Enabling intelligent transferable energy management of series hybrid electric tracked vehicle across motion dimensions via soft actor-critic algorithm

Abstract

Due to the complex driving conditions faced by hybrid electric tracked vehicles, energy management is crucial for improving fuel economy. However, developing an energy management strategy (EMS) is a time-consuming and labor-intensive task, which is challenging to generalize across different driving tasks. To solve this problem and shorten the development cycle of EMSs, this article proposes a novel transferable energy management framework for a series hybrid electric tracked vehicle (SHETV) across motion dimensions. To fully reuse the learned knowledge from longitudinal motion into both longitudinal and lateral motion, this framework merges transfer learning (TL) into the state-of-the-art deep reinforcement learning (DRL) algorithm, soft actor-critic (SAC), to formulate a novel deep transfer reinforcement learning (DTRL) method, with the transfer of both the neural networks and the pre-trained experience replay buffer. Simulation results indicate that the proposed EMS accelerates the convergence speed by 75.38%, enhances the learning ability by 19.05%, and improves the fuel economy by 5.08% compared to the baseline EMS. This article contributes to correlating different energy management tasks and reusing the existing EMS for the rapid development of a new EMS of the hybrid electric tracked vehicle.