Driving Condition-Aware Multi-Agent Integrated Power and Thermal Management for Hybrid Electric Vehicles
For hybrid electric vehicle energy management, this work shows that incorporating driving condition recognition can yield significant fuel savings and thermal efficiency gains.
This paper proposes a driving condition-aware integrated thermal and energy management framework for hybrid electric vehicles, using multi-agent deep reinforcement learning. The method improves fuel economy by 16.14% and reduces thermal management power consumption by 8.22% compared to a benchmark strategy.
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.