A neural network based method for instantaneous power estimation in electric vehicles' Li-ion batteries

Ala A. Hussein

doi:10.1109/APEC.2017.7931143

A neural network based method for instantaneous power estimation in electric vehicles' Li-ion batteries

Ala A. Hussein

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

In electric vehicles (EVs), a battery is used as a main or auxiliary bidirectional power source. In order to optimize the battery operation, the power sourced or sinked by the battery must be estimated in real time under any condition. The battery's power is a function of terminal current, state-of-charge (SOC), ambient temperature and state-of-health (SOH). This paper proposes a method for estimating the battery power using artificial neural networks (ANNs). Experimental data obtained by performing a standardized EVs test on a 12V/150Ah commercial lithium-ion (Li-ion) battery are presented and used for model evaluation.

Original language	English
Title of host publication	2017 IEEE Applied Power Electronics Conference and Exposition, APEC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3122-3126
Number of pages	5
ISBN (Electronic)	9781509053667
DOIs	https://doi.org/10.1109/APEC.2017.7931143
Publication status	Published - May 17 2017
Externally published	Yes
Event	32nd Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2017 - Tampa, United States Duration: Mar 26 2017 → Mar 30 2017

Other

Other	32nd Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2017
Country/Territory	United States
City	Tampa
Period	3/26/17 → 3/30/17

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/APEC.2017.7931143

Cite this

Hussein, AA 2017, A neural network based method for instantaneous power estimation in electric vehicles' Li-ion batteries. in 2017 IEEE Applied Power Electronics Conference and Exposition, APEC 2017., 7931143, Institute of Electrical and Electronics Engineers Inc., pp. 3122-3126, 32nd Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2017, Tampa, United States, 3/26/17. https://doi.org/10.1109/APEC.2017.7931143

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title = "A neural network based method for instantaneous power estimation in electric vehicles' Li-ion batteries",

abstract = "In electric vehicles (EVs), a battery is used as a main or auxiliary bidirectional power source. In order to optimize the battery operation, the power sourced or sinked by the battery must be estimated in real time under any condition. The battery's power is a function of terminal current, state-of-charge (SOC), ambient temperature and state-of-health (SOH). This paper proposes a method for estimating the battery power using artificial neural networks (ANNs). Experimental data obtained by performing a standardized EVs test on a 12V/150Ah commercial lithium-ion (Li-ion) battery are presented and used for model evaluation.",

author = "Hussein, {Ala A.}",

year = "2017",

month = may,

day = "17",

doi = "10.1109/APEC.2017.7931143",

language = "English",

pages = "3122--3126",

booktitle = "2017 IEEE Applied Power Electronics Conference and Exposition, APEC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "32nd Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2017 ; Conference date: 26-03-2017 Through 30-03-2017",

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AB - In electric vehicles (EVs), a battery is used as a main or auxiliary bidirectional power source. In order to optimize the battery operation, the power sourced or sinked by the battery must be estimated in real time under any condition. The battery's power is a function of terminal current, state-of-charge (SOC), ambient temperature and state-of-health (SOH). This paper proposes a method for estimating the battery power using artificial neural networks (ANNs). Experimental data obtained by performing a standardized EVs test on a 12V/150Ah commercial lithium-ion (Li-ion) battery are presented and used for model evaluation.

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M3 - Conference contribution

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EP - 3126

BT - 2017 IEEE Applied Power Electronics Conference and Exposition, APEC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 32nd Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2017

Y2 - 26 March 2017 through 30 March 2017

ER -

A neural network based method for instantaneous power estimation in electric vehicles' Li-ion batteries

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