SOC estimation method based on AFEKF for lithium ion battery
In order to solve the problem that the cumulative error is easy to occur because of the influence of historical data when estimating the charge state of lithium battery by using extended Kalman filtering algorithm,a SOC(state of charge)estimation method based on adaptive fading extended Kalman filtering was proposed.Thevenin equivalent model and recursive least square method were employed to identify battery parameters.By introducing adaptive fading factor into EKF algorithm,the influence of historical data on current state estimation was suppressed,and the SOC estimation of lithium battery was completed.The results show that AFEKF(adaptive fading extended Kalman filtering)algorithm can effectively converge when it is repeated for 20 times,and it has better robustness.The average error of SOC estimation is 1.03%,the root mean square error is 1.21%,and the average running time is 1.476 s,showing a good simulation for the dynamic and static characteristics of batteries.
lithium ion batterystate of chargeKalman filteringSOC estimationestimation methodEKF algorithmleast square methodself-adaption
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