Lithium Iron Phosphate Battery State Of Charge Estimation And Modeling

In recent years, for the energy shortage and environmental pollution problems growing, energy-saving and new energy electric vehicles with its high utilization and low emission characteristics has become the focus of current research. The electric car battery as a power unit is directly related to the development of electric vehicle industry. To further study on battery power, the establishment can accurately reflect the characteristics of the battery and the battery model to accurately estimate the battery state of charge (state of charge, SOC) has great significance for the development of battery systems management and engineering. To achieve improved battery SOC estimation accuracy target, lithium iron phosphate (LiFePO4) battery as experimental subjects, respectively, affect the accuracy of the battery SOC estimation model and the SOC estimation methods are analyzed and studied. To conduct research on the performance characteristics of lithium iron phosphate battery charge and discharge through a lot of experiments to analyze the impact of various factors battery SOC estimation. On this basis, according to the classical equivalent circuit model, the establishment of an equivalent circuit model to reflect the lithium iron phosphate battery rebound and hysteretic characteristics, respectively, equivalent to the battery model and the equivalent voltage source impedance parameters Identification. Then it analyzes the classical SOC estimation method, based on the equivalent circuit model of lithium iron phosphate state space equation, and dual Kalman filter (Dual extended Kalman filter, DEKF) algorithm and battery model based on the SOC estimate. Finally SEV400 battery management system (battery management system, BMS) and the combination of Matlab simulation verified the thesis put forward. By the simulation of the "10-15" condition, the contrast between three different SOC estimation algorithms, experiments show that the battery model and DEKF algorithm for SOC estimation method has more accuracy and better convergence, so this method can improve the accuracy of SOC estimation.