SOC and SOH Monitoring Algorithms for Lithium Batteries Using Multilayer Neural Networks

Jong Hyun Lee; Hyun Sil Kim; In Soo Lee

doi:10.29007/m89x

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SOC and SOH Monitoring Algorithms for Lithium Batteries Using Multilayer Neural Networks

8 pages•Published: March 9, 2020

Jong Hyun Lee, Hyun Sil Kim and In Soo Lee

Abstract

This paper presents a battery monitoring system using a multilayer neural network (MNN) for state of charge (SOC) estimation and state of health (SOH) diagnosis. In this system, the MNN utilizes experimental discharge voltage data from lithium battery operation to estimate SOH and uses present and previous voltages for SOC estimation. From experimental results, we know that the proposed battery monitoring system performs SOC estimation and SOH diagnosis well.

Keyphrases: lithium battery, Multilayer neural network, SOC Estimation, SOH diagnosis

In: Gordon Lee and Ying Jin (editors). Proceedings of 35th International Conference on Computers and Their Applications, vol 69, pages 206--213

Links:	https://easychair.org/publications/paper/nBbH
	https://doi.org/10.29007/m89x

BibTeX entry

@inproceedings{CATA2020:SOC_and_SOH_Monitoring,
  author    = {Jong Hyun Lee and Hyun Sil Kim and In Soo Lee},
  title     = {SOC and SOH Monitoring Algorithms for Lithium Batteries Using Multilayer Neural Networks},
  booktitle = {Proceedings of 35th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {69},
  pages     = {206--213},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/nBbH},
  doi       = {10.29007/m89x}}

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