Battery pack management method, battery management system and vehicle

Abstract

The invention discloses a battery pack management method, a battery management system and a vehicle. The battery pack management method comprises the following steps: acquiring running state data of a battery pack; and based on the operation state data, carrying out multi-item comprehensive management in self-adaptive balance control, energy management, predictive maintenance, safety protection, configuration of the battery pack and capacity expansion processing on the battery pack. According to the method, the battery pack can be comprehensively managed through the running state data of the battery pack, and accurate management and optimal control of the whole life cycle of the battery pack are achieved.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and in particular to a battery pack management method, as well as a battery management system and a vehicle. Background Technology

[0002] In related technologies, battery pack adaptive balancing control, energy management, predictive maintenance, safety protection, and battery pack configuration and capacity expansion are all carried out independently without comprehensive management, making it impossible to accurately manage and optimize the entire life cycle of the battery pack. Summary of the Invention

[0003] This invention aims to at least solve one of the technical problems existing in the prior art. Therefore, one objective of this invention is to propose a battery pack management method that can comprehensively manage the battery pack through its operating status data, achieving precise management and optimized control throughout the battery pack's entire lifecycle.

[0004] The second objective of this invention is to provide a battery management system.

[0005] The third objective of this invention is to provide a vehicle.

[0006] To address the aforementioned problems, a first aspect of the present invention provides a battery pack management method, comprising: acquiring battery pack operating status data; and performing comprehensive management of the battery pack based on the operating status data, including adaptive equalization control, energy management, predictive maintenance, safety protection, and configuration and capacity expansion processing; wherein the predictive maintenance includes life prediction and fault prediction, the life prediction including predicting the SOH value based on a multi-stress life prediction model, and obtaining the remaining service life based on the SOH value, wherein the multi-stress life prediction model is constructed based on a combination of electrochemical mechanisms and data analysis, and the multi-stress life prediction model includes a first term and a second term, the first term characterizing the effect of battery cycle aging, and the second term characterizing the effect of calendar aging on the battery.

[0007] According to the battery pack management method of the present invention, when the battery pack is working, the operating status data of the battery pack is acquired in real time. The battery pack is comprehensively managed through the operating status data, enabling the battery pack to perform multiple functions such as adaptive equalization control, energy management, predictive maintenance, safety protection, and configuration and capacity expansion of the battery pack, thereby achieving precise management and optimized control of the entire life cycle of the battery pack.

[0008] In some embodiments, predictive maintenance further includes fault prediction, which includes constructing a fusion decision function that combines a support vector machine and a deep learning network in a weighted manner, wherein the fusion decision function takes the multi-source operating state data as input to obtain a fault prediction value.

[0009] In some embodiments, adaptive equalization control includes active equalization, multi-parameter consistency equalization, and equalization efficiency evaluation; wherein, the operating state data includes current, voltage, SOC value, and internal resistance; the active equalization includes obtaining an equalization current based on fuzzy PID calculation using the voltage; the multi-parameter consistency equalization includes obtaining an equalization weight factor based on the voltage, the SOC value, the internal resistance, and the corresponding consistency weight and aging compensation weight; the equalization efficiency evaluation includes obtaining an equalization efficiency evaluation value based on an equalization efficiency evaluation model, wherein the equalization efficiency evaluation model is constructed based on the contribution of voltage consistency and SOC value consistency to the equalization effect.

[0010] In some embodiments, energy management includes excess energy conversion and / or hybrid energy storage optimization; wherein, the excess energy conversion includes converting the remaining energy in the battery pack into heat energy and storing the heat energy in a phase change material when there is remaining energy, and converting the heat energy stored in the phase change material into electrical energy through a thermoelectric generator when there is an electricity demand, and providing the electrical energy to the electrical device or to the thermal management system of the battery pack; the hybrid energy storage optimization includes executing a dynamic power allocation strategy based on a battery pack and supercapacitor integrated energy storage architecture, wherein the dynamic power allocation strategy includes allocating the output power of the battery pack based on a correction function of the battery pack's SOC value, temperature and health status and the total power of the battery pack, and determining the output power of the supercapacitor based on the output power of the battery pack and the total power of the supercapacitor.

[0011] In some embodiments, security protection includes a multi-level early warning mechanism, which includes: activating different levels of protection measures according to the magnitude of the risk index, wherein the higher the risk index, the higher the level of protection measures, and the risk index is obtained by weighted fusion of the multi-source operational status data.

[0012] In some embodiments, the safety protection further includes thermal management control, which includes an architecture based on a combination of liquid cooling and phase change materials, controlling the liquid cooling system and the phase change material system with the required total thermal management power; wherein the total thermal management power is determined based on convective heat dissipation power, radiative heat dissipation power, phase change material heat absorption power, and thermoelectric conversion system power.

[0013] In some embodiments, the configuration and capacity expansion of the battery pack includes capacity expansion and reconfiguration optimization of the connection relationships of individual battery cells. The capacity expansion includes automatically identifying and configuring the connection method of a new battery cell when it is added, and performing circulating current suppression on the battery pack when the connection method of the new battery cell is parallel. The reconfiguration optimization includes online reconfiguration of the battery pack's functionality when a faulty or abnormal battery cell appears in the battery pack. Reconfiguration includes isolating the faulty battery or reorganizing the series-parallel connection relationships of individual battery cells within the battery pack. The reconfiguration optimization determines the optimal series-parallel connection relationship based on a reconfiguration optimization objective function, which is constructed based on voltage balancing, SOC balancing, internal resistance balancing, and bypass compensation terms.

[0014] In some embodiments, the operating status data includes multiple parameters such as current, voltage, SOC value, and temperature; wherein the SOC value is obtained by weighted calculation based on the SOC value calculated by the ampere-hour integration method and the SOC value calculated by the open-circuit voltage method.

[0015] A second aspect of the present invention provides a battery management system, including: a processor; a memory connected to the processor; the memory storing a computer program executable by the processor, wherein the processor executes the computer program to implement the battery pack management method described in the above embodiments.

[0016] According to the battery management system of the present invention, the corresponding battery pack management program can be stored in the memory. When implementing the battery pack management method, the operating status data of the battery pack is acquired in real time. The battery pack is comprehensively managed through the operating status data of the battery pack, enabling the battery pack to perform multiple functions such as adaptive equalization control, energy management, predictive maintenance, safety protection, and configuration and capacity expansion of the battery pack, thereby achieving precise management and optimized control of the entire life cycle of the battery pack.

[0017] A third aspect of the present invention provides a vehicle, the vehicle including a battery pack and a battery management system as described in the above embodiments, the battery management system being connected to the battery pack.

