Single battery equalization management method of lithium battery BMS (Battery Management System) protection board

Abstract

The invention belongs to the field of electrical variable measurement, and particularly relates to a single battery equalization management method for a lithium battery BMS (Battery Management System) protection board, which comprises the following steps of: acquiring voltage, temperature, total current and cycle index of each single battery in real time; based on a second-order equivalent circuit model and an extended Kalman filtering algorithm, multi-source information is fused, and the health state and the charge state are estimated with high precision; constructing a dynamic equilibrium trigger threshold fusing the charge state standard deviation, the range and the health state attenuation factor; a bidirectional flyback or multi-winding transformer active equalization strategy is selected according to the inconsistent distribution characteristics and the temperature field, and equalization current is accurately controlled through pulse width modulation; and continuously monitoring the efficiency, the temperature rise rate and the convergence in the equalization process, and dynamically optimizing the equalization parameters by using the fuzzy logic controller until the inconsistency reaches the standard. According to the technical scheme, accurate, self-adaptive, high-efficiency and safe single battery equalization management can be realized, the service life of the battery pack is effectively prolonged, and the operation reliability of the system is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of electric variable measurement, and particularly relates to a single battery equalization management method of a lithium battery BMS protection board. BACKGROUND

[0002] With the rapid development of the new energy industry, lithium batteries have been widely used in electric vehicles, energy storage power stations and portable electronic devices due to their high energy density, long cycle life and environmental friendliness. As the core unit for ensuring the safe and efficient operation of lithium batteries, the battery management system (BMS) undertakes key functions such as voltage monitoring, temperature management, charge and discharge control, and single battery equalization. Among them, single battery equalization is directly related to the consistency of the overall performance, service life and thermal safety of the battery pack, and is one of the core difficulties in BMS technology.

[0003] Among them, single battery equalization aims to eliminate the deviation of state of charge (SOC) and state of health (SOH) caused by manufacturing tolerances, differences in use environment and uneven aging rates through energy transfer or dissipation. Existing equalization methods are mainly divided into active equalization and passive equalization, the former realizes energy redistribution through inductors, capacitors or transformers, and the latter relies on resistors to consume excess energy in the form of heat. Although active equalization has certain advantages in energy efficiency, its control strategy is mostly based on fixed thresholds or static rules, lacking the ability to cooperatively perceive the dynamic working conditions and aging evolution of the battery.

[0004] However, the existing technology has multiple limitations: The equalization triggering mechanism relies on preset voltage or SOC thresholds, and cannot be adjusted adaptively with the degree of battery aging, which is prone to over-equalization in the early stage or response lag in the later stage; Most active equalization topologies only support energy transfer between adjacent single batteries, making it difficult to deal with high / low SOC single batteries that are not continuously distributed, resulting in low equalization efficiency; The equalization process lacks real-time closed-loop regulation of temperature rise, efficiency and convergence speed, which may cause local overheating or energy waste, and weaken the system reliability. Especially under high-rate charging and discharging or extreme temperature environment, the above defects will be further amplified, seriously restricting the long-term stability and safety boundary of the battery pack.

[0005] Therefore, the application provides a single battery equalization management method of a lithium battery BMS protection board. SUMMARY

[0006] In order to make up for the deficiencies of the prior art and solve at least one technical problem proposed in the background art.

