Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles
The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/6/2306 |
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| author | Saadin Oyucu Ferdi Doğan Ahmet Aksöz Emre Biçer |
| author_facet | Saadin Oyucu Ferdi Doğan Ahmet Aksöz Emre Biçer |
| author_sort | Saadin Oyucu |
| collection | DOAJ |
| description | The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the popularity of EVs. The accurate prediction and management of LIBs in EVs are essential, and machine learning-based methods have been explored in order to estimate parameters such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP). Various machine learning techniques, including support vector machines, decision trees, and deep learning, have been employed for predicting LIB states. This study proposes a methodology for comparative analysis, focusing on classical and deep learning approaches, and discusses enhancements to the LSTM (long short-term memory) and Bi-LSTM (bidirectional long short-term memory) methods. Evaluation metrics such as MSE, MAE, RMSE, and R-squared are applied to assess the proposed methods’ performances. The study aims to contribute to technological advancements in the electric vehicle industry by predicting the performance of LIBs. The structure of the rest of the study is outlined, covering materials and methods, LIB data preparation, analysis, the proposal of machine learning models, evaluations, and concluding remarks, with recommendations for future studies. |
| first_indexed | 2024-04-24T18:36:08Z |
| format | Article |
| id | doaj.art-e6e6737dc9b74600a0f6aa188e242c06 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T18:36:08Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e6e6737dc9b74600a0f6aa188e242c062024-03-27T13:19:15ZengMDPI AGApplied Sciences2076-34172024-03-01146230610.3390/app14062306Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric VehiclesSaadin Oyucu0Ferdi Doğan1Ahmet Aksöz2Emre Biçer3Department of Computer Engineering, Adıyaman University, Adıyaman 02040, TürkiyeDepartment of Computer Engineering, Adıyaman University, Adıyaman 02040, TürkiyeMobilers Group, Sivas Cumhuriyet University, Sivas 58140, TürkiyeBattery Research Laboratory, Sivas University of Science and Technology, Sivas 58010, TürkiyeThe significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the popularity of EVs. The accurate prediction and management of LIBs in EVs are essential, and machine learning-based methods have been explored in order to estimate parameters such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP). Various machine learning techniques, including support vector machines, decision trees, and deep learning, have been employed for predicting LIB states. This study proposes a methodology for comparative analysis, focusing on classical and deep learning approaches, and discusses enhancements to the LSTM (long short-term memory) and Bi-LSTM (bidirectional long short-term memory) methods. Evaluation metrics such as MSE, MAE, RMSE, and R-squared are applied to assess the proposed methods’ performances. The study aims to contribute to technological advancements in the electric vehicle industry by predicting the performance of LIBs. The structure of the rest of the study is outlined, covering materials and methods, LIB data preparation, analysis, the proposal of machine learning models, evaluations, and concluding remarks, with recommendations for future studies.https://www.mdpi.com/2076-3417/14/6/2306machine learningstate of healthLi-ionbatteryelectrical vehicle |
| spellingShingle | Saadin Oyucu Ferdi Doğan Ahmet Aksöz Emre Biçer Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles Applied Sciences machine learning state of health Li-ion battery electrical vehicle |
| title | Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles |
| title_full | Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles |
| title_fullStr | Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles |
| title_full_unstemmed | Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles |
| title_short | Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles |
| title_sort | comparative analysis of commonly used machine learning approaches for li ion battery performance prediction and management in electric vehicles |
| topic | machine learning state of health Li-ion battery electrical vehicle |
| url | https://www.mdpi.com/2076-3417/14/6/2306 |
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