example, the number of switching events and the total cycle duration are counted during a complete charge-discharge cycle. The sampling period is determined based on the number of switching events and the total cycle duration, including: Monitor the battery's charge and discharge status, mark the moment when the charging state switches to the discharging state or the moment when the discharging state switches to the charging state as a switching event, and count the number of switching events in a complete charge and discharge cycle. Obtain the total loop duration, divide the number of switching events by the total loop duration to get the switching frequency, and divide the total loop duration by the number of switching events to get the sampling period.

[0021] In this example, the battery management system continuously monitors the battery's current operating state, identifying whether the battery is charging or discharging, and uses logic gates to identify state changes in real time. When the system detects a change in the battery's charging state—either from continuous charging to discharging or vice versa—it marks this instant as a valid state transition event and counts each similar state transition as a switching event, thus establishing a set of switching events within a charge-discharge cycle. During the cycle, the duration of the entire charge-discharge process is accumulated to obtain the total cycle duration, starting from the first charge and ending when the battery returns to the same initial state, covering the complete charge-discharge closed loop. Dividing the number of switching events by the total cycle duration yields the switching frequency per unit time. The switching frequency reflects the intensity of the battery's dynamic state transitions within the operating cycle, characterizing the density of battery operating state fluctuations. Dividing the total cycle duration by the number of switching events yields the sampling period, which is set as the base time step for capacity calculation and temperature response sampling. This means that a sampling point is inserted precisely between every two switching events to achieve synchronous coupling between the sampling process and the battery's operating state. The adaptive adjustment mechanism of the sampling period can effectively avoid the problem of insufficient sampling density in the stage of drastic state change when the sampling is fixed step size, and prevent the sampling points from being too dense in the stage of stable state, which would lead to resource redundancy.

[0022] In one example, the battery's charge / discharge state is monitored, and the instantaneous transition from charging to discharging or from discharging to charging is marked as a switching event. The number of switching events in a complete charge / discharge cycle is counted, including: Real-time acquisition of battery current direction; determines whether the battery is charging or discharging based on current direction. By comparing the charging and discharging states at adjacent times, when a change from charging to discharging or vice versa is detected, the switching event counter is incremented to obtain the number of switching events.

[0023] In this example, a real-time current acquisition module is configured in the battery management system to obtain the current direction information of the battery during operation. The current direction is determined by detecting the sign of the current value. When the acquired current value is positive, it indicates that the battery is in a charging state, that is, an external power source is injecting electrical energy into the battery, and an energy storage process is occurring inside the battery. When the current value is negative, it indicates that the battery is in a discharging state, that is, the battery is outputting electrical energy, and its internal electrochemical energy storage structure is in the release phase. The charging and discharging state at each sampling moment is determined based on the current direction, and the state is encoded and stored as a flag. In each new sampling cycle, the current charging and discharging state flag is read and compared with the state flag at the previous moment. When it is detected that the previous moment was in a charging state and the current moment is in a discharging state, or the previous moment was in a discharging state and the current moment is in a charging state, it is determined as a valid charging and discharging state switching event. When each state transition is confirmed, the switching event counter is incremented, continuously accumulating the number of charging and discharging switching events until the entire charging and discharging cycle is completed.

[0024] In one example, current and temperature values ​​are simultaneously acquired at each sampling moment of the sampling period. The temperature change between adjacent sampling moments is calculated, and the capacity increment is calculated based on the temperature change and current value, including: The battery current and temperature values ​​are collected synchronously at each sampling time of the sampling period, and the difference in temperature values ​​between adjacent sampling times is calculated to obtain the temperature change. The temperature correction factor is obtained by adding 1 to the ratio of the temperature change to the normalized reference temperature difference. The current value and the sampling period are then multiplied by the temperature correction factor to obtain the capacity increment.