[0007] The technical scheme adopted by the present application to solve its technical problems is: a single battery equalization management method of a lithium battery BMS protection board, comprising the following specific steps: Step one: real-time acquisition of battery pack operation data, through a high-precision voltage sampling circuit and a temperature sensor, the end voltage and surface temperature of each single battery are synchronously acquired at a sampling frequency of not less than 1000 Hz, and the total current of the battery pack is monitored through a current Hall sensor, and the cumulative charge and discharge cycle number of the battery pack is recorded; Step two: calculation of the state of health (SOH) and the state of charge (SOC) of the single battery, based on the real-time acquired battery pack operation data, combining a pre-established battery second-order equivalent circuit model, using a recursive least squares method with a forgetting factor to identify the model parameters online, and using an extended Kalman filter algorithm to fuse voltage, current and temperature information, the SOH and SOC of each single battery are estimated in real time, and the estimation accuracy is better than 3% and 2% respectively; Step three: dynamically determining the equalization demand and the trigger threshold, based on the estimated SOH and SOC of each single battery, the standard deviation and range of the SOC of all single batteries in the battery pack are calculated, and a health state attenuation factor reflecting the battery aging rate is introduced, a dynamic equalization trigger threshold function is constructed, which makes the equalization trigger condition adaptively adjust with the battery pack inconsistency degree and the overall aging state; Among them, the health state attenuation factor not only represents the current capacity retention rate, but also quantifies the trend of the capacity attenuation rate in the history cycle, which is used to predict the future inconsistency evolution direction; Step four: selecting and executing an active equalization strategy, when the dynamic equalization trigger condition is met, according to the spatial distribution characteristics (whether continuous or discrete distribution) of the current inconsistency, the available equalization power and the temperature field distribution, the optimal strategy is selected from the pre-stored multiple active equalization topology strategies, the strategy includes an energy transfer strategy based on a bidirectional flyback converter and an energy sharing strategy based on a multi-winding transformer, and the size and direction of the equalization current are accurately controlled through a pulse width modulation signal; In particular, when detecting that the high and low voltage single bodies are in a non-continuous distribution, the multi-winding transformer strategy is preferentially enabled to realize cross-channel energy straight-through, avoiding the efficiency loss caused by traditional cascaded energy transmission; Step five: monitoring and optimizing the equalization process, during the equalization execution process, the key performance indicators are continuously monitored, including the equalization efficiency, the temperature rise rate of each single battery and the SOC convergence condition, and based on the monitoring data, a fuzzy logic-based controller is used to dynamically fine-tune the equalization current and duration, to ensure that the equalization process is efficient and safe, until the battery pack inconsistency index is lower than the preset safety threshold; The rule base of the fuzzy logic controller is trained and optimized by historical equilibrium data, and can dynamically suppress the risk of over-adjustment of the equilibrium current caused by local heat accumulation according to the coupling relationship between the SOC deviation change rate and the temperature gradient.

[0008] As preferably, the high-precision voltage sampling circuit in step one adopts a 24-bit analog-to-digital converter, the voltage measurement range is zero to five volts, the resolution is one hundred microvolts, and the linear error is less than plus or minus one thousandth; the temperature sensor is a patch type negative temperature coefficient thermistor, the temperature measurement range is minus forty degrees Celsius to one hundred twenty-five degrees Celsius, and the accuracy is plus or minus zero point five degrees Celsius; the current Hall sensor has a range of plus or minus two hundred amperes, a bandwidth of zero to fifty kilohertz, and a non-linearity of less than 0.1%.

[0009] As preferably, the battery second-order equivalent circuit model in step two includes an open-circuit voltage source, an ohmic internal resistance, and two series-connected resistance-capacitance networks, which represent electrochemical polarization effect and concentration difference polarization effect respectively; in the recursive least squares method with a forgetting factor, the forgetting factor is set to between zero point nine nine five and zero point nine nine nine to ensure the balance between parameter tracking ability and estimation stability; the process noise covariance matrix and the observation noise covariance matrix of the extended Kalman filter algorithm are trained offline and adjusted online adaptively according to the historical operation data of the battery.

[0010] As preferably, the dynamic equilibrium trigger threshold function in step three is specifically expressed as: when the standard deviation of the battery pack SOC is greater than 2% or the range of the SOC is greater than 5%, and the expected life difference indicated by the health state attenuation factor exceeds 15%, the balancing operation is triggered; the health state attenuation factor is obtained by analyzing the slope of the capacity attenuation curve and its second derivative at the historical cycle number, and is used to identify the accelerated aging inflection point.

