A Real-Time Approach for Thermal Comfort Management in Electric Vehicles

Lahlou, Anas; Ossart, Florence; Boudard, Emmanuel; Roy, Francis; Bakhouya, Mohamed · 2020 · Crossref

DOI: 10.3390/en13154006

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Summary

This paper addresses the critical challenge of balancing energy consumption and passenger thermal comfort in battery electric vehicles (EVs). The Heating, Ventilation, and Air Conditioning (HVAC) system represents a significant auxiliary load that can drastically reduce driving range, particularly in extreme weather conditions. Existing control strategies often rely on short prediction horizons or simplistic temperature targets, failing to account for the total energy required for the entire trip. This oversight can lead to power shortages before reaching the destination. The authors propose a real-time thermal comfort management strategy that dynamically adjusts comfort levels based on the energy available for HVAC operation after accounting for traction needs. The methodology integrates traffic and weather predictions for a planned trip to estimate energy requirements. The system models three sub-systems: the HVAC, the powertrain, and the battery. A key innovation is the use of the Predicted Mean Vote (PMV) index to quantify thermal comfort, which accounts for human perception of temperature and humidity rather than relying solely on cabin temperature. The algorithm first estimates the energy required for traction and the energy available for thermal comfort. It then determines the optimal PMV level achievable within these energetic constraints and controls the HVAC system accordingly. The approach was validated through extensive simulations involving 4,000 test cases across various meteorological and traffic scenarios. The results demonstrate high accuracy in the energy estimation models. The absolute relative error for the traction energy estimator was approximately 1.7%, while the thermal comfort energy estimator showed an error of nearly 4.1%. When compared to an offline optimal control approach based on dynamic programming, the proposed real-time strategy proved to be near-optimal. Specifically, the real-time control achieved a trade-off between energy consumption and thermal discomfort with only a 3% increase in discomfort compared to the offline optimal solution. This confirms that the algorithm can effectively adjust thermal comfort in low battery state-of-charge scenarios without compromising the vehicle's ability to reach its destination. The significance of this work lies in providing a computationally efficient, real-time solution for long-horizon energy management in EVs. By integrating PMV-based comfort metrics and whole-trip energy forecasting, the strategy ensures that passengers maintain acceptable comfort levels while preventing range anxiety caused by excessive HVAC usage. This approach offers a practical alternative to computationally intensive methods like dynamic programming, making it suitable for implementation in onboard computers. The study highlights the importance of considering the entire trip horizon and holistic comfort metrics to optimize the efficiency and usability of electric vehicles.

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