CN-122008945-A - Battery pack management method, battery management system and vehicle

CN122008945ACN 122008945 ACN122008945 ACN 122008945ACN-122008945-A

Abstract

The invention discloses a battery pack management method, a battery management system and a vehicle, wherein the battery pack management method comprises the steps of obtaining running state data of a battery pack; and performing adaptive equalization control, energy management, predictive maintenance, safety protection and multiple comprehensive management in battery pack configuration and capacity expansion processing on the battery pack based on the running state data. The method can comprehensively manage the battery pack through the running state data of the battery pack, and realize accurate management and optimal control of the whole life cycle of the battery pack.

Inventors

GAO HUAN
QIN ZHIDONG
YAN KANGKANG
ZHANG YANCHAO
LIU QUANZHI
SUN HENAN
ZHAI SHUWEI
XU YIDA
CAO RANRAN

Assignees

北京卡文新能源汽车有限公司

Dates

Publication Date: 20260512
Application Date: 20260120

Claims (10)

1. A battery pack management method, comprising: acquiring running state data of a battery pack; Performing adaptive equalization control, energy management, predictive maintenance, safety protection and multiple comprehensive management in battery pack configuration and capacity expansion processing on the battery pack based on the running state data; The predictive maintenance comprises life prediction and fault prediction, wherein the life prediction comprises SOH value prediction based on a multi-stress life prediction model, and residual service life is obtained based on the SOH value, wherein the multi-stress life prediction model is constructed based on a method combining electrochemical mechanism and data analysis, and comprises a first term and a second term, the first term represents the cyclic aging influence of a battery, and the second term represents the influence of calendar aging on the battery.
2. The battery management method of claim 1, wherein the predictive maintenance further comprises a fault prediction, the fault prediction comprising constructing a fusion decision function supporting a weighted combination of a vector machine and a deep learning network, the fusion decision function taking as input the operating state data of multiple sources to obtain a fault prediction value.
3. The battery management method of claim 1, wherein the adaptive equalization control comprises active equalization, multiparameter consistent equalization, and equalization efficiency assessment; wherein the running state data comprises current, voltage, SOC value and internal resistance; The active equalization comprises the step of carrying out fuzzy PID operation based on the voltage to obtain an equalization current; the multi-parameter consistency balancing includes obtaining a balancing weight factor based on the voltage, the SOC value, the internal resistance, and corresponding consistency weights and aging compensation weights; The equalization efficiency evaluation includes obtaining an equalization efficiency evaluation value based on an equalization efficiency evaluation model constructed based on contributions of voltage consistency and SOC value consistency to an equalization effect.
4. The battery pack management method of claim 1, wherein the energy management includes redundant power conversion and/or hybrid energy storage optimization; The energy conversion comprises the steps of converting the residual electric quantity into heat energy when the electric quantity of the battery pack is residual and storing the heat energy into a phase change material, and converting the heat energy stored by the phase change material into electric energy through a thermoelectric generator and providing the electric energy to electric equipment or a thermal management system of the battery pack when the electric quantity is required by useful electricity; The hybrid energy storage optimization process includes executing a dynamic power allocation strategy based on a battery pack and a super-capacitor fusion energy storage architecture, the dynamic power allocation strategy including allocating an output power of the battery pack based on a correction function of an SOC value, a temperature and a state of health of the battery pack and a total power of the battery pack, and determining the output power of the super-capacitor based on the output power of the battery pack and the total power of the super-capacitor.
5. The battery management method according to claim 1, wherein the safety protection comprises a multi-stage early warning mechanism, the multi-stage early warning mechanism comprises starting protection measures of different levels according to the magnitude of risk indexes, wherein the higher the risk index is, the higher the protection measure level is, and the risk indexes are obtained by weighting and fusing based on the multi-source operation state data.
6. The battery pack management method of claim 5, wherein the safety shield further comprises a thermal management control comprising a combined liquid cooling and phase change material based architecture, the liquid cooling system and phase change material system being controlled according to a desired total thermal management power; wherein the total thermal management power is determined based on the convective heat dissipation power, the radiant heat dissipation power, the phase change material endothermic power, and the thermoelectric conversion system power.
7. The battery pack management method according to claim 1, wherein the configuration and capacity expansion processing of the battery pack includes a capacity expansion and a reconfiguration optimization processing of a connection relationship of the battery cells; the capacity expansion comprises automatically identifying a newly-added battery cell and configuring a connection mode of the battery cell when the newly-added battery cell is newly-added, wherein when the connection mode of the newly-added battery cell is parallel connection, circulation suppression processing is carried out on the battery pack; The reconstruction optimization processing comprises a function of reconstructing the battery pack online when a fault single battery or an abnormal single battery occurs in the battery pack, the function of reconstructing the battery pack comprises a function of isolating the fault single battery or a function of reorganizing the serial-parallel connection relation of single batteries in the battery pack, the reconstruction optimization processing is used for determining the optimal serial-parallel connection relation based on a reconstruction optimization objective function, and the reconstruction optimization objective function is constructed based on a voltage balance item, an SOC balance item, an internal resistance balance item and a bypass compensation item.
8. The battery pack management method according to claim 1, wherein the operation state data includes a plurality of current, voltage, SOC value, and temperature; The SOC value is obtained by weighting calculation based on the SOC value calculated by the ampere-hour integration method and the SOC value calculated by the open-circuit voltage method.
9. A battery management system, comprising: A processor; a memory coupled to the processor; The memory stores a computer program executable by the processor, the processor implementing the battery pack management method of any one of claims 1 to 8 when the computer program is executed.
10. A vehicle comprising a battery pack and the battery management system of claim 9, the battery management system being coupled to the battery pack.