[0011] As preferably, the energy transfer strategy based on the bidirectional flyback converter in step four is suitable for energy allocation between adjacent single batteries, and the core element is a high-frequency transformer, the switching frequency is set to one hundred kilohertz, the maximum balancing current is two amperes, and the energy transfer efficiency is higher than 90%; The energy sharing strategy based on the multi-winding transformer is suitable for energy allocation between the whole battery pack or specific high-voltage single battery and low-voltage single battery, the transformer coupling coefficient is greater than zero point nine five, multiple synchronous balancing can be realized, and direct energy bridging between any two single batteries is supported, breaking the dependence of traditional topologies on the physical connection order.

[0012] As preferably, the pulse width modulation signal in step four is generated by a microcontroller unit, the modulation resolution is not less than ten bits, the duty cycle adjustment step is 1%, and the balancing current can be accurately and linearly controlled from zero point one ampere to the maximum balancing current.

[0013] As preferably, the monitoring of the key performance indicators in the step five includes: evaluating the balancing efficiency in real time by calculating the ratio of input and output power, requiring the balancing efficiency to be continuously higher than 85%; monitoring the temperature rise rate by multiple temperature sensors distributed on the surface of the battery, requiring the temperature rise rate of any single battery cell to be no more than 0.5 degrees Celsius per minute; judging the convergence by observing the trend of the change of the SOC standard deviation, requiring the SOC standard deviation to be reduced by more than 30% within ten minutes.

[0014] As preferably, the fuzzy logic-based controller adopts a double-input single-output structure, the input variables are the change rate of SOC deviation and the average battery temperature, and the output variable is the adjustment amount of the balancing current; the fuzzy rule base contains twenty-five rules; Exemplarily, if the change rate of SOC deviation is large and the average temperature is low, the balancing current is increased by a large margin; if the change rate of SOC deviation is small and the average temperature is high, the balancing current is reduced by a small margin; the rule weight is dynamically updated according to the historical balancing effect by a reinforcement learning mechanism.

[0015] As preferably, the method further includes a balancing history database for storing detailed records of each balancing operation, including the triggering reason, the adopted strategy, the duration, the consumed or transferred energy, and the performance indicators before and after balancing; based on the historical data, a time series analysis algorithm is used to predict the future balancing demand trend, and data support is provided for the optimization of long-term balancing strategy, especially for correcting the prediction model of the state of health degradation factor.

[0016] As preferably, the method is integrated in the core processor of the battery management system, the processor is a thirty-two-bit microcontroller with a main frequency of no less than 100 megahertz, and a built-in floating point operation unit and a direct memory access controller, ensuring that all data collection, state estimation and balancing control algorithms can be completed within a control cycle of ten milliseconds.

[0017] Compared with the prior art, the present application has the following beneficial effects: The present application is not simply a combination of known technical means, but through deep integration of the state of health degradation factor and the SOC statistical characteristics, a dynamic triggering mechanism with aging perception ability is constructed; and according to the single voltage distribution form, the strategy of intelligently switching the balancing topology continuously or discontinuously, the energy transfer efficiency under non-ideal layout is significantly improved; and for the first time, a fuzzy logic controller trained based on historical data is introduced into the balancing closed loop, realizing the coordinated optimization of temperature rise and convergence speed. The synergistic effect of the three not only makes the balancing start more accurate and the process safer, but also realizes a significant reduction in balancing energy consumption under typical working conditions. The method breaks through the technical bottlenecks of fixed threshold, single topology and open-loop regulation in traditional balancing control. BRIEF DESCRIPTION OF DRAWINGS

