A method and system for charge-discharge control of series-connected batteries in a battery module

Abstract

The application discloses a charge-discharge control method and system for series-connected batteries in a battery module, and relates to the technical field of battery thermal management. The method comprises the following steps: during the charge-discharge process, performing three-loop cooperative control based on an updated circuit parameter set to generate actual energy flow data containing phase change material heat exchange data; calculating a phase change material phase change progress value through a phase change dynamics model based on the phase change material heat exchange data in the actual energy flow data and a real-time phase change progress parameter, double-verified in combination with voltage and temperature change trends, dynamically correcting a topology reconstruction instruction and triggering a three-level fault diagnosis to obtain a fault-tolerant reconstruction instruction; and storing a reconstruction depth and a phase change utilization rate parameter of the phase change material based on the fault-tolerant reconstruction instruction at the end of a charge-discharge cycle, optimizing a Seebeck coefficient through a self-learning algorithm, and generating a cross-cycle transfer parameter set. The application reversely corrects the Seebeck coefficient through the phase change utilization rate parameter, and suppresses thermoelectric attenuation caused by material aging.

Description

Technical Field

[0001] This invention relates to the field of battery thermal management technology, and in particular to a charging and discharging control method and system for series-connected batteries in a battery module. Background Technology

[0002] With the increasing demands for energy density and safety in electric vehicles and energy storage systems, the application of phase change materials (PCMs) in battery thermal management has become a research hotspot. Existing technologies are typically based on passive phase change heat dissipation structures with fixed temperature thresholds, which absorb battery heat through the latent heat of phase change in the material; combined with active energy recovery systems that utilize thermoelectric conversion, waste heat is converted into electrical energy using the Seebeck effect.

[0003] However, existing technologies still have the following shortcomings: First, the identification of phase change material state is disconnected from battery electrochemical parameters, relying solely on temperature as a single dimension for judgment, which cannot capture the battery activity state reflected by changes in internal resistance, resulting in delayed thermal runaway warnings; Second, topology reconstruction and energy recovery systems are independent of each other, and changes in electrical parameters of the reconstructed battery string cannot be fed back to the ripple recovery circuit in real time, leading to LC resonant frequency mismatch, which not only causes energy recovery losses but also triggers second harmonic interference to the battery management system. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a charging and discharging control method for series-connected batteries in a battery module to solve the problems of disconnect between phase change material state identification and battery electrochemical characteristics, as well as thermal management lag and energy loss caused by topology reconstruction and energy recovery system mismatch.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a charging and discharging control method for series-connected batteries in a battery module, comprising: when the battery module is powered on, synchronously scanning the voltage parameters, temperature parameters and internal resistance parameters of all individual batteries and activating the embedded phase change material to generate an initial circuit parameter set;

[0008] Based on the initial circuit parameter set, the thermoelectric reconfiguration coefficient is calculated through the dynamic coupling of temperature gradient and voltage deviation, and a dynamic topology reconfiguration table is constructed.

[0009] The dynamic topology reconstruction table synchronously triggers the update of the resonant capacitor and inductor current limits of the ripple recovery circuit, and obtains the updated circuit parameter set.

[0010] During the charging and discharging process, three-loop coordinated control is performed based on the updated circuit parameter set to generate actual energy flow data that includes heat exchange data of phase change material.

[0011] Based on the heat exchange data of phase change material and real-time phase change progress parameters in actual energy flow data, the phase change progress value of phase change material is calculated by phase change dynamics model, and dual verification is performed by combining voltage and temperature change trends. The topology reconfiguration command is dynamically corrected and a three-level fault diagnosis is triggered to obtain the fault-tolerant reconfiguration command.

[0012] At the end of the charge-discharge cycle, the reconstruction depth and phase change utilization parameters of the phase change material are stored based on the fault-tolerant reconstruction instructions. The Seebeck coefficient is optimized through a self-learning algorithm to generate a cross-cycle transfer parameter set.

[0013] As a preferred embodiment of the charging and discharging control method for series-connected batteries in the battery module of the present invention, the generation of the initial circuit parameter set refers to marking the embedded phase change material by combining temperature parameters and internal resistance parameters, generating three-level quantized marking data including liquid critical state identifier, semi-solid phase change identifier, and solid stable state identifier; the ratio of internal resistance parameter to temperature parameter is used as a thermal conductivity efficiency factor and bound to the three-level quantized marking data to generate a subset of phase change material state flags, which is then integrated with voltage parameters to generate the initial circuit parameter set.

[0014] As a preferred embodiment of the charging and discharging control method for series-connected batteries in the battery module of the present invention, the calculation of the thermoelectric reconstruction coefficient refers to separating the temperature gradient and voltage deviation from the initial circuit parameter set, using the square value of the temperature gradient as a weighting factor for the voltage deviation, and nonlinearly coupling it with the voltage deviation.

[0015] As a preferred embodiment of the charging and discharging control method for series-connected batteries in the battery module of the present invention, the construction of the dynamic topology reconstruction table includes the following steps.

[0016] Dynamic topology optimization analysis is performed on the thermoelectric reconfiguration coefficient to generate dynamic topology reconfiguration instructions; the dynamic topology reconfiguration instructions include an online battery reduction priority list and a redundant battery activation mapping table.

[0017] Based on the online battery reduction priority list, the control relay array is used to cut off high-heating cells in stages, and the low-resistance redundant cells in the redundant battery mapping table are activated simultaneously to build a recombined battery string.

[0018] Record the topological connections of the recombined battery strings and generate a dynamic topology reconstruction table.

[0019] As a preferred embodiment of the charging and discharging control method for series-connected batteries in the battery module of the present invention, the step of obtaining the updated circuit parameter set includes the following steps:

[0020] Extract the number of individual cells in the recombined battery string from the dynamic topology reconstruction table, input the number of individual cells in the recombined battery string into the resonant frequency adaptive model, and calculate the resonant point offset based on the rate of change of the number of individual cells.

[0021] Based on the resonant point offset, the resonant capacitor adjustment amount is output through a capacitor compensation algorithm.

[0022] Based on the thermal conductivity efficiency factor in the phase change material state indicator set, the inductor current limit is relaxed according to the liquid critical state, semi-solid phase change state, and solid stable state indicators.

[0023] The resonant capacitor adjustment is tied to the relaxed inductor current limit to generate an updated set of circuit parameters.

