Low-cost series high-voltage battery management method and system based on wireless communication

Abstract

The present application provides a low-cost series-type high-voltage battery management method and system based on wireless communication. The method includes: calculating the remaining life and capacity attenuation of each single cell based on aging capacity data and the number of historical cycles; comparing the remaining life of each single cell with a preset life threshold to generate a final balancing control parameter, and dynamically adjusting the compensation parameters of each single cell based on the capacity attenuation and status data of each single cell; transmitting the final balancing control parameters and adjusted compensation parameters of all single cells to the control circuit of the high-voltage battery pack through dual-antenna switching, so that the control circuit performs dynamic balancing management of the high-voltage battery pack based on the final balancing control parameters and adjusted compensation parameters of all single cells. The technical solution provided by the present application dynamically predicts life through monitoring data, and realizes active management of the battery pack through dual-antenna transmission of intelligent balancing parameters, thereby improving performance and safety.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and in particular to a low-cost series high-voltage battery management method and system based on wireless communication. Background Art

[0002] In large-scale power generation applications such as power systems, grid energy storage, and renewable energy, high-voltage battery packs need to maintain the performance consistency of each individual cell over a long period of time to ensure overall stability and safety. Power generation battery packs are usually connected in series to form high-voltage battery packs, which are used to smooth out power fluctuations of renewable energy and achieve peak shaving and valley filling. However, due to manufacturing differences in individual cells within the battery pack, frequent charge and discharge cycles (especially in grid frequency regulation scenarios), and complex environmental factors (such as temperature gradients and high current stress), there are significant differences in their aging rates and capacity attenuation. This inconsistency will exacerbate the "short board effect", resulting in a decrease in the overall available capacity of the high-voltage battery pack, a shortened lifespan, and even safety hazards such as thermal runaway, which seriously threatens the economy and reliability of the power generation system.

[0003] Most existing battery management solutions are based on offline machine learning models. These models predict the remaining life of individual cells by analyzing historical cycle counts, voltage / current curves, and other data. Based on these predictions, they generate static balancing strategies. For example, a "multimodal data fusion model" proposed in some research combines voltage balancing with temperature compensation to generate a fixed-parameter balancing strategy through offline training.

[0004] While existing models perform well in laboratory settings, they rely on offline training and extensive historical data, making them difficult to adapt to the real-time prediction requirements of dynamic power generation conditions (such as random charging and discharging caused by grid frequency fluctuations). Furthermore, the single-channel communication technology used in existing solutions is susceptible to electromagnetic interference in high-voltage isolation scenarios, resulting in control command transmission delays or loss, making it impossible to meet the millisecond-level dynamic balancing requirements. Furthermore, static balancing parameters cannot be dynamically adjusted based on real-time capacity decay, which can easily lead to over- or under-compensation and exacerbate battery pack performance degradation. Summary of the Invention

[0005] The present application provides a low-cost series-type high-voltage battery management method and system based on wireless communication, which can be applied to large-scale power generation applications to solve the problems of poor high-voltage battery management effect, poor safety, and shortened life caused by strong offline training dependence, poor adaptability to dynamic working conditions, insufficient communication reliability, and static balancing parameters not matching the real-time attenuation state in the existing technology.

[0006] In a first aspect, the present application provides a low-cost series high-voltage battery management method based on wireless communication, comprising:

[0007] Obtain aging capacity data, historical cycle counts, and status data for each single cell connected in series within a high-voltage battery pack;

[0008] Calculating the remaining life and capacity attenuation of each of the single cells according to the aging capacity data and the historical cycle counts;

[0009] Based on the comparison result of the remaining life of each single cell and the preset life threshold, generating the final balancing control parameter of each single cell, and dynamically adjusting the compensation parameter of each single cell according to the capacity attenuation of each single cell and the state data;

[0010] The final balancing control parameters and adjusted compensation parameters of all the single cells are transmitted to the control circuit of the high-voltage battery pack through a dual-antenna switching method, so that the control circuit performs dynamic balancing management of the high-voltage battery pack based on the final balancing control parameters and adjusted compensation parameters of all the single cells.

[0011] Optionally, the state data includes operating temperature data, charge and discharge current data, and voltage fluctuation data;

[0012] The dynamically adjusting the compensation parameters of each single cell according to the capacity attenuation of each single cell and the state data includes:

[0013] Calculate the temperature influence factor based on the operating temperature data, calculate the current stress factor based on the charge and discharge current data, and calculate the voltage stability factor based on the voltage fluctuation data;

[0014] Constructing a dynamic compensation evaluation matrix based on the temperature influence factor, the current stress factor and the voltage stability factor;

[0015] Extracting a reference attenuation curve of each of the single cells from a dynamic reference database, and calculating a deviation between the capacity attenuation and the reference attenuation based on the reference attenuation curve and the capacity attenuation of each of the single cells;

[0016] Inputting the dynamic compensation evaluation matrix and the deviation values ​​corresponding to all single cells into a compensation parameter calculation model to obtain initial compensation parameters for all single cells;

[0017] The current health state of each single cell is monitored, and the compensation parameter of each single cell is dynamically adjusted according to the current health state and the initial compensation parameter of each single cell to obtain the compensation parameter of each single cell.

[0018] Optionally, inputting the dynamic compensation evaluation matrix and the deviation values ​​corresponding to all single cells into a compensation parameter calculation model to obtain initial compensation parameters for all single cells includes:

[0019] Performing eigendecomposition on the dynamic compensation evaluation matrix to obtain matrix eigenvectors and eigenvalues, wherein the matrix eigenvectors are used to characterize the contribution direction of operating temperature, charge and discharge current, and voltage fluctuations to capacity decay;

[0020] Performing Gaussian normalization on the deviation values ​​of the reference attenuation values ​​of all single cells to obtain standardized deviation values;

[0021] Couple the matrix eigenvectors with the standardized deviation values ​​of all single cells to generate compensation intermediate parameters of all single cells;

[0022] The compensation intermediate parameters are weightedly corrected according to the characteristic values ​​to obtain initial compensation parameters of all single cells.

[0023] Optionally, generating a final balancing control parameter for each single cell based on a comparison result of the remaining life of each single cell with a preset life threshold includes:

[0024] Comparing the remaining life with the preset life threshold to obtain a life deviation value, and determining the balancing priority according to the absolute value of the life deviation value;

[0025] generating an initial balancing control parameter by combining the balancing priority and the capacity attenuation;

[0026] The initial balancing control parameters are dynamically adjusted according to the status data of each single battery to obtain final balancing control parameters.

[0027] Optionally, the generating an initial balancing control parameter by combining the balancing priority and the capacity attenuation includes:

[0028] Performing linear scaling processing on the balancing priority to obtain a normalized priority parameter, and performing dimension unification processing on the capacity attenuation to obtain a standardized capacity attenuation;

[0029] Obtaining, according to the normalized priority parameter and the standardized capacity attenuation, a charge and discharge direction parameter and a duration parameter corresponding to each single battery from a preset balancing strategy database;

[0030] The charge and discharge direction parameter and the duration parameter are combined to generate an initial balancing control parameter.

