Work

An Adaptive Energy Management Strategy for Off-Road Hybrid Tracked Vehicles

Year: 2025

Type: article

Abstract: Conventional energy management strategies based on reinforcement learning often fail to achieve their intended performance when applied to driving conditions that significantly deviate from their training conditions. Therefore, the conventional reinforcement-learning-based strategy is not suitable for complex off-road conditions. This research suggests an energy management strategy for hybrid tracked vehicles operating in off-road conditions that is based on adaptive reinforcement learning. Power demand is described using a Markov chain model that is updated online in a recursive way. The technique updates the MC model and recalculates the reinforcement learning algorithm using the intrinsic matrix norm (IMN) as a criteria. According to the simulation results, the suggested method can increase the adaptability of energy management based on the reinforcement learning strategy in off-road conditions, as evidenced by the 7.66% reduction in equivalent fuel consumption when compared with the conventional Q-learning based energy management strategy.

Source: Energies

DOI: 10.3390/en18061371

Authors: Lijin Han, Wenhui Shi, Ningkang Yang

Institution: Beijing Institute of Technology

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: $3,137