TY - JOUR
T1 - A Real-Time Battery Thermal Management Strategy for Connected and Automated Hybrid Electric Vehicles (CAHEVs) Based on Iterative Dynamic Programming
AU - Zhu, Chong
AU - Lu, Fei
AU - Zhang, Hua
AU - Sun, Jing
AU - Mi, Chunting Chris
N1 - Funding Information:
Manuscript received February 9, 2018; revised April 27, 2018; accepted May 26, 2018. Date of publication June 6, 2018; date of current version September 17, 2018. The review of this paper was coordinated by Prof. D. Diallo. This work was supported by the U.S. Department of Energy under Grant DE-AR0000797. (Corresponding author: Chunting Chris Mi.) C. Zhu, F. Lu, H. Zhang, and C. Mi are with the Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182 USA (e-mail:,chong.zhu@sdsu.edu; feilu@umich.edu; hzhang@mail.sdsu.edu; cmi@sdsu.edu).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9
Y1 - 2018/9
N2 - Connected and automated hybrid electric vehicles (CAHEVs) are a potential solution to the future transportation due to their improved fuel economy, reduced emissions, and capability to mitigate congestion and improve safety. The battery thermal management (BTM) in CAHEVs is one of the crucial problems, because the lithium-ion batteries are highly temperature sensitive. Therefore, a practical and energy-efficient BTM strategy is required for both improving the operating temperature of batteries and saving energy. In this study, the dynamic programming (DP) is implemented for a BTM system in CAHEVs for achieving the optimal cooling/heating energy savings for batteries. To enhance the real-time capability, an iterative approach is proposed to approximate the optimum control strategy iteratively in a multidimensional search space. The proposed iterative DP strategy can improve the system performance and energy-efficiency by fully exploiting the future road information in CAHEVs combined with a model predictive control method. The hardware-in-the-loop validation of the proposed strategy is conducted on the UDDS and the WLTC drive cycles based on a Toyota Prius PHEV model. The results demonstrate the feasibility and effectiveness of the proposed BTM strategy that leads to a considerable BTM energy reduction.
AB - Connected and automated hybrid electric vehicles (CAHEVs) are a potential solution to the future transportation due to their improved fuel economy, reduced emissions, and capability to mitigate congestion and improve safety. The battery thermal management (BTM) in CAHEVs is one of the crucial problems, because the lithium-ion batteries are highly temperature sensitive. Therefore, a practical and energy-efficient BTM strategy is required for both improving the operating temperature of batteries and saving energy. In this study, the dynamic programming (DP) is implemented for a BTM system in CAHEVs for achieving the optimal cooling/heating energy savings for batteries. To enhance the real-time capability, an iterative approach is proposed to approximate the optimum control strategy iteratively in a multidimensional search space. The proposed iterative DP strategy can improve the system performance and energy-efficiency by fully exploiting the future road information in CAHEVs combined with a model predictive control method. The hardware-in-the-loop validation of the proposed strategy is conducted on the UDDS and the WLTC drive cycles based on a Toyota Prius PHEV model. The results demonstrate the feasibility and effectiveness of the proposed BTM strategy that leads to a considerable BTM energy reduction.
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U2 - 10.1109/TVT.2018.2844368
DO - 10.1109/TVT.2018.2844368
M3 - Article
AN - SCOPUS:85048176028
SN - 0018-9545
VL - 67
SP - 8077
EP - 8084
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 9
M1 - 8373733
ER -