[0031] Optionally, dynamically adjusting the compensation parameters of each single cell according to the current health status and initial compensation parameters of each single cell to obtain the compensation parameters of each single cell includes:

[0032] Quantify the current health status to obtain the status value corresponding to the current health status;

[0033] According to the health state segmented compensation strategy, when it is determined that there is a single battery of the first compensation type whose state value is greater than or equal to a first preset percentage, adjusting the compensation parameters of the single battery of the first compensation type using a linear interpolation method based on the initial compensation parameters;

[0034] When it is determined that there is a single battery of the second compensation type whose state value is greater than the second preset percentage and less than the first preset percentage, adjusting the compensation parameters of the single battery of the second compensation type using an exponential decay method according to the initial compensation parameters;

[0035] When it is determined that there are single cells of the third compensation type whose state value is less than or equal to the second preset percentage, adjusting the compensation parameters of the single cells of the third compensation type using a fixed compensation threshold method according to the initial compensation parameters;

[0036] During the dynamic adjustment process, if the adjustment amplitude of the compensation parameter is less than the preset amplitude threshold within N consecutive cycles, the update of the compensation parameter is frozen until the voltage fluctuation rate is detected to exceed the preset fluctuation rate threshold, and then the update of the compensation parameter is reactivated.

[0037] Optionally, the transmitting of the final balancing control parameters and the adjusted compensation parameters of all the single cells to the control circuit of the high-voltage battery pack by dual-antenna switching includes:

[0038] When the channel quality of the main antenna and the backup antenna is greater than or equal to the preset quality threshold, the final equalization control parameters of all single cells are transmitted to the control circuit of the high-voltage battery pack via the main antenna, and the adjusted compensation parameters of all single cells are transmitted to the control circuit of the high-voltage battery pack via the backup antenna;

[0039] When the channel quality of any of the main antenna and the backup antenna is less than the preset quality threshold, the antenna switching operation is triggered, and the final balancing control parameters and adjusted compensation parameters of all the single cells are transmitted through the antenna whose channel quality is greater than or equal to the preset quality threshold.

[0040] In a second aspect, the present application provides a low-cost series-connected high-voltage battery management system based on wireless communication, comprising:

[0041] An acquisition module is used to obtain aging capacity data, historical cycle counts and status data of each single battery connected in series in the high-voltage battery pack;

[0042] a calculation module, configured to calculate the remaining life and capacity attenuation of each of the single cells based on the aging capacity data and the historical cycle count;

[0043] an adjustment module, configured to generate a final balancing control parameter for each single cell based on a comparison result of the remaining life of each single cell with a preset life threshold, and dynamically adjust a compensation parameter for each single cell according to the capacity attenuation of each single cell and the status data;

[0044] The control module is used to transmit the final balancing control parameters and adjusted compensation parameters of all the single cells to the control circuit of the high-voltage battery pack through dual-antenna switching, so that the control circuit can perform dynamic balancing management of the high-voltage battery pack based on the final balancing control parameters and adjusted compensation parameters of all the single cells.

[0045] In a third aspect, the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a low-cost series high-voltage battery management method based on wireless communication as described in any one of the first aspects.

[0046] In a fourth aspect, the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a low-cost series high-voltage battery management method based on wireless communication as described in any one of the first aspects.

[0047] In the present application, a low-cost series-type high-voltage battery management method based on wireless communication is provided, which includes: obtaining aging capacity data, historical cycle times and status data of each single cell connected in series in a high-voltage battery pack; calculating the remaining life and capacity attenuation of each single cell according to the aging capacity data and the historical cycle times; generating final balancing control parameters of each single cell based on a comparison result of the remaining life of each single cell with a preset life threshold, and dynamically adjusting the compensation parameters of each single cell according to the capacity attenuation of each single cell and the status data; transmitting the final balancing control parameters and adjusted compensation parameters of all the single cells to the control circuit of the high-voltage battery pack through a dual-antenna switching method, so that the control circuit performs dynamic balancing management of the high-voltage battery pack according to the final balancing control parameters and adjusted compensation parameters of all the single cells.

[0048] This application establishes a multi-dimensional degradation model by acquiring real-time aging capacity data, historical cycle counts, and status data for each battery cell. This model improves the accuracy of remaining life prediction and avoids the misjudgment caused by traditional voltage balancing methods that ignore differences in battery aging. By intelligently comparing the remaining life against preset thresholds to generate balancing parameters, the system dynamically adjusts compensation parameters based on capacity degradation and real-time status data, resulting in a faster balancing control response. This approach is particularly suitable for high-cycle battery packs, effectively mitigating overall capacity loss caused by the "barrel effect." Dual-antenna switching transmission technology enables zero-packet loss transmission of control parameters in complex electromagnetic environments, addressing signal interference issues associated with traditional single-channel transmission in high-voltage isolation scenarios and ensuring real-time, synchronous execution of balancing commands. Experimental data demonstrates that this technology can extend the overall life of the battery pack while reducing ineffective balancing energy consumption through precise compensation, improving system energy utilization. This technology is particularly suitable for high-voltage battery systems in new energy vehicles. Dynamic parameter analysis can identify abnormally degraded cells 3-5 cycles in advance, improving early warning accuracy and providing data support for preventive maintenance. This technology enables proactive management of high-voltage battery packs, enhancing performance and safety.

[0049] Furthermore, by collecting data on the operating temperature, charge and discharge current, and voltage fluctuations of individual cells, the temperature impact factor, current stress factor, and voltage stability factor are calculated to construct a dynamic compensation evaluation matrix. Combining the deviation between the historical baseline attenuation curve and the current capacity attenuation, the matrix eigenvectors are coupled with the standardized deviation values ​​to generate intermediate compensation parameters. Initial compensation parameters are then obtained based on eigenvalue weighting corrections, and finally, the compensation parameters are dynamically adjusted based on the current health status of the battery. This solution achieves precise compensation control coupled with multiple physical fields. The coordinated analysis of temperature, current, and voltage parameters improves the compensation response speed, and the eigenvalue weighting mechanism reduces compensation errors. Through dual calibration of the dynamic evaluation matrix and the historical attenuation curve, the accuracy of capacity compensation is improved under abnormal operating conditions, reducing the capacity attenuation of all individual cells (i.e., battery pack inconsistency). Gaussian normalization stabilizes the algorithm and extends the cycle life of the battery pack.

[0050] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0052] Figure 1A flowchart of a low-cost series high-voltage battery management method based on wireless communication provided in an embodiment of the present application;

[0053] Figure 2 A schematic structural diagram of a low-cost series-connected high-voltage battery management system based on wireless communication provided in an embodiment of the present application;

[0054] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0056] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 11, 12, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0057] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0058] In order to solve the problems in the existing technology such as strong dependence on offline training, poor adaptability to dynamic working conditions, high calculation delay, and static balancing parameters not matching the real-time attenuation state, which lead to poor management effect and poor performance and safety, the embodiment of the present application provides a low-cost series high-voltage battery management method based on wireless communication. The method adopts the following concept: by real-time collection of battery aging data, establishing a life prediction model to calculate the remaining life and attenuation, dynamically generating balancing parameters based on threshold comparison and status data, and using dual antennas for reliable transmission, intelligent balancing management of high-voltage battery packs is realized to improve safety and service life.

[0059] Figure 1A flowchart of a low-cost series high-voltage battery management method based on wireless communication provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0060] S11. Obtain aging capacity data, historical cycle counts, and status data of each single battery connected in series in the high-voltage battery pack.

[0061] Among them, the aging capacity data is the ratio of the current effective capacity of the single cell to the rated capacity calculated by the capacity attenuation model. The number of historical cycles is the cumulative number of complete charge and discharge times completed by the single cell. Status data refers to the instantaneous parameters of the battery collected in real time, including short-term dynamic information such as voltage, current, temperature, humidity, and state of charge, which are used to describe the current operating conditions but do not directly reflect the degree of aging. Exemplarily, the status data may include voltage fluctuation data, operating temperature data, charge and discharge current data, etc. It should be understood that the battery may be exposed to a humid outdoor environment in the power generation scenario, so the status data may also include ambient humidity data. Since power fluctuates frequently in the grid frequency modulation scenario, the voltage fluctuation data can be replaced by power fluctuation data.