[0018] According to the vehicle of the present invention, the battery pack management system comprehensively manages the battery pack, acquires the battery pack's operating status data in real time, and comprehensively manages the battery pack through the battery pack's operating status data. This enables the battery pack to perform multiple functions, including adaptive equalization control, energy management, predictive maintenance, safety protection, and configuration and capacity expansion processing, thereby achieving precise management and optimized control of the battery pack throughout its entire life cycle.

[0019] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart of a battery pack management method according to an embodiment of the present invention; Figure 2 A flowchart of a battery pack management process according to an embodiment of the present invention; Figure 3 This is a structural block diagram of a battery management system according to an embodiment of the present invention; Figure 4 This is a structural block diagram of a vehicle according to an embodiment of the present invention.

[0021] Figure label: 200 vehicles; Battery pack 201; Battery management system 100; Processor 101; Memory 102. Detailed Implementation

[0022] The embodiments of the present invention are described in detail below. The embodiments described with reference to the accompanying drawings are exemplary. The embodiments of the present invention are described in detail below.

[0023] In existing technologies, battery pack adaptive balancing control, energy management, predictive maintenance, safety protection, and battery pack configuration and capacity expansion are all carried out independently without integrated management, making it impossible to accurately manage and optimize the entire life cycle of the battery pack.

[0024] To address the above problems, the first aspect of this invention provides a battery pack management method that can comprehensively manage the battery pack through its operating status data, thereby achieving precise management and optimized control of the battery pack throughout its entire life cycle.

[0025] The following is for reference. Figure 1 A battery pack management method according to an embodiment of the first aspect of the present invention is described, such as... Figure 1 As shown, the method includes at least steps S01 to S02.

[0026] Step S01: Obtain the operating status data of the battery pack.

[0027] Specifically, when managing a battery pack, it is necessary to first obtain the battery pack's operating status data. The operating status data may include multiple parameters such as current, voltage, SOC (State of Charge) value, and temperature. The battery pack is managed based on the obtained battery pack status data.

[0028] Step S02 involves comprehensive management of the battery pack based on operating status data, including adaptive equalization control, energy management, predictive maintenance, safety protection, and configuration and capacity expansion.

[0029] Specifically, adaptive equalization control is a key technology in battery pack management used to improve battery pack consistency, extend service life and ensure safe operation. It dynamically adjusts equalization strategies and parameters based on the real-time status of individual battery cells (such as voltage, SOC, temperature, etc.) instead of using a fixed threshold or fixed time to start equalization.

[0030] Battery energy management refers to the process of optimizing and controlling the generation, storage, distribution and consumption of electrical energy. Its core objective is to maximize energy utilization efficiency, extend battery life and ensure system safety and stability.

[0031] The goal of battery pack safety protection is to prevent safety accidents such as thermal runaway, fire, and explosion, and to ensure the safety of personnel, equipment and environment. With the widespread application of high energy density batteries (such as electric vehicles, energy storage power stations and consumer electronics), safety protection has shifted from "passive response" to "active early warning + multi-level defense".

[0032] Battery pack configuration refers to the systems engineering process of selecting battery cells, designing series and parallel topologies, and integrating modules / systems according to application requirements. Battery pack expansion refers to increasing the capacity or energy of an existing battery system to meet demands for longer driving range, longer backup power time, or higher power output. Due to the strong coupling and safety requirements of battery packs, expansion is not simply "adding batteries," but a systems engineering project that requires comprehensive consideration of electrochemical, electrical, thermal management, and safety regulations.

[0033] Furthermore, predictive maintenance can significantly improve battery pack safety, extend lifespan, reduce maintenance costs, and provide early warnings of potential faults (such as single-cell short circuits, loose connections, and thermal runaway risks) several days in advance.

[0034] Predictive maintenance includes life prediction and failure prediction. Life prediction includes predicting the SOH (State of Health) value based on a multi-stress life prediction model and obtaining the remaining service life based on the SOH value. The multi-stress life prediction model is constructed based on a combination of electrochemical mechanisms and data analysis. The multi-stress life prediction model includes a first term and a second term. The first term characterizes the impact of battery cycle aging, and the second term characterizes the impact of calendar aging on the battery.

[0035] Specifically, when performing life prediction, the SOH value is predicted based on the multi-stress life prediction model, and then the remaining life is obtained based on the SOH value. Battery life prediction is a core technical challenge in battery pack management. This invention establishes an improved multi-stress life prediction model based on a combination of electrochemical mechanisms and data analysis, which comprehensively considers the effects of cycle aging and historical aging on the battery.

[0036] The SOH prediction formula in the lifetime prediction model is: SOH(t) = 1-A·exp(-Ea / (R·T))·(t^0.5 + B·N_cycle^C) - D·exp(-F·SOC)·t; Among them, A, B, C, D, and F are aging parameters related to battery materials and operating conditions, which are identified by a combination of accelerated aging experiments and machine learning methods. A·exp(-Ea / (R·T))·(t^0.5 + B·N_cycle^C) represents the first term, loss, characterizing the impact of battery cycle aging; A is the cycle aging baseline coefficient, Ea is the activation energy of the aging reaction, R is the gas constant, T is the battery thermodynamic temperature, t is the cumulative usage time, B is the cycle number weighting coefficient, N_cycle is the cumulative charge-discharge cycle number, and C is the cycle decay index. D·exp(-F·SOC)·t represents the second term, loss, characterizing the impact of calendar aging on the battery; D is the calendar aging baseline coefficient, F is the SOC sensitivity coefficient, SOC is the average state of charge, and t is the cumulative storage time.

[0037] The formula for remaining useful life in the life prediction model is: RUL=t_current·[(1-SOH_threshold) / (1-SOH_current)-1]·k_acceleration; Where RUL is the remaining battery life, t_current is the cumulative battery usage time, SOH_threshold is the life end threshold, usually taken as 0.8; SOH_current is the current SOH value of the battery, and k_acceleration is the aging acceleration factor, taking into account the difference between actual usage conditions and test conditions.

[0038] For example, existing battery pack management methods have significant shortcomings in multi-parameter fusion estimation and thermal management optimization. For instance, calculating SOC using a single ampere-hour integration method or open-circuit voltage method is highly susceptible to factors such as temperature and aging. In thermal management, simple fans or heating elements are often used, failing to achieve precise temperature control. Of particular concern is the battery life prediction field, where cycle-count-based life prediction models fail to consider the impact of actual operating conditions, resulting in prediction errors generally exceeding 20%. Regarding safety protection, existing methods mostly employ passive protection strategies, lacking active early warning and tiered protection mechanisms. These technical deficiencies severely restrict the improvement of overall battery pack performance, especially in fields with extremely high reliability requirements such as aerospace and electric vehicles, where existing battery pack management methods can no longer meet the growing technological demands. Therefore, developing a battery pack management method that integrates efficient balancing, intelligent early warning, and energy optimization is particularly urgent.