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1 This is a flowchart of a single-cell equalization management method for a lithium battery BMS protection board proposed in this invention. Figure 2 This is a schematic diagram of the core principle framework of the dynamic equilibrium triggering mechanism based on the coupling of health state decay factor and state of charge statistical characteristics in this invention. Figure 3 This is a flowchart illustrating the logical flow of the hybrid active balancing topology strategy intelligently selected based on the discontinuous distribution characteristics of individual voltages in this invention. Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow of the closed-loop equilibrium control architecture that integrates fuzzy logic and real-time performance feedback in this invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0021] refer to Figure 1 The overall technical architecture of the single-cell equalization management method of the lithium battery BMS protection board proposed in this invention includes a data acquisition layer, a state estimation layer, a decision triggering layer, an execution control layer, and a closed-loop optimization layer. Each layer interacts with other layers via a high-speed data bus. All algorithm modules are integrated into the BMS core processor, which is a 32-bit microcontroller with a main frequency of no less than 100 MHz. It has a built-in floating-point arithmetic unit and a direct memory access controller, ensuring that all data acquisition, state estimation, and equalization control algorithms can be completed within a 10-millisecond control cycle.

[0022] In the single-cell equalization management method of the aforementioned lithium battery BMS protection board, step one involves real-time acquisition of battery pack operating data. This is achieved through a high-precision voltage sampling circuit and a temperature sensor, synchronously acquiring the terminal voltage and surface temperature of each individual cell at a sampling frequency of at least 1000 Hz. A Hall effect current sensor monitors the total current of the battery pack, and the cumulative charge-discharge cycle count is recorded. Specifically, the high-precision voltage sampling circuit employs a 24-bit analog-to-digital converter (ADC), with a voltage measurement range of 0 to 5 volts, a resolution of 100 microvolts, and a linear error of less than ±1 / ±1 / 1000. This ADC is connected to the microcontroller unit via an isolated digital interface to suppress common-mode interference and ensure signal integrity. The temperature sensor is a surface-mount negative temperature coefficient thermistor with a temperature measurement range of -40°C to 125°C and an accuracy of ±0.5°C. Its mounting position is close to the center area of ​​the outer surface of the individual cell casing to accurately reflect the temperature rise characteristics of the cell itself. The current Hall sensor has a range of ±200 Amperes, a bandwidth of 0 to 50 kHz, and a nonlinearity of less than 0.1%. It is used to monitor the total charging and discharging current of the battery pack in real time, and sends the current signal to the analog-to-digital conversion channel after anti-aliasing filtering. In addition, the system's internal counter continuously accumulates the number of complete charge-discharge cycles. Each cycle is defined as the process of discharging from a fully charged state to the cutoff voltage and then recharging back to a fully charged state. This count value serves as a key input parameter characterizing the degree of battery aging and is stored in non-volatile memory and loaded into the running memory each time the system is powered on.

[0023] In the above-mentioned single-cell equalization management method of lithium battery BMS protection board, step two is to calculate the health state and state of charge of the single cell. Based on the real-time collected battery pack operation data, combined with the pre-established second-order equivalent circuit model of the battery, the recursive least squares method with forgetting factor is used to identify the model parameters online, and the extended Kalman filter algorithm is used to fuse voltage, current and temperature information to estimate the health state and state of charge of each single cell in real time. The estimation accuracy is better than 3% and 2% respectively. Specifically, the second-order equivalent circuit model of the battery includes an open-circuit voltage source, an ohmic internal resistance, and two series-connected RC networks, which respectively characterize the electrochemical polarization effect and the concentration difference polarization effect. The open-circuit voltage source is a nonlinear function of the state of charge, and its lookup table mapping relationship is obtained through offline calibration. The ohmic internal resistance characterizes the instantaneous voltage drop characteristic of the battery's internal resistance. The first RC network has a short time constant and is used to simulate the electrochemical polarization process under high-frequency dynamic response. The second RC network has a longer time constant and is used to simulate the concentration difference polarization effect under low-frequency diffusion process. In the recursive least squares method with a forgetting factor, the forgetting factor is set between 0.995 and 0.999 to ensure a balance between parameter tracking capability and estimation stability. The algorithm iteratively updates the model parameters, including the ohmic internal resistance, the two polarization resistors, and the corresponding capacitance values, with the input being the measured terminal voltage, total current, and their historical sequence, and the output being the optimal parameter estimation vector at the current moment. The state vector of the extended Kalman filter algorithm includes the state of charge, the two polarization voltage components, and the health state decay coefficient. The observation equation is derived from the equivalent circuit model. The process noise covariance matrix and the observation noise covariance matrix are trained offline and adaptively adjusted online based on the battery's historical operating data. For example, when a sudden temperature change or a high-rate charge / discharge event is detected, the system automatically increases the process noise covariance to enhance the filter's response to sudden operating conditions. The health status is estimated based on a capacity decay model, obtained by comparing the ratio of the current available capacity to the initial nominal capacity, and corrected by combining the number of cycles and temperature integral. Finally, the independent health status and state of charge values ​​of each individual cell are output for subsequent decision-making.

