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

W. X. Shen; C. C. Chan; Wai Chau Edward Lo; K. T. Chau

doi:10.1109/TIE.2002.1005395

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

W. X. Shen, C. C. Chan, Wai Chau Edward Lo, K. T. Chau

Research output: Journal article publication › Journal article › Academic research › peer-review

120 Citations (Scopus)

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.

Original language	English
Pages (from-to)	677-684
Number of pages	8
Journal	IEEE Transactions on Industrial Electronics
Volume	49
Issue number	3
DOIs	https://doi.org/10.1109/TIE.2002.1005395
Publication status	Published - 1 Jun 2002
Externally published	Yes

Keywords

Adaptive neuro-fuzzy inference system
Battery modeling
Battery residual capacity
Electric vehicles

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/TIE.2002.1005395

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title = "Adaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles",

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.",

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AB - 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.

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

Abstract

Keywords

ASJC Scopus subject areas

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