Li-ion battery SOC estimation based on EKF algorithm

Li-ion battery is more and more popular in aviation area in recent years for its high energy density, no memory characteristic and long cycle life. The currently used state of charge (SOC) estimation methods based on extended Kalman filter (EKF) doesn´t have a very good accuracy due to the modeling error, which could influence the performance of the battery management system (BMS) and the control of the host machine. Considering about this, 7 ranks Thevenin model is adopted in this article which has a good precision and exponential function and logarithmic function are introduced to increase modeling precision. In the experiment, the max estimation error based on the improved model declined 71.59% comparing to the original model. The experiment result shows through improving the battery model the SOC estimation accuracy basing on EKF is improved greatly, which is significant for the performance of BMS and the host machine.