[0024] In some embodiments, in the above-mentioned single-cell equalization management method of lithium battery BMS protection board, step three is to dynamically determine the equalization requirements and trigger threshold. Based on the estimated health state and state of charge of each single cell, the standard deviation and range of the state of charge of all single cells in the battery pack are calculated, and a health state decay factor reflecting the battery aging rate is introduced to construct a dynamic equalization trigger threshold function. This function enables the equalization trigger condition to be adaptively adjusted according to the degree of inconsistency of the battery pack and the overall aging state. Among them, the health status decay factor not only characterizes the current capacity retention rate, but also quantifies the changing trend of the capacity decay rate in historical cycles, which is used to predict the future direction of inconsistency evolution. Specifically, refer to Figure 2 The diagram shown illustrates the core principle framework of the dynamic equilibrium triggering mechanism based on the coupling of health state decay factor and state of charge statistical characteristics. The system first calculates the state of charge sequence {SOC1, SOC2, ..., SOC...} of N individual cells. n The sample standard deviation of} and range ; Meanwhile, the slope of the capacity decay curve fitted based on historical cyclic data Define the health status decay factor ,in Let i be the decay rate of the i-th individual cell. The average decay rate of the entire group; The dynamic balancing trigger threshold function is set as follows: when or And there exists at least one single cell that satisfies When this occurs, it is determined that a balancing operation needs to be initiated; The health status decay factor is obtained by analyzing the slope of the capacity decay curve and its second derivative under historical cycle counts, and is used to identify the inflection point of accelerated aging. It should be noted that this logic constitutes a two-dimensional decision plane, triggering equilibrium only when both thresholds exceed their respective ranges. This avoids ineffective equilibrium when the battery pack is aging but maintains good consistency, and also prevents accidental triggering when a new battery pack experiences temporary inconsistencies due to transient disturbances. This mechanism significantly improves the accuracy and timeliness of equilibrium decisions.

