Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data
Accurately estimating the State of Charge (SOC) in Electric Vehicles (EVs) is critical for battery management and operational efficiency. This paper presents a Deep Learning (DL) approach to address this challenge, utilizing Feed-Forward Neural Networks (FFNN) to estimate SOC in real-world EV scenar...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier B.V.
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42347/ http://umpir.ump.edu.my/id/eprint/42347/1/Advancing%20battery%20state%20of%20charge%20estimation%20in%20electric%20vehicles.pdf |
| _version_ | 1848826586526121984 |
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| author | Mohd Herwan, Sulaiman Zuriani, Mustaffa Saifudin, Razali Mohd Razali, Daud |
| author_facet | Mohd Herwan, Sulaiman Zuriani, Mustaffa Saifudin, Razali Mohd Razali, Daud |
| author_sort | Mohd Herwan, Sulaiman |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Accurately estimating the State of Charge (SOC) in Electric Vehicles (EVs) is critical for battery management and operational efficiency. This paper presents a Deep Learning (DL) approach to address this challenge, utilizing Feed-Forward Neural Networks (FFNN) to estimate SOC in real-world EV scenarios. The research used data from 70 driving sessions with a BMW i3 EV. Each session recorded key factors like voltage, current, and temperature, providing inputs for the DL model. The recorded SOC values served as outputs. We divided the dataset into training, validation, and testing subsets to develop and evaluate the FFNN model. The results demonstrate that the FFNN model yields minimal errors and significantly improves SOC estimation accuracy. Our comparative analysis with other machine learning techniques shows that FFNN outperforms them, with an approximately 2.87 % lower root mean square error (RMSE) compared to the second-best method, Extreme Learning Machine (ELM). This work has significant implications for electric vehicle battery management, demonstrating that deep learning methods can enhance SOC estimation, thereby improving the efficiency and reliability of EV operations. |
| first_indexed | 2025-11-15T03:47:10Z |
| format | Article |
| id | ump-42347 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:47:10Z |
| publishDate | 2024 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-423472024-08-14T03:45:45Z http://umpir.ump.edu.my/id/eprint/42347/ Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data Mohd Herwan, Sulaiman Zuriani, Mustaffa Saifudin, Razali Mohd Razali, Daud TK Electrical engineering. Electronics Nuclear engineering Accurately estimating the State of Charge (SOC) in Electric Vehicles (EVs) is critical for battery management and operational efficiency. This paper presents a Deep Learning (DL) approach to address this challenge, utilizing Feed-Forward Neural Networks (FFNN) to estimate SOC in real-world EV scenarios. The research used data from 70 driving sessions with a BMW i3 EV. Each session recorded key factors like voltage, current, and temperature, providing inputs for the DL model. The recorded SOC values served as outputs. We divided the dataset into training, validation, and testing subsets to develop and evaluate the FFNN model. The results demonstrate that the FFNN model yields minimal errors and significantly improves SOC estimation accuracy. Our comparative analysis with other machine learning techniques shows that FFNN outperforms them, with an approximately 2.87 % lower root mean square error (RMSE) compared to the second-best method, Extreme Learning Machine (ELM). This work has significant implications for electric vehicle battery management, demonstrating that deep learning methods can enhance SOC estimation, thereby improving the efficiency and reliability of EV operations. Elsevier B.V. 2024-07-25 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/42347/1/Advancing%20battery%20state%20of%20charge%20estimation%20in%20electric%20vehicles.pdf Mohd Herwan, Sulaiman and Zuriani, Mustaffa and Saifudin, Razali and Mohd Razali, Daud (2024) Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data. Cleaner Energy Systems, 8 (100131). pp. 1-9. ISSN 2772-7831. (Published) https://doi.org/10.1016/j.cles.2024.100131 https://doi.org/10.1016/j.cles.2024.100131 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mohd Herwan, Sulaiman Zuriani, Mustaffa Saifudin, Razali Mohd Razali, Daud Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data |
| title | Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data |
| title_full | Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data |
| title_fullStr | Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data |
| title_full_unstemmed | Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data |
| title_short | Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data |
| title_sort | advancing battery state of charge estimation in electric vehicles through deep learning: a comprehensive study using real-world driving data |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/42347/ http://umpir.ump.edu.my/id/eprint/42347/ http://umpir.ump.edu.my/id/eprint/42347/ http://umpir.ump.edu.my/id/eprint/42347/1/Advancing%20battery%20state%20of%20charge%20estimation%20in%20electric%20vehicles.pdf |