[0024] As a preferred embodiment of the charging and discharging control method for series-connected batteries in the battery module of the present invention, the three-loop coordinated control refers to: in the voltage loop, converting the semi-solid identifier of the phase change material into a temperature compensation gain coefficient to correct the virtual voltage calculation value; in the thermoelectric loop, adjusting the temperature difference power transfer path based on the thermal conductivity factor and initializing the Seebeck coefficient; and in the ripple recovery loop, synchronously switching the phase according to the number of individual cells in the recombined battery string to perform directional energy recovery.

[0025] As a preferred embodiment of the charging and discharging control method for series-connected batteries in the battery module of the present invention, the calculation of the phase change progress value of the phase change material includes the following steps.

[0026] The heat flow rate and temperature change rate of phase change material heat exchange data are separated from actual energy flow data. A differential equation for phase change dynamics based on Fourier's law of heat conduction is established, with the heat flow rate as the source term and the temperature change rate as the time derivative term.

[0027] The derivative of the voltage change trend is transformed into a Dirichlet boundary condition, which is embedded into the differential equation constraint terms. The implicit Euler method is used to iteratively solve the phase transition dynamic differential equation and output the phase transition hysteresis quantization value.

[0028] The phase change hysteresis quantification value is mapped to the phase change progress percentage, which is used as the phase change progress value of the phase change material.

[0029] As a preferred embodiment of the charging and discharging control method for series-connected batteries in the battery module of the present invention, the three-level fault diagnosis includes the following steps.

[0030] Level 1 Diagnosis: The voltage surge threshold is dynamically set based on the phase transition hysteresis quantization value. When the real-time monitored voltage surge rate exceeds the voltage surge threshold, it is judged as a Level 1 anomaly.

[0031] Level 2 diagnosis: Compare the attenuation ratio of the actual power of the cogeneration power generation with the predicted power of the phase change progress. When the attenuation ratio is greater than the preset safety threshold, it is judged as a Level 2 anomaly.

[0032] The predicted power for phase change progress is calculated using the phase change progress value.

[0033] Level 3 diagnosis: Detect the deviation between the curing time window of the phase change material and the thermal conductivity factor. When the deviation exceeds the deviation threshold, it is judged as a Level 3 anomaly.

[0034] When any two levels of diagnosis trigger an exception simultaneously, a fault-tolerant refactoring instruction is generated.

[0035] As a preferred embodiment of the charging and discharging control method for series-connected batteries in the battery module of the present invention, the generation of the cross-cycle transfer parameter set includes the following steps:

[0036] Separate the reconstruction depth parameter from the fault-tolerant reconstruction instruction; the reconstruction depth parameter includes the relay action timestamp sequence and the reassembled battery string performance record;

[0037] Analyze the relay action timestamp sequence, calculate the relay action frequency within a unit period, and generate a physical reconstruction depth value;

[0038] Based on the performance records of the reconstructed battery string, the electrical reconfiguration depth value is generated by comparing the rate of decrease in internal resistance and the rate of capacity retention before and after reconfiguration.

[0039] The periodic average value of phase change utilization rate is extracted from phase change materials, and the decay rate of Seebeck coefficient is adjusted by an inverse proportional function.

[0040] The physical reconstruction depth value, electrical reconstruction depth value, and periodic average phase change utilization rate are integrated with the adjusted Seebeck coefficient to generate a cross-cycle transfer parameter set.

[0041] In a second aspect, the present invention provides a charging and discharging control system for series-connected batteries in a battery module, including a parameter acquisition module that synchronously scans the voltage parameters, temperature parameters and internal resistance parameters of all individual batteries and activates the embedded phase change material when the battery module is powered on, thereby generating an initial circuit parameter set.

[0042] The topology reconstruction module, based on the initial parameter set, calculates the thermoelectric reconstruction coefficient through the dynamic coupling of temperature gradient and voltage deviation, and constructs a dynamic topology reconstruction table.

[0043] The circuit tuning module synchronously triggers the update of the resonant capacitor and inductor current limits of the ripple recovery circuit in the dynamic topology reconstruction table, and obtains the updated circuit parameter set.

[0044] During the charging and discharging process, the three-loop control module performs three-loop coordinated control based on the updated circuit parameter set to generate actual energy flow data that includes phase change material heat exchange data.

[0045] The status diagnosis module, based on the phase change material heat exchange data and real-time phase change progress parameters in the actual energy flow data, calculates the phase change progress value of the phase change material through the phase change dynamics model, performs dual verification by combining voltage and temperature change trends, dynamically corrects the topology reconfiguration command and triggers three-level fault diagnosis to obtain the fault-tolerant reconfiguration command.

[0046] At the end of the charge / discharge cycle, the cycle optimization module stores the reconstruction depth and phase change utilization parameters of the phase change material based on the fault-tolerant reconstruction instruction. It then optimizes the Seebeck coefficient and the number of reference cells through a self-learning algorithm to generate a cross-cycle transfer parameter set.

[0047] The beneficial effects of this invention are as follows: the battery string is reassembled based on the thermoelectric reconfiguration coefficient, and the resonant capacitance of the ripple recovery circuit is adjusted simultaneously to improve the energy recovery efficiency; the Seebeck coefficient is corrected in reverse by the phase change utilization rate parameter to suppress the thermoelectric decay caused by material aging, thereby reducing the efficiency fluctuation of the thermoelectric power generation component; the physical and electrical reconfiguration depth values ​​are transferred across cycles to provide a predictive optimization benchmark for the next cycle. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of the charging and discharging control method for series-connected batteries in a battery module.

[0050] Figure 2 A schematic diagram for constructing a dynamic topology reconstruction table.

[0051] Figure 3 This is a schematic diagram of three-ring coordinated control.

[0052] Figure 4 This is a schematic diagram of three-level fault diagnosis and optimization. Detailed Implementation

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0054] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0056] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a charging and discharging control method for series-connected batteries in a battery module, comprising the following steps:

[0057] S1. When the battery module is powered on, it synchronously scans the voltage, temperature and internal resistance parameters of all individual cells and activates the embedded phase change material to generate an initial circuit parameter set.

[0058] S1.1. Combining temperature parameters and internal resistance parameters, the embedded phase change material is labeled to generate three-level quantitative labeling data, including liquid critical state label, semi-solid phase change label, and solid stable state label.

