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.