[0039] This invention provides a battery pack management method applicable to battery management systems. The battery management system adopts a hierarchical distributed architecture, including a data acquisition unit, an energy management unit, an adaptive balancing control unit, a predictive maintenance unit, a safety protection unit, and a classification expansion unit. These units are connected via a high-speed data bus, forming a complete closed-loop control system. The data acquisition unit employs multi-parameter fusion acquisition technology, acquiring real-time battery pack operating status data, including key parameters such as voltage, current, and temperature, through a distributed sensor network, and uses advanced signal processing technology to ensure data accuracy. The energy management unit, based on a multi-objective optimization algorithm, achieves intelligent allocation and conversion of electrical energy, with particular emphasis on the recovery and utilization of excess energy. The adaptive balancing control uses model predictive control technology to achieve dynamic balancing control of the battery pack, effectively solving the problem of battery inconsistency. The predictive maintenance unit integrates machine learning algorithms and physical models to achieve trend prediction of battery status and fault early warning. The safety protection unit provides multi-faceted safety protection from electrical, thermal, and mechanical perspectives, with tiered early warning and rapid response capabilities. The classification expansion unit supports flexible system configuration and expansion, adapting to the application needs of battery packs of different sizes and types. The innovation of this invention lies in the deep integration of traditionally independent functions into multiple comprehensive management systems. Through information sharing and collaborative decision-making, it achieves a significant improvement in overall performance. In particular, it adopts a unique five-step cyclical control strategy of "monitoring-analysis-decision-execution-feedback" to ensure that optimal management decisions can be made at all time scales.

[0040] According to the battery pack management method of the present invention, when the battery pack is working, the operating status data of the battery pack is acquired in real time. The battery pack is comprehensively managed through the operating status data, enabling the battery pack to perform multiple functions such as adaptive equalization control, energy management, predictive maintenance, safety protection, and configuration and capacity expansion of the battery pack, thereby achieving precise management and optimized control of the entire life cycle of the battery pack.

[0041] In some embodiments, predictive maintenance further includes fault prediction, which includes constructing a fusion decision function that combines a support vector machine and a deep learning network in a weighted manner. The fusion decision function takes multi-source runtime data as input to obtain fault prediction values.

[0042] Specifically, a fault prediction framework based on multi-source information fusion is adopted for fault prediction. By analyzing abnormal changes in parameters such as voltage, current, and temperature, potential faults can be identified in advance. The core algorithm adopts a combination of support vector machine and deep learning, which not only ensures training efficiency but also improves prediction accuracy.

[0043] The fusion decision function is: f(x) = sign(∑α_i·y_i·K(x_i,x) + b) + λ·g(x,θ); In this context, sign(∑α_i·y_i·K(x_i,x) + b) is the first term, which is the support vector machine kernel function using a Gaussian radial basis function kernel; α_i is a Lagrange multiplier used to measure the contribution weight of the corresponding sample x_i to the classification decision surface; y_i is the true label of sample x_i; K(x_i,x) is the kernel function, which maps linearly inseparable samples in low-dimensional space to high-dimensional space, making them linearly separable and avoiding the computational explosion problem of direct high-dimensional mapping, K(x_i,x_j) =exp(-γ·||x_i - x_j||²), ||x_i - x_j||² is the squared Euclidean distance between samples, γ is the kernel function bandwidth hyperparameter; and b is the decision bias term of the support vector machine.

[0044] λ·g(x, θ) is the second term, which is the output of the deep learning network. It focuses on key features through the attention mechanism. λ is the fusion weight, which is determined through cross-validation optimization. g(x, θ) is the output of the deep learning network, where θ is all the trainable parameters of the network (weights, biases, etc.).

[0045] In some embodiments, adaptive equalization control includes active equalization, multi-parameter consistency equalization, and equalization efficiency evaluation; wherein, the operating state data includes current, voltage, SOC value, and internal resistance; active equalization includes obtaining the equalization current by performing fuzzy PID (Proportional-Integral-Derivative) calculation based on voltage; Multi-parameter consistency equalization includes obtaining equalization weight factors based on voltage, SOC value, internal resistance, and corresponding consistency weights and aging compensation weights; equalization efficiency evaluation includes obtaining equalization efficiency evaluation values ​​based on the equalization efficiency evaluation model, which is constructed based on the contribution of voltage consistency and SOC value consistency to the equalization effect.

[0046] Specifically, the system acquires the battery pack's current, voltage, SOC value, and internal resistance in real time. During active balancing, a scheme based on a bidirectional DC-DC (Direct Current to Direct Current) converter is employed. High-frequency switching technology enables intelligent energy transfer between batteries, improving energy transfer efficiency from less than 60% to over 85% compared to traditional passive balancing. The active balancing control uses an improved fuzzy PID algorithm, ensuring fast response while avoiding overshoot.

[0047] The formula for calculating the equalization current is: I_bal(i) = Kp·ΔV(i) + Ki·∫ΔV(i)dt + Kd·d(ΔV(i)) / dt +δ·fuzzy_correction; Where Kp·ΔV(i) is the proportional term, Ki·∫ΔV(i)dt is the integral term, Kd·d(ΔV(i)) / dt is the differential term, and δ·fuzzy_correction is the fuzzy compensation term; I_bal(i) is the output current at time i, ΔV(i) = V_avg - V_i represents the deviation between the voltage of the i-th individual cell and the average voltage, V_avg = (1 / n)·ΣV_i is the average voltage; Kp, Ki, and Kd are the PID parameters optimized based on model identification, δ is the fuzzy correction weight, and fuzzy_correction is the output of the fuzzy logic controller.

[0048] Traditional equalization control often only considers voltage consistency. This invention uses multi-parameter consistency equalization, which simultaneously considers the consistency weights of three key parameters: voltage, SOC value, and internal resistance, to obtain an equalization weight factor. The equalization priority of the corresponding battery is determined based on the equalization weight factor. The larger the value of the equalization weight factor, the higher the equalization priority of the battery, thereby improving the battery pack performance at a deeper level.

