Research And Implementation Of SOC Estimation Method For Power Battery In Electric Vehicle

Lithium-ion batteries have gradually become the main power source of electric vehicles due to its advantages of high energy density,long cycle life,low self-discharge rate and good safety.However,due to the inherent characteristics of lithium battery materials,it will cause performance degradation and even safety problems in overcharging,overdischarging,high temperature and group application.Therefore,it is necessary to effectively manage the power lithium batteries.This article focuses on the key technology of battery management system(BMS)-state of charge(SOC)estimation and balance control technology research.The accurate estimation of SOC can improve the accuracy of vehicle range estimation,extend the service life of battery,which is of great significance to the control decision and safety of the whole vehicle;choosing the appropriate balance topology and balance control strategy can improve the battery inconsistency,improve the capacity utilization of the battery pack,and prolong the life of electric vehicles.The specific research work is as follows:Firstly,this article studies the SOC estimation method.Based on the analysis of the working principle of lithium-ion battery,comparing the commonly used SOC estimation methods,the neural network with high nonlinear ability and good generalization ability is selected as the SOC estimation method.Considering its slow convergence speed and easy to fall into local optimum,particle swarm optimization(PSO)is used to optimize BP neural network.The accuracy and convergence of the method are verified by experiments.The simulation results show that the PSO-BP algorithm has high prediction accuracy and fast convergence speed.Secondly,the balance topology of the power cell is studied.Through the analysis of several common battery balance topologies,Buck-Boost converter with bidirectional transformation,simple structure and flexible selection of balance variables is adopted as the equalization topology;At the same time,the traditional balance topology can only balance two adjacent batteries,which leads to the disadvantages of long balance working time and insufficient balance precision.An improved balance topology is proposed,which can balance within group and between groups at the same time.The simulation test is carried out to verify the feasibility and rationality of the balance design.Then,the balance control strategy of power battery pack is studied.The SOC of the battery is used as the balance variable,the balance threshold is quantified,and the model predictive control is used as the balance control algorithm.By controlling the duty cycle of MOSFET on-off in the balance topology circuit,the size of the balance current is dynamically adjusted,and finally the battery pack is balanced.The simulation results show that the algorithm can control the balance current accurately and improve the balance accuracy and speed.Finally,the method for estimating battery SOC based on particle swarm optimization neural network is simulated and verified under different working conditions.The simulation results show that the SOC estimation method designed in this paper has the advantages of rapid convergence and good robustness,and the battery SOC estimation error is less than 4%;Experimental verification of the active balance strategy based on the model predictive control algorithm,the experimental results show that the balance threshold of the battery pack is less than 0.5%,the balance control technology is low in small,high in efficiency,and the balance effect is obvious.