NDLI: A combined method of battery SOC estimation for electric vehicles

Content Provider	IEEE Xplore Digital Library
Author	Naxin Cui Chenghui Zhang Qing Kong Qingsheng Shi
Copyright Year	2010
Description	Author affiliation: School of Control Science and Engineering, Shandong University, Jinan, China (Naxin Cui; Chenghui Zhang) \|\| Department of Electrical Engineering, Henan polytechnical University, Zhengzhou, China (Qingsheng Shi) \|\| School of Electromechanical, College of Jining Professional Technology, China (Qing Kong)
Abstract	Exact estimation of battery state of charge (SOC) is important for a monitoring system, which is the basis of a energy management system(EMS) in electric vehicles. This paper presents a combined method for estimating the battery SOC for electric vehicles. Diagonal recurrent neural network (DRNN) and Kalman filter (KF) were used to estimate battery SOC respectively. Then the two methods were combined to apply alternately. The combined method synthetized the advantages of the neural network and the Kalman filter, so the it can not only estimate SOC accurately, but also reduce computation amount.
Starting Page	1147
Ending Page	1151
File Size	263269
Page Count	5
File Format	PDF
ISBN	9781424450459
e-ISBN	9781424450466
DOI	10.1109/ICIEA.2010.5515866
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-15
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Batteries Electric vehicles Hybrid electric vehicles State estimation Recurrent neural networks Neural networks Vehicle dynamics Neurons Monitoring Computational modeling Kalman Filter electric vehicle battery state of charge estimation Diagonal Recurrent Neural Network
Content Type	Text
Resource Type	Article

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