A Real-Time, Linear, Parameter-Varying, Model Predictive Control Strategy for a Battery-Supercapacitor Hybrid Energy Storage System

Model predictive control (MPC) of the battery-supercapacitor (SC) hybrid energy storage system (HESS) has been studied for electric vehicle (EV) and microgrid applications. However, the conventional models of the HESS reported in the literature, which play an important role in the MPC, cannot guarantee satisfactory modeling and prediction accuracy for the entire range of the battery state of charge (SOC). To address this issue, this work develops a linear, parameter-varying (LPV) model for the HESS, which accounts for the SOC variations of the battery and, therefore, offers higher HESS modeling and prediction accuracy than the conventional models with fixed parameters reported in the literature. Based on the LPV model, a real-time, multiobjective LPV-MPC strategy is proposed to optimally split the load current of the HESS between battery and SC. Simulation studies verify the superiority of the proposed LPV-MPC strategy over the existing current split strategies in reducing the energy losses and battery degradation of the HESS. Finally, a real-time experiment is carried out to further validate the proposed LPV-MPC strategy for an EV application.