State estimation for power battery based on unscented Kalman filter
Accurately predicting the state of charge(SOC)and state of health(SOH)of power battery is vital for the safe operation of electric vehicle battery systems.Kalman filter(KF)algorithm is widely used for power battery state estimation,but with significant nonlinear errors.The unscented Kalman filter(UKF)algorithm is proposed and used to realize accurate power battery state estimation.Firstly,experimental data of power battery are analyzed to establish a first-order equivalent circuit model with a fitting goodness of 0.992.Then,a capacity degradation mechanism is incorporated to simulate Li-ion battery aging.Through galvanostatic charge and random discharge cycles of the battery,actual power battery operating conditions are replicated.The root mean square errors of SOC and SOH estimation are below 0.01 with different initial conditions,and gradually decrease with more cycles.
Li-ion batterystate estimationequivalent circuit modelstate of charge(SOC)state of health(SOH)unscented Kalman filter(UKF)
万方数据
维普
