Toggle Main Menu Toggle Search

Open Access padlockePrints

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

Lookup NU author(s): Dr Cheng Chin, Dr Wai Lok Woo

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Abstract

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.


Publication metadata

Author(s): Gao Z, Chin CS, Woo WL, Jia JB, Toh WD

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 6th IEEE International Conference on Power Electronics Systems and Applications (PESA)

Year of Conference: 2015

Pages: 1-6

Online publication date: 17/12/2015

Acceptance date: 22/09/2015

Publisher: IEEE

URL: http://dx.doi.org/10.1109/PESA.2015.7398911

DOI: 10.1109/PESA.2015.7398911

Library holdings: Search Newcastle University Library for this item

ISBN: 9781509000623


Share