Abstract
The HVAC system represents the main auxiliary load in battery-powered electric vehicles (BEVs) and requires efficient control approaches that balance energy saving and thermal comfort. On the one hand, passengers always demand more comfort, but on the other hand the HVAC system consumption strongly impacts the vehicle’s driving range, which constitutes a major concern in BEVs. In this paper, a thermal comfort management approach that optimizes the thermal comfort while preserving the driving range during a trip is proposed. The electric vehicle is first modeled together with the HVAC and the passengers’ thermo-physiological behavior. Then, the thermal comfort management issue is formulated as an optimization problem solved by dynamic programing. Two representative test-cases of hot climates and traffic situations are simulated. In the first one, the energetic cost and ratio of improved comfort is quantified for different meteorological and traffic conditions. The second one highlights the traffic situation in which a trade-off between the driving speed and thermal comfort is important. A large number of weather and traffic situations are simulated and results show the efficiency of the proposed approach in minimizing energy consumption while maintaining a good comfort.
Highlights
Despite rapid evolution of battery performance and recharging infrastructure, the penetration of EVs in road transportation remains hindered by their limited driving range and by users’ fear of running out of battery
The first one highlights the tradeoffs that can be reached between the energy consumption of the HVAC system and the thermal comfort
Test Case 1: Thermal Discomfort vs. Energy Cost Trade-off. In this test-case, we investigate how the thermal comfort management algorithm adjusts thermal comfort according to the energy available for the HVAC system in different situations
Summary
Despite rapid evolution of battery performance and recharging infrastructure, the penetration of EVs in road transportation remains hindered by their limited driving range and by users’ fear of running out of battery. The thermo-physiological model allows to include the heat exchanges with the driver in the cabin’s heat balance These models have been implemented in the dynamic programing optimization algorithm and simulations were run for a large number of scenarios corresponding to different weather and traffic conditions, from congested urban to highway. The main contributions of the present work are the following ones: (i) development of an off-line optimization approach for long horizon thermal comfort management, (ii) integration of a realistic model of the HVAC and the cabin, (iii) integration of a thermo-physiological model of the driver, (iv) integration of a thermal comfort index representative of human sensations in a vehicle cabin environment, and (v) extensive simulations of the proposed approach for different climatic and traffic scenarios.
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