Work

A Cutting-Edge Energy Management System for a Hybrid Electric Vehicle relying on Soft Actor–Critic Deep Reinforcement Learning

Year: 2025

Type: article

Abstract: Thanks to its superior learning capabilities and its model-free nature, Reinforcement Learning (RL) is increasingly regarded as an effective solution for addressing complex optimization tasks such as energy management in Hybrid Electric Vehicles (HEVs). In this paper, we implement a Soft Actor–Critic (SAC) agent on a digital twin of a plug-in Hybrid Electric Vehicle (pHEV) operating in charge-sustaining mode. We employ multi-cycle training, which significantly improves the SAC model’s ability to generalize across diverse conditions. We fist evaluate the SAC agent capabilities on the Worldwide harmonized Light-duty vehicles Test Cycle (WLTC) by comparing its performance to the global optimum achieved by Dynamic Programming (DP), a local optimization strategy , i.e., Equivalent Consumption Minimization Strategy (ECMS), and a Double Deep Q-Learning (DDQL) algorithm. Furthermore, we test the agent across a broad range of driving cycles to assess its ability to generalize to scenarios beyond those used during training. Simulation results show that the SAC agent achieves results close to the optimal benchmark set by the DP, with CO 2 emissions differing by only 3-4%. The code used in this work is publicly available at:

Source: Transportation Engineering

DOI: 10.1016/j.treng.2025.100308

Authors: Luigi Tresca, Luca Pulvirenti, Luciano Rolando

Institution: Politecnico di Torino

Language: English

Topic: Electric and Hybrid Vehicle Technologies

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,670