[0025] In this example, the BMS system uses an adaptive sampling period to synchronously collect battery operating parameters at each sampling moment, including the current and temperature values ​​at that moment. These current and temperature values ​​are then combined to form a time-series sampled data pair. As sampling continues, a continuous sequence of current and temperature measurements is established. For the temperature sequence, at each current sampling moment, the current temperature value is extracted and subtracted from the historical temperature value corresponding to the previous sampling moment to obtain the temperature change within the current sampling interval. The temperature change serves as a parameter characterizing the battery's thermal behavior within the current time period, reflecting the trend of thermal effects caused by electrochemical reactions. Positive values ​​represent a temperature increase, indicating the battery is in a heat-generating state, while negative values ​​represent a temperature decrease, indicating the battery is in a heat-dissipating state. The temperature change is then calculated as a ratio to a preset normalized reference temperature difference (e.g., 10°C), and 1 is added to the ratio to construct a temperature correction coefficient. This temperature correction coefficient is used to dynamically adjust the capacity calculation results within the sampling interval. When the temperature change is positive, the correction factor is greater than 1, indicating that the internal thermal resistance of the battery has increased, and the capacity should be appropriately reduced. When the temperature change is negative, the correction factor is less than 1, indicating that the internal resistance of the battery has decreased, and the capacity should be appropriately increased. The current value at the current sampling moment is multiplied by the sampling period to obtain the current integral, and then multiplied by the temperature correction factor to calculate the capacity increment value of the current sampling interval.

[0026] In one example, the ratio of the temperature change to the normalized reference temperature difference is incremented by 1 to obtain the temperature correction factor. The current value, sampling period, and the temperature correction factor are then multiplied to obtain the capacity increment, which includes: Divide the temperature change by the normalized reference temperature difference to obtain the temperature deviation ratio, and add 1 to the temperature deviation ratio to obtain the temperature correction factor. Multiply the current value by the sampling period to obtain the initial capacity value, and multiply the initial capacity value by the temperature correction factor to obtain the capacity increment.

[0027] In this example, battery temperature and current data are continuously collected within an adaptively set sampling period. The temperature change is calculated between each pair of adjacent sampling moments, i.e., the temperature value at the current sampling moment minus the temperature value at the previous moment, yielding a temperature difference characterizing the battery's thermal trend within that time period. Dividing the temperature change by a preset normalized reference temperature difference, such as 10 degrees Celsius, yields a temperature deviation ratio. This ratio quantifies the proportional relationship between the temperature change and the standard temperature difference amplitude within the current time period, reflecting the degree of influence of thermal effects on capacity estimation. Adding 1 to the temperature deviation ratio constructs a temperature correction coefficient, ensuring that the coefficient is greater than 1 when the temperature rises and less than 1 when the temperature falls, reflecting different adjustment directions for capacity estimation due to enhanced heat generation or enhanced heat dissipation. Simultaneously, the real-time current value at the current sampling moment is multiplied by the sampling period length to obtain a preliminary capacity value without thermal correction. This value represents the charge throughput per unit time without considering thermal behavior. To incorporate thermal response characteristics, the preliminary capacity value is multiplied again by the temperature correction coefficient to obtain the capacity increment within the sampling interval after temperature adjustment. The capacity estimation results are dynamically adjusted in each sampling period based on the degree of thermal offset, which effectively avoids the systematic errors caused by the traditional coulomb integration method during the process of intense battery heat generation or rapid cooling.

[0028] The normalized reference temperature difference is dynamically determined based on the charge / discharge rate, including: obtaining the charge / discharge rate parameter of the current charge / discharge cycle, determining the rate range to which the charge / discharge rate parameter belongs, where the rate range includes a low rate range, a medium rate range, and a high rate range; querying the corresponding normalized reference temperature difference value from a preset rate-temperature difference mapping table according to the rate range, where the low rate range corresponds to the first reference temperature difference value, the medium rate range corresponds to the second reference temperature difference value, and the high rate range corresponds to the third reference temperature difference value, the first reference temperature difference value is less than the second reference temperature difference value, and the second reference temperature difference value is less than the third reference temperature difference value; using the queried normalized reference temperature difference value as the normalized reference temperature difference of the temperature change, and using it to calculate the temperature correction coefficient.

