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Open AccessArticle

Energy Management Strategy for the Hybrid Energy Storage System of Pure Electric Vehicle Considering Traffic Information

1
State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
2
College of Automotive Engineering, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(8), 1266; https://doi.org/10.3390/app8081266
Received: 8 July 2018 / Revised: 24 July 2018 / Accepted: 28 July 2018 / Published: 31 July 2018
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
The main challenge for the pure electric vehicles (PEVs) with a hybrid energy storage system (HESS), consisting of a battery pack and an ultra-capacitor pack, is to develop a real-time controller that can achieve a significant adaptability to the real road. In this paper, a comprehensive controller considering the traffic information is proposed, which is composed of an adaptive rule-based controller (main controller) and a fuzzy logic controller (auxiliary controller). Through analyzing the dynamic programming (DP) based power allocation of HESS, a general law for the power allocation of HESS is acquired and an adaptive rule-based controller is established. Then, to further enhance the real-time performance of the adaptive rule-based controller, traffic information, which consists of the traffic condition and road grade, is considered, and a novel method combining a K-means clustering algorithm and traffic condition is proposed to predict the future trend of vehicle speed. On the basis of the obtained traffic information, a fuzzy logic controller is constructed to provide the correction for the power allocation in the adaptive rule-based controller. Ultimately, the comparative simulations among the traditional rule-based controller, the adaptive rule-based controller, and the comprehensive controller are conducted, and the results indicate that the proposed adaptive rule-based controller reduces battery life loss by 3.76% and the state of change (SOC) consumption by 3.55% in comparison with the traditional rule-based controller. Furthermore, the comprehensive controller possesses the most excellent performance and reduces the battery life loss by 2.98% and the SOC consumption of the battery by 1.88%, when compared to the adaptive rule-based controller. View Full-Text
Keywords: electric vehicle; hybrid energy storage system; energy management; traffic information electric vehicle; hybrid energy storage system; energy management; traffic information
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MDPI and ACS Style

Hu, J.; Jiang, X.; Jia, M.; Zheng, Y. Energy Management Strategy for the Hybrid Energy Storage System of Pure Electric Vehicle Considering Traffic Information. Appl. Sci. 2018, 8, 1266.

AMA Style

Hu J, Jiang X, Jia M, Zheng Y. Energy Management Strategy for the Hybrid Energy Storage System of Pure Electric Vehicle Considering Traffic Information. Applied Sciences. 2018; 8(8):1266.

Chicago/Turabian Style

Hu, Jianjun; Jiang, Xingyue; Jia, Meixia; Zheng, Yong. 2018. "Energy Management Strategy for the Hybrid Energy Storage System of Pure Electric Vehicle Considering Traffic Information" Appl. Sci. 8, no. 8: 1266.

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