Optimal Management of Thermal Comfort and Driving Range in Electric Vehicles

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

DOI: 10.3390/en13174471

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Summary

This paper addresses the critical challenge of balancing passenger thermal comfort with driving range in battery electric vehicles (BEVs). The Heating, Ventilation, and Air Conditioning (HVAC) system constitutes a major auxiliary load, accounting for up to 60% of total energy consumption in urban or harsh conditions, thereby significantly reducing vehicle autonomy. The authors propose an optimal management strategy that minimizes HVAC energy consumption while maintaining acceptable thermal comfort, ensuring the vehicle can reach its destination or next charging point. The study focuses on hot climates and cooling requirements, aiming to quantify the trade-offs between comfort levels, energy usage, and driving speed. The methodology employs an offline optimization approach using dynamic programming, assuming perfect knowledge of future traffic and weather conditions. The system model integrates four sub-systems: the HVAC system, the powertrain, the battery, and a thermo-physiological model of the driver. The HVAC model is nonlinear, accounting for thermodynamic evolution of the refrigerant and ventilation air, with four control variables: the compressor, fan, and two ventilation valves. Crucially, thermal comfort is not modeled as a simple temperature setpoint but via a thermo-physiological model that calculates skin temperatures for different body segments. This data generates a global thermal comfort index based on equivalent temperature, incorporating environmental factors like humidity, air velocity, and solar irradiance, as well as individual factors like clothing and activity. Simulations were conducted across a wide range of weather and traffic scenarios, from congested urban environments to highways. The results demonstrate the efficiency of the proposed approach in minimizing energy consumption while preserving comfort. The study identifies that thermal comfort has a stronger energetic impact in urban conditions due to lower speeds and higher HVAC relative load. Two specific test cases highlight key findings: first, the energetic cost and ratio of improved comfort were quantified under varying meteorological conditions; second, the study analyzed the trade-off between driving speed and thermal comfort, showing that reducing vehicle speed can allow for better thermal comfort while managing total energy constraints. The optimization successfully identifies compromises where slight reductions in comfort or speed extend the driving range sufficiently to complete the trip. The significance of this work lies in its development of a realistic, holistic framework for energy management in BEVs that moves beyond simplistic temperature controls. By integrating detailed HVAC dynamics and human thermo-physiology, the study provides a benchmark for evaluating potential energy gains. The findings support the development of driver assistance systems that can forecast energy needs and suggest adjustments to comfort settings or speed to prevent range anxiety. While the current study is an offline proof of concept, it lays the groundwork for embedded real-time algorithms, offering a pathway to extend vehicle autonomy without compromising passenger well-being.

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