[0029] The temperature correction coefficient is corrected twice based on the temperature gradient change rate, including: calculating the temperature gradient change amount by the difference between the temperature change amount in the k-th sampling interval and the temperature change amount in the (k-1)-th sampling interval; dividing the temperature gradient change amount by the sampling period to obtain the temperature gradient change rate; determining whether the absolute value of the temperature gradient change rate exceeds a preset gradient change rate threshold; if it does, calculating a second correction factor; dividing the temperature gradient change rate by the preset gradient change rate threshold to obtain the gradient ratio; multiplying the gradient ratio by a preset correction gain coefficient to obtain the second correction factor; multiplying the temperature correction coefficient by 1 plus the second correction factor to obtain the second-corrected temperature correction coefficient; and using the second-corrected temperature correction coefficient to replace the original temperature correction coefficient to calculate the capacity increment.

[0030] In one example, the capacity increments are summed in the i-th loop to obtain the initial loop capacity for the i-th loop. The ratio of the peak temperature deviation and the ratio of the temperature integral area deviation between the i-th and (i-1)-th loops are calculated. The corrected loop capacity for the i-th loop is obtained through iterative correction, including: The capacity increments in the i-th cycle are summed to obtain the initial cycle capacity of the i-th cycle. The maximum temperature value is extracted from the temperature value sequence of the i-th cycle as the temperature peak value of the i-th cycle. The temperature integral area of ​​the i-th cycle is obtained by multiplying each temperature value by the sampling period and summing them. Divide the difference between the temperature peak value of the i-th round and the temperature peak value of the (i-1)-th round by the temperature peak value of the (i-1)-th round to obtain the temperature peak deviation ratio. Divide the difference between the temperature integral area of ​​the i-th round and the temperature integral area of ​​the (i-1)-th round by the temperature integral area of ​​the (i-1)-th round to obtain the temperature integral area deviation ratio. The average of the temperature peak deviation ratio and the temperature integral area deviation ratio is calculated to obtain the comprehensive deviation ratio. The decreasing weight coefficient is determined based on the distance between each historical round and the round of round i. The cycle capacity of each historical round is multiplied by the difference between 1 and the product of the decreasing weight coefficient and the comprehensive deviation ratio for correction. The initial cycle capacity of round i is multiplied by the difference between 1 and the comprehensive deviation ratio to obtain the corrected cycle capacity of round i.

[0031] In this example, after completing the i-th full charge-discharge cycle, the capacity increments calculated for each sampling period in the i-th cycle are summed to obtain the initial capacity value corresponding to the i-th cycle. The initial capacity reflects the battery's capacity output level after temperature correction integration during the i-th cycle, before historical trend correction. Simultaneously, the maximum temperature value is extracted from the temperature value sequence collected throughout the i-th cycle and defined as the i-th cycle temperature peak, characterizing the extreme state of the battery's thermal behavior during the i-th cycle. Furthermore, the temperature value at each sampling moment is multiplied by the corresponding sampling period length, and all products are summed to calculate the temperature integration area of ​​the i-th cycle, serving as an indicator of the cumulative effect of heat over time. To construct a cross-cycle thermal behavior evolution model, the temperature peak and temperature integration area corresponding to the (i-1)-th cycle are retrieved from storage and differencing them with the corresponding values ​​from the i-th cycle. These differences are then divided by the data value from the previous cycle to obtain the temperature peak deviation ratio and the temperature integration area deviation ratio, respectively measuring the relative changes in the battery's heat generation peak and heat accumulation trend between adjacent cycles. The arithmetic mean of the temperature peak deviation ratio and the temperature integral area deviation ratio is used to obtain the comprehensive deviation ratio of the current i-th cycle relative to the (i-1)-th cycle. This ratio is used to uniformly characterize the overall evolution trend of this cycle in the thermal dimension. To achieve dynamic correction of historical capacity estimates, a corresponding decreasing weight coefficient is set based on the cycle interval distance between each historical cycle and the current i-th cycle, so that the longer the cycle is, the smaller the correction magnitude, thereby maintaining the stability of historical capacity estimates. The initial cycle capacity of each historical cycle is multiplied by "1 minus the product of the decreasing weight coefficient and the comprehensive deviation ratio" to obtain the historical capacity value after correction for this cycle. At the same time, the initial cycle capacity of the i-th cycle is multiplied by "1 minus the comprehensive deviation ratio" to obtain the corrected cycle capacity for this cycle.