[0025] In some embodiments, in the above-mentioned single-cell equalization management method of lithium battery BMS protection board, step four is to select and execute an active equalization strategy. When the dynamic equalization triggering condition is met, the optimal strategy is selected from a variety of pre-stored active equalization topology strategies based on the current inconsistent spatial distribution characteristics (whether it is continuous or discrete distribution), available equalization power and temperature field distribution. The strategies include energy transfer strategies based on bidirectional flyback converters and energy sharing strategies based on multi-winding transformers. The magnitude and direction of the equalization current are precisely controlled by pulse width modulation signals. In particular, when a discontinuous distribution of high and low voltage units is detected, a multi-winding transformer strategy is prioritized to achieve cross-channel energy direct transmission, avoiding the efficiency loss caused by traditional cascaded energy transfer. Specifically, refer to Figure 3 The diagram shown illustrates the logical flow framework for intelligently selecting a hybrid active balancing topology strategy based on the discontinuous distribution characteristics of individual cell voltages. The system first performs pattern recognition on the state-of-charge (POC) sequence of individual cells: if high-POC cells and low-POC cells exhibit a continuous block distribution in their physical arrangement (e.g., the first three are high voltage, the last three are low voltage), it is determined to be a continuous distribution, and an energy transfer strategy based on a bidirectional flyback converter is activated. This strategy is suitable for energy allocation between adjacent individual cells. Its core component is a high-frequency transformer with a switching frequency set at 100 kHz, a maximum balancing current of 2 amps, and an energy transfer efficiency exceeding 90%. It should be noted that each flyback unit consists of a pair of MOSFET switches, a coupling inductor, and a freewheeling diode. By alternating their conduction, energy can flow unidirectionally or bidirectionally from the high-charge state cell to the low-charge state cell. In some embodiments, if high / low state-of-charge cells are interleaved (exemplarily, cells 1, 3, and 5 are high voltage, and cells 2, 4, and 6 are low voltage), it is determined to be a discontinuous distribution. In this case, an energy sharing strategy based on a multi-winding transformer is preferentially activated. This strategy is applicable to energy allocation between the entire battery pack or specific high-voltage cells and low-voltage cells. The transformer coupling coefficient is greater than 0.95, enabling multi-path synchronous equalization and supporting direct energy bridging between any two cells, breaking through the dependence of traditional topologies on the physical connection sequence. Its structure includes a common magnetic core and N independent windings. Each winding is connected to the corresponding cell via a half-bridge circuit. By coordinating the switching timing of each bridge arm, flexible energy scheduling between any cells is achieved. The pulse width modulation signal is generated by the microcontroller unit, with a modulation resolution of no less than 10 bits and a duty cycle adjustment step of one percent, enabling precise linear control of the equalization current from 0.1 amperes to the maximum equalization current. After being amplified by the drive circuit, the modulation signal controls the on and off of the power switching devices, thereby adjusting the average equalization current. The strategy selection logic comprehensively considers the balanced speed requirements, thermal management constraints, and system efficiency objectives to ensure optimal energy redistribution within the safety boundary.

[0026] In some embodiments, in the above-mentioned single-cell equalization management method of lithium battery BMS protection board, step five is to monitor and optimize the equalization process. During the equalization process, key performance indicators are continuously monitored, including equalization efficiency, temperature rise rate of each single cell and state of charge convergence. Based on the monitoring data, a controller based on fuzzy logic is used to dynamically fine-tune the equalization current and duration to ensure that the equalization process is efficient and safe until the battery pack inconsistency index is lower than the preset safety threshold. Among them, the rule base of the fuzzy logic controller is trained and optimized with historical equalization data, which can dynamically suppress the risk of over-adjustment of equalization current caused by local heat accumulation based on the coupling relationship between the rate of change of state of charge deviation and temperature gradient. Specifically, refer to Figure 4 The diagram shown illustrates the multi-level interaction relationships and data flow of the closed-loop equalization control architecture that integrates fuzzy logic and real-time performance feedback. The system collects real-time data during the equalization process at 100-millisecond intervals: the equalization efficiency η is calculated by the input power. With output power The ratio is evaluated in real time, that is It requires that η be consistently higher than 85%. If it is lower than this threshold for three consecutive cycles, it is judged as an abnormal energy loss, triggering power reduction or suspension of equalization. Temperature rise rate Multiple temperature sensors distributed across the battery surface monitor the temperature, ensuring that the temperature rise rate of any single cell does not exceed 0.5 degrees Celsius per minute. If the temperature rise rate of a single cell exceeds this limit, the current in the relevant equalization channel in that area is immediately reduced, or a heat dissipation priority mode is switched. The state-of-charge convergence is observed... The judgment of the changing trend requires a timeframe of 10 minutes. A reduction of more than 30% is considered insufficient to achieve a balanced effect, and the strategy needs to be reassessed. The fuzzy logic-based controller employs a dual-input, single-output structure, with the input variable being the rate of change of the state-of-charge deviation. and average battery temperature The output variable is the adjustment amount ΔI of the equalization current; The fuzzy rule base contains 25 rules, covering 7 levels of linguistic variables, including "negative large", "negative medium", "negative small", "zero", "positive small", "positive medium", and "positive large". For example, if If the value is "negatively large" (indicating a rapid decrease in inconsistency) and T_avg is "low", then the output ΔI is "positively large" (significantly increasing the balancing current to accelerate convergence). like The value is "zero" (convergence stalled) and If the value is "high", then the output ΔI is "negative small" (the current is slightly reduced to prevent overheating); Understandably, the rule weights are dynamically updated based on historical equalization results through a reinforcement learning mechanism, enabling the controller to adapt to the characteristics of different battery batches and aging stages. The controller output is superimposed on the basic PWM command to achieve closed-loop dynamic adjustment. The equalization process continues until... ≤1.5% and If the value is ≤3%, it is determined that the safety threshold has been reached, the system terminates the balancing operation and records the event.