[0059] Specifically, at the moment the battery module is powered on, the voltage sensor is activated to collect the voltage parameters of all individual cells, the temperature sensor to collect the temperature parameters, and the internal resistance detection circuit to collect the internal resistance parameters; at the same time, an activation command is sent to the embedded phase change material driver.

[0060] Based on the collected temperature and internal resistance parameters, a phase transition state identification algorithm is used to perform three-level quantization labeling on the embedded phase transition material:

[0061] When the temperature parameter exceeds 45℃ and the rate of decrease in internal resistance parameter is >5% / min, it is marked as a liquid critical state.

[0062] When the temperature parameter is between 40-45℃ and the internal resistance fluctuation is <3%, it is marked as a semi-solid phase change indicator.

[0063] When the temperature parameter is below 40℃ and the rate of change of internal resistance approaches 0, it is marked as a solid-state stable state.

[0064] It should be noted that the temperature parameter for the liquid critical state indicator is greater than 45℃, based on differential scanning calorimetry (DSC) data of the paraffin / expanded graphite composite. When this temperature is exceeded, the material's heat absorption rate increases sharply, and marking it as liquid critical can trigger heat dissipation control in a timely manner. The internal resistance parameter is set to a decrease rate of 5% / min because the lithium-ion mobility increases when the temperature exceeds 45℃, and a decrease in internal resistance of more than 5% per minute is a critical characteristic for the battery to enter a highly active state. When the battery temperature breaks through the melting point of the phase change material and the internal resistance decreases rapidly, it is marked as liquid critical state. This state represents the synergy between the material's heat absorption phase change and the battery's high activity, and a strong heat dissipation strategy (such as liquid cooling power +30%) must be initiated immediately to avoid the risk of thermal runaway.

[0065] The semi-solid phase change indicator is set in the 40-45℃ temperature range because the phase change material exists in a solid-liquid coexistence state within this range. While this increases energy density by 15%, thermal conductivity remains unsaturated, requiring special monitoring. Internal resistance fluctuations of <3% are determined through cycle testing, meeting the lifespan requirements of energy storage batteries. When the temperature enters the phase change transition region and the internal resistance stabilizes, it is marked as a semi-solid phase change indicator. This state requires maintaining precise temperature control (±1℃), utilizing the material's latent heat absorption to slow temperature rise, and optimizing the charge / discharge rate to extend lifespan.

[0066] The fixed stable state is marked below 40℃ because at temperatures below 40℃, the material has a higher degree of crystallinity and stable thermal conductivity; the rate of change of internal resistance approaches zero, meeting the battery's safety redundancy requirements; when the temperature drops below the phase transition point and the internal resistance does not change significantly, it is marked as a solid-state stable state. In this state, charge and discharge efficiency can be maximized, and the solidification characteristics of the phase change material can be used to continuously balance the module's temperature gradient and improve cycle stability.

[0067] S1.2. The ratio of internal resistance parameter to temperature parameter is used as the heat conduction efficiency factor, which is bound to the three-level quantization marker data to generate a subset of phase change material state markers, and integrated with the voltage parameter to generate the initial circuit parameter set.

[0068] Specifically, the three-level quantization marker data and the thermal conductivity efficiency factor value are input to the binding processor; the binding processor assigns dynamic weight coefficients according to the phase transition state type: when a liquid critical state identifier is detected, a high thermal conductivity response weight is applied; when a semi-solid phase transition identifier is identified, a medium phase transition buffer weight is enabled; when a solid stable state identifier is found, a steady-state maintenance base weight is applied, and the binding processor outputs dynamic weight coefficients.

[0069] When the dynamic weighting coefficient carries the liquid critical state identifier, the heat dissipation priority enhancement operation is performed: the high thermal conductivity response weight is multiplied and coupled with the thermal conductivity efficiency factor to generate the heat dissipation priority gain parameter.

[0070] When the dynamic weighting coefficient carries a semi-solid phase change identifier, a buffer compensation operation is performed: the medium phase change buffer weight is additively coupled with the thermal conductivity efficiency factor to generate phase change process compensation parameters.

[0071] When the dynamic weighting coefficient carries the solid-state steady-state identifier, the benchmark locking operation is performed: the steady-state maintenance base weight is multiplied and coupled with the thermal conductivity efficiency factor to generate the steady-state maintenance parameter;

[0072] Input the heat dissipation priority gain parameter, phase change process compensation parameter, or steady-state maintenance parameter (based on the actual phase change state) into the state flag generator and encode it into a subset of phase change material state flags with weighted coefficients.

[0073] The voltage parameters are deconstructed topologically, and the original waveform data of voltage sampling is retained at the individual unit level to generate individual unit level waveform data. Based on the individual unit level waveform data, the average series and parallel voltage is calculated at the module level. The average series and parallel voltage is used to record the voltage range fluctuation characteristics at the global level, thus completing the construction of a hierarchical structured voltage parameter set.

[0074] The matrix integrator synchronously receives a subset of phase change material state flags with weighted coefficients and a hierarchical structured voltage parameter set; maps the phase change state type to the individual layer waveform data in the voltage hierarchy; embeds the thermal conductivity efficiency factor into the module layer voltage mean; and dynamically correlates the global layer voltage range characteristics with the phase change state change trend to generate a three-dimensional parameter fusion matrix.

[0075] Dimensional compilation is performed based on a three-dimensional parameter fusion matrix: a temperature control benchmark is defined in the phase transition state dimension (X-axis), and the temperature response characteristics in the single-unit level waveform data are associated; a current adjustment threshold is set in the heat conduction dimension (Y-axis), and the heat conduction efficiency factor in the module level mean is bound; a virtual voltage feedback mechanism is established in the voltage stratification dimension (Z-axis), and the global level range characteristics and phase transition trends are linked to generate the initial circuit parameter set.

[0076] It should be noted that the current regulation threshold is obtained by calculating the safe upper limit of the current in real time through a phase change state compensation mechanism based on the dynamic embedded value of the thermal conductivity efficiency factor at the module level.

[0077] S2. Based on the initial parameter set of the circuit, the thermoelectric reconfiguration coefficient is calculated through the dynamic coupling of temperature gradient and voltage deviation, and a dynamic topology reconfiguration table is constructed.

