Research on Fault Diagnosis Method of Lithium Battery Based on Fuzzy Neural Network

Meng Chen, Zongyang Liu, Jiang Wu, Kun Ma

Abstract

Aiming at the problem that the battery lithium battery has poor generalization performance for different health conditions, a fault diagnosis method based on fuzzy neural network is proposed. The neural network is used to diagnose the battery fault, and then the fuzzy system rules are used to output three fault states of the lithium battery, namely Corresponding Capacity reduction, Increase of internal resistance, SOC Reduction, and finally simulation analysis. The effectiveness of this method for fault diagnosis of lithium battery systems is demonstrated.

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Paper Citation


in Harvard Style

Chen M., Liu Z., Wu J. and Ma K. (2018). Research on Fault Diagnosis Method of Lithium Battery Based on Fuzzy Neural Network.In Proceedings of the 2nd International Conference on Intelligent Manufacturing and Materials - Volume 1: ICIMM, ISBN 978-989-758-345-2, pages 593-596. DOI: 10.5220/0007536405930596


in Bibtex Style

@conference{icimm18,
author={Meng Chen and Zongyang Liu and Jiang Wu and Kun Ma},
title={Research on Fault Diagnosis Method of Lithium Battery Based on Fuzzy Neural Network},
booktitle={Proceedings of the 2nd International Conference on Intelligent Manufacturing and Materials - Volume 1: ICIMM,},
year={2018},
pages={593-596},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007536405930596},
isbn={978-989-758-345-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Intelligent Manufacturing and Materials - Volume 1: ICIMM,
TI - Research on Fault Diagnosis Method of Lithium Battery Based on Fuzzy Neural Network
SN - 978-989-758-345-2
AU - Chen M.
AU - Liu Z.
AU - Wu J.
AU - Ma K.
PY - 2018
SP - 593
EP - 596
DO - 10.5220/0007536405930596