[0032] Before extracting the temperature peak and temperature integral area of ​​the i-th cycle, segmented feature extraction is performed on the temperature value sequence of the i-th cycle, including: dividing the temperature value sequence of the i-th cycle into a heating stage, a constant temperature stage, and a cooling stage according to time sequence; calculating the temperature rise rate in the heating stage, the temperature fluctuation amplitude in the constant temperature stage, and the temperature fall rate in the cooling stage; determining whether the temperature rise rate exceeds a preset rise rate threshold or the temperature fluctuation amplitude exceeds a preset fluctuation amplitude threshold; if either condition is met, the cycle is marked as an abnormal cycle; if marked as an abnormal cycle, the temperature peak and temperature integral area of ​​the cycle are abnormally corrected by multiplying the temperature peak by an abnormal attenuation coefficient to obtain the corrected temperature peak, and multiplying the temperature integral area by an abnormal attenuation coefficient to obtain the corrected temperature integral area.

[0033] During the iterative correction process, the convergence status of the correction is dynamically determined, including: calculating the absolute value of the difference between the correction loop capacity of round i and round i-1 to obtain the change in round capacity; dividing the change in round capacity by the correction loop capacity of round i-1 to obtain the capacity change rate; determining whether the capacity change rate is less than the preset convergence threshold and whether the number of consecutive rounds has reached the preset number of consecutive determinations; if both conditions are met, the correction process is determined to have converged, and the current round i is recorded as the effective number of loops; if the correction process has converged and the effective number of loops is greater than or equal to the minimum number of loops required, the subsequent loops are terminated and the evaluation capacity is calculated based on the current completed correction loop capacity, where the minimum number of loops required is 5.

[0034] In one example, the difference between the temperature peak value of the i-th round and the temperature peak value of the (i-1)-th round is divided by the temperature peak value of the (i-1)-th round to obtain the temperature peak deviation ratio. Similarly, the difference between the temperature integral area of ​​the i-th round and the temperature integral area of ​​the (i-1)-th round is divided by the temperature integral area of ​​the (i-1)-th round to obtain the temperature integral area deviation ratio, which includes: The difference between the temperature peak value in the i-th round and the temperature peak value in the (i-1)-th round is used to obtain the temperature peak value difference. The temperature peak value difference is divided by the temperature peak value in the (i-1)-th round to obtain the temperature peak value deviation ratio. The temperature integral area difference is obtained by subtracting the temperature integral area of ​​the i-th round from the temperature integral area of ​​the (i-1)-th round. The temperature integral area deviation ratio is obtained by dividing the temperature integral area difference by the temperature integral area of ​​the (i-1)-th round. The overall deviation ratio is obtained by adding the peak temperature deviation ratio to the integral area temperature deviation ratio and then dividing by 2.

[0035] In this example, the peak temperature in the i-th cycle is extracted, and the corresponding peak temperature value is retrieved from the historical data of the previous cycle (i-1). The difference between the peak temperature in the i-th cycle and the peak temperature in the i-1 cycle is calculated to obtain the peak temperature difference, which describes the maximum temperature change amplitude achieved by the battery in terms of thermal behavior between the two cycles. The peak temperature difference is divided by the peak temperature in the i-1 cycle to calculate the peak temperature deviation ratio. This ratio serves as a dimensionless relative change indicator to measure the degree of difference between the i-th and i-1 cycles under extreme heat generation conditions. Simultaneously, the temperature integral area in each cycle is calculated, which is the sum of the products of the temperature value at each sampling time and the sampling period, characterizing the cumulative heat output level of the battery within a complete cycle. The temperature integral area difference is obtained by subtracting the temperature integral area in the i-th cycle and then dividing the temperature integral area difference by the temperature integral area of ​​the previous cycle to obtain the temperature integral area deviation ratio, which measures the evolution trend of the battery's thermal accumulation effect between adjacent cycles. The ratio of peak temperature deviation to integral area temperature deviation is added together and the result is divided by 2 to obtain the comprehensive deviation ratio. This integrates the dual trends of thermal behavior at both the peak and cumulative levels and serves as the input correction factor in capacity correction calculation. This expands capacity calculation from single-round temperature response correction to iterative adjustment of thermal characteristics across rounds, improving the continuity and reliability of battery health status assessment in the time dimension.

