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Design and Analysis of Battery-Aware Automotive Climate Control for Electric Vehicles

Design and Analysis of Battery-Aware Automotive Climate Control for Electric Vehicles Electric Vehicles (EV) as a zero-emission means of transportation encounter challenges in battery design that cause a range anxieties for the drivers. Besides the electric motor, the Heating, Ventilation, and Air Conditioning (HVAC) system is another major contributor to the power consumption that may influence the EV battery lifetime and driving range. In the state-of-the-art methodologies for battery management systems, the battery performance is monitored and improved. While in the automotive climate control, the passenger’s thermal comfort is the main objective. Hence, the influence of the HVAC power on the battery behavior for the purpose of jointly optimized battery management and climate control has not been considered. In this article, we propose an automotive climate control methodology that is aware of the battery behavior and performance, while maintaining the passenger’s thermal comfort. In our methodology, battery parameters and cabin temperature are modeled and estimated, and the HVAC utilization is optimized and adjusted with respect to the electric motor and HVAC power requests. Therefore, the battery stress reduces, while the cabin temperature is maintained by predicting and optimizing the system states in the near-future. We have implemented our methodology and compared its performance to the state-of-the-art in terms of battery lifetime improvement and energy consumption reduction. We have also conducted experiments and analyses to explore multiple control window sizes, drive profiles, ambient temperatures, and modeling error rates in the methodology. It is shown that our battery-aware climate control can extend the battery lifetime by up to 13.2% and reduce the energy consumption by up to 14.4%. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Embedded Computing Systems (TECS) Association for Computing Machinery

Design and Analysis of Battery-Aware Automotive Climate Control for Electric Vehicles

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References (64)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2018 ACM
ISSN
1539-9087
eISSN
1558-3465
DOI
10.1145/3203408
Publisher site
See Article on Publisher Site

Abstract

Electric Vehicles (EV) as a zero-emission means of transportation encounter challenges in battery design that cause a range anxieties for the drivers. Besides the electric motor, the Heating, Ventilation, and Air Conditioning (HVAC) system is another major contributor to the power consumption that may influence the EV battery lifetime and driving range. In the state-of-the-art methodologies for battery management systems, the battery performance is monitored and improved. While in the automotive climate control, the passenger’s thermal comfort is the main objective. Hence, the influence of the HVAC power on the battery behavior for the purpose of jointly optimized battery management and climate control has not been considered. In this article, we propose an automotive climate control methodology that is aware of the battery behavior and performance, while maintaining the passenger’s thermal comfort. In our methodology, battery parameters and cabin temperature are modeled and estimated, and the HVAC utilization is optimized and adjusted with respect to the electric motor and HVAC power requests. Therefore, the battery stress reduces, while the cabin temperature is maintained by predicting and optimizing the system states in the near-future. We have implemented our methodology and compared its performance to the state-of-the-art in terms of battery lifetime improvement and energy consumption reduction. We have also conducted experiments and analyses to explore multiple control window sizes, drive profiles, ambient temperatures, and modeling error rates in the methodology. It is shown that our battery-aware climate control can extend the battery lifetime by up to 13.2% and reduce the energy consumption by up to 14.4%.

Journal

ACM Transactions on Embedded Computing Systems (TECS)Association for Computing Machinery

Published: Jul 5, 2018

Keywords: Electric vehicle

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