Genetic Algorithm based Back-Propagation Neural Network approach for fault diagnosis in lithium-ion battery system

Gao, Zuchang, Chin, Cheng Siong, Woo, Wai Lok, Jia, Junbo and Toh, Wei Da (2016) Genetic Algorithm based Back-Propagation Neural Network approach for fault diagnosis in lithium-ion battery system. In: PESA 2015 - 2015 6th International Conference on Power Electronics Systems and Applications, 15th - 17th December 2015, Melboune, Australia.

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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.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Back-propagation neural network, fault diagnosis, genetic algorithm, lithium-ion battery
Subjects:	H600 Electronic and Electrical Engineering
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	05 Apr 2019 12:49
Last Modified:	10 Oct 2019 20:34
URI:	http://nrl.northumbria.ac.uk/id/eprint/38801

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