Strategies of Different Hybrid Energy Storage Systems for Electric Vehicles Applications

Abstract

Integrating energy storage systems with complementing features increases the efficiency and longevity of electric cars.  A high-energy-density battery, when combined with a high-power-density supercapacitor, efficiently manages peak energy demands during acceleration and regenerative braking while reducing battery deterioration.  This system, known as a Hybrid Energy Storage System (HESS), provides considerable financial and operational savings due to battery deterioration over time.  This work presents a control approach for optimizing power flow between two storage components.  The energy management problem is treated as an infinite-horizon inventory control scenario and reconstructed as a linear program to improve computing efficiency. To manage the complexity typically associated with dynamic programming, we employ a value function approximation using basis functions, allowing the policy to be computed offline and minimizing the need for intensive real-time calculations. In contrast to existing heuristic and model predictive control techniques, which either lack future demand forecasting or rely on suboptimal solutions, our approach offers a scalable and flexible approximation of the optimal strategy. Moreover, unlike previous dynamic programming methods that often suffer from the curse of dimensionality, our optimization programming-based method remains computationally viable even in high-dimensional state spaces. Simulation results on an electric vehicle equipped with both a battery and supercapacitor validate the proposed method. The findings show that the derived suboptimal control policy effectively approximates the optimal one, especially when a sufficient number of basis functions are utilized.