[0062] For example, the battery management system first collects real-time data on each cell's terminal voltage, charge / discharge current, and surface temperature using its voltage and current Hall effect sensors. Simultaneously, the cycle count data stored in the storage module is used to calculate the current capacity decay using the ampere-hour integration method. Finally, a neural network model is used to extract the cell's aging capacity data, historical cycle counts, and status data.

[0063] S12. Calculate the remaining life and capacity attenuation of each single battery cell based on the aging capacity data and the historical number of cycles.

[0064] Remaining life can refer to the predicted lifespan output by a nonlinear regression model based on the number of cycles and capacity decay rate. Capacity decay is the absolute difference between the current capacity and the initial capacity.

[0065] Exemplarily, the remaining service life algorithm framework is used to input the aging capacity data into the double exponential decay model to calculate the remaining service life prediction value. At the same time, according to the two-dimensional scatter plot analysis method, the capacity retention rate and the number of cycles are nonlinearly fitted to generate a dynamic curve of capacity decay. Finally, the optimal prediction model is screened out by the Akaike Information Criterion (AIC), and finally a quantitative table of the life difference of each single cell is output. The remaining service life algorithm framework is the overall calculation process, and the double exponential decay model is the specific implementation tool in the framework. The remaining service life calculation process first constructs the model, and the decay rate parameters are fitted to calculate the remaining service life prediction. The formula used in the double exponential decay model is:

[0066] ,in, is the battery capacity at the nth cycle, is the initial capacity, is the weight coefficient, is the decay rate parameter of the corresponding item.

[0067] S13. Based on the comparison result of the remaining life of each single cell and the preset life threshold, generate the final balancing control parameter of each single cell, and dynamically adjust the compensation parameter of each single cell according to the capacity attenuation and status data of each single cell.

[0068] The preset lifespan threshold is dynamically set based on the battery model and application scenario (such as grid frequency regulation or energy time shifting) and can be 15,000 cycles, 30,000 cycles, or other values. The comparison result can be the difference between the two. Optionally, the final balancing control parameter can be a three-tuple consisting of a balancing trigger threshold, a maximum balancing current, and a balancing priority. Alternatively, the final balancing control parameter can be an instruction set consisting of a balancing target value, a maximum balancing current, and a cutoff voltage. Still further, the final balancing control parameter can be a two-tuple consisting of a balancing intensity (e.g., current intensity) and a charge / discharge duration (or time window). For example, this embodiment of the present application provides initial balancing control parameters for charging at 0.3A for 120 seconds. Compensation parameters include a temperature compensation coefficient, an internal resistance compensation coefficient, and a self-discharge compensation time. The adjusted compensation parameters are dynamic correction data consisting of a temperature compensation coefficient correction weight and a polarization matching offset. The polarization matching offset is a dynamic parameter used to correct for deviations between the battery voltage and the actual state of charge due to polarization effects.

[0069] For example, a hybrid optimal control strategy compares remaining life data with a preset threshold: when the difference in lifespan exceeds 50 cycles, strong balancing mode is activated; when the difference is ≤50 cycles, weak balancing mode is adopted. Simultaneously, based on a mapping table, a compensation factor γ = 1.2 is applied to cells with a capacity decay greater than 5%. This embodiment dynamically adjusts the balancing resistance value through a fuzzy controller, generating a final set of control parameters including balancing strength and time window. The fuzzy controller's input variables are directly derived from the real-time parameters of the individual cells.

[0070] S14. The final balancing control parameters and adjusted compensation parameters of all single cells are transmitted to the control circuit of the high-voltage battery pack through dual-antenna switching, so that the control circuit performs dynamic balancing management of the high-voltage battery pack according to the final balancing control parameters and adjusted compensation parameters of all single cells.

[0071] Dual-antenna switching transmission is a time-division multiple access communication protocol that dynamically switches between the primary and backup antennas based on signal strength and bit error rate. It should be noted that power generation scenarios experience significant electromagnetic interference. To provide a reliable transmission mechanism, embodiments of this application can replace dual-antenna switching with multi-channel redundant transmission.

[0072] For example, a dual-antenna redundant transmission protocol is used, alternating between 2.4 GHz and dual-band frequencies, to transmit a control parameter packet containing the final balancing control parameters and adjusted compensation parameters for all cells to the balancing control circuit. Based on an active balancing topology, this circuit controls the switch array to perform energy transfer: discharging the startup circuit of the high-capacity cell while simultaneously activating the resonant charging module corresponding to the low-capacity cell.

[0073] Here's a specific example:

[0074] Taking a high-voltage battery pack as an example, the system first collects real-time data on each battery's aging capacity, historical cycle count, and status. Then, based on a capacity decay curve model and combined with the cycle count, it calculates the remaining lifespan and capacity decay of each individual battery. This remaining lifespan is then compared with preset thresholds to generate differentiated balancing control parameters. Compensation parameters are dynamically adjusted based on real-time capacity decay, temperature, and current data. Finally, all parameters are synchronized with the control circuit via dual-antenna redundant communication, driving the active balancing module to precisely transfer energy to the target individual battery, achieving millisecond-level dynamic balancing.

[0075] By executing S11~S14, the embodiment of the present application obtains the aging capacity data, historical cycle counts and status data of each single cell in the high-voltage battery pack in real time, accurately calculates its remaining life and capacity attenuation, and generates the optimal balancing control parameters based on life threshold comparison and dynamic compensation adjustment, and then efficiently transmits them to the control circuit with the help of dual-antenna switching technology, ultimately realizing intelligent dynamic balancing management of the high-voltage battery pack, and ultimately improving safety and service life. Moreover, compared with the static balancing strategy, the dynamic balancing management in the embodiment of the present application can reduce unnecessary balancing operations, thereby reducing energy consumption and extending the overall service life of the battery pack. The embodiment of the present application improves the accuracy of remaining life prediction by establishing an attenuation model that includes multi-dimensional factors such as operating temperature, charge and discharge current, and voltage fluctuations. Accurate prediction can identify abnormally attenuated single cells in advance, provide data support for preventive maintenance, and thus avoid greater losses caused by battery failure.

[0076] In one possible embodiment, the state data includes operating temperature data, charge and discharge current data, and voltage fluctuation data. S13, dynamically adjusting the compensation parameters of each single cell according to the capacity attenuation and state data of each single cell, including:

[0077] Step 111 : Calculate the temperature influence factor based on the operating temperature data, calculate the current stress factor based on the charge and discharge current data, and calculate the voltage stability factor based on the voltage fluctuation data.

[0078] Among them, the temperature impact factor includes the battery surface temperature gradient, temperature difference change rate and thermal distribution discreteness, which is used to reflect the nonlinear effect of temperature on capacity attenuation; the current stress factor includes the charge and discharge rate, current ripple coefficient and peak current duration ratio, which characterizes the cumulative damage of current fluctuations to the battery polarization effect; the voltage stability factor is composed of the voltage extreme difference, voltage standard deviation and relaxation voltage recovery rate, which quantifies the dynamic characteristics of voltage fluctuations.

[0079] Step 112: Construct a dynamic compensation evaluation matrix based on the temperature influence factor, the current stress factor, and the voltage stability factor.

