Intelligent Computing for Extended Kalman Filtering SOC Algorithm of Lithium-Ion Battery

The accurate estimation of battery state of charge (SOC) is an important function of the battery management system, and the precise state of battery is estimated makes for the stability of the system. Based on the working characteristics of lithium-ion batteries, the article which used intelligent computing method establishes the mathematical model of the lithium-ion power battery by using the Thevenin model to accurately estimate the battery SOC. Besides, this paper adopts extended Kalman filtering algorithm based on an ampere-hour integral method and the open circuit voltage method for the estimation of battery SOC. Finally in simulation and hardware, the algorithm is verified. The simulation results show that the intelligent computing model can well reflect dynamic and static characteristics of the battery and the extended Kalman filtering algorithm has better estimation accuracy and can meet the system requirements. Similar to the simulation, hardware experiments also show that the algorithm has the high precision and a good anti-jamming ability.