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Home List of Titles Adaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/196451
- Adaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles
- Shen, W. X.; Chan, C. C.; Lo, E. W. C.; Chau, K. T.
- This paper proposes and implements a new method for the estimation of the battery residual capacity (BRC) for electric vehicles (EVs). The key of the proposed method is to model the EV battery by using the adaptive neuro-fuzzy inference system. Different operating profiles of the EV battery are investigated, including the constant current discharge and the random current discharge as well as the standard EV driving cycles in Europe, the U.S., and Japan. The estimated BRCs are directly compared with the actual BRCs, verifying the accuracy and effectiveness of the proposed modeling method. Moreover, this method can be easily implemented by a low-cost microcontroller and can readily be extended to the estimation of the BRC for other types of EV batteries.
- Publication type
- Journal article
- IEEE Transactions on Industrial Electronics, Vol. 49, no. 3 (Jun 2002), pp. 677-684
- Publication year
- FOR Code(s)
- 0906 Electrical and Electronic Engineering; 0910 Manufacturing Engineering
- Adaptive neuro-fuzzy inference system; Adaptive systems; Battery modeling; Battery residual capacity; BRC; Electric batteries; Electric discharges; Electric vehicles; Microcontrollers; Neural networks
- Publisher URL
- Copyright © 2002.
- Peer reviewed