A novel combined estimation method for state of energy and predicted maximum available energy based on fractional-order modeling

Although accurate SOE estimation can enhance the reliability of residual energy prediction, the environmental temperature, parameter coupling, and multiple state constraints increase the difficulty of obtaining SOE accu-rately. A combined estimation method for SOE and predicted maximum available energy based on fractional -order composite equivalent circuit model is proposed to ensure SOE accuracy in the whole battery life cycle. Firstly, the fixed fractional-order forgetting factor recursive least square method is used to realize the online identification of full parameters. Secondly, the adaptive dual fractional-order extended Kalman filter algorithm is applied to realize the co-estimation of SOC and SOE to solve parameter constraints and state coupling. Finally, the fourth-order extended Kalman filter algorithm is exploited to realize the joint estimation of the predicted maximum available energy and SOE, effectively avoiding the divergence of results caused by fixed maximum available energy. The longitudinal comparison experiment results show that the proposed algorithm has the highest accuracy and the smallest root mean square error, which proves the necessity of updating the maximum available energy in real-time. The horizontal comparison experiment further illustrates that real-time correction of multiple factors affecting the SOE estimation accuracy is a necessary way to achieve high accuracy and strong robustness.