[0036] Monitoring the switching frequency change and dynamically compensating the sampling period in multiple consecutive cycles includes: calculating the difference between the switching frequency of the i-th cycle and the switching frequency of the (i-1)-th cycle to obtain the switching frequency change; dividing the switching frequency change by the switching frequency of the (i-1)-th cycle to obtain the switching frequency fluctuation rate; determining whether the absolute value of the switching frequency fluctuation rate exceeds a preset fluctuation rate threshold; if it does, calculating the sampling period compensation coefficient; adding 1 to half of the switching frequency fluctuation rate to obtain the sampling period compensation coefficient; multiplying the initial sampling period of the i-th cycle by the sampling period compensation coefficient to obtain the compensated sampling period; and using the compensated sampling period to replace the sampling period of the i-th cycle for current and temperature value acquisition.

[0037] In one example, the decreasing weighting coefficient is determined based on the distance between each historical round and the round of round i. The cycle capacity of each historical round is corrected by multiplying the difference between 1 and the product of the decreasing weighting coefficient and the overall deviation ratio. The corrected cycle capacity of round i is obtained by multiplying the initial cycle capacity of round i by the difference between 1 and the overall deviation ratio, including: The round distance is obtained by calculating the difference between the round number of the i-th round and each historical round. The decreasing weight coefficient corresponding to each historical round is calculated based on the round distance and the preset decay coefficient. The historical correction factor is obtained by multiplying the decreasing weight coefficient by the overall deviation ratio and then subtracting it from 1. The corrected cycle capacity of each historical round is obtained by multiplying the cycle capacity of each historical round by the corresponding historical correction factor. The current correction factor is obtained by subtracting the overall deviation ratio from 1, and the correction cycle capacity of the i-th round is obtained by multiplying the initial cycle capacity of the i-th round by the current correction factor.

[0038] In this example, the round distance between the current round and all historical rounds is calculated by subtracting the index j of each historical round from the current round index i, resulting in the round distance Δij = i. The round distance (Δij) reflects the time span between each historical capacity data point and the current thermal state. Based on a preset decay coefficient β, the round distance Δij is substituted into the decreasing function model, for example, using linear or exponential decay, to calculate the decreasing weight coefficient ηj corresponding to each historical round. The weight coefficient gradually decreases as the round distance increases, with historical data further removed from the current data receiving less correction. Each decreasing weight coefficient is multiplied by the current comprehensive deviation ratio to obtain the historical correction term. This historical correction term is then subtracted from 1, forming the historical correction factor for each historical round, used to make differentiated adjustments to the historical capacity data. The original cyclic capacity value of each historical round is multiplied by its corresponding historical correction factor to obtain the corrected cyclic capacity of the historical round, achieving iterative backtracking and updating of the historical capacity results. Simultaneously, for the current capacity data of the i-th round, the comprehensive deviation ratio is directly subtracted from 1 to obtain the current correction factor. This current correction factor fully reflects the impact of the current round's temperature evolution on capacity changes without any decay. The current correction factor is then multiplied by the initial cycle capacity of the i-th round to obtain the corrected capacity value for the i-th round. The immediate effect of temperature response is considered during capacity assessment, and a recursive relationship is established between multiple rounds of data, enabling dynamic feedback of thermal effect changes in each round to historical capacity estimation. This establishes a multi-round rolling correction model based on thermal deviation.

[0039] The decay rate of the decreasing weight coefficient is dynamically adjusted based on the overall deviation ratio, including: calculating the ratio of the absolute value of the overall deviation ratio to the preset standard deviation ratio to obtain the deviation intensity coefficient; determining whether the deviation intensity coefficient is greater than 1; if it is greater than 1, calculating the decay rate adjustment factor; subtracting 1 from the deviation intensity coefficient and multiplying it by the preset adjustment factor to obtain the decay rate adjustment factor; multiplying the preset decay coefficient by the product of 1 minus the decay rate adjustment factor to obtain the dynamically adjusted decay coefficient; using the dynamically adjusted decay coefficient to replace the preset decay coefficient; calculating the decreasing weight coefficient corresponding to each historical round based on the round distance and the dynamically adjusted decay coefficient; and reducing the decay rate when the overall deviation ratio is large to maintain a high weight for the historical rounds.

