Lithium battery protection board software management method and system based on multi-battery-pack cooperation

Abstract

The invention relates to the technical field of lithium batteries. The lithium battery protection board software management method and system based on multi-battery-pack cooperation are provided, and the method comprises the following steps: carrying out chemical system dynamic identification processing based on electrochemical characteristic parameters of each battery pack, and generating a battery pack type label; according to the battery pack type labels, differential equalization strategy generation processing is carried out, and a multi-chemical-system cooperative control model is generated; dynamic working mode switching processing is carried out, and a parallel operation control instruction is generated; according to the parallel operation control instruction and the energy flow direction between the battery packs monitored in real time, reverse charging blocking processing is carried out, and a current suppression signal is generated; and performing health state collaborative evaluation processing, generating dynamically optimized equilibrium strategy parameters, and feeding back the equilibrium strategy parameters to the multi-chemical system collaborative control model, so as to solve the problems of recessive energy backflow, dynamic threshold loss and strategy stiffness in a multi-chemical system mixed use scene in related technologies.

Description

Technical Field

[0001] The present invention relates to the field of lithium battery technology, and in particular to a software management method and system for a lithium battery protection board coordinated by multiple battery packs. Background Art

[0002] With the rapid development of renewable energy and energy storage technologies, lithium batteries are playing an increasingly important role in electric vehicles, smart grids, distributed energy storage, and other fields. Collaborative management of multiple battery packs is a core technology for improving system energy utilization and safety. In particular, hybrid networking of batteries with different chemistries (such as ternary lithium and lithium iron phosphate) has become a key industry trend in second-use scenarios.

[0003] Traditional battery management systems (BMSs) assume all battery packs are of the same chemistry by default. Their unified balancing strategy faces a core contradiction in mixed-chemistry scenarios: due to differences in voltage platforms, high-voltage battery packs can reverse charge low-voltage packs at low states of charge (SOC), triggering hidden energy backflow and accelerated polarization. However, due to a lack of dynamic threshold adjustment capabilities, traditional balancing strategies are unable to effectively block reverse currents or adapt to the differentiated needs of mixed-use scenarios, ultimately leading to component aging and reduced system reliability. Summary of the Invention

[0004] Based on this, it is necessary to provide a software management method and system for lithium battery protection boards with coordinated multi-battery packs to address the above technical issues, so as to solve the problems of hidden energy backflow, lack of dynamic thresholds and rigid strategies in related technologies under the mixed use of multiple chemical systems, and achieve the technical effects of reverse current suppression, dynamic adaptation of differentiated needs and improvement of system reliability.

[0005] In a first aspect, the present application provides a software management method for a lithium battery protection board in collaboration with multiple battery packs, the method comprising:

[0006] Based on the electrochemical characteristic parameters of each battery pack, dynamic chemical system identification processing is performed to generate battery pack type labels;

[0007] Based on the battery pack type label, differentiated balancing strategies are generated to create a multi-chemical system collaborative control model;

[0008] Based on the multi-chemical system collaborative control model and battery pack type labels, dynamic working mode switching is performed to generate parallel operation control instructions;

[0009] According to the parallel operation control instructions and the real-time monitoring of the energy flow direction between battery packs, reverse charging is blocked and a current suppression signal is generated;

[0010] Based on the current suppression signal, parallel operation control instructions and multi-dimensional aging indicators, a collaborative health status assessment is performed to generate dynamically optimized equilibrium strategy parameters, which are then fed back to the multi-chemical system collaborative control model.

[0011] Furthermore, based on the multi-chemical system collaborative control model and battery pack type labels, dynamic working mode switching is performed to generate parallel operation control instructions, including:

[0012] The following formula is used to verify the voltage platform compatibility of battery packs with different chemical systems based on the multi-chemical system collaborative control model, and generate dynamic compatibility verification results:

[0013]

[0014] Among them, V comp represents the voltage compatibility index, n represents the number of battery packs, α i Represents the weight coefficient, V i Represents the voltage of the i-th battery pack, V ref Indicates the reference voltage, V max represents the maximum allowable voltage deviation, η represents the voltage platform stability coefficient, T represents the observation time period, Indicates the rate of voltage change;

[0015] Based on the dynamic compatibility check results and battery pack type labels, a multi-modal decision-making process is performed between active balancing mode, hierarchical isolation mode, and hybrid scheduling mode to generate the initial operating mode instruction;

[0016] Based on the polarization characteristic parameters corresponding to the battery pack type label, dynamic inertia correction processing is performed on the initial operating mode instructions to eliminate the transient response differences of battery packs with different chemical systems and generate inertia-corrected parallel operation control instructions.

[0017] Furthermore, based on the polarization characteristic parameters corresponding to the battery pack type label, dynamic inertia correction processing is performed on the initial operating mode instructions to eliminate the transient response differences of battery packs with different chemical systems and generate inertia-corrected parallel operation control instructions, including:

[0018] The polarization voltage relaxation time parameter and polarization current response slope parameter are extracted based on the polarization characteristic parameters corresponding to the battery type label using the following formula:

[0019] U p (t) = U p0 ·e -t / τ

[0020] I p (t) = k·(1-e -t / T)

[0021] Among them, U p (t) represents the change of polarization voltage over time, U p0 represents the initial polarization voltage value, t represents the time variable, τ represents the polarization voltage relaxation time constant, I p (t) represents the change of polarization current over time, k represents the polarization current response slope coefficient, and T represents the polarization current response time constant;

[0022] Dynamically smoothing the mode switching rate in the initial working mode instruction according to the polarization voltage relaxation time parameter to generate a first correction instruction;

[0023] Based on the polarization current response slope parameter, dynamically compensate the current distribution ratio in the initial working mode instruction to generate a second correction instruction;

[0024] The first correction instruction and the second correction instruction are subjected to multi-objective fusion processing to generate an inertia-corrected parallel operation control instruction.

[0025] Furthermore, according to the parallel operation control instruction and the energy flow direction between the battery packs monitored in real time, reverse charging blocking processing is performed to generate a current suppression signal, including:

[0026] Based on the parallel operation control instructions, multi-dimensional vector analysis is performed on the energy flow direction between battery packs to generate reverse energy flow characteristic signals;

[0027] According to the reverse energy flow characteristic signal and the polarization hysteresis characteristics of battery packs with different chemical systems, a phased blocking decision-making process is performed to generate multi-level blocking trigger instructions;

[0028] Based on the multi-level blocking trigger instruction, the MOSFET gate drive signal of the target battery pack is dynamically duty-cycle modulated to generate a current suppression signal with phase compensation function.