[0080] The dynamic compensation evaluation matrix is ​​a 3×3 weighted matrix constructed from temperature impact factors, current stress factors, and voltage stability factors. It is used to quantify the coupled impact of environmental stress on capacity degradation. The update frequency of the dynamic compensation evaluation matrix is ​​primarily determined by factors such as the rate of change of environmental parameters, balancing strategy requirements, and hardware performance limitations.

[0081] Step 113 : extracting a reference attenuation curve of each single battery from the dynamic reference database, and calculating a deviation between the capacity attenuation and the reference attenuation based on the reference attenuation curve and the capacity attenuation of each single battery.

[0082] The dynamic benchmark database is a real-time or periodically updated data storage system used to store and generate benchmark reference data for high-voltage battery packs under different operating conditions, such as capacity decay curves, internal resistance growth models, and health status thresholds. For example, in photovoltaic / wind power storage systems, the dynamic benchmark database generates adaptive capacity decay benchmarks in real time based on seasonal temperature changes and fluctuations in charge and discharge depth, supporting accurate lifespan prediction and balancing decisions. A benchmark decay curve is an idealized reference curve of battery capacity versus cycle number or time under specific operating conditions (such as ambient temperature, charge and discharge mode, and application scenario), generated by the dynamic benchmark database. This curve represents the expected aging trajectory of the battery under standard or typical conditions and is used to compare actual capacity decay to quantify the degree of performance deviation of individual cells. The deviation value is the absolute difference between the current capacity decay and the value predicted by the benchmark curve. For example, if the measured capacity decay of a battery after 1000 cycles is 12%, while the benchmark curve predicts 8%, the deviation value is +4%, indicating that the battery is aging faster than expected.

[0083] Step 114 : Input the dynamic compensation evaluation matrix and the deviation values ​​corresponding to all the single cells into a compensation parameter calculation model to obtain initial compensation parameters for all the single cells.

[0084] The compensation parameter calculation model is based on a regression model, which inputs the dynamic compensation evaluation matrix and the capacity attenuation deviation value, and outputs the compensation current, balancing time, and temperature compensation coefficient. The compensation parameter calculation model can be used using the following formula:

[0085] ,in, is the dependent variable, which represents the initial compensation parameter of the i-th single cell. is the intercept term, i.e. the benchmark compensation amount, is the regression coefficient of the jth factor, is the random error term, is the jth factor of the i-th single cell, and the jth factor is an element in the dynamic compensation evaluation matrix or a capacity attenuation deviation value, etc. =4 is the total number of factors, and factors are elements or deviation values ​​in the dynamic compensation evaluation matrix.

[0086] Step 115 : Monitor the current health status of each single cell, and dynamically adjust the compensation parameters of each single cell according to the current health status and initial compensation parameters of each single cell to obtain the compensation parameters of each single cell.

[0087] The current health status may include capacity retention rate, internal resistance growth rate, and relaxation voltage decay rate.

[0088] Here's a specific example:

[0089] In a high-voltage battery management system, the system monitors the operating status of individual cells in real time. First, the system detects the battery's current operating temperature of 52°C. Using a temperature-aging relationship model, the system calculates a temperature impact factor of 1.35. It also detects that the battery is charging at a 2.5C rate, and using a current stress model, the system calculates a current stress factor of 1.8. Furthermore, by analyzing voltage fluctuation data from the last 20 cycles, the system calculates a voltage stability factor of 1.25. The system combines these three key indicators into a dynamic compensation evaluation matrix [1.35, 1.8, 1.25], which comprehensively reflects the impact of current operating conditions on battery aging. The system then retrieves a baseline degradation curve for a similar battery model under standard operating conditions from a historical database and finds that the typical capacity degradation for this battery model after 600 cycles is 10%. However, the measured capacity degradation for the current battery after 600 cycles reached 16%, resulting in a calculated deviation of 6%. The system inputs the dynamic compensation evaluation matrix and the deviation into a trained compensation parameter calculation model. After comprehensive analysis, the model outputs an initial compensation parameter of 8.6 mA·h. Finally, in the three ranges of 85% and above for mild degradation, 75%-85% for moderate degradation, and below 75% for severe degradation, the system monitors the battery's current health status at 82%, placing it in the moderate degradation range. Based on the preset dynamic adjustment rules, the initial compensation parameters for this healthy battery need to be increased by 12%. The system inputs the dynamic compensation evaluation matrices of 1.35, 1.8, and 1.25 and a capacity decay deviation of 6% into the trained compensation parameter calculation model. Using a weighted multi-factor regression algorithm, combined with temperature compensation coefficient correction weights and polarization matching offsets, the model outputs an initial compensation parameter of 16.3 mA·h. Based on the real-time health status, the compensation intensity is increased by 12% according to the moderate degradation rule, and a balancing current correction of 4 mA·h corresponding to the polarization offset is added, resulting in a final compensation parameter of 20.3 mA·h. Based on this compensation parameter, calibrated based on both operating condition analysis and health status, the system will perform precise energy compensation for the battery during the next balancing cycle, effectively slowing its aging while ensuring balanced and stable performance of the overall battery pack. Through such refined compensation parameter calculation and dynamic adjustment mechanism, the accuracy and reliability of battery management are improved.

[0090] By executing steps 111-115, the battery compensation parameter calculation scheme in this embodiment of the present application achieves precise battery health management through multi-dimensional data fusion and dynamic adjustment. The system first collects temperature, current, and voltage data in real time and converts them into quantitative influencing factors, constructing a comprehensive evaluation matrix reflecting the current operating conditions. Simultaneously, by comparing historical baseline attenuation curves, it calculates the actual attenuation deviation of each battery. The operating condition matrix and attenuation deviation are input into an intelligent calculation model to generate preliminary compensation parameters. Finally, dynamic fine-tuning is performed based on the real-time health status of the battery, and the final compensation value is output. This closed-loop calculation method, which integrates operating condition monitoring, historical comparison, and status feedback, accurately identifies the cause of abnormal battery attenuation and provides a personalized optimal compensation solution for each individual battery cell. This embodiment of the present application considers both the immediate impact of the current operating environment on the battery and long-term aging trends, achieving full-cycle optimization from short-term compensation to long-term life extension. Through a dynamic parameter adjustment mechanism, the compensation strength is consistently matched to the actual battery requirements, effectively improving battery pack consistency, extending the overall service life, and ensuring system operational safety.

[0091] In one possible embodiment, step 114, inputting the dynamic compensation evaluation matrix and the deviation values ​​corresponding to all single cells into a compensation parameter calculation model to obtain initial compensation parameters for all single cells, includes:

[0092] Step a1: perform eigendecomposition on the dynamic compensation evaluation matrix to obtain matrix eigenvectors and eigenvalues. The matrix eigenvectors are used to characterize the contribution direction of operating temperature, charge and discharge current, and voltage fluctuations to capacity attenuation.

[0093] Among them, the eigenvectors and eigenvalues ​​are obtained through matrix decomposition. The eigenvectors represent the contribution direction of temperature, current, and voltage fluctuations to capacity attenuation, and the eigenvalues ​​reflect the weight ratio of each environmental factor.

[0094] Step a2: Perform Gaussian normalization on the deviation values ​​of the baseline attenuation of all single cells to obtain standardized deviation values.

[0095] Among them, the standardized deviation value is to convert the deviation between the capacity attenuation deviation value of the single battery and the historical benchmark curve into a statistic that obeys the standard normal distribution, eliminating the dimensional difference.

[0096] Step a3: Couple the matrix eigenvectors with the standardized deviation values ​​of all single cells to generate compensation intermediate parameters of all single cells.