[0078] S2.1. Separate temperature gradient and voltage deviation from the initial circuit parameter set; the temperature gradient includes the rate of change of temperature difference between adjacent units, and the voltage deviation represents the difference between the unit voltage and the module average voltage; the temperature gradient is squared to generate the temperature gradient square value; the temperature gradient square value is used as a dynamic weighting factor for the voltage deviation to strengthen the weighting influence of the high-heat region on the voltage deviation.

[0079] Input the temperature gradient square weighting factor and voltage deviation dataset into the nonlinear coupler: use a piecewise exponential function for dynamic superposition calculation (linear superposition in the low temperature region, exponential gain in the medium temperature region, and logarithmic suppression in the high temperature region) to output the thermoelectric reconstruction coefficient.

[0080] S2.2. Perform topology optimization on the thermoelectric reconfiguration coefficients, sort them in descending order according to the magnitude of the thermoelectric reconfiguration coefficients, and generate an online battery reduction priority sequence; at the same time, analyze the coupling relationship between the thermoelectric coefficients and internal resistance, and establish a redundant cell activation mapping table;

[0081] Specifically, analyzing the coupling relationship between the thermoelectric reconfiguration coefficient and internal resistance, and establishing a redundant cell activation mapping table, refers to using the three-dimensional thermal distribution map of the current battery module as a spatial reference parameter, and using a multivariate regression algorithm to analyze the coupling relationship between the thermoelectric coefficient and internal resistance.

[0082] The frequency component characterizing high-temperature sensitivity in the thermoelectric reconfiguration coefficient is extracted as the principal analysis variable. Based on the principal analysis variable, the battery internal resistance parameter is decomposed into ohmic internal resistance and polarization internal resistance components through hybrid pulse power characteristic testing, forming independent dual-channel data streams. A first regression equation is established with temperature gradient as the input variable and the changing trend of ohmic internal resistance as the output variable, while a second regression equation is established with voltage deviation as the input variable and the changing trend of polarization internal resistance as the output variable. The first and second regression equations are integrated into a bivariate regression equation through weighted coupling.

[0083] Matching decisions are made based on bivariate regression equations: redundant monomers with ohmic resistance lower than the population mean are selected for high-heat regions to enhance electron conduction efficiency; redundant monomers with polarization resistance fluctuations at high temperatures are matched for the intermediate-temperature transition region to ensure predictability of ion diffusion; and redundant monomers with excellent uniformity of internal resistance across the entire low-temperature stable region are used to maintain chemical equilibrium.

[0084] The selected redundant cells are associated with their physical locations. The core coordinates of the high-heat-generating module are located based on the 3D thermal distribution map. The battery compartment coordinates of the selected redundant cells are mapped to the target area, generating a coordinate offset matrix with a temperature compensation coefficient. The matching decision and spatial coordinate data are integrated to output a machine-executable redundant cell activation mapping table.

[0085] Extract high-heat-risk cells from the online battery reduction priority sequence to generate a cut-off list; select medium-heat-risk cells to generate a standby list; simultaneously, based on redundant cells, use a mapping relationship table to match the coordinates of low-internal-resistance redundant cells and high-heat-generating modules to generate a redundant cell call relationship matrix; complete the construction of dynamic topology reconstruction instructions containing the cut-off list and call matrix;

[0086] Receive the cut-off list from the dynamic topology reconfiguration command; control the relay array to perform tiered cut-off operations: the first stage cuts off high-heat-risk cells in the list; the second stage monitors the temperature gradient change after cut-off; the third stage initiates selective cut-off from the standby list based on temperature feedback; the cut-off operation is linked to the temperature feedback data in real time.

[0087] Based on the redundant unit call relationship matrix in the dynamic topology reconfiguration instruction; synchronously execute the redundant unit call operation: locate the relay group corresponding to the coordinates of the high-heat module; call the low internal resistance redundant unit bound in the relationship matrix, and establish the physical connection between the redundant unit and the cut-off vacancy;

[0088] The retained cells formed after removing cells with high heat generation risk are reassembled in series with newly activated cells from the spare battery; the cell arrangement is optimized according to the principle of ascending internal resistance value: the cell with the lowest internal resistance is placed at the current input end; the cell with the highest internal resistance is placed at the current output end; cells with the same resistance value are evenly arranged according to temperature distribution to form a reassembled battery string with optimized electrothermal characteristics.

[0089] Scan the physical connection path of the recombined battery string: record the coordinates of the series nodes between cells, mark the replacement positions of redundant cells, and map the internal resistance-temperature correlation parameters to generate a topology connection matrix containing spatial coordinates and electrical characteristics.

[0090] The topology connection matrix is ​​compiled to include a sequence of individual connection coordinates in the spatial dimension, a heat map of internal resistance-temperature distribution in the electrical dimension, and operation timing marks in the time dimension, and integrated into a machine-resolvable dynamic topology reconstruction table.

[0091] S3. The dynamic topology reconstruction table synchronously triggers the update of the resonant capacitor and inductor current limits of the ripple recovery circuit, and obtains the updated circuit parameter set.

[0092] S3.1. Extract the number of individual cells in the recombined battery string from the dynamic topology reconstruction table, input the number of individual cells in the resonant frequency adaptive model, and calculate the resonant point offset based on the rate of change of the number of individual cells.

[0093] Specifically, the number of individual cells in the recombined battery string is parsed from the dynamic topology reconfiguration table; the number of individual cells includes the total number of battery cells actually participating in charging and discharging in the current series circuit, and records the ratio of redundant cells to be replaced and the coordinates of the empty spaces of the removed cells.

[0094] The number of individual cells in the recombined battery string is input into the resonant frequency adaptive model; the number of reference cells is loaded as the calculation reference value, and the change rate of the number of individual cells is obtained by comparing the difference rate between the current number of individual cells and the number of reference cells;

[0095] The resonant frequency offset equation is used to calculate the resonant point offset based on the product of the rate of change of the number of individual cells and the circuit topology constant. The resonant frequency offset equation is obtained by establishing a negative correlation between the rate of change of the number of individual cells and the LC oscillation frequency. A decrease in the number of cells leads to an upward shift in the resonant frequency, while an increase in the number of cells leads to a downward shift in the frequency.

