Journal
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
Volume 11, Issue 1, Pages 251-263Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JESTPE.2021.3130795
Keywords
Batteries; Predictive models; Real-time systems; Computational modeling; Energy management; State of charge; Load modeling; Battery; electric vehicle (EV); hybrid energy storage system (HESS); linear parameter-varying (LPV) model; model predictive control (MPC); supercapacitor (SC)
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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. The linear parameter-varying (LPV) models of the HESS are developed, offering higher modeling and prediction accuracy compared to conventional linear time invariant (LTI) models. The real-time LPV-MPC strategy optimally allocates the load current of the HESS between battery and SC to achieve the goals of minimizing power loss, mitigating battery degradation, and regulating the SOC of the SC.
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.
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