Frontiers in Energy Research (Jul 2023)
An energy management strategy to reduce the comprehensive cost of hybrid energy storage systems in electric vehicles
Abstract
A 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 an operation being 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 cell were not overcharged or overdischarged. 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 HESSs. 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 losses of 18.41%, 13.94%, and 20.37% and 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 losses of 14.87%, 10.77%, and 4.87% and increased comprehensive costs of 8.48%, 2.98%, and 1.55%, respectively. These results proved the effectiveness of the HIOC-EMS in reducing the usage cost of electric vehicles with HESSs.
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