Description

Battery pack management method, battery management system and vehicle Technical Field The present invention relates to the field of battery technologies, and in particular, to a battery pack management method, a battery management system, and a vehicle. Background In the related art, the self-adaptive equalization control, the energy management, the predictive maintenance, the safety protection and the configuration and the capacity expansion of the battery pack are all independently carried out, the comprehensive management is not carried out, and the full life cycle of the battery pack cannot be accurately managed and optimally controlled. Disclosure of Invention The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, an object of the present invention is to provide a battery pack management method, which can comprehensively manage a battery pack through operation state data of the battery pack, and realize accurate management and optimal control of a full life cycle of the battery pack. A second object of the present invention is to provide a battery management system. A third object of the invention is to propose a vehicle. In order to solve the problems, the embodiment of the first aspect of the invention provides a battery pack management method, which comprises the steps of obtaining operation state data of a battery pack, carrying out adaptive equalization control, energy management, predictive maintenance, safety protection and multiple comprehensive management in configuration and capacity expansion processing of the battery pack on the basis of the operation state data, wherein the predictive maintenance comprises life prediction and fault prediction, the life prediction comprises SOH value prediction based on a multi-stress life prediction model, and residual service life is obtained based on the SOH value, the multi-stress life prediction model is constructed based on a method combining electrochemical mechanism and data analysis, and comprises a first term and a second term, the first term represents the effect of battery cycle aging, and the second term represents the effect of calendar aging on the battery. According to the battery pack management method provided by the embodiment of the invention, when the battery pack works, the running state data of the battery pack is acquired in real time, and the battery pack is comprehensively managed through the running state data of the battery pack, so that the battery pack performs multiple of self-adaptive balance control, energy management, predictive maintenance, safety protection and configuration and capacity expansion processing on the battery pack, and the accurate management and optimization control on the whole life cycle of the battery pack are realized. In some embodiments, predictive maintenance further includes fault prediction including constructing a fusion decision function supporting a weighted combination of a vector machine and a deep learning network, the fusion decision function taking as input the operating state data of multiple sources to obtain a fault prediction value. In some embodiments, the adaptive equalization control includes active equalization, multi-parameter consistent equalization, and equalization efficiency assessment, wherein the operating state data includes current, voltage, SOC value, and internal resistance, the active equalization includes obtaining an equalization current based on a fuzzy PID operation of the voltage, the multi-parameter consistent equalization includes obtaining an equalization weight factor based on the voltage, the SOC value, the internal resistance, and corresponding consistent weights and aging compensation weights, and the equalization efficiency assessment includes obtaining an equalization efficiency assessment value based on an equalization efficiency assessment model constructed based on contributions of voltage consistency and SOC value consistency to an equalization effect. In some embodiments, energy management includes excess electrical energy conversion and/or hybrid energy storage optimization processes, wherein the excess electrical energy conversion includes converting remaining electrical energy into thermal energy and storing the thermal energy to a phase change material when the electrical energy of a battery is remaining, and converting thermal energy stored by the phase change material into electrical energy by a thermoelectric generator and providing the electrical energy to a powered device or to a thermal management system of the battery when there is a useful electrical demand, the hybrid energy storage optimization processes include executing a dynamic power distribution strategy based on a battery and super-capacitor fusion energy storage architecture, the dynamic power distribution strategy including distributing an output power of the battery based on a correction function of a SOC value, temperatur