Traffic-aware Hierarchical Integrated Thermal and Energy Management for Connected HEVs

TL;DR

This paper introduces a traffic-aware hierarchical control strategy for connected hybrid electric vehicles that leverages traffic data to optimize thermal and energy management, significantly improving fuel efficiency and thermal comfort.

Contribution

It presents a novel integrated control framework combining traffic information, a Transformer-based speed predictor, and model predictive control for HEVs.

Findings

56.36% average fuel consumption reduction

Superior thermal regulation and cabin comfort

Effective generalization across driving conditions

Abstract

The energy and thermal management systems of hybrid electric vehicles (HEVs) are inherently interdependent. With the ongoing deployment of intelligent transportation systems (ITSs) and increasing vehicle connectivity, the integration of traffic information has become crucial for improving both energy efficiency and thermal comfort in modern vehicles. To enhance fuel economy, this paper proposes a novel traffic-aware hierarchical integrated thermal and energy management (TA-ITEM) strategy for connected HEVs. In the upper layer, global reference trajectories for battery state of charge (SOC) and cabin temperature are planned using traffic flow speed information obtained from ITSs. In the lower layer, a real-time model predictive control (MPC)-based ITEM controller is developed, which incorporates a novel Transformer-based speed predictor with driving condition recognition (TF-DCR) to enable anticipatory tracking of the reference trajectories. Numerical simulations are conducted under various driving cycles and ambient temperature conditions. The results demonstrate that the proposed TA-ITEM approach outperforms conventional rule-based and MPC-SP approaches, with average fuel consumption reductions of 56.36\% and 5.84\%, respectively, while maintaining superior thermal regulation and cabin comfort. These findings confirm the effectiveness and strong generalization capability of TA-ITEM and underscore the advantages of incorporating traffic information.