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

Jie Han, Arash Khalatbarisoltani, Hai L. Vu, Xiaosong Hu, Jun Yang

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.

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

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.
Paper Structure (23 sections, 30 equations, 15 figures, 3 tables)

This paper contains 23 sections, 30 equations, 15 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Modeling of Powertrain and Thermal Management Systems
  3. Longitudinal Dynamics
  4. Power-split Device
  5. Engine Model
  6. Motor/Generator Model
  7. Battery Model
  8. Cabin Thermal Model
  9. Traffic Information Description
  10. Traffic Dataset
  11. Traffic Flow Speed Extraction
  12. Traffic-aware Hierarchical Integrated Thermal and Energy Management
  13. Global Reference Trajectory Planning Formulation
  14. TF-DCR Speed Predictor
  15. Unsupervised Driving Condition Recognition
  16. ...and 8 more sections

Figures (15)

  • Figure 1: Schematic of the powertrain and thermal management system of Prius HEV.
  • Figure 2: Individual vehicle trajectories for northbound trips collected during the Mobile Century field experiment conducted on the I-880 highway herrera2010evaluation.
  • Figure 3: The average spatio-temporal traffic flow speed.
  • Figure 4: Comparison between extracted traffic flow speed and individual vehicle speed for two trajectories. (a) No.418 and (b) No. 1377.
  • Figure 5: Control framework for traffic-aware hierarchical integrated thermal and energy management.
  • ...and 10 more figures