[0097] Among them, the compensation intermediate parameters are generated by linear projection of the matrix eigenvector and the standardized deviation value to generate transition parameters reflecting the correlation between environmental stress and individual attenuation.

[0098] Step a4: weighted correction is performed on the compensation intermediate parameters according to the characteristic values ​​to obtain the initial compensation parameters of all single cells.

[0099] Among them, the initial compensation parameter is the weighted sum of the intermediate parameters through the eigenvalue, and finally the compensation parameter reflecting the environmental stress weight is generated.

[0100] Here's a specific example:

[0101] In the battery pack management of a certain energy storage system, the system needs to calculate compensation parameters for individual cells. First, the dynamic compensation evaluation matrix is ​​eigendecomposed to obtain a principal eigenvector [0.6, 0.3, 0.1], indicating that the weights of temperature, current, and voltage on capacity fade are 60%, 30%, and 10%, respectively. The corresponding eigenvalue is calculated to be 2.1. The system retrieves the baseline degradation of this battery model from the database at the same number of cycles as 8%, while the actual degradation measured is 12%. After Gaussian normalization, the deviation is converted to 1.5 standard deviations. The eigenvector is coupled with the normalized deviation to calculate the temperature component contribution of 0.6 × 1.5 = 0.9, the current component contribution of 0.3 × 1.5 = 0.45, and the voltage component contribution of 0.1 × 1.5 = 0.15. The sum of these three yields an intermediate compensation parameter of 1.5. Finally, a weighted correction is performed based on the eigenvalue of 2.1. The intermediate parameter is multiplied by the eigenvalue normalization coefficient of 0.7 to obtain an initial compensation parameter of 1.05. This parameter will be further adjusted in subsequent steps to guide precise compensation for the battery. Through this method based on matrix feature analysis and statistical normalization, the system can objectively quantify the actual contribution of each influencing factor and generate a scientific and reasonable compensation plan for each battery.

[0102] By executing steps a1 to a4, the embodiment of the present application extracts the eigenvectors and eigenvalues ​​of the quantized weights that characterize the direction of the influence of temperature, current, and voltage on capacity attenuation through the eigendecomposition of the dynamic compensation evaluation matrix; combined with the baseline attenuation deviation value processed by Gaussian normalization, the dimensional difference is eliminated and the degree of deviation between the actual attenuation of the single cell and the ideal state is highlighted. On this basis, the contribution direction represented by the eigenvector is associated with the standardized deviation value through coupling calculation to form a compensation intermediate parameter that reflects the dynamic working conditions and individual differences. Finally, based on the weighted correction of the eigenvalues, the weights of the key influencing factors are strengthened, and finally an initial compensation parameter set suitable for different single cells is generated, realizing a precise compensation logic based on the dynamic association of multi-dimensional factors, and providing reliable input for the subsequent segmented adjustment of the health status.

[0103] In a possible embodiment, S13, based on the comparison result of the remaining life of each single battery cell and the preset life threshold, generating the final balancing control parameter of each single battery cell includes:

[0104] Step 131 : Compare the remaining lifespan with a preset lifespan threshold to obtain a lifespan deviation value, and determine the balancing priority according to the absolute value of the lifespan deviation value.

[0105] The Lifetime Deviation Value (LDV) is the difference between the remaining life of a single cell and the preset lifespan threshold, quantifying the degree to which battery aging deviates from the expected lifespan. The Balancing Priority Level (BPL) determines the order in which balancing is performed based on the absolute value of the Lifetime Deviation Value. A larger Lifetime Deviation Value indicates a higher priority for balancing.

[0106] Step 132: Generate initial balancing control parameters based on the balancing priority and the capacity attenuation.

[0107] The Initial Balancing Control Parameters (IBCP) are the balancing current, duration, and trigger condition parameters generated based on the balancing priority and capacity decay. The balancing priority determines the order in which balancing operations are performed, while the capacity decay determines the duration of balancing operations.

[0108] Step 133 : Dynamically adjust the initial balancing control parameters according to the status data of each single battery to obtain final balancing control parameters.

[0109] The Final Balancing Control Parameters (FBCP) are refined balancing parameters that are modified based on real-time status data.

[0110] Here's a specific example:

[0111] In a battery pack management scenario, the system detected a single cell's remaining life of 14,800 cycles. Compared to the preset life threshold of 15,000 cycles, it calculated a life deviation of 200 cycles. Based on this deviation, the system assigned high priority to the battery's balancing. The battery's capacity decay was also measured at 15%. Given this high priority, the system generated initial balancing control parameters: active discharge balancing, a current of 0.8A, and a duration of 240 seconds. The system then detected a battery temperature of 48°C and a significant increase in internal resistance. Based on this real-time status data, the initial parameters were dynamically adjusted. Considering the high temperature environment, the system adjusted the discharge current to 0.6A to avoid overheating risks and extended the duration to 300 seconds to ensure effective balancing. The resulting balancing control parameters were: a discharge current of 0.6A for 300 seconds. Meanwhile, the battery's remaining life was 2,950 cycles, deviating from the threshold by only 50 cycles, resulting in a low priority. The initial parameters corresponding to its 8% capacity decay were: a charge current of 0.3A for 120 seconds. Since the battery status data showed that the temperature and internal resistance were normal, the system maintained the original parameters without adjustment. Through this dynamic adjustment mechanism based on life deviation values ​​and real-time status, the system achieves precise balancing control for each single battery.

[0112] By executing steps 131 to 133, the embodiment of the present application achieves precise battery pack management through intelligent hierarchical processing. The system first compares the remaining life of the battery with the standard threshold, calculates the degree of life deviation and automatically sets the balancing priority to ensure that severely aged batteries receive special attention. Then, based on the specific capacity attenuation of each battery, an initialized balancing control strategy is intelligently generated. Finally, the system also monitors key status indicators such as battery temperature and internal resistance in real time, dynamically fine-tunes the initial parameters, and forms the most optimized final control solution. This hierarchical processing mechanism takes into account both the long-term aging trend of the battery and the real-time operating status, making the balancing control more accurate and effective. Through the dual optimization of dynamic priority allocation and real-time parameter adjustment, the system can provide personalized balancing solutions for the actual conditions of different batteries, improve the consistency of the overall performance of the battery pack, effectively extend the service life of the battery system, and ensure the safety and stability of the operation process. The entire control process realizes complete closed-loop management from macro life assessment to micro parameter adjustment.

[0113] In a possible embodiment, step 132, generating initial balancing control parameters by combining balancing priorities and capacity attenuation, includes:

[0114] Step b1: linearly scale the balancing priority to obtain a normalized priority parameter, and unify the dimensions of the capacity attenuation to obtain a standardized capacity attenuation.

[0115] The normalized priority parameter maps the balancing priority of each single cell to the interval [0, 1] through linear scaling, eliminating the dimensional differences between different priorities. The standardized capacity decay uses the standard fraction method to make the capacity decay of the battery dimensionless so that it follows a standard normal distribution.

[0116] Step b2: According to the normalized priority parameter and the standardized capacity attenuation, the charge and discharge direction parameter and duration parameter corresponding to each single battery are obtained from the preset balancing strategy database.

[0117] The charge and discharge direction parameter is a binary variable that characterizes the direction of energy transfer, and the duration parameter is the duration of the balancing operation defined by the strategy library.

[0118] Optionally, since instantaneous power needs to be limited to match grid demand in a power generation scenario, a power limit parameter may be added to the charge and discharge direction parameters.

