Available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles

C. C. Chan; Wai Chau Edward Lo; Shen Weixiang

doi:10.1016/S0378-7753(99)00502-9

Available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles

C. C. Chan, Wai Chau Edward Lo, Shen Weixiang

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

176 Citations (Scopus)

Abstract

The available capacity computation model based on the artificial neural network (ANN) for lead-acid batteries in an electric vehicle (EV) is presented. Comparing with the methods based on the Peukert equation, which is often used for the calculation of the available capacity for lead-acid batteries in EVs, this model is more accurate. The results of the experiment have proven the accuracy of the proposed model; the computation values are in good agreement with experimental data, the associated error has been considered acceptable from an engineering point of view.

Original language	English
Pages (from-to)	201-204
Number of pages	4
Journal	Journal of Power Sources
Volume	87
Issue number	1
DOIs	https://doi.org/10.1016/S0378-7753(99)00502-9
Publication status	Published - 1 Jan 2000
Externally published	Yes

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Physical and Theoretical Chemistry
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

More information

10.1016/S0378-7753(99)00502-9

Cite this

@article{22a6a02b99fa45919f9076b40ff81d96,

title = "Available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles",

abstract = "The available capacity computation model based on the artificial neural network (ANN) for lead-acid batteries in an electric vehicle (EV) is presented. Comparing with the methods based on the Peukert equation, which is often used for the calculation of the available capacity for lead-acid batteries in EVs, this model is more accurate. The results of the experiment have proven the accuracy of the proposed model; the computation values are in good agreement with experimental data, the associated error has been considered acceptable from an engineering point of view.",

author = "Chan, {C. C.} and Lo, {Wai Chau Edward} and Shen Weixiang",

year = "2000",

month = jan,

day = "1",

doi = "10.1016/S0378-7753(99)00502-9",

language = "English",

volume = "87",

pages = "201--204",

journal = "Journal of Power Sources",

issn = "0378-7753",

publisher = "Elsevier",

number = "1",

}

TY - JOUR

T1 - Available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles

AU - Chan, C. C.

AU - Lo, Wai Chau Edward

AU - Weixiang, Shen

PY - 2000/1/1

Y1 - 2000/1/1

N2 - The available capacity computation model based on the artificial neural network (ANN) for lead-acid batteries in an electric vehicle (EV) is presented. Comparing with the methods based on the Peukert equation, which is often used for the calculation of the available capacity for lead-acid batteries in EVs, this model is more accurate. The results of the experiment have proven the accuracy of the proposed model; the computation values are in good agreement with experimental data, the associated error has been considered acceptable from an engineering point of view.

AB - The available capacity computation model based on the artificial neural network (ANN) for lead-acid batteries in an electric vehicle (EV) is presented. Comparing with the methods based on the Peukert equation, which is often used for the calculation of the available capacity for lead-acid batteries in EVs, this model is more accurate. The results of the experiment have proven the accuracy of the proposed model; the computation values are in good agreement with experimental data, the associated error has been considered acceptable from an engineering point of view.

UR - http://www.scopus.com/inward/record.url?scp=0033905301&partnerID=8YFLogxK

U2 - 10.1016/S0378-7753(99)00502-9

DO - 10.1016/S0378-7753(99)00502-9

M3 - Journal article

SN - 0378-7753

VL - 87

SP - 201

EP - 204

JO - Journal of Power Sources

JF - Journal of Power Sources

IS - 1

ER -

Available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles

Abstract

ASJC Scopus subject areas

UN SDGs

More information

Other files and links

Fingerprint

Cite this