Joint estimation method for maximum available energy and state-of-energy of lithium-ion battery under various temperatures

Abstract Accurate estimation for state-of-energy (SOE), defined as the ratio of residual available energy to maximum available energy, is an important task in battery management system. Nevertheless, the reduction in maximum available energy caused by battery degradation may lower SOE estimation accuracy, which is almost ignored in existing SOE estimation algorithms. To ensure precise SOE prediction over battery's whole lifetime, a joint estimation method for maximum available energy and SOE is proposed in this paper. Firstly, the parameters of the first-order RC battery model are online identified by forgetting factor recursive least square, where rough SOE is inferred directly from identified open-circuit-voltage (OCV). Considering OCV change at adjacent sampling time, a third-order extended Kalman filter is established to correct OCV and estimate maximum available energy with identified parameters and rough SOE. Finally, the predicted maximum available energy is further transmitted into adaptive extended Kalman filter to estimate SOE. The feasibility, prediction accuracy and robustness ability are verified with Federal Urban Driving Schedule tests under the temperature range of 0 °C–50 °C. Validation results indicate that the proposed joint estimation method can still provide accurate maximum available energy and SOE prediction results even though there exist various forms of interferences.