A new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system

被引：53

作者：

Chau, KT ^{[1
]}

Wu, KC ^{[1
]}

Chan, CC ^{[1
]}

机构：

[1] Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Hong Kong, Peoples R China

来源：

ENERGY CONVERSION AND MANAGEMENT | 2004年 / 45卷 / 11-12期

关键词：

adaptive neuro-fuzzy inference system; battery residual capacity; electric vehicles; lithium-ion battery; state of available capacity;

D O I：

10.1016/j.enconman.2003.09.031

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

This paper describes a new adaptive neuro-fuzzy inference system (ANFIS) model to estimate accurately the battery residual capacity (BRC) of the lithium-ion (Li-ion) battery for modern electric vehicles (EVs). The key to this model is to adopt newly both the discharged/regenerative capacity distributions and the temperature distributions as the inputs and the state of available capacity (SOAC) as the output, which represents the BRC. Moreover, realistic EV discharge current profiles are newly used to formulate the proposed model. The accuracy of the estimated SOAC obtained from the model is verified by experiments under various EV discharge current profiles. (C) 2003 Elsevier Ltd. All rights reserved.

引用

页码：1681 / 1692

页数：12

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