Battery Management System In The Lithium Ion Battery Soc Estimation Method Of Research

As global energy and environment crisis increasing heavily, new energydevelopment and application is being the mainstream of the energy developmentstrategy. Electric car with outstanding advantages of energy saving, low noise,low emission and energy configured has become a hot spot in the study ofautomobile transportation. Li-ion power battery for electric vehicle energysource, is the core of the electric car industry chain. The reasonable control andprotection of the power battery is an important factor to change the performanceof the vehicle. The battery management system which has high reliability andperformance can provide security good operation guarantee for power batteryand reduce the cost of electric vehicles. The estimation method of SOC (State ofCharge) in the battery management system was mainly studied and carried onthe simulation in this paper.Fist of all, from the characteristics of the battery, it expounded theimportance of Li-ion battery management system for Li-ion battery pack and thenecessity of research. It introduced the structure of the automotive Li-ion batterymanagement system, the domestic and foreign present development situationand the development trend of power Li-ion battery management system.Then the estimation method of the lithium ion battery SOC was researchedmainly. Based on the analysis of factors affecting the SOC and traditional SOCalgorithm, the method to estimate stalling state and charge-discharge state ofbattery respectively is applied to predict SOC of lithium-ion. Especially incharge and discharge state, on the basis of quantity method extended Kalmanfilter and fuzzy adaptive Kalman filter are applied in the estimation respectively.A state space model of a lithium-ion battery derived from Thevenin model isproposed, which has the advantage of simplicity and could be easilyimplemented. The simulation results show that the SOC estimation method offuzzy kalman filtering has lower estimation error and higher accuracy than themethod based on extended kalman filtering algorithm.