[0096] The LC oscillation frequency is the inherent physical frequency obtained by combining the equivalent inductance of the battery series circuit with the resonant capacitance of the ripple recovery circuit.

[0097] It should be noted that the resonant frequency adaptive model is a static calculation model based on physical laws, requiring no data training. Its construction principle is as follows:

[0098] The resonant frequency adaptive model is established based on the inherent physical laws of LC circuits (inductor-capacitor circuits), where changes in the number of battery cells directly affect the total inductance of the circuit. The calculation rules are preset through the initial parameters of the battery module (reference inductance value, reference capacitance value, and equivalent inductance coefficient of each cell). The resonant frequency adaptive model dynamically adjusts the output according to the input number of cells, automatically adapting to the changes in the circuit topology after reconfiguration.

[0099] S3.2. Based on the resonant point offset, the resonant capacitor adjustment amount is output through a capacitor compensation algorithm.

[0100] Specifically, the resonant point offset is input into the capacitor compensation algorithm: when the resonant point is positively offset, a capacitor increase command is output, and when it is negatively offset, a capacitor decrease command is output; the compensation amount is linearly adjusted according to the offset to generate the resonant capacitor adjustment amount.

[0101] S3.3. Based on the thermal conductivity efficiency factor in the phase change material state flag set, relax the inductor current limit according to the liquid critical state flag, semi-solid phase change flag, and solid stable state flag; bind the resonant capacitor adjustment to the relaxed inductor current limit to generate an updated circuit parameter set.

[0102] Extract the thermal conductivity factor from a subset of phase change material state indicators; perform differentiated current limit adjustments based on phase change state classification:

[0103] Liquid critical state identification: relax the upper limit of current according to the thermal conductivity factor;

[0104] Semi-solid phase transition identification: establishing buffer current margin based on factor values;

[0105] Solid-state stable state indicator: Maintains reference current limit;

[0106] The resonant capacitor adjustment is parameter-bound to the adjusted current limit to establish an electrical characteristic mapping relationship between capacitor and current, and compiled into an updated circuit parameter set that includes the switching frequency setting and ripple suppression coefficient.

[0107] S4. During the charging and discharging process, three-loop coordinated control is performed based on the updated circuit parameter set to generate actual energy flow data that includes heat exchange data of phase change materials.

[0108] S4.1. During the charging and discharging process, the updated circuit parameter set is input to the three-loop collaborative controller; the updated circuit parameter set includes the resonant capacitor adjustment, the relaxed inductor current limit and the switching frequency setting, and a subset of phase change material status flags is loaded as auxiliary input parameters.

[0109] Extract the virtual voltage calculation reference value from the updated circuit parameter set; detect the semi-solid phase change identifier from the phase change material state identifier subset; convert the semi-solid identifier into a temperature compensation gain coefficient; correct the virtual voltage calculation value using the temperature compensation gain coefficient to generate a temperature compensation voltage value;

[0110] It should be noted that converting the semi-solid identifier into a temperature compensation gain coefficient refers to mapping the identifier signal to a thermal buffer strength level based on the thermodynamic characteristics of the semi-solid phase transition (latent heat absorption capacity and temperature hysteresis effect); according to the thermal buffer strength level, the temperature compensation gain coefficient is output through linear interpolation, and the larger the value, the stronger the temperature fluctuation that needs to be compensated.

[0111] Correcting the virtual voltage calculation value by temperature compensation gain coefficient refers to inputting the virtual voltage calculation reference value and the temperature compensation gain coefficient into the voltage corrector; the voltage corrector performs a multiplication coupling operation between the temperature compensation gain coefficient and the virtual voltage calculation reference value: the temperature compensation gain coefficient acts as a scaling factor to dynamically amplify the temperature influence component in the voltage calculation reference value, eliminate the voltage measurement temperature drift error caused by phase change heat absorption, and generate a temperature-compensated voltage value.

[0112] The power transfer path of the thermoelectric generator is adjusted using the thermal conductivity efficiency factor of the updated circuit parameter set: the series and parallel connection relationship of thermocouples is reconstructed based on the temperature gradient; at the same time, the Seebeck coefficient is initialized, and the thermal conductivity efficiency factor is used as a dynamic weighting term of the Seebeck coefficient to output the power transfer efficiency value.

[0113] The number of individual cells in the recombined battery string is analyzed from the dynamic topology reconstruction table; based on the linear proportional relationship between the rate of change of the number of individual cells and the reference phase difference, the switch phase offset is directly calculated through product operation: the phase lags when the number of individual cells in the recombined battery string increases and the phase leads when it decreases; the MOSFET drive timing of the ripple recovery circuit is controlled to ensure that the switch phase is strictly synchronized with the charge and discharge cycle of the recombined battery string; energy-oriented recovery operation is performed to generate the recovered energy value.

[0114] It should be noted that the reference phase difference is the switching phase angle corresponding to the peak value of ripple energy recovery efficiency under the rated number of units and standard load conditions, and is then fixed as the reference phase difference.

[0115] S4.2. Input the temperature compensation voltage value output by the voltage loop, the power transfer efficiency value output by the thermoelectric loop, and the recovered energy value output by the ripple recovery loop into the data aggregator; at the same time, based on the temperature sensor feedback in the three-loop control process, collect the heat exchange rate and temperature change gradient of the phase change material in real time; the aggregator maps the four types of data to a four-dimensional structure to generate actual energy flow data.

[0116] The four-dimensional structure includes the electrothermal coupling dimension, the thermoelectric conversion dimension, the energy recovery dimension, and the phase change material dimension; the actual energy flow data includes phase change material heat exchange data, actual thermoelectric power generation power, cumulative energy recovery, and thermal management power consumption distribution map.

[0117] S5. Based on the phase change material heat exchange data and real-time phase change progress parameters in the actual energy flow data, the phase change progress value of the phase change material is calculated through the phase change dynamics model. The voltage and temperature change trends are combined for dual verification. The topology reconfiguration command is dynamically corrected and a three-level fault diagnosis is triggered to obtain the fault-tolerant reconfiguration command.

[0118] S5.1. Based on the heat exchange data of phase change materials and real-time phase change progress parameters in the actual energy flow data, the phase change progress value of the phase change material is calculated through the phase change kinetic model.

[0119] Specifically, phase change material heat exchange data are separated from actual energy flow data, and heat flow rate and temperature change rate values ​​are extracted; at the same time, real-time phase change progress parameters are loaded as a state reference.

