Real-Time Model Predictive Control for Battery-Supercapacitor Hybrid Energy Storage Systems Using Linear Parameter-Varying Models
Chao Jia, Junwei Cui, Wei Qiao, Liyan Qu
Abstract
This article proposes a new model predictive control (MPC) strategy for the energy management of a battery-supercapacitor (SC) hybrid energy storage system (HESS) for electric vehicle (EV) applications. First, linear parameter-varying (LPV) models of the HESS are developed, which account for battery parameter variations along its state of charge (SOC). Compared with the conventional linear time invariant (LTI) models reported in the literature, the LPV models of the HESS offer higher modeling and prediction accuracy. Based on the LPV prediction model, a real-time LPV-MPC strategy is then proposed to optimally allocate the load current of the HESS between battery and SC to achieve three goals: minimizing the power loss of the HESS, mitigating the battery degradation, and regulating the SOC of the SC. The optimization problem of the LPV-MPC strategy is transformed into a quadratic programming problem, which can be efficiently solved in real time. Simulation studies verify the superiority of the proposed LPV-MPC strategy over the existing energy management 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 EV applications.