Optimal parameter identification strategy applied to lithium-ion battery model for electric vehicles using drive cycle data
The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which...
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| Published in: | Energy reports Vol. 11; pp. 2049 - 2058 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.06.2024
Elsevier |
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| ISSN: | 2352-4847, 2352-4847 |
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| Abstract | The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which can be time-consuming and yield suboptimal results. In recent years, metaheuristic optimization algorithms have emerged as powerful tools for efficiently searching and identifying optimal parameter values. This paper proposes an optimal parameter identification strategy using a metaheuristic optimization algorithm applied to a Shepherd model for EV applications. The identification technique that was based on the Self-adaptive Bonobo Optimizer (SaBO) performed extremely well when it came to the process of identifying the battery's unidentified properties. Because of this, the overall voltage error of the suggested identification technique has been lowered to 4.2377 × 10−3, and the root mean square error (RMSE) between the model and the data has been calculated to be 8.64 × 10−3. In addition, compared to the other optimization methods, the optimization efficiency was able to attain 96.6%, which validated its efficiency. |
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| AbstractList | The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which can be time-consuming and yield suboptimal results. In recent years, metaheuristic optimization algorithms have emerged as powerful tools for efficiently searching and identifying optimal parameter values. This paper proposes an optimal parameter identification strategy using a metaheuristic optimization algorithm applied to a Shepherd model for EV applications. The identification technique that was based on the Self-adaptive Bonobo Optimizer (SaBO) performed extremely well when it came to the process of identifying the battery's unidentified properties. Because of this, the overall voltage error of the suggested identification technique has been lowered to 4.2377 × 10 -3 , and the root mean square error (RMSE) between the model and the data has been calculated to be 8.64 × 10 -3 . In addition, compared to the other optimization methods, the optimization efficiency was able to attain 96.6%, which validated its efficiency. The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which can be time-consuming and yield suboptimal results. In recent years, metaheuristic optimization algorithms have emerged as powerful tools for efficiently searching and identifying optimal parameter values. This paper proposes an optimal parameter identification strategy using a metaheuristic optimization algorithm applied to a Shepherd model for EV applications. The identification technique that was based on the Self-adaptive Bonobo Optimizer (SaBO) performed extremely well when it came to the process of identifying the battery's unidentified properties. Because of this, the overall voltage error of the suggested identification technique has been lowered to 4.2377 × 10−3, and the root mean square error (RMSE) between the model and the data has been calculated to be 8.64 × 10−3. In addition, compared to the other optimization methods, the optimization efficiency was able to attain 96.6%, which validated its efficiency. |
| Author | Al-Dhaifallah, Mujahed Ghadbane, Houssam Eddine Ferahtia, Seydali Rezk, Hegazy Barkat, Said |
| Author_xml | – sequence: 1 givenname: Houssam Eddine surname: Ghadbane fullname: Ghadbane, Houssam Eddine organization: Electrical Engineering Laboratory of Guelma (LGEG), Electrotechnical and Automatic Engineering Department, Université 8 Mai 1945, Guelma 24000, Algeria – sequence: 2 givenname: Hegazy surname: Rezk fullname: Rezk, Hegazy organization: Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Saudi Arabia – sequence: 3 givenname: Seydali surname: Ferahtia fullname: Ferahtia, Seydali organization: University of Nantes, IREENA Laboratory, Industrial Engineering Department, France – sequence: 4 givenname: Said surname: Barkat fullname: Barkat, Said organization: University of M’sila, Electrical Engineering Laboratory, Electrical Engineering Departement, Algeria – sequence: 5 givenname: Mujahed surname: Al-Dhaifallah fullname: Al-Dhaifallah, Mujahed email: mujahed@kfupm.edu.sa organization: Control and Instrumentation Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia |
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| Keywords | Metaheuristic optimization algorithms Electric vehicles Li-ion battery Parameters identification Electric vehicles, Li-ion battery, Metaheuristic optimization algorithms, Parameters identification |
| Language | English |
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