[0120] The heat flow rate is used as the source term of the equation (thermal input driving force); the temperature change rate is used as the time derivative term (thermal inertia characterization); the voltage change trend derivative collected by the voltage sensor is applied; the voltage change derivative is transformed into a Dirichlet boundary condition and embedded into the constraint term of the phase transition dynamics differential equation to construct the phase transition dynamics differential equation;

[0121] The implicit Euler method is used to iteratively solve the differential equation of phase change dynamics: the latent heat parameters of the phase change material are initialized, and the temperature change rate feedback correction is introduced in each iteration to output the phase change hysteresis quantization value; the phase change hysteresis quantization value represents the deviation between the actual phase change rate and the theoretical value;

[0122] Progress mapping of phase change hysteresis quantization value: The quantization value is mapped to a phase change progress percentage of 0-100% through a piecewise linear interpolation algorithm; when the phase change hysteresis quantization value > 0, it is marked as endothermic delay state, and when the phase change hysteresis quantization value < 0, it is marked as exothermic advance state; finally, the phase change progress value of the phase change material is output.

[0123] S5.2. Combine voltage and temperature change trends for dual verification, dynamically correct topology reconfiguration instructions and trigger three-level fault diagnosis to obtain fault-tolerant reconfiguration instructions.

[0124] Specifically, the timing deviation is calculated by comparing the temporal correlation between the phase transition progress value and the sudden change rate of the real-time voltage.

[0125] When the timing deviation is greater than the timing tolerance threshold, a voltage correction factor is generated; the spatial matching degree between the phase transition progress change rate and the temperature gradient is analyzed, and the spatial deviation is calculated. When the spatial deviation is greater than the spatial tolerance threshold, a temperature correction factor is generated; the voltage correction factor and the temperature correction factor are weighted and superimposed to generate a topology reconstruction correction factor.

[0126] It should be noted that the expression for calculating the timing deviation is:

[0127] ;

[0128] in, Indicates the time series deviation. This indicates the number of data points within the sampling time window. Indicates the first Phase transition change over time intervals Indicates the first Voltage change over time intervals This represents the voltage-phase-change coupling coefficient. Indicates the sampling time interval;

[0129] The expression for calculating spatial deviation is:

[0130] ;

[0131] in, Indicates spatial deviation. Represents the temperature gradient vector. Represents the spatial derivative vector of the rate of change of phase transition. The standard deviation of the temperature gradient vector. This represents the standard deviation of the temperature gradient vector and the spatial derivative vector of the rate of change of phase transition. represents the covariance of the temperature gradient vector and the spatial derivative vector of the phase transition rate, and represents the spatial coordinate variables of the monitoring points on the surface of the positioning battery module.

[0132] The timing tolerance threshold is set based on the maximum timing deviation of the battery module under rated operating conditions through charge-discharge cycles, ensuring coverage of normal electrochemical response fluctuations; the spatial tolerance threshold is determined through material thermal diffusion anisotropy testing, ensuring that the temperature field and phase transition field distributions are not mismatched.

[0133] Input the topology reconstruction correction factor into the instruction corrector; adjust the sorting weight of the online battery reduction priority list according to the ratio of the topology reconstruction correction factor, add temperature gradient safety boundary conditions to the redundant battery enable mapping table, and output the corrected dynamic topology reconstruction instruction.

[0134] The coordinates of the voltage surge sensitive area are extracted from the corrected dynamic topology reconstruction command. A dynamic threshold is generated for the phase transition hysteresis quantization value. Based on the magnitude of the phase transition hysteresis quantization value, the regional voltage surge threshold is calculated using an inverse proportional function (the larger the phase transition hysteresis quantization value, the lower the regional voltage surge threshold). The voltage surge rate of the sensitive area is monitored in real time. When the regional voltage surge threshold is exceeded, a first-level abnormal signal is activated.

[0135] Extract the redundant unit distribution map from the corrected reconstruction instruction and locate the coordinates of the thermoelectric converter; extract the actual power of thermoelectric generation from the coordinates of the thermoelectric converter, calculate the predicted power of phase change progress through the phase change progress value, and activate the secondary abnormal signal when the actual power attenuation ratio is greater than the preset safety threshold.

[0136] Furthermore, the expression for calculating the predicted power of the phase change progress using the phase change progress value is as follows:

[0137] ;

[0138] in, This indicates the power used to predict the phase transition progress. Represents the Seebeck coefficient. Indicates temperature difference. Indicates the internal resistance of thermoelectric materials. This represents the equivalent heat capacity of the phase change material.

[0139] ;

[0140] in, Indicates the actual power attenuation ratio. This indicates the actual output power of the thermoelectric converter;

[0141] The safety threshold is based on the critical definition of thermoelectric conversion failure and is verified through accelerated aging tests. For example, when the power decay rate is >15%, the failure probability exceeds 98.5%, and abnormal diagnosis needs to be triggered immediately to prevent thermal runaway chain reaction.

[0142] Based on the thermal management constraints in the corrected reconstruction instruction, the phase change material monitoring area is determined; the curing time window of the monitoring area is recorded, and the deviation between the curing window and the thermal conductivity factor is calculated. When the deviation is greater than the deviation threshold, the level 3 abnormal signal is activated; when any two levels of diagnosis trigger an abnormality simultaneously, a fault-tolerant reconstruction instruction is generated.

[0143] Furthermore, the expression for calculating the deviation between the curing window and the thermal conductivity factor is as follows:

[0144] ;

[0145] in, This indicates the time offset, or the degree of deviation. Indicates the measured curing time. Indicates the heat transfer efficiency factor. Indicates the phase transformation constant of the material;

[0146] The deviation threshold is based on the tolerance requirement of the phase change material curing time and is verified through battery module cycle testing.

[0147] It should be noted that when a Level 1+Level 2 abnormal signal is triggered, the thermo-electric coupling fault area is marked (the voltage-sensitive area overlaps with the thermoelectric module coordinates); when a Level 1+Level 3 abnormal signal is triggered, the phase change material failure area is located (the intersection of the voltage change area and the curing abnormal area); when a Level 2+Level 3 abnormal signal is triggered, the degradation of the thermoelectric power generation component is diagnosed (the intersection of the power attenuation area and the curing abnormal area).