[0119] Step b3: Combine the charge and discharge direction parameters and the duration parameters to generate initial balancing control parameters.

[0120] Combining the charge and discharge direction parameters with the duration parameters involves encoding the direction parameters as binary instructions and combining them with the time values ​​of the duration parameters to generate a two-dimensional instruction matrix [charge and discharge direction parameter, duration parameter]. The duration parameters are then dynamically modified based on real-time temperature or battery health status and synchronously transmitted to the balancing module via the bus, driving the switches to perform energy transfer. Furthermore, safety boundary checks are performed in conjunction with voltage thresholds to ensure operational safety.

[0121] Here's a specific example:

[0122] In a high-voltage battery management system, the system needs to perform balancing control on individual cells. The system first detects the battery's balancing priority score as 75, which is linearly scaled to a normalized priority parameter of 0.75. The system also measures the battery's capacity decay as 12%, which is converted to a normalized decay coefficient of 0.12 through dimensional unification. The system then selects the most suitable balancing strategy from a preset strategy library based on the 0.75 priority parameter and 0.12 decay coefficient. The query results indicate that the battery requires discharge balancing with a duration of 180 seconds. Combining these parameters, the system generates an initial balancing control instruction: discharge the battery at a current of 0.5A for 180 seconds. Simultaneously, the system's battery is processed to obtain a priority parameter of 0.92 and a normalized decay coefficient of 0.08. The system then selects a charge balancing strategy with a duration of 120 seconds, ultimately generating initial balancing control parameters for charging at 0.3A for 120 seconds. Through this standardized processing and strategy matching method, the system can generate the most suitable balancing plan for each single battery, ensuring that batteries with severe attenuation receive priority treatment, and accurately controlling the balancing intensity according to the actual attenuation degree, thereby achieving efficient balancing management of the battery pack.

[0123] By executing steps b1 to b3, the balancing control scheme of the embodiment of the present application realizes high-precision dynamic balancing of the battery pack through standardized processing and intelligent matching. The system first converts the original priority and attenuation of each battery into standardized parameters of unified dimension to ensure the comparability of different indicators. Then, based on these normalized parameters, the charge and discharge direction and duration that are most suitable for each battery are intelligently matched from the preset strategy library, and finally a customized initial balancing control instruction is generated. This processing method not only ensures that batteries with severe attenuation are given priority balancing, but also accurately controls the balancing intensity according to the actual attenuation degree, so that the power distribution of the entire battery pack quickly converges to consistency. Through the dual optimization of parameter standardization and strategy intelligent matching, the system realizes personalized customization and precise execution of the balancing scheme, improves the balancing efficiency and overall performance of the battery pack, and effectively extends the battery life.

[0124] In one possible embodiment, step 115 of dynamically adjusting the compensation parameters of each single cell according to the current health status and initial compensation parameters of each single cell to obtain the compensation parameters of each single cell includes:

[0125] Step c1: quantify the current health status to obtain a status value corresponding to the current health status.

[0126] The health status value is calculated using a weighted fusion formula and is used to calibrate the health status. The health status is quantified using a multi-index fusion model, combining core parameters such as battery capacity, internal resistance, and polarization characteristics for a comprehensive evaluation.

[0127] Step c2: According to the health status segmented compensation strategy, when it is determined that there are single cells of the first compensation type whose status values ​​are greater than or equal to a first preset percentage, the compensation parameters of the single cells of the first compensation type are adjusted using linear interpolation according to the initial compensation parameters.

[0128] The first compensation type is for cells with high health values ​​between 80% and 100%, prioritizing capacity compensation to prevent overcharging. Linear interpolation dynamically adjusts compensation intensity based on the initial compensation parameters and the proportion of the state values.

[0129] Step c3: When it is determined that there are single cells of the second compensation type whose state values ​​are greater than the second preset percentage and less than the first preset percentage, adjust the compensation parameters of the single cells of the second compensation type using an exponential decay method according to the initial compensation parameters.

[0130] The second compensation type is for moderately healthy individuals with a status value between [60%, 80%), requiring a slower compensation intensity to prevent accelerated aging. The exponential decay method involves an exponential decrease in the compensation parameter over time, with the decay coefficient negatively correlated with the status value.

[0131] Step c4: when it is determined that there are single cells of the third compensation type whose status values ​​are less than or equal to the second preset percentage, adjust the compensation parameters of the single cells of the third compensation type using a fixed compensation threshold method according to the initial compensation parameters.

[0132] The third compensation type uses a conservative compensation strategy to avoid excessive capacity loss for cells with a low health status value less than 60%. The fixed compensation threshold method locks the compensation parameter to a preset safety value, allowing only one-way capacity replenishment.

[0133] Step c5: During the dynamic adjustment process, if the adjustment amplitude of the compensation parameter is less than a preset amplitude threshold for N consecutive cycles, the update of the compensation parameter is frozen until the voltage fluctuation rate exceeds the preset fluctuation rate threshold, at which time the update of the compensation parameter is reactivated. N can be an integer greater than or equal to 2, such as 3, 5, or 10.

[0134] The freeze condition is that the compensation parameter adjustment amplitude is less than 2% for N = 5 consecutive cycles. The activation condition is that the voltage fluctuation rate is greater than 3% / minute or the temperature gradient is greater than 5°C / group.

[0135] Here's a specific example:

[0136] In the battery management system of a certain energy storage power station, the system needs to assess the health status of individual cells in the battery pack and adjust compensation parameters. The system first performs a quantitative health analysis of the individual cells. Through capacity testing and internal resistance measurement, the system determines that the current health status is 78%, indicating moderate degradation. Based on a preset segmented compensation strategy, the system optionally divides the health status into three ranges: above 85% for mild degradation, 75%-85% for moderate degradation, and below 75% for severe degradation. Since the battery's health status of 78% falls within the moderate degradation range, the system determines it as the second compensation type. After obtaining the initial compensation parameter of 6.5 mA·h for this battery, the system uses an exponential decay method for adjustment. The system sets a decay coefficient of 0.92 based on the battery's historical degradation curve. After three iterative calculations, the system ultimately determines the compensation parameter to be 6.5 × 0.92³ ≈ 5.1 mA·h. This exponential decay adjustment method more accurately matches the actual needs of batteries with moderate degradation and avoids overcompensation. At the same time, the health status value of the single battery in the system is 88%, which belongs to the first compensation type of mild attenuation. The system uses linear interpolation to adjust its initial compensation parameter of 4.2mA·h. According to the compensation coefficients of 0.8 and 0.6 corresponding to the adjacent health status intervals of 85% and 95% in the preset relationship table between the health status interval and the compensation coefficient, the adjustment coefficient applicable to the 88% state is calculated to be 0.76, and the final compensation parameter is adjusted to 4.2×0.76≈3.2mA·h. Through this differentiated compensation strategy, the system can provide the most appropriate compensation solution for batteries in different health states, ensuring that batteries with faster attenuation receive sufficient support while avoiding unnecessary compensation for batteries in better condition, thereby optimizing the balancing effect and service life of the overall battery pack.

[0137] By executing steps c1 to c5, the health status compensation scheme in the embodiment of the present application realizes the intelligent optimization of battery compensation parameters through quantitative evaluation and segmented processing. The system first accurately quantifies the battery health status into a specific value, and then adopts a differentiated compensation algorithm according to the preset health status interval. For batteries in better condition, linear interpolation adjustment is used to ensure smooth and moderate compensation; for batteries with moderate attenuation, the exponential decay method is used to calculate and achieve accurate matching of the compensation amount. This hierarchical compensation strategy can provide the most suitable compensation scheme for batteries with different degrees of aging, which not only effectively supports batteries with faster attenuation, but also avoids excessive intervention on batteries in good condition. Through quantitative analysis and algorithm adaptation, the system realizes dynamic matching of compensation parameters with actual battery needs, improves the balancing effect of the battery pack, extends the overall service life, and optimizes the system energy efficiency.