[0040] In one example, the evaluated capacity is obtained by calculating the arithmetic mean of the capacity in the i-th correction cycle. The battery health status index is then calculated based on the evaluated capacity and the rated capacity, where i ≥ 5, including: The total correction capacity is obtained by summing the correction cycle capacity from the first round to the i-th round. The total correction capacity is then divided by i to obtain the evaluation capacity, where i ≥ 5. The rated capacity is read from the battery parameter database, the evaluated capacity is divided by the rated capacity to obtain the capacity ratio, and the capacity ratio is multiplied by 100 to obtain the battery health status index.

[0041] In this example, after completing the capacity correction calculation for the i-th cycle, all corrected cycle capacity values ​​from cycle 1 to cycle i are sequentially called to construct a capacity correction sequence. Each item in the capacity correction sequence is then summed to obtain the total corrected capacity containing capacity information from cycle i. This total corrected capacity comprehensively reflects the battery's energy output level after current integration and temperature response correction in each cycle, and incorporates a decreasing weight feedback mechanism from historical cycles. Therefore, statistically, it can accurately represent the battery's average performance state over a certain operating cycle. Dividing the total corrected capacity by the cycle number i yields the evaluated capacity. The evaluated capacity value is a capacity estimate derived by eliminating occasional errors in a single cycle, trends in absorbed heat, and the stability of multi-cycle averages. The value of i is no less than 5, meaning at least five cycles of cumulative average calculation of corrected capacity are performed to ensure a sufficiently rich statistical sample for the evaluated capacity, improving its approximation of the true capacity. The rated capacity value of the battery model is read from a preset battery parameter database. The rated capacity value is the standard capacity defined under ideal operating conditions during factory calibration and serves as a reference benchmark for SOH evaluation. Dividing the assessed capacity by the rated capacity yields the capacity ratio, which reflects the degree to which the battery retains its capacity relative to its theoretical performance under real-world operating conditions. By multiplying the capacity ratio by 100, the result is converted into a percentage and output as the battery state of health (SOH) index.

[0042] Reference Figure 2 This embodiment provides a BMS battery health monitoring system, including: The statistics module 1 is used to count the number of switching events and the total cycle time of the battery in a complete charge-discharge cycle, and to determine the sampling period based on the number of switching events and the total cycle time. Calculation module 2 is used to synchronously collect current and temperature values ​​at each sampling moment of the sampling period, calculate the temperature change between adjacent sampling moments, and calculate the capacity increment based on the temperature change and current value. Iterative correction module 3 is used to accumulate and sum the capacity increment in the i-th cycle to obtain the initial cycle capacity of the i-th cycle, and calculate the ratio of the temperature peak deviation and the ratio of the temperature integral area deviation between the i-th and i-1-th cycles, and obtain the corrected cycle capacity of the i-th cycle through iterative correction. Evaluation module 4 is used to obtain the evaluation capacity by calculating the arithmetic mean of the capacity of the i-th round of correction cycle, and to calculate the battery health status index based on the evaluation capacity and the rated capacity, where i≥5.

[0043] In this embodiment, the specific implementation of each unit in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0044] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, system, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, system, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, system, article, or method that includes that element.

[0045] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for monitoring battery health using a battery management system, characterized in that, include: The number of switching events and the total cycle duration of the battery during a complete charge-discharge cycle are counted, and the sampling period is determined based on the number of switching events and the total cycle duration. At each sampling moment of the sampling period, current and temperature values ​​are collected synchronously, the temperature change between adjacent sampling moments is calculated, and the capacity increment is calculated based on the temperature change and the current value. In the i-th cycle, the capacity increments are accumulated and summed to obtain the initial cycle capacity of the i-th cycle. The ratio of the temperature peak deviation and the ratio of the temperature integral area deviation between the i-th and i-1-th cycles are calculated, and the corrected cycle capacity of the i-th cycle is obtained through iterative correction. The evaluated capacity is obtained by calculating the arithmetic mean of the capacity of the i-th round of correction cycle. The battery health status index is calculated based on the evaluated capacity and the rated capacity, where i ≥ 5.

