Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries

Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries

Accurately evaluating the State of Health (SOH) of batteries is crucial for guaranteeing the secure and dependable functioning of Electric Vehicles (EVs). This paper presents a novel strategy for tackling the difficulties associated with intricate preprocessing and the demand for extensive data in c...

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Bibliographic Details
Main Authors: Sadiqa Jafari, Jisoo Kim, Wonil Choi, Yung-Cheol Byun
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Lithium-ion battery
SOH
optimization algorithms
ensemble learning
machine learning
battery performance
Online Access:https://ieeexplore.ieee.org/document/10752534/
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Summary:Accurately evaluating the State of Health (SOH) of batteries is crucial for guaranteeing the secure and dependable functioning of Electric Vehicles (EVs). This paper presents a novel strategy for tackling the difficulties associated with intricate preprocessing and the demand for extensive data in conventional approaches to SOH measurement. Using sophisticated machine learning algorithms, we suggest an all-encompassing methodology for predicting the SOH. Our approach includes meticulous data preparation, which includes analyzing crucial operating elements such as voltage, current, and temperature. We utilized Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models, which were fine-tuned using hyperparameter optimization. The models were assessed using evaluation metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>. In order to improve the accuracy of our predictions, we combined these models into a stacked ensemble using a Random Forest (RF) meta-model. This resulted in an <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> value of 0.987, MAE of 0.02559, MSE of 0.0013, and RMSE of 0.00624. The results indicate that the ensemble outperforms individual models in predicting SOH. This research highlights the capacity of ensemble learning in predictive maintenance and battery management.
ISSN:2169-3536

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