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A new battery available capacity indicator for electric vehicles using neural network

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 Apr 2002 China (People's Republic of), Australia, Australia Publisher:Elsevier BVJournal:Energy Conversion and Management, volume 43, pages 817-826 (issn: 0196-8904,

Authors: Shen, WX; Chan, CC; Lo, EWC; Chau, KT;

doi: 10.1016/s0196-8904(01)00078-4

handle: 1959.3/196474 , 10722/73903

A new battery available capacity indicator for electric vehicles using neural network

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Abstract

Abstract The ability to calculate the battery available capacity (BAC) for electric vehicles (EVs) is very important. Knowing the BAC and, thus, the driving range cannot only prevent EVs from being stranding on the road but also optimize the utilization of the battery energy storage in EVs. In order to determine the BAC, this paper presents a new neural network (NN) model of the lead–acid battery, based on the battery discharge current and temperature. Comparisons between the calculated BAC from the NN model and the measured BAC from experiments show good agreement. Furthermore, this new approach can readily be extended to the calculation of the BAC for other types of batteries.

Countries

China (People's Republic of), Australia, Australia

Related Organizations

University of Hong Kong
China (People's Republic of)
University of Hong Kong
China (People's Republic of)
Swinburne University of Technology
Australia
University of Hong Kong (香港大學)
China (People's Republic of)
University of Hong Kong (香港大學)
China (People's Republic of)

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Keywords

Electric vehicles, Neural network model, Battery available capacity

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	101
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	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average