[0027] In addition, the present invention also includes a balancing history database for storing detailed records of each balancing operation, including the triggering reason (e.g., (Exceeding limits, ΔSOH exceeding limits, etc.), the type of strategy employed, duration, energy consumed or transferred, and performance indicators before and after equilibration (for example, initial...). ,final The database, which includes data on average temperature rise and equalization efficiency, employs a circular buffer structure to retain data from the most recent 100 equalization events. Based on this historical data, the system periodically runs time series analysis algorithms (such as ARIMA models or LSTM neural networks) to predict equalization demand trends over the next 10 to 50 charge-discharge cycles, providing data support for optimizing long-term equalization strategies. In particular, it is used to correct predictive models of health state degradation factors, enabling them to more accurately predict future inconsistency evolution directions, thereby achieving preventative equalization, reducing the number of emergency equalizations, and extending battery life.

[0028] In summary, this embodiment constructs a single-cell battery equalization management system integrating high-precision sensing, intelligent decision-making, efficient execution, and closed-loop optimization through a five-step collaborative mechanism. This system not only solves the core defects of traditional methods, such as static threshold lag, serious energy waste, and lack of aging coupling, but also achieves a unity of safety, efficiency, and adaptability in the equalization process through deep collaboration between hardware, algorithms, and strategies.

[0029] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for single-cell equalization management of a lithium battery BMS protection board, characterized in that: The specific steps include the following: Step 1: Real-time acquisition of battery pack operation data. The terminal voltage and surface temperature of each individual cell are obtained synchronously through a high-precision voltage sampling circuit and a temperature sensor. The total current of the battery pack is monitored through a current Hall sensor, and the cumulative number of charge-discharge cycles of the battery pack is recorded. Step 2: Calculate the health status and state of charge of individual cells. Based on the real-time collected battery pack operation data, combined with the pre-established second-order equivalent circuit model of the battery, the recursive least squares method with forgetting factor is used to identify the model parameters online, and the extended Kalman filter algorithm is used to fuse voltage, current and temperature information to estimate the health status and state of charge of each individual cell in real time. Step 3: Dynamically determine the equalization demand and trigger threshold. Based on the estimated health state and state of charge of each individual cell, calculate the standard deviation and range of the state of charge of all individual cells in the battery pack, and introduce a health state decay factor that reflects the battery aging rate to construct a dynamic equalization trigger threshold function. This function enables the equalization trigger condition to be adaptively adjusted according to the degree of inconsistency of the battery pack and the overall aging state. Step 4: Select and execute the active balancing strategy. When the dynamic balancing triggering condition is met, select the optimal strategy from a variety of pre-stored active balancing topology strategies based on the current inconsistent spatial distribution characteristics, available balancing power, and temperature field distribution. The strategies include energy transfer strategies based on bidirectional flyback converters and energy sharing strategies based on multi-winding transformers. The magnitude and direction of the balancing current are precisely controlled by pulse width modulation signals. Step 5: Monitor and optimize the equalization process. During the equalization process, continuously monitor key performance indicators, including equalization efficiency, temperature rise rate of each individual cell, and state of charge convergence. Based on the monitoring data, use a fuzzy logic-based controller to dynamically fine-tune the equalization current and duration to ensure that the equalization process is efficient and safe until the battery pack inconsistency index is lower than the preset safety threshold.

