Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques

M. S.Hossain Lipu, Aini Hussain, M. H.M. Saad, M. A. Hannan

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

© 2017 IEEE. This paper presents an optimal state of energy (SOE) estimation strategy of a lithium-ion battery using the back-propagation neural network (BPNN). Two heuristic optmization techniques named backtracketing search algorithm (BSA) and particle swarm optimization (PSO) algorithm are applied to improve the accuracy of BPNN model. Optimization algorithms are developed to determine the optimal value of hidden layer neurons and learning rate of BPNN model. Three most influencing factors including current, voltage and temperature are considered as the inputs to the optimal BPNN model. Federal Urban Driving Schedule (FUDS) is used to check the model robustness at 0°C, 25°C and 45°C. The model performance is evaluated based on the root mean square error (RMSE) and mean absolute error (MAE). The results show that the proposed model obtains good accuracy with an absolute error of ±5%. The BPNN based BSA model improves the SOE estimation accuracy by reducing RMSE and MAE by 2.8% and 4.4% compared to BPNN based PSO model at 25°C.
Original languageEnglish
Pages1-6
Number of pages0
DOIs
Publication statusPublished - 09 Mar 2018
EventProceedings of the 2017 6th International Conference on Electrical Engineering and Informatics: Sustainable Society Through Digital Innovation, ICEEI 2017 -
Duration: 09 Mar 2018 → …

Conference

ConferenceProceedings of the 2017 6th International Conference on Electrical Engineering and Informatics: Sustainable Society Through Digital Innovation, ICEEI 2017
Period09/03/18 → …

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lithium
heuristics
back propagation
Backpropagation
Neural networks
ion
energy
Mean square error
Particle swarm optimization (PSO)
battery
Lithium-ion batteries
Neurons
learning
Electric potential

All Science Journal Classification (ASJC) codes

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

Cite this

Lipu, M. S. H., Hussain, A., Saad, M. H. M., & Hannan, M. A. (2018). Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques. 1-6. Paper presented at Proceedings of the 2017 6th International Conference on Electrical Engineering and Informatics: Sustainable Society Through Digital Innovation, ICEEI 2017, . https://doi.org/10.1109/ICEEI.2017.8312418
Lipu, M. S.Hossain ; Hussain, Aini ; Saad, M. H.M. ; Hannan, M. A. / Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques. Paper presented at Proceedings of the 2017 6th International Conference on Electrical Engineering and Informatics: Sustainable Society Through Digital Innovation, ICEEI 2017, .
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Lipu, MSH, Hussain, A, Saad, MHM & Hannan, MA 2018, 'Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques' Paper presented at Proceedings of the 2017 6th International Conference on Electrical Engineering and Informatics: Sustainable Society Through Digital Innovation, ICEEI 2017, 09/03/18, pp. 1-6. https://doi.org/10.1109/ICEEI.2017.8312418

Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques. / Lipu, M. S.Hossain; Hussain, Aini; Saad, M. H.M.; Hannan, M. A.

2018. 1-6 Paper presented at Proceedings of the 2017 6th International Conference on Electrical Engineering and Informatics: Sustainable Society Through Digital Innovation, ICEEI 2017, .

Research output: Contribution to conferencePaper

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Lipu MSH, Hussain A, Saad MHM, Hannan MA. Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques. 2018. Paper presented at Proceedings of the 2017 6th International Conference on Electrical Engineering and Informatics: Sustainable Society Through Digital Innovation, ICEEI 2017, . https://doi.org/10.1109/ICEEI.2017.8312418