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

Anas Lahlou, Florence Ossart, Emmanuel Boudard, Francis Roy and Mohamed Bakhouya
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Anas Lahlou: Laboratoire de Génie Electrique et Electronique de Paris, CNRS, Université Paris-Saclay, CentraleSupélec, 91192 Gif-sur-Yvette, France
Florence Ossart: Laboratoire de Génie Electrique et Electronique de Paris, CNRS, Université Paris-Saclay, CentraleSupélec, 91192 Gif-sur-Yvette, France
Emmanuel Boudard: Groupe PSA Centre technique Vélizy A, 78140 Vélizy Villacoublay, France
Francis Roy: Groupe PSA Centre technique Vélizy A, 78140 Vélizy Villacoublay, France
Mohamed Bakhouya: LERMA Lab, College of Engineering and Architecture, International University of Rabat, Parc Technopolis, 11100 Sala Al Jadida, Morocco

Energies, 2020, vol. 13, issue 15, 1-22

Abstract: The HVAC system represents the main auxiliary load in electric vehicles, but passengers’ thermal comfort expectations are always increasing. Hence, a compromise is needed between energy consumption and thermal comfort. The present paper proposes a real-time thermal comfort management strategy that adapts the thermal comfort according to the energy available for operating the HVAC system. The thermal comfort is evaluated thanks to the “Predicted Mean Vote”, representative of passenger’s thermal sensations. Based on traffic and weather predictions for a given trip, the algorithm first estimates the energy required for the traction and the energy available for thermal comfort. Then, it determines the best thermal comfort that can be provided in these energetic conditions and controls the HVAC system accordingly. The algorithm is tested for a wide variety of meteorological and traffic scenarios. Results show that the energy estimators have a good accuracy. The absolute relative error is about 1.7% for the first one (traction), and almost 4.1% for the second one (thermal comfort). The effectiveness of the proposed thermal comfort management strategy is assessed by comparing it to an off-line optimal control approach based on dynamic programming. Simulation results show that the proposed approach is near-optimal, with a slight increase of discomfort by only 3%.

Keywords: battery electric vehicle; thermal comfort; HVAC; energy management; real-time control; dynamic programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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