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

Author

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

Author_Institution

Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, China

Volume

49

Issue

3

fYear

2002

fDate

6/1/2002 12:00:00 AM

Firstpage

677

Lastpage

684

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 US, 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

Keywords

electric vehicles; fuzzy neural nets; inference mechanisms; microcontrollers; power engineering computing; secondary cells; Europe; Japan; USA; adaptive neuro-fuzzy inference system; adaptive neuro-fuzzy modeling; battery residual capacity; constant current discharge; electric vehicles; low-cost microcontroller; operating profiles; random current discharge; standard EV driving cycles; Adaptive systems; Artificial neural networks; Battery charge measurement; Electric vehicles; Energy conservation; Europe; Impedance measurement; Input variables; Microcontrollers; Voltage;

fLanguage

English

Journal_Title

Industrial Electronics, IEEE Transactions on

Publisher

ieee

ISSN

0278-0046

Type

jour

DOI

10.1109/TIE.2002.1005395

Filename

1005395