2. The BMS battery health monitoring method according to claim 1, characterized in that, The number of switching events and the total cycle duration of the battery during a complete charge-discharge cycle are counted, and the sampling period is determined based on the number of switching events and the total cycle duration, including: Monitor the battery's charge and discharge status, mark the moment when the charging state switches to the discharging state or the moment when the discharging state switches to the charging state as a switching event, and count the number of switching events in a complete charge and discharge cycle. Obtain the total loop duration, divide the number of switching events by the total loop duration to obtain the switching frequency, and divide the total loop duration by the number of switching events to obtain the sampling period.

3. The BMS battery health monitoring method according to claim 2, characterized in that, The monitoring of the battery's charge / discharge state marks the instant when the charging state switches to the discharging state or vice versa as a switching event, and counts the number of switching events in the complete charge / discharge cycle, including: The direction of the battery current is collected in real time, and the battery is determined to be in a charging or discharging state based on the direction of the current. By comparing the charging and discharging states at adjacent times, when it is detected that the charging state changes to the discharging state or the discharging state changes to the charging state, the switching event counter is incremented to obtain the number of switching events.

4. The BMS battery health monitoring method according to claim 1, characterized in that, At each sampling moment of the sampling period, current and temperature values ​​are simultaneously acquired, the temperature change between adjacent sampling moments is calculated, and the capacity increment is calculated based on the temperature change and the current value, including: The current and temperature values ​​of the battery are collected synchronously at each sampling time in the sampling period, and the difference in temperature values ​​between adjacent sampling times is calculated to obtain the temperature change. The ratio of the temperature change to the normalized reference temperature difference is increased by 1 to obtain the temperature correction coefficient. The current value, the sampling period, and the temperature correction coefficient are then multiplied to obtain the capacity increment.

5. The BMS battery health monitoring method according to claim 4, characterized in that, The ratio of the temperature change to the normalized reference temperature difference is increased by 1 to obtain the temperature correction coefficient. The current value, the sampling period, and the temperature correction coefficient are then multiplied to obtain the capacity increment, including: Divide the temperature change by the normalized reference temperature difference to obtain the temperature deviation ratio, and add 1 to the temperature deviation ratio to obtain the temperature correction coefficient. Multiply the current value by the sampling period to obtain a preliminary capacity value, and multiply the preliminary capacity value by the temperature correction coefficient to obtain the capacity increment.

6. The BMS battery health monitoring method according to claim 1, characterized in that, In the i-th cycle, the capacity increments are accumulated and summed to obtain the initial cycle capacity of the i-th cycle. The ratio of the peak temperature deviation and the ratio of the temperature integral area deviation between the i-th and (i-1)-th cycles are calculated. The corrected cycle capacity of the i-th cycle is obtained through iterative correction, including: The capacity increments in the i-th cycle are summed to obtain the initial cycle capacity of the i-th cycle. The maximum temperature value is extracted from the temperature value sequence of the i-th cycle as the temperature peak value of the i-th cycle. The temperature integral area of ​​the i-th cycle is obtained by multiplying each temperature value by the sampling period and summing them. Divide the difference between the temperature peak value of the i-th round and the temperature peak value of the (i-1)-th round by the temperature peak value of the (i-1)-th round to obtain the temperature peak deviation ratio; divide the difference between the temperature integral area of ​​the i-th round and the temperature integral area of ​​the (i-1)-th round by the temperature integral area of ​​the (i-1)-th round to obtain the temperature integral area deviation ratio. The average of the temperature peak deviation ratio and the temperature integral area deviation ratio is calculated to obtain the comprehensive deviation ratio. The decreasing weight coefficient is determined based on the distance between each historical round and the round of round i. The cycle capacity of each historical round is corrected by multiplying the difference between 1 and the product of the decreasing weight coefficient and the comprehensive deviation ratio. The corrected cycle capacity of round i is obtained by multiplying the initial cycle capacity of round i by the difference between 1 and the comprehensive deviation ratio.

