Traffic-aware Hierarchical Integrated Thermal and Energy Management for Connected HEVs
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911467624726528 |
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| author | Han, Jie Khalatbarisoltani, Arash Vu, Hai L. Hu, Xiaosong Yang, Jun |
| author_facet | Han, Jie Khalatbarisoltani, Arash Vu, Hai L. Hu, Xiaosong Yang, Jun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_21914 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Traffic-aware Hierarchical Integrated Thermal and Energy Management for Connected HEVs Han, Jie Khalatbarisoltani, Arash Vu, Hai L. Hu, Xiaosong Yang, Jun Systems and Control 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. |
| title | Traffic-aware Hierarchical Integrated Thermal and Energy Management for Connected HEVs |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2602.21914 |