Improved Whale Optimization Algorithm and Support Vector Machine for Remaining Useful Life Prediction of Lithium-ion Batteries

Y. Wang, Y. Ni, Y. Zhang, Z. Shen, S. Zhang, J. Wang

2021

Abstract

Prediction of remaining useful life (RUL) of Lithium-ion batteries (LIBs) is a key component of the prognostics and health management (PHM). A method based on improved whale optimization algorithm and support vector machine (IWOA-SVM) is proposed, which can improve the prediction accuracy for RUL of LIBs and timely maintain and replace the battery to ensure the safety and stability of the energy storage system. With the number of iterations increase, the WOA algorithm inevitably falls into local optimal solution. Therefore, the adaptive weights are introduced to improve the global search ability of the WOA algorithm. To verify the performance of the proposed method, the five test functions are utilized to compare with WOA algorithm. Experimental data simulations were performed using NASA Ames Prognostics Center of Excellence (PCoE) datasets to verify the proposed method. Compared with the SVM and WOA-SVM methods, the results show that the proposed method can accurately ensure RUL prediction accuracy.

Paper Citation

in Harvard Style

Wang Y., Ni Y., Zhang Y., Shen Z., Zhang S. and Wang J. (2021). Improved Whale Optimization Algorithm and Support Vector Machine for Remaining Useful Life Prediction of Lithium-ion Batteries. In Proceedings of the 1st International Joint Conference on Energy and Environmental Engineering - Volume 1: CoEEE, ISBN 978-989-758-599-9, pages 115-121. DOI: 10.5220/0011359300003355

in Bibtex Style

@conference{coeee21,
author={Y. Wang and Y. Ni and Y. Zhang and Z. Shen and S. Zhang and J. Wang},
title={Improved Whale Optimization Algorithm and Support Vector Machine for Remaining Useful Life Prediction of Lithium-ion Batteries},
booktitle={Proceedings of the 1st International Joint Conference on Energy and Environmental Engineering - Volume 1: CoEEE,},
year={2021},
pages={115-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011359300003355},
isbn={978-989-758-599-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Joint Conference on Energy and Environmental Engineering - Volume 1: CoEEE,
TI - Improved Whale Optimization Algorithm and Support Vector Machine for Remaining Useful Life Prediction of Lithium-ion Batteries
SN - 978-989-758-599-9
AU - Wang Y.
AU - Ni Y.
AU - Zhang Y.
AU - Shen Z.
AU - Zhang S.
AU - Wang J.
PY - 2021
SP - 115
EP - 121
DO - 10.5220/0011359300003355