Study On Power Lithium-ion Batteries Management System And SOC Estimation Method

With the development of economic society, governments around the world pay more attention to the energy and environmental protection, which have became a global focus. The use of battery as a green energy is becoming more and more widely. Especially, the traction lithium-ion battery get extensive use on the electric vehicles. With the application of battery, to get more safety and higher efficiency, the study of battery management system(BMS) become more and more important. BMS is one of the core technologies of electric vehicles. This thesis taken the traction lithium-ion battery as the research object, a BMS with complete functions was designed, and put forward a solution of BMS. The specific research works are as follows:Firstly, according to the working principle and performance of traction lithium-ion battery, a BMS based on PIC18F45K80 MCU was designed. The special battery monitoring chip LTC6802G-1 was used to realize the collecting of voltage and controlling of balance. Based on the traditional functions, the BMS designed in this thesis increase the detection circuit of insulation resistance, which is used to detect the insulation between battery and vehicle, improved the security of the use of traction lithium-ion battery.Secondly, this thesis analyzes the basic performance parameters and working characteristics(voltage characteristic, internal resistance and capacity characteristics, etc) of traction lithium-ion battery. Several kinds of equivalent circuit models are introduced, and dual power supply equivalent circuit model is put forward based on Thevenin model combined with the working characteristics of traction lithium-ion battery. Besides, the thesis verifies the accuracy and practicability of the proposed model by using Matlab/Simulink simulation platform.Finally, the extended kalman filter(EKF) algorithm is analyzed based on the classical kalman filtering theory. The thesis applies EKF algorithm to the estimation of SOC, and proposes a estimation method of SOC based on EKF algorithm. The validity and practicability of the algorithm is proved by using the Matlab/Simulink simulation tools.The research on this thesis has made great progress both in theory and practice, which provides some effective bases for the further study of BMS.