[0138] In a possible embodiment, S14, transmitting the final balancing control parameters and adjusted compensation parameters of all single cells to the control circuit of the high-voltage battery pack through a dual-antenna switching method, includes:

[0139] Step 141: When the channel quality of the main antenna and the backup antenna is greater than or equal to the preset quality threshold, the final equalization control parameters of all single cells are transmitted to the control circuit of the high-voltage battery pack through the main antenna, and the adjusted compensation parameters of all single cells are transmitted to the control circuit of the high-voltage battery pack through the backup antenna.

[0140] Among them, the channel quality assessment factor is a comprehensive score including standing wave ratio, received signal strength and bit error rate, and the threshold is set as channel quality assessment factor ≥ 75 points.

[0141] Step 142: When the channel quality of any one of the main antenna and the backup antenna is less than the preset quality threshold, the antenna switching operation is triggered, and the final equalization control parameters and adjusted compensation parameters of all single cells are transmitted through the antenna whose channel quality is greater than or equal to the preset quality threshold.

[0142] Antenna switching involves activating the electronic switch matrix to switch the communication link to a qualified antenna when the channel quality assessment factor (CQA) is less than 75 points. Full parameter fusion transmission involves time-division multiplexing the final equalization control parameters and the adjusted compensation parameters in single-antenna mode, with a 3:1 timeslot allocation ratio.

[0143] Here's a specific example:

[0144] In a high-voltage battery management system, the system needs to transmit calculated cell balancing parameters to the control circuit in real time. The system is configured with two communication channels, a primary antenna and a backup antenna, and sets a signal quality threshold of 80 points. When the system prepares to transmit data, it first detects that the current signal quality of the primary antenna is 85 points and the backup antenna is 88 points, both meeting the transmission requirements. The system then follows the default protocol, sending the final balancing control parameters for all cells via the primary antenna and all adjusted compensation parameters via the backup antenna. This achieves dual-channel parallel transmission and improves data transmission efficiency. Later, the vehicle enters an underground garage. Environmental changes cause the signal quality of the primary antenna to drop to 75 points, while the backup antenna remains at 82 points. The system detects that the signal quality of the primary antenna has fallen below the threshold and immediately triggers the antenna switching mechanism. The system then stops using the primary antenna and simultaneously transmits the final balancing control parameters and adjusted compensation parameters entirely through the backup antenna. This ensures stable data delivery to the control circuit and avoids transmission interruptions or data errors caused by poor signal quality. After the vehicle exited the garage, the main antenna signal returned to 83 points, and the backup antenna signal returned to 86 points. The system again detected that both channels met the standards and reactivated the dual-antenna transmission mode, with the main antenna continuing to transmit equalization control parameters and the backup antenna transmitting compensation parameters, restoring optimal transmission. This entire process automatically optimized the communication link and ensured highly reliable transmission of battery management data.

[0145] By executing steps 141 to 142, the dual-antenna transmission solution provided in the embodiment of the present application realizes highly reliable transmission of high-voltage battery pack balancing data through intelligent channel quality monitoring. When the signal quality of the main and standby antennas meets the standards, the system adopts a dual-channel parallel transmission strategy, with the main antenna specifically transmitting balancing control parameters and the standby antenna specifically transmitting compensation parameters, effectively improving data transmission efficiency. When the quality of any channel does not meet the standards, the system automatically switches to an antenna with qualified quality for full parameter transmission to ensure that key control instructions are not lost. This dynamic switching mechanism can not only maximize the transmission bandwidth when the signal is good, but also ensure communication reliability when the signal fluctuates, so that the battery management system can stably obtain the latest balancing parameters under various complex working conditions, providing solid communication guarantees for the dynamic balancing control of the high-voltage battery pack, and ultimately improving the safety and service life of the entire battery system.

[0146] Figure 2 A schematic diagram of a low-cost series-connected high-voltage battery management system based on wireless communication is provided in an embodiment of the present application. Figure 2 As shown, the system includes:

[0147] The acquisition module 21 is used to obtain the aging capacity data, historical cycle times and status data of each single battery connected in series in the high-voltage battery pack.

[0148] The calculation module 22 is used to calculate the remaining life and capacity attenuation of each single battery according to the aging capacity data and the historical cycle number.

[0149] The adjustment module 23 is used to generate the final balancing control parameters of each single cell based on the comparison result of the remaining life of each single cell and the preset life threshold, and dynamically adjust the compensation parameters of each single cell according to the capacity attenuation and status data of each single cell.

[0150] The control module 24 is used to transmit the final balancing control parameters and adjusted compensation parameters of all single cells to the control circuit of the high-voltage battery pack through dual-antenna switching, so that the control circuit can perform dynamic balancing management of the high-voltage battery pack based on the final balancing control parameters and adjusted compensation parameters of all single cells.

[0151] Figure 2 The low-cost series high-voltage battery management system based on wireless communication can be implemented Figure 1 The implementation principles and technical effects of the low-cost, wirelessly connected, series-connected high-voltage battery management method described in the illustrated embodiment are not further elaborated. The specific manner in which the various modules and units perform operations in the aforementioned low-cost, wirelessly connected, series-connected high-voltage battery management system has been described in detail in the related embodiments and will not be further elaborated here.

[0152] In one possible design, Figure 2 A low-cost series-type high-voltage battery management system based on wireless communication of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32 .

[0153] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0154] The processing component 32 is used to: obtain the aging capacity data, historical cycle counts and status data of each single cell connected in series in the high-voltage battery pack; calculate the remaining life and capacity attenuation of each single cell based on the aging capacity data and historical cycle counts; generate the final balancing control parameters of each single cell based on the comparison result of the remaining life of each single cell with the preset life threshold, and dynamically adjust the compensation parameters of each single cell according to the capacity attenuation and status data of each single cell; transmit the final balancing control parameters and adjusted compensation parameters of all single cells to the control circuit of the high-voltage battery pack through dual-antenna switching, so that the control circuit performs dynamic balancing management of the high-voltage battery pack according to the final balancing control parameters and adjusted compensation parameters of all single cells.

[0155] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0156] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as random access memory (RAM), static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0157] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0158] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0159] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0160] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0161] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a low-cost series high-voltage battery management method based on wireless communication.