[0029] Furthermore, based on the parallel operation control instruction, multi-dimensional vector analysis processing is performed on the energy flow direction between the battery packs to generate a reverse energy flow characteristic signal, including:

[0030] Based on the parallel operation control instructions, the spatiotemporal distribution characteristics of energy flow between battery packs are synchronously analyzed and processed to generate spatiotemporal synchronized flow direction vector parameters;

[0031] Based on the spatiotemporally synchronized flow direction vector parameters and the polarization phase characteristics of battery packs with different chemical systems, multi-band phase correlation detection processing is performed to generate the reverse energy phase offset;

[0032] Based on the reverse energy phase offset, the forward and reverse weights of the energy flow direction are dynamically allocated to generate a multi-dimensional fusion reverse energy flow characteristic signal.

[0033] Furthermore, based on the reverse energy flow characteristic signal and the polarization hysteresis characteristics of battery packs with different chemical systems, a phased blocking decision-making process is performed to generate multi-level blocking trigger instructions, including:

[0034] Based on the polarization hysteresis characteristics of battery packs with different chemical systems, dynamic hysteresis compensation parameters are extracted and a polarization hysteresis compensation table is generated;

[0035] According to the energy accumulation rate of the reverse energy flow characteristic signal and the polarization hysteresis compensation table, the gradient blocking threshold adaptive matching processing is performed to generate the dynamic blocking trigger threshold;

[0036] Based on the dynamic blocking trigger threshold, the reverse energy flow characteristic signal is segmented into multiple time windows to generate phased blocking trigger conditions.

[0037] According to the staged blocking trigger conditions, the trigger intensity and duration of the blocking instruction are gradient-superimposed to generate a multi-level blocking trigger instruction.

[0038] Furthermore, based on the current suppression signal, parallel operation control instructions and multi-dimensional aging indicators, a coordinated health status assessment is performed to generate dynamically optimized balancing strategy parameters, including:

[0039] Based on the blocking characteristic parameters in the current suppression signal and the mode switching frequency in the parallel operation control instruction, multi-source data fusion processing is performed to generate a dynamic health weight coefficient;

[0040] Based on the cycle life attenuation rate parameters in the multi-dimensional aging index and the polarization decay difference parameters of battery packs with different chemical systems, aging difference compensation is performed to generate a polarization decay compensation factor;

[0041] Based on the dynamic health weight coefficient and polarization decay compensation factor, the priority allocation of the balancing strategy is dynamically reconstructed to generate a cross-system balancing priority mapping table;

[0042] According to the cross-system balancing priority mapping table and the energy allocation logic in the parallel operation control instructions, the trigger path of the balancing strategy is topologically optimized to generate dynamically optimized balancing strategy parameters.

[0043] In a second aspect, the present application also provides a multi-battery pack collaborative lithium battery protection board software management system, the system comprising:

[0044] A chemical system identification module is used to dynamically identify the chemical system based on the electrochemical characteristic parameters of each battery pack and generate a battery pack type label;

[0045] The strategy generation module is used to generate differentiated balancing strategies based on battery pack type labels and generate a multi-chemical system collaborative control model;

[0046] The mode switching module is used to perform dynamic working mode switching based on the multi-chemical system collaborative control model and battery pack type labels and generate parallel operation control instructions;

[0047] The reverse blocking module is used to perform reverse charging blocking processing and generate a current suppression signal based on the parallel operation control instructions and the energy flow direction between battery packs monitored in real time;

[0048] The health assessment module is used to perform collaborative health status assessment based on current suppression signals, parallel operation control instructions and multi-dimensional aging indicators, generate dynamically optimized equilibrium strategy parameters, and feed back the equilibrium strategy parameters to the multi-chemical system collaborative control model.

[0049] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any method in the first aspect of the present application are implemented.

[0050] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any method in the first aspect of the present application when the computer program is executed by a processor.

[0051] The technical solution provided by the present application includes the following technical effects: by providing a software management method and system for a lithium battery protection board that coordinates multiple battery packs, the method includes: based on the electrochemical characteristic parameters of each battery pack, performing dynamic identification processing of the chemical system to generate a battery pack type label; based on the battery pack type label, performing differentiated balancing strategy generation processing to generate a multi-chemical system collaborative control model; based on the multi-chemical system collaborative control model and the battery pack type label, performing dynamic working mode switching processing to generate parallel operation control instructions; based on the parallel operation control instructions and the real-time monitored energy flow direction between battery packs, performing reverse charging blocking processing to generate a current suppression signal; based on the current suppression signal, the parallel operation control instructions and the multi-dimensional aging indicators, performing health status collaborative assessment processing to generate dynamically optimized balancing strategy parameters, and feeding back the balancing strategy parameters to the multi-chemical system collaborative control model to solve the problems of hidden energy backflow, lack of dynamic thresholds and strategy rigidity in related technologies in the mixed use scenario of multiple chemical systems, and achieve the technical effects of reverse current suppression, dynamic adaptation of differentiated needs and improved system reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 This is a flowchart of a software management method for a lithium battery protection board in collaboration with multiple battery packs in one embodiment of the present invention;

[0054] Figure 2 A flowchart of performing reverse charge blocking processing and generating a current suppression signal based on a parallel operation control instruction and real-time monitoring of the energy flow direction between battery packs in one embodiment of the present invention;

[0055] Figure 3 This is a structural diagram of the software management of the lithium battery protection board for multiple battery packs in one embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned purposes, features and advantages of the present application more clearly understood, the specific implementation methods of the present application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to fully understand the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar improvements without violating the connotation of the application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0057] like Figure 1 As shown, the present application provides a software management method for lithium battery protection boards in collaboration with multiple battery packs, the method comprising:

[0058] S101: Based on the electrochemical characteristic parameters of each battery pack, a chemical system dynamic identification process is performed to generate a battery pack type label.

[0059] Specifically, the electrochemical characteristic parameters of each battery pack are collected. The above parameters include the open circuit voltage of the battery, the characteristics of the charge and discharge curve, the internal resistance and other key information that can characterize the differences in the battery chemical system. Afterwards, the collected parameters are analyzed and processed, and the corresponding algorithms or models are used to identify the chemical system to which each battery pack belongs based on the unique performance of the above parameters in batteries of different chemical systems. During the identification process, the characteristic parameter database of known chemical systems is compared, and the type of chemical system corresponding to the current battery pack is determined based on matching rules such as similarity. After completing the chemical system identification of each battery pack, a unique identifying type label is generated for each battery pack based on the identification results. This label is used for subsequent differentiated management and strategy formulation of battery packs with different chemical systems.

