Driving Condition-Aware Multi-Agent Integrated Power and Thermal Management for Hybrid Electric Vehicles

TL;DR

This paper introduces a driving condition-aware integrated power and thermal management framework for hybrid electric vehicles, utilizing deep learning and reinforcement learning to enhance fuel efficiency and thermal regulation.

Contribution

It presents a novel real-time driving recognition model combined with multi-agent deep reinforcement learning for optimized energy and thermal management in HEVs.

Findings

Fuel economy improved by 16.14% with the new framework.

Thermal management power consumption reduced by 8.22%.

Effective driving condition recognition enhances management strategies.

Abstract

Effective co-optimization of energy management strategy (EMS) and thermal management (TM) is crucial for optimizing fuel efficiency in hybrid electric vehicles (HEVs). Driving conditions significantly influence the performance of both EMS and TM in HEVs. This study presents a novel driving condition-aware integrated thermal and energy management (ITEM) framework. In this context, after analyzing and segmenting driving data into micro-trips, two primary features (average speed and maximum acceleration) are measured. Using the K-means approach, the micro-trips are clustered into three main groups. Finally, a deep neural network is employed to develop a real-time driving recognition model. An ITEM is then developed based on multi-agent deep reinforcement learning (DRL), leveraging the proposed real-time driving recognition model. The primary objectives are to improve the fuel economy and reduce TM power consumption while maintaining a pleasant cabin temperature for passengers. Our simulation results illustrate the effectiveness of the suggested framework and the positive impact of recognizing driving conditions on ITEM, improving fuel economy by 16.14% and reducing TM power consumption by 8.22% compared to the benchmark strategy.