State co-estimation for lithium-ion batteries based on multi-innovations online identification

Abstract

It is very crucial to accurately estimate the state-of-charge (SOC) and state-of-health (SOH) of electric vehicles. Considering that the ordinary least square method and Kalman filter have low data utilization and poor tracking ability, this research put forward a novel co-estimator on the ground of the multi-innovations (MI) principle. In this method, the parameters are calculated by forgetting factor MI least squares, SOC is estimated by the MI unscented Kalman filter, and the SOH is predicted by the extended Kalman filter. The proposed method is confirmed under the urban dynamometer driving schedule condition and the dynamic stress test condition at different temperatures. In the co-estimation, the maximum absolute error and root-mean-square error of SOC are only 0.53% and 0.3% respectively, 0.025% and 0.00852% respectively for SOH when the estimated effect is optimal. Under multiple test cycles, the estimated accuracy of SOH can also remain within 2%, but is slightly higher than that of SOC. The results also indicate that the proposed method has high precision and robustness in extreme environment.