Genetic Algorithm Based Back-Propagation Neural Network Approach for Fault Diagnosis in Lithium-ion Battery System

Zuchang Gao, Cheng Siong Chin, Wai Lok Woo, Junbo Jia, Wei Da Toh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Citations (Scopus)

Abstract

Safety is important in a lithium-ion battery power system. It is necessary to adopt an effective fault diagnosis method to keep the battery power system in the good working status. In this paper, Genetic Algorithm (GA) is integrated to build a single hidden layer Back-Propagation Neural Network (BPNN) for fault diagnosis. In the process of training the neural network, GA is used to initialize and optimize the connection weights and thresholds of the neural network. Several faults are detected by the proposed GA optimized fault diagnosis scheme. Simulation results show that the proposed fault diagnosis scheme provides satisfactory results.
Original languageEnglish
Title of host publication2015 6th International Conference on Power Electronics Systems and Applications (PESA)
Subtitle of host publicationElectric Transportation - Automotive, Vessel and Aircraft, PESA 2015
PublisherIEEE
Number of pages6
ISBN (Electronic)9781509000623
DOIs
Publication statusPublished - 4 Feb 2016
Event6th International Conference on Power Electronics Systems and Applications, PESA 2015 - Hong Kong, Hong Kong
Duration: 15 Dec 201517 Dec 2015

Conference

Conference6th International Conference on Power Electronics Systems and Applications, PESA 2015
CountryHong Kong
CityHong Kong
Period15/12/1517/12/15

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