[0049] The formula for calculating the equilibrium weight factor is: W(i) = α·(V_avg - V_i) / V_avg + β·(SOC_avg - SOC_i) + γ·(R_avg -R_i) / R_avg + ζ·aging_factor; The constraint is: α + β + γ + ζ = 1, where α = 0.35, β = 0.35, γ = 0.2, and ζ = 0.1, representing voltage consistency weight, SOC consistency weight, internal resistance consistency weight, and aging compensation weight, respectively. These weight coefficients can be dynamically adjusted according to the actual operating state of the battery pack through an online learning algorithm; W(i) is the balance weight coefficient of the i-th battery, V_avg is the average single-cell voltage of the battery pack, V_i is the single-cell voltage of the i-th battery, SOC_avg is the average SOC of the battery pack, SOC_i is the SOC of the i-th battery, R_avg is the average single-cell internal resistance of the battery pack, R_i is the single-cell internal resistance of the i-th battery, and aging_factor is the aging factor.

[0050] The equalization efficiency evaluation value is obtained by using the equalization efficiency evaluation model. The equalization efficiency evaluation model comprehensively considers the contribution of voltage consistency and SOC consistency to the equalization effect, and can more comprehensively evaluate the performance of the equalization system.

[0051] Equilibrium efficiency evaluation model: η_balance = [1-(Σ|V_i - V_avg|) / (n·V_avg)]×[1 - (Σ|SOC_i- SOC_avg|) / (n·SOC_avg)]×100%; Where η_balance is the battery pack balancing efficiency, used to evaluate the target, with a value range of 0-100%, and the higher the value, the better the balancing effect; V_i is the terminal voltage of the i-th cell; V_avg is the average voltage of the cells in the battery pack; n is the number of cells in the battery pack connected in series; SOC_i is the remaining capacity of the i-th cell; and SOC_avg is the average SOC of the cells in the battery pack.

[0052] In some embodiments, energy management includes excess energy conversion and / or hybrid energy storage optimization; wherein, excess energy conversion includes converting the remaining energy in the battery pack into heat energy and storing the heat energy in a phase change material when there is remaining energy, and converting the heat energy stored in the phase change material into electrical energy through a thermoelectric generator and supplying the electrical energy to the electrical device or to the thermal management system of the battery pack when there is an electricity demand; the hybrid energy storage optimization includes executing a dynamic power allocation strategy based on a battery pack and supercapacitor integrated energy storage architecture, wherein the dynamic power allocation strategy includes allocating the output power of the battery pack based on the SOC value, temperature and health status of the battery pack and the total power of the battery pack, and determining the output power of the supercapacitor based on the output power of the battery pack and the total power of the supercapacitor.

[0053] Specifically, in applications such as photovoltaic energy storage and grid peak shaving, battery packs often experience overcharging. Traditionally, this energy is dissipated through braking resistors, which not only wastes energy but also generates significant heat. This invention proposes a combined thermoelectric energy storage scheme for battery pack management. This scheme converts excess electrical energy into thermal energy for storage, which is then converted back into electrical energy by a thermoelectric generator when needed, or directly used for the thermal management of the battery pack.

[0054] The thermoelectric conversion efficiency model is as follows: η_tec=η_max·[(T_h-T_c) / T_h]·[(√(1+Z·T_avg)-1) / (√(1+Z·T_avg)+T_c / T_h)]·η_controller; Where η_tec is the overall efficiency of the thermoelectric conversion system; η_max is the theoretical maximum efficiency of the thermoelectric material; T_h is the hot end temperature; T_c is the cold end temperature; T_avg is the average temperature, T_avg = (T_h + T_c) / 2; Z is the figure of merit of the thermoelectric material, which is related to the material properties; and η_controller is the efficiency of the power point tracking controller, which can typically reach over 98%.

[0055] The thermal energy storage capacity calculation model is: Q_storage = m·[∫c_p(T)dT + L·φ+∫c_pm(T)dT]; Where Q_storage is the total heat storage; m is the mass of the heat storage material; c_p(T) is the specific heat capacity of the solid state; L is the latent heat of phase change; φ is the phase change ratio; and c_pm(T) is the specific heat capacity of the phase change material in the liquid state. A staged phase change material is used to achieve efficient energy storage at different temperature ranges. Phase change materials are a class of materials that can undergo a change of state at a specific temperature (such as solid-liquid, liquid-gas, or solid-solid phase change) and absorb or release a large amount of latent heat during the phase change process.

[0056] The thermal energy storage capacity calculation model, combined with the thermoelectric conversion efficiency model, can fully describe the entire energy conversion relationship in the combined thermoelectric energy storage scheme, from electrical energy to thermal energy and from thermal energy to electrical energy.

[0057] Furthermore, for applications with frequent charging and discharging characteristics, such as electric vehicles and rail transit, the hybrid energy storage optimization process includes implementing a dynamic power allocation strategy based on a battery pack and supercapacitor integrated energy storage architecture. The battery provides high energy density to ensure the system's driving time, while the supercapacitor provides high power density to meet instantaneous high current demands.

[0058] The output power of the battery pack is: P_battery = P_total·[1 - exp(-(SOC-SOC_min) / τ)]·f(T, SOH); Where P_battery is the battery pack power; P_total is the total power; τ is the time constant, which is adaptively adjusted within the range of 10-30 seconds according to the battery characteristics; f(T, SOH) is the correction function for temperature and health status, ensuring that the power distribution strategy can adapt to the actual state of the battery.

[0059] The supercapacitor output power is: P_capacitor = P_total - P_battery; Where P_capacitor is the supercapacitor power; P_battery is the battery pack power; and P_total is the total power.

[0060] In high-frequency charging and discharging scenarios such as electric vehicles and rail transit, the integrated energy storage architecture of battery packs and supercapacitors can effectively reduce the instantaneous impact on batteries, extend their lifespan, and ensure power response speed through complementary power characteristics.

[0061] The lifetime optimization objective function is: min J = w1·(I_rms / I_max) + w2·(ΔSOC / ΔSOC_max) + w3·(T / T_max); Where I_rms is the effective value of the battery operating current; I_max is the maximum allowable continuous operating current of the battery; ΔSOC is the amplitude of the SOC change of the battery in a certain operating cycle; ΔSOC_max is the maximum allowable SOC fluctuation range of the battery; T is the real-time operating temperature of the battery; T_max is the maximum allowable operating temperature threshold of the battery; w1, w2 and w3 are weighting coefficients that satisfy the normalization condition (the sum of the weighting coefficients is 1), that is, w1+w2+w3=1.

