A comparative study of deep reinforcement learning based energy management strategy for hybrid electric vehicle

Abstract

Energy management strategies (EMSs) are essential for hybrid electric vehicles (HEVs), as they can further exploit the potential of HEVs to save energy and reduce emissions. Research on deep reinforcement learning (DRL)-based EMSs is developing rapidly. However, most studies have ignored the impact of uniform test benchmarks on the performance of DRL-based EMS and focus too much on fuel economy improvement resulting in a single optimization objective. In this study, four DRL-based EMSs are designed for HEVs with a multi-objective optimization reward function that considers battery health furtherly. The optimal learning rates and weight coefficients of the four EMSs are determined first. Based on this, the monetary cost, fuel cost, and battery health of each EMS are intensively studied under nine driving cycles. The EMSs perform better in high-speed conditions and worse in suburban conditions are initially concluded. A comparative analysis under unlearned mixed driving cycles validates this conclusion and shows that the SAC-based EMS achieves a fuel consumption of 4.218L per 100 km and 99.96 % battery health, which are the lowest of the four EMSs. This paper can provide a theoretical basis for the parametric and driving cycle study of DRL-based EMSs.