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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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10752534/ |
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| author | Sadiqa Jafari Jisoo Kim Wonil Choi Yung-Cheol Byun |
| author_facet | Sadiqa Jafari Jisoo Kim Wonil Choi Yung-Cheol Byun |
| author_sort | Sadiqa Jafari |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f8cdf52d4a244979a079225c48b2b3e5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f8cdf52d4a244979a079225c48b2b3e52025-01-24T00:01:47ZengIEEEIEEE Access2169-35362025-01-0113114631147810.1109/ACCESS.2024.349765610752534Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion BatteriesSadiqa Jafari0https://orcid.org/0000-0001-9308-1062Jisoo Kim1https://orcid.org/0000-0002-1954-8805Wonil Choi2Yung-Cheol Byun3https://orcid.org/0000-0003-1107-9941Department of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju-si, South KoreaDepartment of Computer Engineering, Faculty of Software (Artificial Intelligence Major), College of Engineering, Jeju National University, Jeju-si, South KoreaNanoom Energy Company Ltd., Cheomdan-ro, Jeju-si, Jeju-do, South KoreaDepartment of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju-si, South KoreaAccurately 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.https://ieeexplore.ieee.org/document/10752534/Lithium-ion batterySOHoptimization algorithmsensemble learningmachine learningbattery performance |
| spellingShingle | Sadiqa Jafari Jisoo Kim Wonil Choi Yung-Cheol Byun Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries IEEE Access Lithium-ion battery SOH optimization algorithms ensemble learning machine learning battery performance |
| title | Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries |
| title_full | Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries |
| title_fullStr | Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries |
| title_full_unstemmed | Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries |
| title_short | Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries |
| title_sort | integrating multilayer perceptron and support vector regression for enhanced state of health estimation in lithium ion batteries |
| topic | Lithium-ion battery SOH optimization algorithms ensemble learning machine learning battery performance |
| url | https://ieeexplore.ieee.org/document/10752534/ |
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