[0062] The lifespan optimization objective function is a multi-objective weighted optimization function. By coupling the three core lifespan loss factors of battery current stress, SOC fluctuation, and temperature rise, a balance is achieved between "power demand satisfaction" and "battery lifespan extension". After calculating the objective function value J in real time, the key parameters of the power allocation strategy are adaptively adjusted to minimize J. By optimizing the objective function in real time, the battery lifespan is extended to the maximum extent while satisfying the power demand.

[0063] In some embodiments, security protection includes a multi-level early warning mechanism, which includes: activating different levels of protection measures according to the magnitude of the risk index, wherein the higher the risk index, the higher the level of protection measures, and the risk index is obtained by weighted fusion of multi-source operational status data.

[0064] Specifically, a comprehensive tiered early warning mechanism is established for security protection functions, and different response measures are taken according to the severity of the risk. The severity of the risk is represented by a risk index, which is calculated by integrating multiple parameters. The higher the severity of the risk, the greater the risk index.

[0065] The formula for calculating the comprehensive risk index is: R = w1·(ΔT / ΔT_max) + w2·(ΔV / ΔV_max) + w3·(I / I_max) + w4·(dV / dt) / (dV / dt)_max + w5·(dT / dt) / (dT / dt)_max; Wherein, ΔT is the temperature change; ΔT_max is the maximum temperature change; ΔV is the voltage change; ΔV_max is the maximum voltage change; I is the operating current; I_max is the maximum operating current; (dV / dt) is the voltage change rate; (dV / dt)_max is the maximum voltage change rate; (dT / dt) is the temperature change rate; (dT / dt)_max is the maximum temperature change rate; the weighting coefficients satisfy w1+ w2+ w3+ w4+ w5= 1, and are determined jointly by the analytic hierarchy process and actual operating data.

[0066] Based on the magnitude of the risk index, different levels of protective measures are activated: R<0.3: normal monitoring, recording operating data; 0.3 ≤ R<0.6: Level 1 warning, optimizing operating parameters; 0.6 ≤ R<0.8: Level 2 warning, limiting power output; R ≥0.8: Level 3 warning, executing emergency shutdown.

[0067] In some embodiments, the safety protection also includes thermal management control, which includes an architecture based on the combination of liquid cooling and phase change materials, controlling the liquid cooling system and the phase change material system with the total thermal management power as needed; wherein the total thermal management power is determined based on convective heat dissipation power, radiative heat dissipation power, heat absorption by the phase change material, and the contribution power of the thermoelectric conversion system.

[0068] Specifically, the thermal management control system addresses the dynamic heating characteristics and differentiated heat dissipation needs of the battery pack by employing a composite cooling architecture that combines liquid-cooled forced convection and latent heat storage of phase change materials. This, along with multi-loop topology design and intelligent control strategies, enables high-precision temperature control of the battery pack under all operating conditions, ensuring battery cycle life and charge / discharge performance.

[0069] A shaped phase change material (such as paraffin-based composite phase change material) is placed close to the surface of the battery cell, and its phase change temperature matches the battery's optimal operating range (25-40℃). When the battery experiences thermal shock due to short-term high-rate charging and discharging, the phase change material absorbs a large amount of latent heat through solid-liquid phase change, quickly suppressing the sudden rise in battery temperature and preventing the formation of local hot spots. When the battery's heat generation decreases, the phase change material undergoes liquid-solid phase change to release heat, which is continuously discharged through the liquid cooling circuit to maintain the system's thermal balance.

[0070] Liquid-cooled forced convection uses an aluminum microchannel cold plate integrated into the gap between battery modules. It transfers battery heat to the heat sink by pumping a 50% ethylene glycol aqueous solution as the heat exchange medium and using forced convection.

[0071] Convection cooling is the core heat exchange path of liquid cooling circuit. Its power is positively correlated with heat transfer coefficient, contact area and temperature difference. The heat transfer coefficient changes dynamically with temperature. The equation is: Q_convection = h(T)·A·(T_batt - T_coolant); Where Q_convection is the convective heat dissipation power; h(T) is the temperature-dependent convective heat transfer coefficient, obtained by fitting experimental data; A is the effective heat transfer area; T_batt is the battery temperature; and T_coolant is the coolant temperature.

[0072] Radiation-based heat dissipation is a passive heat dissipation method that follows the Stefan-Boltzmann law. It is suitable for heat dissipation in areas within the battery pack where there is no forced convection. The equation is: Q_radiation = ε·σ·A·(T_batt)4 - T_ambient 4 ); Where Q_radiation is the radiative heat dissipation power; ε is the emissivity of the battery surface; σ is the Stefan-Boltzmann constant; A is the effective heat transfer area; T_batt is the battery temperature; and T_ambient is the ambient temperature.

[0073] When the battery temperature reaches the phase change temperature of the phase change material, the phase change material absorbs heat through a combination of sensible and latent heat absorption, thus suppressing the battery temperature rise. The equation is as follows (when T ≥ T_m): Q_pcm = m_pcm·[c_ps·(T_m - T) + L + c_pl·(T - T_m)]; Where Q_pcm is the heat absorption power of the phase change material; m_pcm is the mass of the phase change material; c_ps is the solid specific heat capacity of the phase change material; T_m is the phase change temperature of the phase change material; T is the initial temperature of the phase change material; L is the latent heat of phase change; and c_pl is the liquid specific heat capacity of the phase change material.

[0074] The total thermal management power is the algebraic sum of the power of each component, reflecting the synergistic effect of heat dissipation power and cooling power. The equation is: Q_total = Q_convection + Q_radiation + Q_pcm + Q_tec; Where Q_total is the total thermal management power; Q_convection is the convective heat dissipation power; Q_radiation is the radiative heat dissipation power; Q_pcm is the heat absorption power of the phase change material; Q_tec is the power of the thermoelectric conversion system, which serves as a redundancy means for active temperature control and is used for rapid cooling under extreme conditions. Its value is determined by the power level and operating current of the thermoelectric conversion device. When rapid cooling is required, it can work in reverse to enhance the cooling effect.

[0075] In some embodiments, the configuration and capacity expansion of the battery pack includes capacity expansion and reconfiguration optimization of the connection relationship of individual battery cells. Capacity expansion includes automatically identifying and configuring the connection method of newly added battery cells when they are added. When the connection method of the newly added battery cells is parallel, circulating current suppression is performed on the battery pack. Reconfiguration optimization includes online reconfiguration of the battery pack's functions when a faulty or abnormal battery cell appears in the battery pack. The reconfiguration functions include isolating the faulty battery or reorganizing the series and parallel connections of individual battery cells within the battery pack. The reconfiguration optimization determines the optimal series and parallel connections based on a reconfiguration optimization objective function, which is constructed based on voltage balancing, SOC balancing, internal resistance balancing, and bypass compensation terms.