[0060] S102: Perform differentiated balancing strategy generation processing based on the battery pack type label to generate a multi-chemical system collaborative control model.

[0061] Specifically, when generating differentiated balancing strategies, the system uses battery pack type labels, which are generated based on the electrochemical characteristics of each battery pack and can clearly identify battery packs of different chemistries, such as lithium iron phosphate and ternary lithium batteries. Based on these type labels, the system to which each battery pack belongs is identified. For each chemistry, strategy parameters such as the balancing starting voltage difference and balancing current are then set to match the characteristics of the battery pack, generating differentiated balancing strategies. Simultaneously, a multi-chemistry coordinated control model is constructed to integrate the differentiated balancing strategies for battery packs of different chemistries. By analyzing the characteristics of each battery pack and their interactions, it determines how to coordinate balancing control across the battery packs under different operating conditions. During the model construction process, the charge / discharge characteristics and capacity characteristics of the battery packs of different chemistries are fully considered to achieve coordinated management of the multi-chemistry battery packs, ensuring optimal overall performance and improving energy utilization and safety.

[0062] S103: Based on the multi-chemical system collaborative control model and the battery pack type label, dynamic working mode switching processing is performed to generate parallel operation control instructions.

[0063] Specifically, the operating status of the battery pack, including parameters such as voltage, current, and temperature, is monitored in real time. Combined with the application scenario, the current operating condition of the battery pack is determined. A multi-chemical system collaborative control model is used to extract the voltage platform characteristic parameters of each battery pack based on its type label. A specific algorithm is used to calculate the voltage compatibility index between battery packs of different chemical systems, and to evaluate the degree of voltage matching when they operate in parallel. Based on the dynamic compatibility check results and the battery pack type label, a decision is made between active balancing mode, hierarchical isolation mode, and hybrid scheduling mode to generate initial operating mode instructions. The active balancing mode is used for active energy regulation between battery packs; the hierarchical isolation mode is used to isolate some battery packs when the battery pack voltage difference is too large to ensure safe system operation; the hybrid scheduling mode combines active balancing and hierarchical isolation to flexibly adjust the charge and discharge status of the battery pack.

[0064] Obtain the polarization characteristic parameters corresponding to the battery pack type label, including the polarization voltage relaxation time and polarization current response slope. These parameters reflect the polarization phenomenon and dynamic response characteristics of the battery pack during the charge and discharge process. Based on the polarization characteristic parameters, correct the initial operating mode command. Based on the polarization voltage relaxation time, smooth the mode switching rate to avoid battery pack voltage fluctuations caused by excessively fast mode switching. Based on the polarization current response slope, compensate for the current distribution ratio to ensure reasonable current distribution among the battery packs during parallel operation, and generate inertia-corrected parallel operation control commands.

[0065] S104: According to the parallel operation control instruction and the energy flow direction between the battery packs monitored in real time, reverse charging blocking processing is performed to generate a current suppression signal.

[0066] Specifically, it receives parallel operation control instructions and identifies key information such as the connection relationship, operating status, and target charge and discharge mode of each battery pack. It also monitors the energy flow direction between battery packs in real time. Using energy sensors and voltage and current detection equipment, it obtains the energy transmission paths and flow rates between different battery packs, as well as the voltage and current changes of each battery pack in real time. It performs vector analysis on the monitored energy flow direction to determine the energy source and destination, as well as the energy flow components along each path. By combining the polarization phase characteristics of battery packs with different chemistries, it uses multi-band phase correlation detection technology to detect the reverse energy phase offset. Based on the offset, it dynamically adjusts the weight distribution of forward and reverse energy flows to generate a multi-dimensional fused reverse energy flow signature signal. It analyzes the polarization hysteresis characteristics of battery packs with different chemistries, extracts dynamic hysteresis compensation parameters, and constructs a polarization hysteresis compensation table. Based on the energy accumulation rate of the reverse energy flow signature signal and the compensation table, it performs adaptive gradient blocking threshold matching to determine the dynamic blocking trigger threshold. Based on the dynamic blocking trigger threshold, it segments the reverse energy flow signature signal into multiple time windows to form phased blocking trigger conditions.

[0067] Based on the trigger conditions at different stages, the trigger intensity and duration of the blocking instructions are gradient-superimposed to generate multi-level blocking trigger instructions. These multi-level blocking trigger instructions are sent to the battery pack's MOSFET control module, dynamically modulating the MOSFET gate drive signal of the target battery pack. By adjusting the duty cycle, the on- and off-times of the MOSFET are controlled to suppress reverse charging current. A current suppression signal with phase compensation is then generated to block reverse charging current, preventing hidden energy backflow and polarization acceleration.

[0068] S105: Based on the current suppression signal, the parallel operation control instruction and the multi-dimensional aging index, a health status collaborative assessment process is performed to generate dynamically optimized equilibrium strategy parameters, and the equilibrium strategy parameters are fed back to the multi-chemical system collaborative control model.

[0069] Specifically, the blocking characteristic parameters in the current suppression signal are combined with the mode switching frequency in the parallel operation control instruction, and multi-source data fusion processing is performed to generate a dynamic health weight coefficient. The blocking characteristic parameters reflect the working status and health status of the battery pack during the reverse charge blocking process, and the mode switching frequency reflects the switching of the battery pack in different operating modes. By fusing the above data, the health status of the battery pack can be more comprehensively evaluated. Based on the cycle life attenuation rate parameters in the multi-dimensional aging indicators, combined with the polarization decay difference parameters of battery packs with different chemical systems, aging difference compensation processing is performed to generate a polarization decay compensation factor. Batteries of different chemical systems have different aging characteristics during long-term use. By considering the above differences and making compensation, the aging degree of the battery pack can be more accurately evaluated.

[0070] Based on the dynamic health weight coefficient and polarization degradation compensation factor, the priority allocation of the balancing strategy is dynamically reconfigured to generate a cross-system balancing priority mapping table. The priorities of different chemistry battery packs in the balancing strategy are adjusted based on the health status and aging of the battery packs. This allows the battery management system to more rationally allocate resources and prioritize the balancing needs of battery packs with better health and lower aging. Based on the cross-system balancing priority mapping table and the energy allocation logic in the parallel operation control instructions, the triggering path of the balancing strategy is topologically optimized to generate dynamically optimized balancing strategy parameters. The triggering path of the balancing strategy is optimized based on the battery pack priorities and energy allocation requirements, improving the operating efficiency and balancing effectiveness of the battery management system. The dynamically optimized balancing strategy parameters are fed back to the multi-chemistry coordinated control model, allowing the model to more accurately coordinate the multi-chemistry battery packs based on the latest balancing strategy parameters, thereby improving the performance and reliability of the entire battery system.

