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Lookup NU author(s): Professor Cheng Chin,
Dr Wai Lok Woo
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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.
Author(s): Gao ZC, Chin CS, Woo WL, Jia JB, Toh WD
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Power Electronics Systems and Applications (PESA), 2015 6th International Conference on
Year of Conference: 2015
Print publication date: 01/01/2015
Online publication date: 04/02/2016
Acceptance date: 01/01/1900