2. The single-cell equalization management method of a lithium battery BMS protection board according to claim 1, characterized in that: The high-precision voltage sampling circuit uses an analog-to-digital converter, with a voltage measurement range of 0 to 5 volts; the temperature sensor is a surface-mount negative temperature coefficient thermistor, with a temperature measurement range of -40 degrees Celsius to 125 degrees Celsius; and the current Hall sensor has a range of ±200 amperes.

3. The single-cell equalization management method of a lithium battery BMS protection board according to claim 1, characterized in that: The second-order equivalent circuit model of the battery includes an open-circuit voltage source, an ohmic internal resistance, and two series-connected RC networks; the process noise covariance matrix and observation noise covariance matrix of the extended Kalman filter algorithm are trained offline and adaptively adjusted online based on the battery's historical operating data.

4. The single-cell equalization management method of a lithium battery BMS protection board according to claim 1, characterized in that: The dynamic equalization trigger threshold function is set as follows: when the standard deviation of the battery pack's state of charge is greater than 2% or the range of the state of charge is greater than 5%, and the expected life difference indicated by the health state degradation factor exceeds 15%, the equalization operation is triggered; the health state degradation factor is obtained by analyzing the slope of the capacity degradation curve and its second derivative under historical cycle counts.

5. The single-cell equalization management method of a lithium battery BMS protection board according to claim 1, characterized in that: The energy transfer strategy based on the bidirectional flyback converter is suitable for energy allocation between adjacent single cells; the energy sharing strategy based on the multi-winding transformer is suitable for energy allocation between discontinuously distributed high-voltage cells and low-voltage cells, and supports direct energy bridging between any two cells.

6. The single-cell equalization management method of a lithium battery BMS protection board according to claim 1, characterized in that: The pulse width modulation signal is generated by the microcontroller unit, which can achieve precise linear control of the equalization current from 0.1 ampere to the maximum equalization current.

7. The single-cell equalization management method of a lithium battery BMS protection board according to claim 1, characterized in that: The equalization efficiency is evaluated in real time by calculating the ratio of input to output power, and the equalization efficiency is required to be consistently higher than 85%; the temperature rise rate is monitored by multiple temperature sensors distributed on the battery surface, and the temperature rise rate of any single battery cell is required to be no more than 0.5 degrees Celsius per minute; the state of charge convergence is judged by observing the trend of the change in the standard deviation of the state of charge, and the standard deviation of the state of charge is required to decrease by more than 30% within ten minutes.

8. The single-cell equalization management method of a lithium battery BMS protection board according to claim 1, characterized in that: The fuzzy logic-based controller adopts a dual-input single-output structure. The input variables are the rate of change of state of charge deviation and the average battery temperature, and the output variable is the adjustment amount of the balancing current. The fuzzy rule base contains multiple rules, and the rule weights are dynamically updated based on the historical balancing effect through a reinforcement learning mechanism.

9. A method for single-cell equalization management of a lithium battery BMS protection board according to claim 1, characterized in that: The method also includes a balancing history database for storing detailed records of each balancing operation, including the triggering reason, the strategy adopted, the duration, the energy consumed or transferred, and the performance indicators before and after balancing. Based on this historical data, a time series analysis algorithm is used to predict future equilibrium demand trends and to correct the prediction model of the health status decay factor.

10. A method for single-cell equalization management of a lithium battery BMS protection board according to claim 1, characterized in that: The method is integrated into the core processor of the battery management system, with a built-in floating-point unit and a direct memory access controller.

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