[0071] An embodiment of the present application provides a software management method for a lithium battery protection board in collaboration with multiple battery packs, including: performing dynamic chemical system identification processing based on the electrochemical characteristic parameters of each battery pack to generate a battery pack type label; performing differentiated balancing strategy generation processing based on the battery pack type label to generate a multi-chemical system collaborative control model; performing dynamic working mode switching processing based on the multi-chemical system collaborative control model and the battery pack type label to generate a parallel operation control instruction; performing reverse charging blocking processing based on the parallel operation control instruction and the energy flow direction between battery packs monitored in real time to generate a current suppression signal; performing health status collaborative assessment processing based on the current suppression signal, parallel operation control instruction and multi-dimensional aging indicators to generate dynamically optimized balancing strategy parameters, and feeding back the balancing strategy parameters to the multi-chemical system collaborative control model to solve the problems of hidden energy backflow, lack of dynamic thresholds and strategy rigidity in related technologies in the mixed use scenario of multiple chemical systems, and to achieve the technical effects of reverse current suppression, dynamic adaptation of differentiated needs and improved system reliability.

[0072] Furthermore, based on the multi-chemical system collaborative control model and battery pack type labels, dynamic working mode switching is performed to generate parallel operation control instructions, including:

[0073] The following formula is used to verify the voltage platform compatibility of battery packs with different chemical systems based on the multi-chemical system collaborative control model, and generate dynamic compatibility verification results:

[0074]

[0075]

[0076] Among them, V comp represents the voltage compatibility index, n represents the number of battery packs, α i Represents the weight coefficient, V i Represents the voltage of the i-th battery pack, V ref Indicates the reference voltage, V max represents the maximum allowable voltage deviation, η represents the voltage platform stability coefficient, T represents the observation time period, Indicates the rate of voltage change;

[0077] Based on the dynamic compatibility check results and battery pack type labels, a multi-modal decision-making process is performed between active balancing mode, hierarchical isolation mode, and hybrid scheduling mode to generate the initial operating mode instruction;

[0078] Based on the polarization characteristic parameters corresponding to the battery pack type label, dynamic inertia correction processing is performed on the initial operating mode instructions to eliminate the transient response differences of battery packs with different chemical systems and generate inertia-corrected parallel operation control instructions.

[0079] Specifically, based on a multi-chemistry collaborative control model, the voltage platform compatibility of battery packs with different chemistries is verified and processed to generate dynamic compatibility verification results. By analyzing the voltage platform characteristic parameters of each battery pack, such as voltage level, fluctuation range, and stability, and combining multi-dimensional factors such as weight coefficients, reference voltage, and maximum allowable voltage deviation, the voltage compatibility between different battery packs is comprehensively evaluated. Based on the dynamic compatibility verification results and the battery pack type labels, a multi-modal decision process is made between active balancing mode, hierarchical isolation mode, and hybrid scheduling mode to generate initial operating mode instructions. Active balancing mode actively adjusts energy between battery packs to achieve balanced charge levels. Hierarchical isolation mode isolates some battery packs when voltage differences between battery packs are too large to ensure safe system operation. Hybrid scheduling mode combines active balancing and hierarchical isolation to flexibly adjust the charge and discharge states of battery packs to adapt to complex operating conditions.

[0080] Based on the battery pack type label, the corresponding polarization characteristic parameters are obtained, including polarization voltage relaxation time and polarization current response slope. Polarization characteristics reflect the voltage and current variation patterns of the battery due to polarization during the charge and discharge process. Dynamic inertia correction processing is performed on the initial working mode instructions to eliminate the transient response differences of battery packs with different chemical systems and generate inertia-corrected parallel operation control instructions. According to the polarization voltage relaxation time, the mode switching rate is dynamically smoothed to avoid battery pack voltage fluctuations caused by too fast mode switching; according to the polarization current response slope, the current distribution ratio is dynamically compensated to ensure that the current distribution of each battery pack is reasonable when operating in parallel, thereby achieving stable parallel operation of battery packs with different chemical systems.

[0081] Furthermore, based on the polarization characteristic parameters corresponding to the battery pack type label, dynamic inertia correction processing is performed on the initial operating mode instructions to eliminate the transient response differences of battery packs with different chemical systems and generate inertia-corrected parallel operation control instructions, including:

[0082] The polarization voltage relaxation time parameter and polarization current response slope parameter are extracted based on the polarization characteristic parameters corresponding to the battery type label using the following formula:

[0083] U p (t) = U p0 ·e -t / τ

[0084] I p (t) = k·(1-e -t / T )

[0085] Among them, U p (t) represents the change of polarization voltage over time, U p0represents the initial polarization voltage value, t represents the time variable, τ represents the polarization voltage relaxation time constant, I p (t) represents the change of polarization current over time, k represents the polarization current response slope coefficient, and T represents the polarization current response time constant;

[0086] Dynamically smoothing the mode switching rate in the initial working mode instruction according to the polarization voltage relaxation time parameter to generate a first correction instruction;

[0087] Based on the polarization current response slope parameter, dynamically compensate the current distribution ratio in the initial working mode instruction to generate a second correction instruction;

[0088] The first correction instruction and the second correction instruction are subjected to multi-objective fusion processing to generate an inertia-corrected parallel operation control instruction.

[0089] Specifically, the corresponding polarization characteristic parameters, including the polarization voltage relaxation time and the polarization current response slope, are obtained from the battery pack type label. The polarization voltage relaxation time reflects the time required for the battery pack voltage to return to a stable state during the charge and discharge process, while the polarization current response slope reflects the dynamic response speed of the battery pack current to the charge and discharge operation. Based on the polarization voltage relaxation time parameter, the mode switching rate in the initial operating mode instruction is dynamically smoothed to generate a first correction instruction. By adjusting the mode switching rate, battery pack voltage fluctuations caused by excessively fast switching are avoided, ensuring a smooth transition between different operating modes. Based on the polarization current response slope parameter, the current distribution ratio in the initial operating mode instruction is dynamically compensated to generate a second correction instruction. Based on the current response characteristics of the battery pack, the current distribution of each battery pack during parallel operation is appropriately adjusted to ensure coordinated operation during the charge and discharge process and improve overall performance. The first and second correction instructions are then fused together to generate an inertia-corrected parallel operation control instruction, taking into account both the smoothness of the mode switching and the rationality of the current distribution. Through fusion processing, the parallel operation performance of battery packs is optimized while eliminating the transient response differences of battery packs with different chemical systems.

