Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning

Abstract

An energy management strategy (EMS) is important for hybrid electric vehicles (HEVs) since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions online. Therefore, it is useful that the EMS can learn from the environment or driving cycle. In this paper, a deep reinforcement learning (DRL)-based EMS is designed such that it can learn to select actions directly from the states without any prediction or predefined rules. Furthermore, a DRL-based online learning architecture is presented. It is significant for applying the DRL algorithm in HEV energy management under different driving conditions. Simulation experiments have been conducted using MATLAB and Advanced Vehicle Simulator (ADVISOR) co-simulation. Experimental results validate the effectiveness of the DRL-based EMS compared with the rule-based EMS in terms of fuel economy. The online learning architecture is also proved to be effective. The proposed method ensures the optimality, as well as real-time applicability, in HEVs.

Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid

“…In addition, DRL is a powerful method used to handle complex control problems and large state spaces by using a deep neural network to calculate the value estimation and associated the pairs of state and action. Thus, the DRL method has been rapidly applied in robotics [27], building HVAC control [28], hybrid electric vehicles [29], etc. Some researchers have studied the use of RL and DRL energy management systems for hybrid electric vehicles and smart building [30,31].…”

Examination on Classification of EVs and Energy Management Strategies of HEV

“…Based on the analysis , among all energy management strategies, learning based EMS are preferred over rule based and optimization based EMS [25], [27].Driving range and SOC offered by learning Based EMS is also more [25], [28].Fuel consumption for various energy management strategies is listed in Fig.7.…”

Simulation Model and Method for Active Torsional Vibration Control of an HEV

“…Hybrid electric vehicles (HEV) have been widely adopted by the automotive industry as a practical solution to increasing fuel efficiency and extending driving range [1][2][3][4][5][6][7][8]. However, because the configuration is more complicated, if the design is not properly executed torsional vibration problems are more likely to occur, such as torsional damper damage and broken shafts, but it also provides a new means for torsional vibration control [9][10][11].…”