Study On SOC Estimation Of Lithium Iron Phosphate Battery For EV

As energy and environmental problems have become increasingly prominent, the electric vehicle (EV) car with the advantages of zero emissions has attracted a great deal of attention over the world. Battery energy management system (BMS) is one of key technologies of development of EV, while prediction the State of Charge (SOC) of battery is necessary and important for BMS to run well in EV. Based on the study of battery model and parameter identification, we are committed to the SOC estimation of the Lithium Iron Phosphate Battery for EV.Firstly, the background of project is briefly introduced, introduces the development situation of EV. After this, the definition of SOC is given, and several different kinds of SOC estimation method are introduced. In addition, the key content of this paper is summarized.Secondly, the history and structure and principle of the Lithium Iron Phosphate Battery are briefed. The Lithium Iron Phosphate Battery is found to be a quite power for the EV. After this, several common equivalent circuit model of battery were summarized and from the battery of electrochemical perspective, the Thevenin equivalent circuit model has been improved as the equivalent circuit model of lithium iron battery in this paper.Then, introduces the experiment method and steps of the laboratory measurement the relationship between Eoc and SOC of Lithium Iron Phosphate Battery; used the method of circuit analysis to get the design formulas of the battery equivalent circuit model parameters, and through the pulse experiment to get the parameters of the battery equivalent circuit model and validate the model is more veracity and validity which we used. The real-time identification of battery model parameter was achieved by the method Fading Memory Delivery Recursive Least Squares.Then, used the relationship between the Eoc and SOC as a mathematical model of kalman filtering; used the algorithm of kalman filtering to calculate the initial value of SOC. Compare the result of simulation and experiment, this method could estimate the initial value of SOC more efficient and provides matting for improve the accuracy of on-line estimation battery SOC.Finally, use the parameters identification and kalman filter combined algorithm (That is parameter identification is carried out first and the parameters are used to estimate SOC later) to estimate the SOC of Lithium Iron Phosphate Battery, compared this method with Ah counting and Kalman filter under the parameters with the Constants, the result of simulation show that this method could improve the accuracy estimation of SOC.