[0076] Specifically, to adapt to the flexible capacity requirements of battery packs in various scenarios, a capacity expansion function was designed and implemented. The core functionality utilizes standardized interfaces and intelligent recognition algorithms to enable plug-and-play use of new battery modules. For the circulating current problem in parallel expansion scenarios, a quantitative analysis model was established and a closed-loop suppression strategy was proposed to ensure the safety and energy efficiency of the expanded system. In multi-battery pack parallel expansion scenarios, due to differences in open-circuit voltage, internal resistance, and equilibrium state among the batteries, reactive circulating currents will occur between them. These circulating currents not only cause energy loss and reduce system efficiency but also accelerate battery aging and even trigger the risk of thermal runaway.

[0077] To adapt to the needs of different application scenarios, the system supports online capacity expansion. Through standardized interfaces and intelligent recognition algorithms, newly added battery modules can be automatically identified and configured by the system. During parallel capacity expansion, circulating current suppression is emphasized; the circulating current suppression model is: I_circulating = (V_m - V_s) / (R_m + R_s + R_balance). Where I_circulating is the inter-module circulating current control threshold; V_m is the open-circuit voltage of the battery cell to be expanded; V_s is the open-circuit voltage of the original battery; R_m is the equivalent internal resistance of the battery cell to be expanded; R_s is the equivalent internal resistance of the original battery; and R_balance is the equivalent resistance of the balancing circuit.

[0078] To ensure battery pack safety and energy efficiency, the circulating current control threshold is set as: I_circulating ≤ 0.02·I_rated; In the formula, I_rated is the rated charge / discharge current of the battery pack, which is achieved through active balancing and dynamic impedance matching. The threshold is set based on the fact that when the circulating current is less than 2% of the rated current, its energy loss and thermal effect are within an acceptable range and will not have a significant impact on the battery cycle life.

[0079] When a faulty or abnormal cell appears in the battery pack, that is, when there is a serious inconsistency in the performance of a cell with other cells, online reconstruction is performed to address abnormal operating conditions such as cell failure and serious performance inconsistency that occur during the operation of the battery pack. By bypassing and isolating the faulty cell and dynamically reorganizing the series and parallel topology of the remaining healthy cells, the performance of the battery pack is maintained to the maximum extent.

[0080] When the battery pack malfunctions or exhibits significant performance inconsistencies, an online reconfiguration function is supported. This function isolates the faulty battery and reorganizes the series and parallel connections of the remaining batteries to maximize system performance. The reconfiguration optimization is based on a multi-objective weighted minimization as its core objective, and the mathematical model is as follows: min J = λ1·Σ|V_i - V_avg| + λ2·Σ|SOC_i - SOC_avg| + λ3·Σ|R_i -R_avg| + λ4·N_bypass; Where V_i is the cell voltage; V_avg is the average voltage; SOC_i is the cell state of charge; SOC_avg is the average SOC; R_i is the cell internal resistance; R_avg is the average internal resistance; λ1-λ4 are weighting coefficients; and N_bypass is the number of cells bypassed. This optimization problem is solved using an improved genetic algorithm, obtaining an approximate optimal solution while ensuring real-time performance.

[0081] The reconfiguration process must meet the safety operating boundaries and performance constraints of the battery pack. Discharge current constraint: I_discharge ≤ I_max, limiting the maximum discharge current of the reconfigured battery pack to avoid exceeding the current tolerance threshold of individual cells or modules, preventing the risk of thermal runaway. Temperature constraint: T ≤ T_max, constraining the maximum operating temperature of the battery pack to ensure stable electrochemical performance and prevent accelerated aging at high temperatures. Individual cell voltage constraint: V_min ≤ Vi ≤ V_max, limiting the charge and discharge voltage range of individual cells to prevent permanent battery damage caused by overcharging or over-discharging. Individual cell SOC constraint: SOC_min ≤ SOC_i ≤ SOC_max, limiting the SOC operating range of individual cells to avoid deep discharge or overcharging.

[0082] In some embodiments, the operating status data includes multiple parameters such as current, voltage, SOC value, and temperature; wherein the SOC value is obtained by weighted calculation based on the SOC value calculated by the ampere-hour integration method and the SOC value calculated by the open-circuit voltage method.

[0083] Specifically, the operating status data includes multiple parameters such as current, voltage, SOC value, and temperature. A distributed sensor network architecture is employed to synchronously acquire these key parameters of the battery pack. For voltage acquisition, a 16-bit high-precision ADC (Analog-to-Digital Converter) chip is used, achieving a sampling accuracy of ±1mV. The sampling frequency is programmable from 1Hz to 10kHz to adapt to different application scenarios. Each battery cell is equipped with an independent voltage acquisition channel, and differential measurement technology eliminates common-mode interference. The current acquisition system uses a closed-loop Hall effect sensor with an accuracy of ±0.5% and a bandwidth covering up to 100kHz, accurately capturing rapid current changes during charging and discharging. The sensor has built-in temperature compensation, maintaining measurement stability within an operating temperature range of -40℃ to 85℃. The temperature monitoring system uses a digital temperature sensor network with an accuracy of ±0.5℃. Sensors are placed on the surface of each battery cell, on the tabs, and at key locations on the module, forming a complete temperature field monitoring network. The sensors are connected via a single bus, greatly simplifying wiring complexity.

[0084] After acquiring the operating status data, data fusion processing is performed. To improve data reliability, the system adopts an improved extended Kalman filter algorithm to fuse multi-source acquired data, fully considering the nonlinear characteristics of the battery system, and achieving optimal estimation through a state-space model.

[0085] In multi-source data fusion scenarios, the improved extended Kalman filter algorithm achieves optimal estimation of the battery's core states (such as SOC, current, and voltage) by linearizing the battery's nonlinear state-space model, thereby improving data reliability and state observation accuracy.

[0086] The state prediction model is: x k - = f(x k-1 , u k ) + w k ;P k - = A k ·P k-1 ·A k + Q k Based on the optimal estimate from the previous moment, predict the prior state and prior covariance at the current moment. Where, x k - x is the prior state estimate of the system at time k; k-1u is the posterior optimal state estimate of the system at time k-1; k The system input at time k; w k For process noise; P k - Let A be the prior covariance matrix at time k; k P is the state transition Jacobian matrix; k-1 Q is the posterior covariance matrix at time k-1; k Let be the process noise covariance matrix.