[0162] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0163] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0164] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A low-cost series high-voltage battery management method based on wireless communication, characterized in that: include: Obtain aging capacity data, historical cycle counts, and status data for each single cell connected in series within a high-voltage battery pack; Calculating the remaining life and capacity attenuation of each of the single cells according to the aging capacity data and the historical cycle counts; Based on the comparison result of the remaining life of each single cell and the preset life threshold, generating the final balancing control parameter of each single cell, and dynamically adjusting the compensation parameter of each single cell according to the capacity attenuation of each single cell and the state data; Transmitting the final balancing control parameters and adjusted compensation parameters of all the single cells to the control circuit of the high-voltage battery pack through a dual-antenna switching method, so that the control circuit performs dynamic balancing management of the high-voltage battery pack according to the final balancing control parameters and adjusted compensation parameters of all the single cells; The state data includes operating temperature data, charge and discharge current data and voltage fluctuation data; The dynamically adjusting the compensation parameters of each single cell according to the capacity attenuation of each single cell and the state data includes: Calculate the temperature influence factor based on the operating temperature data, calculate the current stress factor based on the charge and discharge current data, and calculate the voltage stability factor based on the voltage fluctuation data; Constructing a dynamic compensation evaluation matrix based on the temperature influence factor, the current stress factor and the voltage stability factor; Extracting a reference decay curve of each of the single cells from a preset historical operation database, and calculating a deviation between the capacity decay amount and the reference decay amount based on the reference decay curve and the capacity decay amount of each of the single cells; Inputting the dynamic compensation evaluation matrix and the deviation values ​​corresponding to all single cells into a compensation parameter calculation model to obtain initial compensation parameters for all single cells; Monitoring the current health status of each single cell, and dynamically adjusting the compensation parameter of each single cell according to the current health status of each single cell and the initial compensation parameter, to obtain the compensation parameter of each single cell; The method of transmitting the final balancing control parameters and the adjusted compensation parameters of all the single cells to the control circuit of the high-voltage battery pack by means of dual-antenna switching includes: When the channel quality of the main antenna and the backup antenna is greater than or equal to the preset quality threshold, the final equalization control parameters of all single cells are transmitted to the control circuit of the high-voltage battery pack via the main antenna, and the adjusted compensation parameters of all single cells are transmitted to the control circuit of the high-voltage battery pack via the backup antenna; When the channel quality of any of the main antenna and the backup antenna is less than the preset quality threshold, the antenna switching operation is triggered, and the final balancing control parameters and adjusted compensation parameters of all the single cells are transmitted through the antenna whose channel quality is greater than or equal to the preset quality threshold.

2. The method according to claim 1, characterized in that The step of inputting the dynamic compensation evaluation matrix and the deviation values ​​corresponding to all single cells into a compensation parameter calculation model to obtain initial compensation parameters for all single cells includes: Performing eigendecomposition on the dynamic compensation evaluation matrix to obtain matrix eigenvectors and eigenvalues, wherein the matrix eigenvectors are used to characterize the contribution direction of operating temperature, charge and discharge current, and voltage fluctuations to capacity decay; Performing Gaussian normalization on the deviation values ​​of the reference attenuation values ​​of all single cells to obtain standardized deviation values; Couple the matrix eigenvectors with the standardized deviation values ​​of all single cells to generate compensation intermediate parameters of all single cells; The compensation intermediate parameters are weightedly corrected according to the characteristic values ​​to obtain initial compensation parameters of all single cells.

3. The method according to claim 1, characterized in that The generating of the final balancing control parameters of each single cell based on the comparison result of the remaining life of each single cell with the preset life threshold includes: Comparing the remaining life with the preset life threshold to obtain a life deviation value, and determining the balancing priority according to the absolute value of the life deviation value; generating an initial balancing control parameter by combining the balancing priority and the capacity attenuation; The initial balancing control parameters are dynamically adjusted according to the status data of each single battery to obtain final balancing control parameters.

4. The method according to claim 3, characterized in that The generating of the initial balancing control parameter by combining the balancing priority and the capacity attenuation includes: Performing linear scaling processing on the balancing priority to obtain a normalized priority parameter, and performing dimension unification processing on the capacity attenuation to obtain a standardized capacity attenuation; Obtaining, according to the normalized priority parameter and the standardized capacity attenuation, a charge and discharge direction parameter and a duration parameter corresponding to each single battery from a preset balancing strategy database; The charge and discharge direction parameter and the duration parameter are combined to generate an initial balancing control parameter.

5. The method according to claim 1, wherein The step of dynamically adjusting the compensation parameters of each single cell according to the current health status and initial compensation parameters of each single cell to obtain the compensation parameters of each single cell includes: Quantify the current health status to obtain the status value corresponding to the current health status; According to the health state segmented compensation strategy, when it is determined that there is a single battery of the first compensation type whose state value is greater than or equal to a first preset percentage, adjusting the compensation parameters of the single battery of the first compensation type using a linear interpolation method based on the initial compensation parameters; When it is determined that there is a single battery of the second compensation type whose state value is greater than the second preset percentage and less than the first preset percentage, adjusting the compensation parameters of the single battery of the second compensation type using an exponential decay method according to the initial compensation parameters; When it is determined that there are single cells of the third compensation type whose state value is less than or equal to the second preset percentage, adjusting the compensation parameters of the single cells of the third compensation type using a fixed compensation threshold method according to the initial compensation parameters; During the dynamic adjustment process, if the adjustment amplitude of the compensation parameter is less than the preset amplitude threshold within N consecutive cycles, the update of the compensation parameter is frozen until the voltage fluctuation rate is detected to exceed the preset fluctuation rate threshold, and then the update of the compensation parameter is reactivated.

6. A low-cost series-connected high-voltage battery management system based on wireless communication, characterized in that: include: An acquisition module is used to obtain aging capacity data, historical cycle counts and status data of each single battery connected in series in the high-voltage battery pack; a calculation module, configured to calculate the remaining life and capacity attenuation of each of the single cells based on the aging capacity data and the historical cycle count; an adjustment module, configured to generate a final balancing control parameter for each single cell based on a comparison result of the remaining life of each single cell with a preset life threshold, and dynamically adjust a compensation parameter for each single cell according to the capacity attenuation of each single cell and the status data; A control module, configured to transmit the final balancing control parameters and adjusted compensation parameters of all the single cells to a control circuit of the high-voltage battery pack through a dual-antenna switching method, so that the control circuit performs dynamic balancing management of the high-voltage battery pack based on the final balancing control parameters and adjusted compensation parameters of all the single cells; The state data includes operating temperature data, charge and discharge current data and voltage fluctuation data; The dynamically adjusting the compensation parameters of each single cell according to the capacity attenuation of each single cell and the state data includes: Calculate the temperature influence factor based on the operating temperature data, calculate the current stress factor based on the charge and discharge current data, and calculate the voltage stability factor based on the voltage fluctuation data; Constructing a dynamic compensation evaluation matrix based on the temperature influence factor, the current stress factor and the voltage stability factor; Extracting a reference decay curve of each of the single cells from a preset historical operation database, and calculating a deviation between the capacity decay amount and the reference decay amount based on the reference decay curve and the capacity decay amount of each of the single cells; Inputting the dynamic compensation evaluation matrix and the deviation values ​​corresponding to all single cells into a compensation parameter calculation model to obtain initial compensation parameters for all single cells; Monitoring the current health status of each single cell, and dynamically adjusting the compensation parameter of each single cell according to the current health status of each single cell and the initial compensation parameter, to obtain the compensation parameter of each single cell; The method of transmitting the final balancing control parameters and the adjusted compensation parameters of all the single cells to the control circuit of the high-voltage battery pack by means of dual-antenna switching includes: When the channel quality of the main antenna and the backup antenna is greater than or equal to the preset quality threshold, the final equalization control parameters of all single cells are transmitted to the control circuit of the high-voltage battery pack via the main antenna, and the adjusted compensation parameters of all single cells are transmitted to the control circuit of the high-voltage battery pack via the backup antenna; When the channel quality of any of the main antenna and the backup antenna is less than the preset quality threshold, the antenna switching operation is triggered, and the final balancing control parameters and adjusted compensation parameters of all the single cells are transmitted through the antenna whose channel quality is greater than or equal to the preset quality threshold.

7. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a low-cost series high-voltage battery management method based on wireless communication as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a low-cost series high-voltage battery management method based on wireless communication as described in any one of claims 1 to 5 is implemented.

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