A new battery capacity indicator for nickel-metal hydride battery powered electric vehicles using adaptive neuro-fuzzy inference system

K. T. Chau; K. C. Wu; C. C. Chan; W. X. Shen

doi:10.1016/S0196-8904(02)00249-2

A new battery capacity indicator for nickel-metal hydride battery powered electric vehicles using adaptive neuro-fuzzy inference system

K. T. Chau, K. C. Wu, C. C. Chan, W. X. Shen

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

34 Citations (Scopus)

Abstract

This paper describes a new approach to estimate accurately the battery residual capacity (BRC) of the nickel-metal hydride (Ni-MH) battery for modern electric vehicles (EVs). The key to this approach is to model the Ni-MH battery in EVs by using the adaptive neuro-fuzzy inference system (ANFIS) with newly defined inputs and output. The inputs are the temperature and the discharged capacity distribution describing the discharge current profile, while the output is the state of available capacity (SOAC) representing the BRC. The estimated SOAC from ANFIS model and the measured SOAC from experiments are compared, and the results confirm that the proposed approach can provide an accurate estimation of the SOAC under variable discharge currents.

Original language	English
Pages (from-to)	2059-2071
Number of pages	13
Journal	Energy Conversion and Management
Volume	44
Issue number	13
DOIs	https://doi.org/10.1016/S0196-8904(02)00249-2
Publication status	Published - Aug 2003
Externally published	Yes

Keywords

Adaptive neuro-fuzzy inference system
Battery residual capacity
Electric vehicles
Nickel-metal hydride 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/S0196-8904(02)00249-2

Cite this

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abstract = "This paper describes a new approach to estimate accurately the battery residual capacity (BRC) of the nickel-metal hydride (Ni-MH) battery for modern electric vehicles (EVs). The key to this approach is to model the Ni-MH battery in EVs by using the adaptive neuro-fuzzy inference system (ANFIS) with newly defined inputs and output. The inputs are the temperature and the discharged capacity distribution describing the discharge current profile, while the output is the state of available capacity (SOAC) representing the BRC. The estimated SOAC from ANFIS model and the measured SOAC from experiments are compared, and the results confirm that the proposed approach can provide an accurate estimation of the SOAC under variable discharge currents.",

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AB - This paper describes a new approach to estimate accurately the battery residual capacity (BRC) of the nickel-metal hydride (Ni-MH) battery for modern electric vehicles (EVs). The key to this approach is to model the Ni-MH battery in EVs by using the adaptive neuro-fuzzy inference system (ANFIS) with newly defined inputs and output. The inputs are the temperature and the discharged capacity distribution describing the discharge current profile, while the output is the state of available capacity (SOAC) representing the BRC. The estimated SOAC from ANFIS model and the measured SOAC from experiments are compared, and the results confirm that the proposed approach can provide an accurate estimation of the SOAC under variable discharge currents.

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A new battery capacity indicator for nickel-metal hydride battery powered electric vehicles using adaptive neuro-fuzzy inference system

Abstract

Keywords

ASJC Scopus subject areas

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