An energy management strategy to reduce the comprehensive cost of hybrid energy storage systems in electric vehicles - Zhao, Jianyou; Zhou, Juanying; Wang, Lufeng; Zhao, Yang (Frontiers in Energy Research, 2023)
The real-time adaptive energy management strategy (EMS) used a model-based predictive control algorithm that continuously adapted to the changing driving patterns and traffic conditions. With battery degradation in operation considered, the algorithm was designed to minimize the total cost of electricity consumed by vehicles with hybrid energy storage systems (HESSs) while ensuring that the battery and supercapacitor cells were not overcharged or over-discharged. First, the objective function was taken as the instantaneous minimization of the comprehensive cost. Second, a Hierarchical Instantaneous Optimal Control EMS (HIOC-EMS) was suggested to solve the optimal power coupling coefficient of the supercapacitor that satisfied the constraints at any moment. Third, the HIOC-EMS was proven to be an efficient and robust method for optimizing the energy management system of HESS. The experimental results of three different driving cycles showed that the HIOC-EMS, when compared to the Particle Swarm Optimized Fuzzy EMS (PFZY-EMS), achieved reductions in battery loss of 18.41%, 13.94%, and 20.37%, as well as comprehensive cost reductions of 11.16%, 7.37%, and 9.61%, respectively, in the three cycles. Furthermore, compared to the Dynamic Programming EMS (DP-EMS), the HIOC-EMS resulted in increased battery loss of 14.87%, 10.77%, and 4.87%, and increased comprehensive cost of 8.48%, 2.98%, and 1.55%, respectively. These results proved the effectiveness of HIOC-EMS in reducing the usage cost of electric vehicles with HESSs.