Accurate and reliable state of charge estimation of lithium ion batteries using time-delayed recurrent neural networks through the identification of overexcited neurons

Zhimin Xi, Rui Wang, Yuhong Fu, Chris Mi

Research output: Contribution to journalArticlepeer-review

Abstract

Various neural network models have been adopted for lithium ion battery state of charge (SOC) estimation with good accuracy. However, problems for battery states estimation from neural networks were usually not reported, which is mainly due to the lack of effective solutions other than a trial and error training process. This paper firstly proposes time-delayed recurrent neural network for lithium ion battery modeling and SOC estimation. Both exceptional performances and unexpected overfitting or poor performances are reported with in-depth analysis of the root cause. With explicit formulation of the network, each hidden neuron's output is examined. It is discovered that overexcited neurons could be the root cause for unexpected poor performances of the neural network. Without overexcited neurons, expectational SOC estimation accuracy is consistently obtained with estimation error being less than 1% for lithium ion magnesium phosphate (LiFeMgPO4) batteries considering a fair comparison in literature.

Original languageEnglish (US)
Article number117962
JournalApplied Energy
Volume305
DOIs
StatePublished - Jan 1 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Mechanical Engineering
  • Energy(all)
  • Management, Monitoring, Policy and Law

Keywords

  • Equivalent circuit models
  • Lithium ion battery
  • Overexcited neurons
  • State of charge
  • Time-delayed recurrent neural network

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