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Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Van Mierlo, J. and Van Den Bossche, P. (2016) Critical Review of State of Health Estimation Methods of Li-Ion Batteries for Real Applications. Renewable and Sustainable Energy Reviews, 56, 572-587.
https://doi.org/10.1016/j.rser.2015.11.042

has been cited by the following article:

• TITLE: State of Health Estimation of Lithium-Ion Batteries Using Support Vector Regression and Long Short-Term Memory

  AUTHORS: Inioluwa Obisakin, Chikodinaka Vanessa Ekeanyanwu

  KEYWORDS: Support Vector Regression (SVR), Long Short-Term Memory (LSTM) Network, State of Health (SOH) Estimation

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.12 No.8, August 19, 2022

  ABSTRACT: Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model.