[0090] like Figure 2 As shown, according to the parallel operation control instruction and the real-time monitored energy flow direction between battery packs, reverse charging blocking processing is performed to generate a current suppression signal, including:

[0091] S201: Based on the parallel operation control instruction, perform multi-dimensional vector analysis processing on the energy flow direction between battery packs to generate a reverse energy flow characteristic signal;

[0092] S202: Based on the reverse energy flow characteristic signal and the polarization hysteresis characteristics of battery packs with different chemical systems, a phased blocking decision process is performed to generate a multi-level blocking trigger instruction;

[0093] S203: Based on the multi-level blocking trigger instruction, dynamically perform duty cycle modulation processing on the MOSFET gate drive signal of the target battery pack to generate a current suppression signal with a phase compensation function.

[0094] Specifically, based on parallel operation control instructions, multi-dimensional vector analysis technology is used to analyze the direction of energy flow between battery packs. This analysis takes into account factors such as the magnitude, direction, and trend of energy flow, generating a reverse energy flow signature signal that reflects the characteristics of energy flow. This reverse energy flow signature signal is then combined with the polarization hysteresis characteristics of battery packs with different chemistries. Polarization hysteresis describes the nonlinear relationship between voltage and current during the charge and discharge process, which affects the reverse charging process. Based on these factors, a phased blocking decision is made. The reverse charging process is divided into multiple stages, and corresponding blocking conditions and strategies are determined based on the characteristics of each stage, generating multi-level blocking trigger instructions. Based on the multi-level blocking trigger instructions, the MOSFET gate drive signal of the target battery pack is dynamically duty-cycle modulated. By adjusting the on and off times of the MOSFETs, the current flow between the battery packs is controlled, generating a current suppression signal with phase compensation, effectively blocking the reverse charging current and ensuring the safe and stable operation of the battery pack.

[0095] Furthermore, based on the parallel operation control instruction, multi-dimensional vector analysis processing is performed on the energy flow direction between the battery packs to generate a reverse energy flow characteristic signal, including:

[0096] Based on the parallel operation control instructions, the spatiotemporal distribution characteristics of energy flow between battery packs are synchronously analyzed and processed to generate spatiotemporal synchronized flow direction vector parameters;

[0097] Based on the spatiotemporally synchronized flow direction vector parameters and the polarization phase characteristics of battery packs with different chemical systems, multi-band phase correlation detection processing is performed to generate the reverse energy phase offset;

[0098] Based on the reverse energy phase offset, the forward and reverse weights of the energy flow direction are dynamically allocated to generate a multi-dimensional fusion reverse energy flow characteristic signal.

[0099] Specifically, based on parallel operation control instructions, the spatiotemporal distribution characteristics of energy flow between battery packs are synchronously analyzed to generate spatiotemporally synchronized flow direction vector parameters. This process fully considers the variations in energy flow across different temporal and spatial dimensions, including its magnitude, direction, and how it changes over time and space. This yields vector parameters that accurately describe the energy flow state. Based on these spatiotemporally synchronized flow direction vector parameters and the polarization phase characteristics of battery packs with different chemistries, multi-band phase correlation detection is performed to generate a reverse energy phase offset. Battery packs with different chemistries exhibit different phase characteristics during polarization. Multi-band phase correlation detection can accurately detect the phase deviation of energy flow from the normal direction, providing a key basis for subsequent processing. Based on the reverse energy phase offset, forward and reverse weights for the energy flow direction are dynamically assigned to generate a multi-dimensional fused reverse energy flow characteristic signal. According to the size and direction of the phase offset, the weight distribution of forward and reverse energy flows is reasonably adjusted so that the reverse energy flow characteristic signal can more accurately reflect the abnormal situation of the energy flow direction and provide accurate signal support for subsequent reverse charging blocking processing.

[0100] Furthermore, based on the reverse energy flow characteristic signal and the polarization hysteresis characteristics of battery packs with different chemical systems, a phased blocking decision-making process is performed to generate multi-level blocking trigger instructions, including:

[0101] Based on the polarization hysteresis characteristics of battery packs with different chemical systems, dynamic hysteresis compensation parameters are extracted and a polarization hysteresis compensation table is generated;

[0102] According to the energy accumulation rate of the reverse energy flow characteristic signal and the polarization hysteresis compensation table, the gradient blocking threshold adaptive matching processing is performed to generate the dynamic blocking trigger threshold;

[0103] Based on the dynamic blocking trigger threshold, the reverse energy flow characteristic signal is segmented into multiple time windows to generate phased blocking trigger conditions.

[0104] According to the staged blocking trigger conditions, the trigger intensity and duration of the blocking instruction are gradient-superimposed to generate a multi-level blocking trigger instruction.

[0105] Specifically, based on the polarization hysteresis characteristics of battery packs with different chemistries, dynamic hysteresis compensation parameters are extracted and a polarization hysteresis compensation table is generated. Polarization hysteresis refers to the hysteresis between the changes in voltage and current during the charge and discharge process of a battery, which affects the battery's energy flow and voltage performance. Through experiments and data analysis, dynamic hysteresis compensation parameters for battery packs with different chemistries under different operating conditions are obtained. These parameters reflect the hysteresis effect of the battery pack and are organized into a polarization hysteresis compensation table for subsequent use. Based on the energy accumulation rate of the reverse energy flow characteristic signal and the polarization hysteresis compensation table, a gradient blocking threshold adaptive matching process is performed to generate a dynamic blocking trigger threshold. The energy accumulation rate reflects the intensity and speed of the reverse energy flow. By combining this rate with the parameters in the polarization hysteresis compensation table, the blocking threshold is dynamically adjusted based on the current energy flow and the battery pack's hysteresis characteristics. This ensures that under different operating conditions and energy flow intensities, a relatively accurate determination of when blocking is required is made to protect the battery pack's safety.

[0106] Based on the dynamic blocking trigger threshold, the reverse energy flow characteristic signal is segmented into multiple time windows to generate phased blocking trigger conditions. The entire reverse energy flow process is divided into multiple time windows, each corresponding to a phase. The blocking trigger conditions for each phase are determined based on the dynamic blocking trigger threshold. This phased processing approach allows for more precise control of the blocking operation, making it more consistent with the actual changes in energy flow and improving the accuracy and timeliness of blocking decisions. Based on the phased blocking trigger conditions, the trigger strength and duration of the blocking instruction are gradient-superimposed to generate multi-level blocking trigger instructions. Based on the blocking trigger conditions for each phase, the corresponding trigger strength and duration are determined, and these parameters are gradient-superimposed to generate multi-level blocking trigger instructions. This gradient superposition approach allows the blocking operation to gradually increase in intensity, preventing sudden and drastic changes from impacting the battery pack. It also better adapts to the changing trend of reverse energy flow and effectively blocks reverse charging.

