Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm

Mahammad A. Hannan, Molla S.Hossain Lipu, Aini Hussain, Mohamad H. Saad, Afida Ayob

Research output: Contribution to journalArticle

45 Citations (Scopus)


© 2013 IEEE. The state of charge (SOC) is a critical evaluation index of battery residual capacity. The significance of an accurate SOC estimation is great for a lithium-ion battery to ensure its safe operation and to prevent from over-charging or over-discharging. However, to estimate an accurate capacity of SOC of the lithium-ion battery has become a major concern for the electric vehicle (EV) industry. Therefore, numerous researches are being conducted to address the challenges and to enhance the battery performance. The main objective of this paper is to develop an accurate SOC estimation approach for a lithium-ion battery by improving back-propagation neural network (BPNN) capability using backtracking search algorithm (BSA). BSA optimization is utilized to improve the accuracy and robustness of BPNN model by finding the optimal value of hidden layer neurons and learning rate. In this paper, Dynamic Stress Test and Federal Urban Driving Schedule drive profiles are applied for testing the model at three different temperatures. The obtained results of the BPNN based BSA model are compared with the radial basis function neural network, generalized regression neural network and extreme learning machine model using statistical error values of root mean square error, mean absolute error, mean absolute percentage error, and SOC error to check and validate the model performance. The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures.
Original languageEnglish
Pages (from-to)10069-10079
Number of pages9061
JournalIEEE Access
Publication statusPublished - 24 Jan 2018

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Energy(all)
  • Energy Engineering and Power Technology
  • Materials Science(all)
  • Computer Science(all)
  • Signal Processing

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