Simultaneous Identification and Control for Hybrid Energy Storage System Using Model Predictive Control and Active Signal Injection

A battery/supercapacitor hybrid energy storage system (HESS) is overactuated in the sense that there are two power sources providing a single power output. This feature of HESS is exploited in this article to simultaneously achieve accurate identification of the battery states/parameters and high system efficiency. By actively injecting current signals, the state of charge and state of health, together with other battery parameters, can be identified sequentially. Sufficient richness in the input (i.e., battery current) is necessary to ensure identification accuracy. Since signal richness for identification can be in conflict with efficient operation, a novel model predictive control (MPC) strategy is used to simultaneously consider both objectives to determine the optimal power distribution between supercapacitor and battery. The tradeoff between identification accuracy and system efficiency is investigated. Simulation results show that the proposed MPC can significantly improve identification accuracy at the expense of a slight decrease in system efficiency when compared to the baseline MPC, which does not consider the signal richness. Therefore, it is validated that the proposed MPC can effectively achieve simultaneous identification and efficient operation.