[0148] S6. At the end of the charge / discharge cycle, the reconstruction depth and phase change utilization parameters of the phase change material are stored based on the fault-tolerant reconstruction instruction. The Seebeck coefficient is optimized through a self-learning algorithm to generate a cross-cycle transfer parameter set.

[0149] At the end of the charge / discharge cycle, the reconstruction depth parameter is separated from the fault-tolerant reconstruction instruction; the reconstruction depth parameter includes the relay action timestamp sequence and the reassembled battery string performance record.

[0150] Analyze the relay action timestamp sequence; count the relay action frequency within a unit period; generate a physical reconstruction depth value by the ratio of the action frequency to the reference frequency;

[0151] Extract the average internal resistance values ​​before and after reconstruction from the performance record of the reconstructed battery string, and calculate the rate of decrease in internal resistance, expressed as:

[0152] ;

[0153] in, Indicates the rate of decrease in internal resistance. This represents the average internal resistance value before reconstruction. This represents the average internal resistance value after reconstruction.

[0154] Simultaneously, the capacity retention rate before and after reconfiguration is extracted, and the electrical reconfiguration depth value is obtained by the ratio of the weighted internal resistance reduction rate to the capacity retention rate before and after reconfiguration.

[0155] The periodic average value of phase change utilization rate is extracted from the heat exchange data of phase change materials. The decay rate of the Seebeck coefficient is adjusted using an inverse proportional function to obtain the adjusted Seebeck coefficient, expressed as:

[0156] ;

[0157] in, This represents the adjusted Seebeck coefficient. This represents the material aging compensation constant. This represents the periodic average of phase change utilization rate;

[0158] The physical reconstruction depth value, electrical reconstruction depth value, and periodic average phase change utilization rate are integrated with the adjusted Seebeck coefficient. The parameters are then integrated according to the dimensions of reconstruction efficiency, material optimization, and time transfer to generate a cross-cycle transfer parameter set.

[0159] This embodiment also provides a charging and discharging control system for series-connected batteries in a battery module, including:

[0160] The parameter acquisition module synchronously scans the voltage, temperature, and internal resistance parameters of all individual cells when the battery module is powered on, and activates the embedded phase change material to generate an initial circuit parameter set.

[0161] The topology reconstruction module, based on the initial parameter set, calculates the thermoelectric reconstruction coefficient through the dynamic coupling of temperature gradient and voltage deviation, and constructs a dynamic topology reconstruction table.

[0162] The circuit tuning module synchronously triggers the update of the resonant capacitor and inductor current limits of the ripple recovery circuit in the dynamic topology reconstruction table, and obtains the updated circuit parameter set.

[0163] During the charging and discharging process, the three-loop control module performs three-loop coordinated control based on the updated circuit parameter set to generate actual energy flow data that includes phase change material heat exchange data.

[0164] The status diagnosis module, based on the phase change material heat exchange data and real-time phase change progress parameters in the actual energy flow data, calculates the phase change progress value of the phase change material through the phase change dynamics model, performs dual verification by combining voltage and temperature change trends, dynamically corrects the topology reconfiguration command and triggers three-level fault diagnosis to obtain the fault-tolerant reconfiguration command.

[0165] At the end of the charge / discharge cycle, the cycle optimization module stores the reconstruction depth and phase change utilization parameters of the phase change material based on the fault-tolerant reconstruction instruction. It then optimizes the Seebeck coefficient and the number of reference cells through a self-learning algorithm to generate a cross-cycle transfer parameter set.

[0166] This embodiment also provides a computer device applicable to the charging and discharging control method of series-connected batteries in a battery module, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the charging and discharging control method of series-connected batteries in a battery module as proposed in the above embodiment.

[0167] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0168] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the charging and discharging control method for series-connected batteries in a battery module as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0169] In summary, this invention drives battery string recombination based on thermoelectric reconfiguration coefficient and simultaneously adjusts the resonant capacitance of the ripple recovery circuit to improve energy recovery efficiency; it reverses the Seebeck coefficient by using phase change utilization rate parameter to suppress thermoelectric decay caused by material aging, thereby reducing the efficiency fluctuation of thermoelectric power generation components; and it transfers the physical and electrical reconfiguration depth values ​​across cycles to provide a predictive optimization benchmark for the next cycle.

[0170] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for controlling the charging and discharging of series-connected batteries in a battery module, characterized in that: include, When the battery module is powered on, it synchronously scans the voltage, temperature and internal resistance parameters of all individual cells and activates the embedded phase change material to generate an initial circuit parameter set. Based on the initial circuit parameter set, the thermoelectric reconfiguration coefficient is calculated through the dynamic coupling of temperature gradient and voltage deviation, and a dynamic topology reconfiguration table is constructed. The calculation of the thermoelectric reconstruction coefficient refers to separating the temperature gradient and voltage deviation from the initial circuit parameter set, using the squared value of the temperature gradient as a weighting factor for the voltage deviation, and nonlinearly coupling it with the voltage deviation. The construction of the dynamic topology reconstruction table includes the following steps. Dynamic topology optimization analysis is performed on the thermoelectric reconfiguration coefficient to generate dynamic topology reconfiguration instructions; The dynamic topology reconfiguration command includes an online battery reduction priority list and a redundant battery activation mapping table; Based on the online battery reduction priority list, the control relay array is used to cut off high-heating cells in stages, and the low-resistance redundant cells in the redundant battery mapping table are activated simultaneously to build a recombined battery string. Record the topological connection relationships of the recombined battery strings and generate a dynamic topology reconstruction table; The dynamic topology reconstruction table synchronously triggers the update of the resonant capacitor and inductor current limits of the ripple recovery circuit, and obtains the updated circuit parameter set. During the charging and discharging process, three-loop coordinated control is performed based on the updated circuit parameter set to generate actual energy flow data that includes heat exchange data of phase change material. The aforementioned three-loop coordinated control refers to the following: in the voltage loop, converting the semi-solid identifier of the phase change material into a temperature compensation gain coefficient to correct the virtual voltage calculation value; in the thermoelectric loop, adjusting the thermoelectric power transfer path based on the thermal conductivity efficiency factor and initializing the Seebeck coefficient; and in the ripple recovery loop, synchronously switching the phase according to the number of cells in the recombined battery string to perform directional energy recovery. The thermal conductivity efficiency factor refers to the ratio of the internal resistance parameter to the temperature parameter. Based on the heat exchange data of phase change material and real-time phase change progress parameters in actual energy flow data, the phase change progress value of phase change material is calculated by phase change dynamics model, and dual verification is performed by combining voltage and temperature change trends. The topology reconfiguration command is dynamically corrected and a three-level fault diagnosis is triggered to obtain the fault-tolerant reconfiguration command. At the end of the charge-discharge cycle, the reconstruction depth and phase change utilization parameters of the phase change material are stored based on the fault-tolerant reconstruction instruction. The Seebeck coefficient is optimized through a self-learning algorithm to generate a cross-cycle transfer parameter set. The reconstruction depth parameter includes the relay action timestamp sequence and the reassembled battery string performance record.