[0087] The state update model is: K k = P k - ·H k ·(H k ·P k - ·H k + R k ) - ¹(Formula 1); k = k - + K k ·(z k - h( k - )) (Formula 2); P k = (I - K k ·H k )·P k - (Formula 3); By combining the current observations, the prior estimate is corrected to obtain the optimal posterior estimate; where K in Formula 1... k P is the Kalman gain matrix at time k; k - H is the Kalman gain matrix at time k; k To observe the Jacobian matrix; H k To observe the Jacobian matrix H k The transpose of R; k The observation noise covariance matrix is ​​given; Formula 1 is used to calculate the Kalman gain matrix K. k This is the core weight matrix used in extended Kalman filtering to balance the reliability of prior state estimates and actual observations.

[0088] In formula 2 kThis is the posterior optimal state estimate at time k (output of the filtering algorithm). k - z is the prior state estimate at time k; k The actual observed value of the system at time k; z k - h( k - ) represents the observation residual; Formula 2 utilizes the Kalman gain K. k Prior state estimates k - After correction, the posterior optimal state estimate at time k is obtained. k This is the final output of the extended Kalman filter algorithm (e.g., the optimal estimates of current, voltage, SOC, and SOH).

[0089] In Formula 3, P k Let I be the posterior covariance matrix at time k; I be the identity matrix; K k ·H k P represents the product of the Kalman gain and the observation Jacobian matrix, used to correct for the uncertainty of the prior covariance; k - Let P be the prior covariance matrix at time k; the posterior covariance matrix P at time k is obtained by updating it in Equation 3. k Characterizes the posterior optimal state estimate k The uncertainty level is considered, and this matrix will serve as the input for the state prediction stage at the next time step (k+1), forming the closed-loop process of the extended Kalman filter. State vector x = [SOC, V, I, T, R_internal] It includes key state variables such as state of charge, terminal voltage, current, temperature and internal resistance. All parameters have been verified and optimized through a large amount of experimental data.

[0090] Furthermore, to obtain an accurate SOC value, the SOC value is obtained by weighted calculation based on the SOC value calculated by the ampere-hour integration method and the SOC value calculated by the open-circuit voltage method.

[0091] Specifically, a fusion calculation method combining the ampere-hour integration method and the open-circuit voltage method is adopted to fully leverage the advantages of both methods. The ampere-hour integration method has the advantage of continuous estimation, but it suffers from cumulative error; the open-circuit voltage method has high accuracy but requires static conditions. Through intelligent fusion, both real-time performance and accuracy are guaranteed.

[0092] The formula for the integral of ampere-hours is: SOC(t) = SOC(t0) + (1 / C_nom)·∫[η(I,T)·I(τ)]dτ; Where SOC(t) is the state of charge of the battery at time t; SOC(t0) is the SOC value at the initial time t0 (initial value); C_nom is the rated capacity of the battery; η(I,T) is the coulomb efficiency coefficient related to current and temperature, which is obtained in real time by a two-dimensional lookup table method; I(τ) is the battery operating current at time τ.

[0093] The correction formula for the ampere-hour integral method-open circuit voltage method is: SOC_corrected = α·SOC_Ah + (1-α)·SOC_OCV; Where α = f(T, I, SOC_Ah), α is an adaptive weighting coefficient, a function of temperature, current, and the current SOC value, adjusted in real time by a fuzzy logic controller; SOC_corrected is the corrected SOC value; SOC_Ah is the ampere-hour integral SOC value; and SOC_OCV is the open-circuit voltage SOC value. The weighted fusion SOC correction model combining the ampere-hour integral method and the open-circuit voltage method leverages the advantages of both methods to improve the accuracy and robustness of battery SOC estimation.

[0094] For example, the purpose of this invention is to provide a battery pack management method that can be applied to a battery management system. This system, through an innovative multi-level architecture design and intelligent algorithm integration, achieves precise management and optimized control of the battery pack throughout its entire lifecycle. The battery management system includes a data acquisition unit, an energy management unit, an adaptive balancing control unit, a predictive maintenance unit, a safety protection unit, and a classification extension unit. These units form a complete closed-loop control system through a collaborative working mechanism. At the data acquisition level, the acquisition unit uses an improved extended Kalman filter algorithm to fuse multi-source data and achieves optimal estimation through a state-space model. The state vector includes key parameters such as SOC, terminal voltage, current, temperature, and internal resistance. The state prediction equation and update equation ensure the accuracy and real-time performance of data processing. SOC calculation employs a fusion algorithm combining the ampere-hour integral method and the open-circuit voltage method, and introduces an adaptive weight coefficient adjustment mechanism, significantly improving calculation accuracy. In the adaptive balancing control unit, an innovative multi-objective active balancing strategy is proposed, comprehensively considering the consistency of three key parameters: voltage, SOC, and internal resistance. The balancing current uses a fuzzy PID control algorithm, achieving precise control through a weight factor calculation model. Experiments show that this scheme reduces voltage inconsistency from >5% in traditional systems to <2%, achieving a balancing efficiency of over 85%. The energy management unit employs a combined thermoelectric and electric energy storage scheme, achieving 15%-25% recovery of excess energy through thermoelectric conversion efficiency models and thermal energy storage capacity calculations. The dynamic power allocation strategy of hybrid energy storage effectively extends battery life. The predictive maintenance unit, based on an improved multi-stress life prediction model, accurately predicts battery health status and remaining lifespan. Fault prediction uses an algorithm combining support vector machines and deep learning, achieving a warning accuracy rate exceeding 90%. The safety protection unit establishes a comprehensive hierarchical early warning system, ensuring safe system operation through comprehensive risk index calculations and multiple protection mechanisms. The classification expansion unit supports online capacity expansion and dynamic reconfiguration, significantly improving system adaptability. Through systematic and innovative design, comprehensive improvements have been achieved in balancing performance, energy utilization efficiency, battery life, and safety reliability, providing a reliable battery management solution for applications such as electric vehicles and energy storage power stations.

[0095] The specific process of battery pack management is as follows: Figure 2 As shown, Step S1: The data acquisition unit collects parameters such as V, I, and T in real time.

[0096] Step S2: The data fusion processing uses an improved extended Kalman filter algorithm to obtain the optimal estimates of SOC, internal resistance, etc.

[0097] Step S3: Calculate the risk index R. If R ≥ 0.8, proceed to step S4; otherwise, proceed to step S5.

[0098] Step S4: The safety protection unit performs an emergency shutdown.

[0099] Step S5: The predictive maintenance unit predicts SOH / RUL based on the multi-stress model.