[0107] Furthermore, based on the current suppression signal, parallel operation control instructions and multi-dimensional aging indicators, a coordinated health status assessment is performed to generate dynamically optimized balancing strategy parameters, including:

[0108] Based on the blocking characteristic parameters in the current suppression signal and the mode switching frequency in the parallel operation control instruction, multi-source data fusion processing is performed to generate a dynamic health weight coefficient;

[0109] Based on the cycle life attenuation rate parameters in the multi-dimensional aging index and the polarization decay difference parameters of battery packs with different chemical systems, aging difference compensation is performed to generate a polarization decay compensation factor;

[0110] Based on the dynamic health weight coefficient and polarization decay compensation factor, the priority allocation of the balancing strategy is dynamically reconstructed to generate a cross-system balancing priority mapping table;

[0111] According to the cross-system balancing priority mapping table and the energy allocation logic in the parallel operation control instructions, the trigger path of the balancing strategy is topologically optimized to generate dynamically optimized balancing strategy parameters.

[0112] Specifically, blocking characteristic parameters are extracted from the current suppression signal. These parameters reflect the operating status and health of the battery pack during the reverse charge blocking process. Simultaneously, the mode switching frequency in the parallel operation control command is obtained, reflecting the battery pack's switching between different operating modes. Multi-source data fusion is performed on these two parameters to comprehensively consider their impact on the battery pack's health status and generate a dynamic health weight coefficient. This coefficient dynamically reflects the health weight of the battery pack in its current operating state, providing a basis for subsequent health status assessment. The cycle life decay rate parameter within the multi-dimensional aging indicator is used to understand the battery pack's life degradation over long-term use. Furthermore, the polarization decay difference parameter for battery packs of different chemistries is combined to account for the differences in degradation during the polarization process caused by factors such as material and structure. By compensating for aging differences, a polarization decay compensation factor is generated to quantify and compensate for the aging degree of battery packs of different chemistries, thereby more accurately assessing the actual health status of the battery pack.

[0113] Based on the dynamic health weight coefficient and polarization degradation compensation factor, the priority allocation of balancing strategies is dynamically reconfigured. Based on the health and aging of the battery packs, the priorities of battery packs of different chemistry systems in the balancing strategy are redefined, generating a cross-chemistries balancing priority mapping table. This mapping table guides the battery management system to prioritize energy regulation for battery packs with better health and lower aging during balancing control, thereby improving the performance and lifespan of the entire battery system. By combining the cross-chemistries balancing priority mapping table with the energy allocation logic in the parallel operation control instructions, the triggering path of the balancing strategy is topologically optimized. Based on the battery pack priorities and energy allocation requirements, the triggering conditions and execution sequence of the balancing strategy are optimized, generating dynamically optimized balancing strategy parameters. These parameters guide the battery management system to more efficiently perform energy balancing control in actual operation, improving the overall performance and reliability of the battery system.

[0114] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0115] In one embodiment, if Figure 3 As shown, the present application also provides a multi-battery pack collaborative lithium battery protection board software management system 300, the system 300 including:

[0116] A chemical system identification module 301 is used to perform dynamic chemical system identification processing based on the electrochemical characteristic parameters of each battery pack and generate a battery pack type label;

[0117] Strategy generation module 302, for performing differentiated balancing strategy generation processing based on battery pack type labels to generate a multi-chemical system collaborative control model;

[0118] A mode switching module 303 is used to perform dynamic working mode switching processing and generate parallel operation control instructions based on the multi-chemical system coordinated control model and battery pack type labels;

[0119] The reverse blocking module 304 is used to perform reverse charging blocking processing and generate a current suppression signal according to the parallel operation control instruction and the energy flow direction between the battery packs monitored in real time;

[0120] The health assessment module 305 is used to perform collaborative health status assessment based on the current suppression signal, parallel operation control instructions and multi-dimensional aging indicators, generate dynamically optimized equilibrium strategy parameters, and feed back the equilibrium strategy parameters to the multi-chemical system collaborative control model.

[0121] Specifically, the chemical system identification module 301 collects the electrochemical characteristic parameters of each battery pack, such as open-circuit voltage, internal resistance, and charge-discharge curves. By analyzing these parameters and applying machine learning algorithms or database matching techniques, it dynamically identifies the chemical system of each battery pack and generates a unique type label for each battery pack. This label serves as the basis for subsequent differentiated management, ensuring that battery packs with different chemical systems can be distinguished. The strategy generation module 302 develops personalized balancing strategies for battery packs with different chemical systems based on the battery pack type labels generated by the chemical system identification module. These strategies take into account the characteristics of each battery pack, such as voltage platform and capacity characteristics, and integrate them into a multi-chemical system coordinated control model. This model comprehensively coordinates the operation of battery packs with different chemical systems to ensure their performance and safety when operated in parallel. The mode switching module 303 dynamically adjusts the operating mode of the battery pack based on the multi-chemical system coordinated control model and the battery pack type labels. It monitors the battery pack's operating status in real time, such as voltage, current, and temperature, and, based on application requirements, determines whether to adopt active balancing, hierarchical isolation, or hybrid scheduling, and generates corresponding parallel operation control instructions. This dynamic adjustment ensures efficient battery pack operation under varying operating conditions.

[0122] Reverse charge blocking module 304: Based on parallel operation control instructions and real-time monitoring of the direction of energy flow between battery packs, this module performs reverse charge blocking. By monitoring the direction and characteristics of energy flow and combining it with the polarization hysteresis characteristics of the battery pack, the module can promptly detect and prevent reverse charge that could damage the battery, generating a current suppression signal to protect the battery pack's safety. Health assessment module 305: This module comprehensively considers the current suppression signal, parallel operation control instructions, and multi-dimensional aging indicators to assess the health of the battery pack. Through multi-source data fusion and aging difference compensation technology, it generates dynamic health weight coefficients and polarization degradation compensation factors. These indicators are used to dynamically adjust the priority of balancing strategies, optimize the triggering paths of balancing strategies, generate dynamically optimized balancing strategy parameters, and feed these parameters into the multi-chemistry coordinated control model for continuous optimized management of the battery pack. The collaborative operation of these modules enables refined management and optimized control of multi-chemistry battery packs, improving their efficiency, safety, and lifespan. Furthermore, by considering the differences in the characteristics of battery packs with different chemistries, dynamic adjustment and optimization strategies ensure stable operation under complex operating conditions.

