Enhanced Q-learning for real-time hybrid electric vehicle energy management with deterministic rule

Abstract

Power allocation plays an important and challenging role in fuel cell and supercapacitor hybrid electric vehicle because it influences the fuel economy significantly. We present a novel Q-learning strategy with deterministic rule for real-time hybrid electric vehicle energy management between the fuel cell and the supercapacitor. The Q-learning controller (agent) observes the state of charge of the supercapacitor, provides the energy split coefficient satisfying the power demand, and obtains the corresponding rewards of these actions. By processing the accumulated experience, the agent learns an optimal energy control policy by iterative learning and maintains the best Q-table with minimal fuel consumption. To enhance the adaptability to different driving cycles, the deterministic rule is utilized as a complement to the control policy so that the hybrid electric vehicle can achieve better real-time power allocation. Simulation experiments have been carried out using MATLAB and Advanced Vehicle Simulator, and the results prove that the proposed method minimizes the fuel consumption while ensuring less and current fluctuations of the fuel cell.