State of Health Estimation for Lithium-Ion Batteries Using Enhanced Whale Optimization Algorithm for Feature Selection and Support Vector Regression Model

Evaluating the state of health (SOH) of lithium-ion batteries (LIBs) is essential for their safe deployment and the advancement of electric vehicles (EVs). Existing machine learning methods face challenges in the automation and effectiveness of feature extraction, necessitating improved computationa...

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Veröffentlicht in:	Processes Jg. 13; H. 1; S. 158
Hauptverfasser:	Wang, Rui, Xu, Xikang, Zhou, Qi, Zhang, Jingtao, Wang, Jing, Ye, Jilei, Wu, Yuping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.01.2025
Schlagworte:	Accuracy Aging Algorithms Automation Batteries Cetacea Datasets Electric vehicles Feature extraction Feature selection Gaussian process Health Lithium Lithium-ion batteries Machine learning Mathematical optimization Methods Neural networks Noise threshold Optimization Optimization algorithms Parameter identification Random noise Regression models Root-mean-square errors Support vector machines Technology application Temperature
ISSN:	2227-9717, 2227-9717
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Abstract	Evaluating the state of health (SOH) of lithium-ion batteries (LIBs) is essential for their safe deployment and the advancement of electric vehicles (EVs). Existing machine learning methods face challenges in the automation and effectiveness of feature extraction, necessitating improved computational efficiency. To address this issue, we propose a collaborative approach integrating an enhanced whale optimization algorithm (EWOA) for feature selection and a lightweight support vector regression (SVR) model for SOH estimation. Key features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The EWOA selects features by initially assigning weights based on importance scores from a random forest model. Gaussian noise increases population diversity, while a dynamic threshold method optimizes the selection process, preventing local optima. The selected features construct the SVR model for SOH estimation. This method is validated using four aging datasets from the NASA database, conducting 50 prediction experiments per battery. The results indicate optimal average absolute error (MAE) and root mean square error (RMSE) within 0.41% and 0.71%, respectively, with average errors below 1% and 1.3%. This method enhances automation and accuracy in feature selection while ensuring efficient SOH estimation, providing valuable insights for practical LIB applications.
AbstractList	Evaluating the state of health (SOH) of lithium-ion batteries (LIBs) is essential for their safe deployment and the advancement of electric vehicles (EVs). Existing machine learning methods face challenges in the automation and effectiveness of feature extraction, necessitating improved computational efficiency. To address this issue, we propose a collaborative approach integrating an enhanced whale optimization algorithm (EWOA) for feature selection and a lightweight support vector regression (SVR) model for SOH estimation. Key features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The EWOA selects features by initially assigning weights based on importance scores from a random forest model. Gaussian noise increases population diversity, while a dynamic threshold method optimizes the selection process, preventing local optima. The selected features construct the SVR model for SOH estimation. This method is validated using four aging datasets from the NASA database, conducting 50 prediction experiments per battery. The results indicate optimal average absolute error (MAE) and root mean square error (RMSE) within 0.41% and 0.71%, respectively, with average errors below 1% and 1.3%. This method enhances automation and accuracy in feature selection while ensuring efficient SOH estimation, providing valuable insights for practical LIB applications.
Audience	Academic
Author	Zhou, Qi Ye, Jilei Xu, Xikang Wu, Yuping Wang, Jing Wang, Rui Zhang, Jingtao
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SubjectTerms	Accuracy Aging Algorithms Automation Batteries Cetacea Datasets Electric vehicles Feature extraction Feature selection Gaussian process Health Lithium Lithium-ion batteries Machine learning Mathematical optimization Methods Neural networks Noise threshold Optimization Optimization algorithms Parameter identification Random noise Regression models Root-mean-square errors Support vector machines Technology application Temperature
Title	State of Health Estimation for Lithium-Ion Batteries Using Enhanced Whale Optimization Algorithm for Feature Selection and Support Vector Regression Model
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