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

K. T. Chau; K. C. Wu; C. C. Chan

doi:10.1016/j.enconman.2003.09.031

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

K. T. Chau, K. C. Wu, C. C. Chan

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

80 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	1681-1692
Number of pages	12
Journal	Energy Conversion and Management
Volume	45
Issue number	11-12
DOIs	https://doi.org/10.1016/j.enconman.2003.09.031
Publication status	Published - Jul 2004
Externally published	Yes

Keywords

Adaptive neuro-fuzzy inference system
Battery residual capacity
Electric vehicles
Lithium-ion battery
State of available capacity

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Nuclear Energy and Engineering
Fuel Technology
Energy Engineering and Power Technology

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10.1016/j.enconman.2003.09.031

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abstract = "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.",

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AU - Wu, K. C.

AU - Chan, C. C.

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Y1 - 2004/7

N2 - 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.

AB - 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.

KW - Adaptive neuro-fuzzy inference system

KW - Battery residual capacity

KW - Electric vehicles

KW - Lithium-ion battery

KW - State of available capacity

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JO - Energy Conversion and Management

JF - Energy Conversion and Management

IS - 11-12

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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