Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/196451

Title

Adaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles

Author(s)

Shen, W. X.;  Chan, C. C.;  Lo, E. W. C.;  Chau, K. T.

Abstract

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

Source

IEEE Transactions on Industrial Electronics, Vol. 49, no. 3 (Jun 2002), pp. 677-684

Publication year

2002

FOR Code(s)

0906 Electrical and Electronic Engineering;  0910 Manufacturing Engineering

Keyword(s)

Adaptive neuro-fuzzy inference system;  Adaptive systems;  Battery modeling;  Battery residual capacity;  BRC;  Electric batteries;  Electric discharges;  Electric vehicles;  Microcontrollers;  Neural networks

Publisher

IEEE

ISSN

0278-0046

Publisher URL

http://dx.doi.org/10.1109/tie.2002.1005395

Copyright

Copyright © 2002.

Peer reviewed