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Publication Type : Conference Paper
Publisher : 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE)
Source : 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) (2020)
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Year : 2020
Abstract : This paper elucidates an effective fault classification method for lithium-ion batteries employed in electric vehicles. Lithium-ion batteries are eccentric in nature with non-linear behavior which may lead to dreadful events, especially in the case of electric vehicles. This work takes into consideration the eventual, thermal runaway fault and the battery-life degrading faults such as the under-voltage and overvoltage faults. A mathematical model of the lithium-ion battery is deployed. The model is then exercised to collect sufficient data under conventional and non-conventional operating conditions. By supervised learning, a layered perceptron is trained, effectively enabling the system to efficiently classify the faults in lithium-ion batteries. Multi-Layer perceptron is employed in this work as the classifying algorithm which is trained using backpropagation. The validation of the trained algorithm is executed in a software workbench and, the discussions and results are presented.
Cite this Research Publication : R. R. Suresh, Shanmughasundaram R., and Mohanrajan S. R., “Model Based Fault Classification Method for Electric Vehicle Pertained Lithium-Ion Batteries Using Multi Layer Perceptron”, in 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 2020.