7. The BMS battery health monitoring method according to claim 6, characterized in that, The difference between the temperature peak value of the i-th round and the temperature peak value of the (i-1)-th round is divided by the temperature peak value of the (i-1)-th round to obtain the temperature peak value deviation ratio. The difference between the temperature integral area of ​​the i-th round and the temperature integral area of ​​the (i-1)-th round is divided by the temperature integral area of ​​the (i-1)-th round to obtain the temperature integral area deviation ratio, including: The difference between the temperature peak value in the i-th round and the temperature peak value in the (i-1)-th round is used to obtain the temperature peak value difference. The temperature peak value difference is then divided by the temperature peak value in the (i-1)-th round to obtain the temperature peak value deviation ratio. The temperature integral area difference is obtained by subtracting the temperature integral area of ​​the i-th round from the temperature integral area of ​​the (i-1)-th round, and the temperature integral area deviation ratio is obtained by dividing the temperature integral area difference by the temperature integral area of ​​the (i-1)-th round. The comprehensive deviation ratio is obtained by adding the peak temperature deviation ratio to the integral area deviation ratio and then dividing by 2.

8. The BMS battery health monitoring method according to claim 6, characterized in that, The decreasing weighting coefficient is determined based on the distance between each historical round and the round of round i. The cycle capacity of each historical round is corrected by multiplying the difference between 1 and the product of the decreasing weighting coefficient and the comprehensive deviation ratio. The corrected cycle capacity of round i is obtained by multiplying the initial cycle capacity of round i by the difference between 1 and the comprehensive deviation ratio, including: The round distance is obtained by calculating the difference between the round number of the i-th round and each historical round, and the decreasing weight coefficient corresponding to each historical round is calculated based on the round distance and the preset attenuation coefficient. The historical correction factor is obtained by multiplying the decreasing weight coefficient by the comprehensive deviation ratio and then subtracting it from 1. The corrected cycle capacity of each historical cycle is obtained by multiplying the cycle capacity of each historical cycle by the corresponding historical correction factor. The current correction factor is obtained by subtracting the comprehensive deviation ratio from 1, and the correction cycle capacity of the i-th round is obtained by multiplying the initial cycle capacity of the i-th round by the current correction factor.

9. The BMS battery health monitoring method according to claim 1, characterized in that, The evaluated capacity is obtained by calculating the arithmetic mean of the capacity in the i-th round of correction cycles. The battery health status index is then calculated based on the evaluated capacity and the rated capacity, where i ≥ 5, including: The total corrected capacity is obtained by summing the corrected capacity from the first round of correction cycle capacity to the i-th round of correction cycle capacity. The total corrected capacity is then divided by i to obtain the evaluation capacity, where i ≥ 5. The rated capacity is read from the battery parameter database, the evaluated capacity is divided by the rated capacity to obtain the capacity ratio, and the capacity ratio is multiplied by 100 to obtain the battery health status index.

10. A BMS battery health monitoring system, characterized in that, The steps for implementing the BMS battery health monitoring method according to any one of claims 1 to 7 include: The statistics module is used to count the number of switching events and the total cycle time of the battery in a complete charge-discharge cycle, and to determine the sampling period based on the number of switching events and the total cycle time. The calculation module is used to synchronously collect current and temperature values ​​at each sampling moment of the sampling period, calculate the temperature change between adjacent sampling moments, and calculate the capacity increment based on the temperature change and the current value. The iterative correction module is used to accumulate and sum the capacity increment in the i-th cycle to obtain the initial cycle capacity of the i-th cycle, and calculate the ratio of the temperature peak deviation and the ratio of the temperature integral area deviation between the i-th and i-1-th cycles, and obtain the corrected cycle capacity of the i-th cycle through iterative correction. The evaluation module is used to obtain the evaluation capacity by calculating the arithmetic mean of the i-th round of corrected cycle capacity, and to calculate the battery health status index based on the evaluation capacity and the rated capacity, where i ≥ 5.

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