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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Vydáno v:Processes Ročník 13; číslo 1; s. 158
Hlavní autoři: Wang, Rui, Xu, Xikang, Zhou, Qi, Zhang, Jingtao, Wang, Jing, Ye, Jilei, Wu, Yuping
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.01.2025
Témata:
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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Shrnutí: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.
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr13010158