2. The charging and discharging control method for series-connected batteries in a battery module as described in claim 1, characterized in that: The generation of the initial circuit parameter set refers to marking the embedded phase change material by combining temperature parameters and internal resistance parameters, generating three-level quantized labeling data including liquid critical state identifier, semi-solid phase change identifier, and solid stable state identifier; the ratio of internal resistance parameter to temperature parameter is used as the heat conduction efficiency factor and bound to the three-level quantized labeling data to generate a subset of phase change material state flags, which is then integrated with voltage parameters to generate the initial circuit parameter set.

3. The charging and discharging control method for series-connected batteries in a battery module as described in claim 2, characterized in that: Obtaining the updated circuit parameter set includes the following steps. Extract the number of individual cells in the recombined battery string from the dynamic topology reconstruction table, input the number of individual cells in the resonant frequency adaptive model, and calculate the resonant point offset based on the rate of change of the number of individual cells. Based on the resonant point offset, the resonant capacitor adjustment amount is output through a capacitor compensation algorithm. Based on the thermal conductivity efficiency factor in the phase change material state indicator set, the inductor current limit is relaxed according to the liquid critical state, semi-solid phase change state, and solid stable state indicators. The resonant capacitor adjustment is tied to the relaxed inductor current limit to generate an updated set of circuit parameters.

4. The charging and discharging control method for series-connected batteries in a battery module as described in claim 1, characterized in that: The calculation of the phase change progress value of the phase change material includes the following steps. The heat flow rate and temperature change rate of phase change material heat exchange data are separated from actual energy flow data. A differential equation for phase change dynamics based on Fourier's law of heat conduction is established, with the heat flow rate as the source term and the temperature change rate as the time derivative term. The derivative of the voltage change trend is transformed into a Dirichlet boundary condition, which is embedded into the differential equation constraint terms. The implicit Euler method is used to iteratively solve the phase transition dynamic differential equation and output the phase transition hysteresis quantization value. The phase change hysteresis quantification value is mapped to the phase change progress percentage, which is used as the phase change progress value of the phase change material.

5. The charging and discharging control method for series-connected batteries in a battery module as described in claim 1, characterized in that: The three-level fault diagnosis includes the following steps. Level 1 Diagnosis: The voltage surge threshold is dynamically set based on the phase transition hysteresis quantization value. When the real-time monitored voltage surge rate exceeds the voltage surge threshold, it is judged as a Level 1 anomaly. Level 2 diagnosis: Compare the attenuation ratio of the actual power of the cogeneration power generation with the predicted power of the phase change progress. When the attenuation ratio is greater than the preset safety threshold, it is judged as a Level 2 anomaly. The predicted power for phase change progress is calculated using the phase change progress value. Level 3 diagnosis: Detect the deviation between the curing time window of the phase change material and the thermal conductivity factor. When the deviation exceeds the deviation threshold, it is judged as a Level 3 anomaly. When any two levels of diagnosis trigger an exception simultaneously, a fault-tolerant refactoring instruction is generated.

6. The charging and discharging control method for series-connected batteries in a battery module as described in claim 1, characterized in that: The generation of the cross-cycle transfer parameter set includes the following steps. Separate the reconstruction depth parameter from the fault-tolerant reconstruction instruction; the reconstruction depth parameter includes the relay action timestamp sequence and the reassembled battery string performance record; Analyze the relay action timestamp sequence, calculate the relay action frequency within a unit period, and generate a physical reconstruction depth value; Based on the performance records of the reconstructed battery string, the electrical reconfiguration depth value is generated by comparing the rate of decrease in internal resistance and the rate of capacity retention before and after reconfiguration. The periodic average value of phase change utilization rate is extracted from phase change materials, and the decay rate of Seebeck coefficient is adjusted by an inverse proportional function. The physical reconstruction depth value, electrical reconstruction depth value, and periodic average phase change utilization rate are integrated with the adjusted Seebeck coefficient to generate a cross-cycle transfer parameter set.

7. A charging and discharging control system for series-connected batteries in a battery module, based on the charging and discharging control method for series-connected batteries in a battery module according to any one of claims 1 to 6, characterized in that: include, The parameter acquisition module synchronously scans the voltage, temperature, and internal resistance parameters of all individual cells when the battery module is powered on, and activates the embedded phase change material to generate an initial circuit parameter set. The topology reconstruction module, based on the initial parameter set, calculates the thermoelectric reconstruction coefficient through the dynamic coupling of temperature gradient and voltage deviation, and constructs a dynamic topology reconstruction table. The circuit tuning module synchronously triggers the update of the resonant capacitor and inductor current limits of the ripple recovery circuit in the dynamic topology reconstruction table, and obtains the updated circuit parameter set. During the charging and discharging process, the three-loop control module performs three-loop coordinated control based on the updated circuit parameter set to generate actual energy flow data that includes phase change material heat exchange data. The status diagnosis module, based on the phase change material heat exchange data and real-time phase change progress parameters in the actual energy flow data, calculates the phase change progress value of the phase change material through the phase change dynamics model, performs dual verification by combining voltage and temperature change trends, dynamically corrects the topology reconfiguration command and triggers three-level fault diagnosis to obtain the fault-tolerant reconfiguration command. At the end of the charge / discharge cycle, the cycle optimization module stores the reconstruction depth and phase change utilization parameters of the phase change material based on the fault-tolerant reconstruction instruction. It then optimizes the Seebeck coefficient and the number of reference cells through a self-learning algorithm to generate a cross-cycle transfer parameter set.

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