[0100] Step S6: The energy management unit performs power allocation for combined thermoelectric and hybrid energy storage.

[0101] Step S7: The adaptive equilibrium control unit performs multi-objective active equilibrium.

[0102] Step S8: The safety protection unit performs intelligent thermal management.

[0103] Step S9: Does the system need to be expanded or restructured? If yes, proceed to step S10; otherwise, proceed to step S1.

[0104] Step S10: The adaptive equilibrium control unit performs multi-objective active equilibrium.

[0105] A second aspect of the present invention provides a battery management system, such as... Figure 3 As shown, the battery management system 100 includes a processor 101 and a memory 102.

[0106] The processor 101 is connected to the memory 102; the memory 102 stores a computer program that can be executed by the processor 101, and the processor 101 executes the computer program to implement the battery pack management method.

[0107] According to the battery management system of the present invention, the corresponding battery pack management program can be stored in the memory. When implementing the battery pack management method, the operating status data of the battery pack is acquired in real time. The battery pack is comprehensively managed through the operating status data of the battery pack, enabling the battery pack to perform multiple functions such as adaptive equalization control, energy management, predictive maintenance, safety protection, and configuration and capacity expansion of the battery pack, thereby achieving precise management and optimized control of the entire life cycle of the battery pack.

[0108] A third aspect of the present invention provides a vehicle, such as Figure 4 As shown, the vehicle 200 includes a battery pack 201 and a battery management system 100, with the battery management system 100 connected to the battery pack 201.

[0109] According to the vehicle of the present invention, the battery pack management system comprehensively manages the battery pack, acquires the battery pack's operating status data in real time, and comprehensively manages the battery pack through the battery pack's operating status data. This enables the battery pack to perform multiple functions, including adaptive equalization control, energy management, predictive maintenance, safety protection, and configuration and capacity expansion processing, thereby achieving precise management and optimized control of the battery pack throughout its entire life cycle.

[0110] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, substrate, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example.

[0111] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A battery pack management method, characterized in that, include: Obtain battery pack operating status data; Based on the aforementioned operating status data, the battery pack undergoes comprehensive management including adaptive equalization control, energy management, predictive maintenance, safety protection, and configuration and capacity expansion. The predictive maintenance includes life prediction and failure prediction. The life prediction includes predicting the SOH value based on a multi-stress life prediction model and obtaining the remaining life based on the SOH value. The multi-stress life prediction model is constructed based on a combination of electrochemical mechanisms and data analysis. The multi-stress life prediction model includes a first term and a second term. The first term characterizes the effect of battery cycle aging, and the second term characterizes the effect of calendar aging on the battery.

2. The battery pack management method according to claim 1, characterized in that, The predictive maintenance also includes fault prediction, which involves constructing a weighted fusion decision function that combines support vector machines and deep learning networks. The fusion decision function takes the multi-source operating state data as input to obtain fault prediction values.

3. The battery pack management method according to claim 1, characterized in that, The adaptive equilibrium control includes active equilibrium, multi-parameter consistency equilibrium, and equilibrium efficiency evaluation. The operating status data includes current, voltage, SOC value, and internal resistance; The active balancing includes obtaining a balancing current based on the voltage using fuzzy PID calculation. The multi-parameter consistency equalization includes obtaining an equalization weight factor based on the voltage, the SOC value, the internal resistance, and the corresponding consistency weight and aging compensation weight; The equalization efficiency assessment includes obtaining an equalization efficiency assessment value based on an equalization efficiency assessment model, which is constructed based on the contribution of voltage consistency and SOC value consistency to the equalization effect.

4. The battery pack management method according to claim 1, characterized in that, The energy management includes excess electrical energy conversion and / or hybrid energy storage optimization; The excess electrical energy conversion includes converting the remaining electrical energy into heat energy and storing the heat energy in a phase change material when the battery pack has excess power, and converting the heat energy stored in the phase change material into electrical energy through a thermoelectric generator when there is a need for electricity and supplying the electrical energy to the electrical equipment or to the thermal management system of the battery pack. The hybrid energy storage optimization process includes executing a dynamic power allocation strategy based on a battery pack and supercapacitor integrated energy storage architecture. The dynamic power allocation strategy includes allocating the output power of the battery pack based on the SOC value, temperature and health status of the battery pack and the total power of the battery pack, and determining the output power of the supercapacitor based on the output power of the battery pack and the total power of the supercapacitor.

5. The battery pack management method according to claim 1, characterized in that, The security protection includes a multi-level early warning mechanism, which includes: activating different levels of protection measures according to the magnitude of the risk index, wherein the higher the risk index, the higher the level of protection measures. The risk index is obtained by weighted fusion of the multi-source operational status data.

6. The battery pack management method according to claim 5, characterized in that, The safety protection also includes thermal management control, which includes an architecture based on the combination of liquid cooling and phase change materials, controlling the liquid cooling system and the phase change material system according to the required total thermal management power; The total thermal management power is determined based on the convective heat dissipation power, the radiative heat dissipation power, the heat absorption power of the phase change material, and the power of the thermoelectric conversion system.

7. The battery pack management method according to claim 1, characterized in that, The configuration and expansion of the battery pack includes capacity expansion and reconstruction and optimization of the connection relationship of individual battery cells; The capacity expansion includes automatically identifying the new battery cell and configuring its connection method when a new battery cell is added. When the new battery cell is connected in parallel, the battery pack is subjected to circulating current suppression processing. The reconfiguration optimization process includes online reconfiguration of the battery pack's function when a faulty or abnormal individual cell appears in the battery pack. The reconfiguration of the battery pack's function includes isolating the faulty cell or reorganizing the series-parallel relationship of the individual cells in the battery pack. The reconfiguration optimization process determines the optimal series-parallel relationship based on a reconfiguration optimization objective function, which is constructed based on voltage balancing, SOC balancing, internal resistance balancing, and bypass compensation terms.

8. The battery pack management method according to claim 1, characterized in that, The operating status data includes multiple parameters such as current, voltage, SOC value, and temperature. The SOC value is obtained by weighting the SOC value calculated based on the ampere-hour integration method and the SOC value calculated based on the open-circuit voltage method.

9. A battery management system, characterized in that, include: processor; Memory connected to the processor; The memory stores a computer program that can be executed by the processor, and when the processor executes the computer program, it implements the battery pack management method according to any one of claims 1-8.

10. A vehicle, characterized in that, The vehicle includes a battery pack and a battery management system as described in claim 9, wherein the battery management system is connected to the battery pack.

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