[0123] Mode switching mode 303 is also used to:

[0124] The following formula is used to verify the voltage platform compatibility of battery packs with different chemical systems based on the multi-chemical system collaborative control model, and generate dynamic compatibility verification results:

[0125]

[0126] Among them, V comp represents the voltage compatibility index, n represents the number of battery packs, α i Represents the weight coefficient, V i Represents the voltage of the i-th battery pack, V ref Indicates the reference voltage, V max represents the maximum allowable voltage deviation, η represents the voltage platform stability coefficient, T represents the observation time period, Indicates the rate of voltage change;

[0127] Based on the dynamic compatibility check results and battery pack type labels, a multi-modal decision-making process is performed between active balancing mode, hierarchical isolation mode, and hybrid scheduling mode to generate the initial operating mode instruction;

[0128] Based on the polarization characteristic parameters corresponding to the battery pack type label, dynamic inertia correction processing is performed on the initial operating mode instructions to eliminate the transient response differences of battery packs with different chemical systems and generate inertia-corrected parallel operation control instructions.

[0129] Mode switching mode 303 is also used to:

[0130] The polarization voltage relaxation time parameter and polarization current response slope parameter are extracted based on the polarization characteristic parameters corresponding to the battery type label using the following formula:

[0131] U p (t) = U p0 ·e -t / τ

[0132] I p (t) = k·(1-e -t / T )

[0133] Among them, U p (t) represents the change of polarization voltage over time, U p0 represents the initial polarization voltage value, t represents the time variable, τ represents the polarization voltage relaxation time constant, I p (t) represents the change of polarization current over time, k represents the polarization current response slope coefficient, and T represents the polarization current response time constant;

[0134] Dynamically smoothing the mode switching rate in the initial working mode instruction according to the polarization voltage relaxation time parameter to generate a first correction instruction;

[0135] Based on the polarization current response slope parameter, dynamically compensate the current distribution ratio in the initial working mode instruction to generate a second correction instruction;

[0136] The first correction instruction and the second correction instruction are subjected to multi-objective fusion processing to generate an inertia-corrected parallel operation control instruction.

[0137] The reverse blocking module 304 is further configured to:

[0138] Based on the parallel operation control instructions, multi-dimensional vector analysis is performed on the energy flow direction between battery packs to generate reverse energy flow characteristic signals;

[0139] According to the reverse energy flow characteristic signal and the polarization hysteresis characteristics of battery packs with different chemical systems, a phased blocking decision-making process is performed to generate multi-level blocking trigger instructions;

[0140] Based on the multi-level blocking trigger instruction, the MOSFET gate drive signal of the target battery pack is dynamically duty-cycle modulated to generate a current suppression signal with phase compensation function.

[0141] The reverse blocking module 304 is further configured to:

[0142] Based on the parallel operation control instructions, the spatiotemporal distribution characteristics of energy flow between battery packs are synchronously analyzed and processed to generate spatiotemporal synchronized flow direction vector parameters;

[0143] Based on the spatiotemporally synchronized flow direction vector parameters and the polarization phase characteristics of battery packs with different chemical systems, multi-band phase correlation detection processing is performed to generate the reverse energy phase offset;

[0144] Based on the reverse energy phase offset, the forward and reverse weights of the energy flow direction are dynamically allocated to generate a multi-dimensional fusion reverse energy flow characteristic signal.

[0145] The reverse blocking module 304 is further configured to:

[0146] Based on the polarization hysteresis characteristics of battery packs with different chemical systems, dynamic hysteresis compensation parameters are extracted and a polarization hysteresis compensation table is generated;

[0147] According to the energy accumulation rate of the reverse energy flow characteristic signal and the polarization hysteresis compensation table, the gradient blocking threshold adaptive matching processing is performed to generate the dynamic blocking trigger threshold;

[0148] Based on the dynamic blocking trigger threshold, the reverse energy flow characteristic signal is segmented into multiple time windows to generate phased blocking trigger conditions.

[0149] According to the staged blocking trigger conditions, the trigger intensity and duration of the blocking instruction are gradient-superimposed to generate a multi-level blocking trigger instruction.

[0150] The health assessment module 305 is also used to:

[0151] Based on the blocking characteristic parameters in the current suppression signal and the mode switching frequency in the parallel operation control instruction, multi-source data fusion processing is performed to generate a dynamic health weight coefficient;

[0152] Based on the cycle life attenuation rate parameters in the multi-dimensional aging index and the polarization decay difference parameters of battery packs with different chemical systems, aging difference compensation is performed to generate a polarization decay compensation factor;

[0153] Based on the dynamic health weight coefficient and polarization decay compensation factor, the priority allocation of the balancing strategy is dynamically reconstructed to generate a cross-system balancing priority mapping table;

[0154] According to the cross-system balancing priority mapping table and the energy allocation logic in the parallel operation control instructions, the trigger path of the balancing strategy is topologically optimized to generate dynamically optimized balancing strategy parameters.

[0155] In one embodiment, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0156] In one embodiment, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the above-mentioned method embodiments when the computer program is executed by a processor.

[0157] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0158] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. A software management method for lithium battery protection boards with multi-battery pack collaboration, characterized in that: The method comprises: Based on the electrochemical characteristic parameters of each battery pack, dynamic chemical system identification processing is performed to generate battery pack type labels; Performing differentiated balancing strategy generation processing according to the battery pack type label to generate a multi-chemical system collaborative control model; Based on the multi-chemical system coordinated control model and the battery pack type label, dynamic working mode switching processing is performed to generate parallel operation control instructions; Perform reverse charging blocking processing and generate a current suppression signal according to the parallel operation control instruction and the energy flow direction between the battery packs monitored in real time; Based on the current suppression signal, the parallel operation control instruction and the multi-dimensional aging index, a health status collaborative assessment process is performed to generate dynamically optimized balancing strategy parameters, which are then fed back to the multi-chemical system collaborative control model.

2. The method for managing software of lithium battery protection boards for multi-battery group collaboration according to claim 1, characterized in that: The performing of dynamic working mode switching processing based on the multi-chemical system coordinated control model and the battery pack type label to generate parallel operation control instructions includes: The following formula is used to verify the voltage platform compatibility of battery packs with different chemical systems based on the multi-chemical system coordinated control model to generate a dynamic compatibility verification result: Among them, V comp represents the voltage compatibility index, n represents the number of battery packs, α i Represents the weight coefficient, V i Represents the voltage of the i-th battery pack, V ref Indicates the reference voltage, V max represents the maximum allowable voltage deviation, η represents the voltage platform stability coefficient, T represents the observation time period, Indicates the rate of voltage change; Performing multimodal decision processing among active balancing mode, hierarchical isolation mode, and hybrid scheduling mode according to the dynamic compatibility check result and the battery pack type label to generate an initial operating mode instruction; Based on the polarization characteristic parameters corresponding to the battery pack type label, dynamic inertia correction processing is performed on the initial working mode instruction to eliminate the transient response differences of battery packs with different chemical systems and generate the parallel operation control instruction after inertia correction.

