Naturalistic data-driven and emission reduction-conscious energy management for hybrid electric vehicle based on improved soft actor-critic algorithm

Abstract

Energy management strategies (EMSs) are critical to saving fuel and reducing emissions for hybrid electric vehicles (HEVs). Given that, this article proposes a naturalistic data-driven and emission reduction-conscious EMS based on deep reinforcement learning (DRL) for a power-split HEV. In this article, for the purpose of evaluating the practical fuel economy of an HEV driving in a certain city region, a specific driving cycle is constructed by using a naturalistic data-driven method. Furthermore, to realize the multi-objective optimization in terms of fuel conservation and emission reduction as well as the state of charge (SOC) sustaining, an intelligent EMS based on the improved soft actor-critic (SAC) algorithm with a novel experience replay method is innovatively proposed. Finally, the effectiveness and optimality of the proposed EMS are verified. Simulation results indicate that the constructed driving cycle can effectively reflect the real traffic scenarios of the test region. Moreover, the proposed EMS achieves 95.25% fuel economy performance of the global optimum, improving the fuel economy by 5.29% and reducing the emissions by 10.42% compared with the emission reduction-neglecting EMS based on standard SAC. This article contributes to energy conservation and emission reduction for the transportation industry through advanced DRL methods.