Research And Realization Of State Estimation For Lithium-ion Batteries

In recent years, the face of the energy crisis and the threat of these increasingly serious environmental pollution problems, around the world are stepping up research and development of electric vehicles. Electric vehicles with its excellent energy saving and pollution-free characteristics become the focus of future development of the automobile industry. Among them, battery as the power source for electric vehicles to become a bottleneck restricting the development of electric vehicles. Battery Management System(BMS) as a key comprehensive monitoring and management of the battery, charge and discharge and the balance tests guarantee reasonable battery normal work, can effectively improve the useful life of the battery, to avoid improper use and reduce unnecessary risk. Among them, the battery and the State of Chaerge(SOC) and State of Health(SOH) online estimate is the key point of battery management system efficient operation. Research has high SOC and SOH estimation accuracy of the algorithm for the battery management system is extremely important, and it can provide effective support for extended battery life and improve battery efficiency and the like.In this paper, the main research content is the lithium-ion battery state estimation algorithm, on the basis of the analysis of the existing BMS research level, combined with the characteristics of the lithium-ion battery research and realize their own lithium-ion battery management algorithms. Firstly, through the study of battery model introduction, select second-order RC equivalent circuit model and through the establishment of a lithium-ion battery MATLAB nonlinear model and the effectiveness of the experimental data parameter identification and the model is verified. Then by now several common battery state of charge(SOC) estimation algorithm to analyze the advantages and disadvantages of lithium-ion batteries based on second-order model established to study the estimation based on the extended Kalman filter(EKF) algorithm lithium-ion battery SOC, using Simulink SOC estimation algorithm for the simulation. Lithium-ion battery health, through in-depth analysis of the experimental data, we propose a two-pulse-method SOH detection algorithm. Finally, based on estimates of the lithium-ion battery SOH using Particle Filter(PF) algorithm for the remaining useful life of lithium-ion batteries were predicted for code implementation and simulation in MATLAB comparison with experimental data validation, to achieve a better prediction accuracy.Based on the remaining capacity of lithium-ion battery state estimation methods and health research, as well as battery life prediction, estimation method by MATLAB simulation, experimental data were compared, laid the technical foundation to achieve a better estimation accuracy for the lithium-ion battery electric vehicles and energy storage system application.