3. The method for managing the software of the lithium battery protection board in cooperation with multiple battery packs according to claim 2, characterized in that: The method of performing dynamic inertia correction processing on the initial operating mode instruction based on the polarization characteristic parameters corresponding to the battery pack type label to eliminate transient response differences between battery packs of different chemical systems and generate the parallel operation control instruction after inertia correction includes: The polarization voltage relaxation time parameter and the polarization current response slope parameter are extracted based on the polarization characteristic parameters corresponding to the battery type label using the following formula: U p (t)=U p0 ·e -t / τ I p (t)=k·(1-e -t / T ) Among them, U p (t) represents the change of polarization voltage over time, U p0 represents the initial polarization voltage value, t represents the time variable, τ represents the polarization voltage relaxation time constant, I p (t) represents the change of polarization current over time, k represents the polarization current response slope coefficient, and T represents the polarization current response time constant; Dynamically smoothing the mode switching rate in the initial working mode instruction according to the polarization voltage relaxation time parameter to generate a first correction instruction; Based on the polarization current response slope parameter, dynamically compensate the current distribution ratio in the initial working mode instruction to generate a second correction instruction; The first correction instruction and the second correction instruction are subjected to multi-objective fusion processing to generate the parallel operation control instruction after inertia correction.

4. The method for managing the software of the lithium battery protection board in cooperation with multiple battery packs according to claim 1, characterized in that: The reverse charging blocking process is performed according to the parallel operation control instruction and the energy flow direction between the battery packs monitored in real time to generate a current suppression signal, including: Based on the parallel operation control instruction, multi-dimensional vector analysis processing is performed on the energy flow direction between the battery packs to generate a reverse energy flow characteristic signal; According to the reverse energy flow characteristic signal, combined with the polarization hysteresis characteristics of battery packs with different chemical systems, a staged blocking decision process is performed to generate a multi-level blocking trigger instruction; Based on the multi-level blocking trigger instruction, dynamic duty cycle modulation processing is performed on the MOSFET gate drive signal of the target battery pack to generate the current suppression signal with phase compensation function.

5. The method for managing software of lithium battery protection boards in cooperation with multiple battery packs according to claim 4, characterized in that: The method of performing multi-dimensional vector analysis processing on the energy flow direction between battery packs based on the parallel operation control instruction to generate a reverse energy flow characteristic signal includes: Based on the parallel operation control instructions, synchronously analyzing and processing the spatiotemporal distribution characteristics of energy flow between battery packs to generate spatiotemporally synchronized flow direction vector parameters; Based on the spatiotemporally synchronized flow direction vector parameters and the polarization phase characteristics of battery packs with different chemical systems, a multi-band phase correlation detection process is performed to generate a reverse energy phase offset; Based on the reverse energy phase offset, the forward and reverse weights of the energy flow direction are dynamically allocated to generate the multi-dimensional fused reverse energy flow characteristic signal.

6. The method for managing software of lithium battery protection boards for multi-battery group collaboration according to claim 4, characterized in that: The method of performing phased blocking decision processing based on the reverse energy flow characteristic signal and combining the polarization hysteresis characteristics of battery packs with different chemical systems to generate multi-level blocking trigger instructions includes: Extracting dynamic hysteresis compensation parameters based on the polarization hysteresis characteristics of the battery packs with different chemical systems and generating a polarization hysteresis compensation table; According to the energy accumulation rate of the reverse energy flow characteristic signal, combined with the polarization hysteresis compensation table, a gradient blocking threshold adaptive matching process is performed to generate a dynamic blocking trigger threshold; Based on the dynamic blocking trigger threshold, performing multi-level time sequence window segmentation processing on the reverse energy flow characteristic signal to generate staged blocking trigger conditions; According to the staged blocking triggering conditions, the triggering intensity and duration of the blocking instruction are subjected to gradient superposition processing to generate the multi-level blocking triggering instruction.

7. The method for managing software of lithium battery protection boards in cooperation with multiple battery packs according to claim 1, characterized in that: The health status collaborative evaluation process is performed based on the current suppression signal, the parallel operation control instruction, and the multi-dimensional aging index to generate dynamically optimized balancing strategy parameters, including: Based on the blocking characteristic parameter in the current suppression signal and the mode switching frequency in the parallel operation control instruction, multi-source data fusion processing is performed to generate a dynamic health weight coefficient; Based on the cycle life attenuation rate parameter in the multi-dimensional aging index and the polarization decay difference parameter of battery packs with different chemical systems, aging difference compensation processing is performed to generate a polarization decay compensation factor; Based on the dynamic health weight coefficient and the polarization decay compensation factor, dynamically reconstruct the priority allocation of the balancing strategy to generate a cross-system balancing priority mapping table; According to the cross-system balancing priority mapping table and the energy allocation logic in the parallel operation control instruction, a topology optimization process is performed on the trigger path of the balancing strategy to generate the dynamically optimized balancing strategy parameters.

8. The software management system for lithium battery protection board with multi-battery pack collaboration is characterized by: The system comprises: A chemical system identification module is used to dynamically identify the chemical system based on the electrochemical characteristic parameters of each battery pack and generate a battery pack type label; A strategy generation module, configured to generate a differentiated balancing strategy based on the battery pack type label and generate a multi-chemical system collaborative control model; A mode switching module, configured to perform dynamic operating mode switching processing based on the multi-chemical system coordinated control model and the battery pack type label, and generate parallel operation control instructions; a reverse blocking module, configured to perform reverse charging blocking processing and generate a current suppression signal according to the parallel operation control instruction and the energy flow direction between the battery packs monitored in real time; A health assessment module is used to perform collaborative health status assessment processing based on the current suppression signal, the parallel operation control instruction and the multi-dimensional aging index, generate dynamically optimized equilibrium strategy parameters, and feed back the equilibrium strategy parameters to the multi-chemical system collaborative control model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the multi-battery pack coordinated lithium battery protection board software management method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-battery pack coordinated lithium battery protection board software management method according to any one of claims 1 to 7 are implemented.

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