Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm

Molla S. Hossain Lipu, Hannan M A, Aini Hussain, Mohamad Hanif Md Saad, Afida Ayob, Mohammad Nasir Uddin

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper develops a state-of-charge (SOC) estimation model for a lithium-ion battery using an improved extreme learning machine (ELM) algorithm. ELM is suitable for an SOC estimation since the ELM algorithm has fast estimation speed, good generalization performance, and high accuracy. However, the performance of ELM is highly dependent on training accuracy and the number of neurons in a hidden layer. Hence, a gravitational search algorithm (GSA) is applied to improve the ELM computational intelligence by searching for the optimal value hidden layer neurons. The optimal ELM-based GSA model does not require internal battery knowledge and mathematical model for an SOC estimation. The model robustness is validated at different temperatures using different electric vehicle drive cycles. The performance of the ELM-GSA model is verified with two popular neural network methods: Back-propagation neural network (BPNN) and radial basis function neural network (RBFNN). The results are evaluated using different error rates and computation costs. The results demonstrate that the ELM-based GSA model offers a higher accuracy and lower SOC error rate than those of BPNN-based GSA and RBFNN-based GSA models. Furthermore, a detailed comparative study between the proposed model and existing SOC strategies is conducted, which also demonstrates the superiority of the proposed model.

Original languageEnglish
Article number8656510
Pages (from-to)4225-4234
Number of pages10
JournalIEEE Transactions on Industry Applications
Volume55
Issue number4
DOIs
Publication statusPublished - 1 Jul 2019

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Learning systems
Neural networks
Backpropagation
Neurons
Lithium-ion batteries
Electric vehicles
Artificial intelligence
Mathematical models
Costs

Keywords

  • Electric vehicle
  • extreme learning machine
  • gravitational search algorithm
  • lithium-ion NMC battery
  • state of charge (SOC)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm. / Hossain Lipu, Molla S.; M A, Hannan; Hussain, Aini; Md Saad, Mohamad Hanif; Ayob, Afida; Uddin, Mohammad Nasir.

In: IEEE Transactions on Industry Applications, Vol. 55, No. 4, 8656510, 01.07.2019, p. 4225-4234.

Research output: Contribution to journalArticle

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