Comparative Evaluation of Fractional-Order Models for Lithium-Ion Batteries Response to Novel Drive Cycle Dataset
The high-fidelity lithium-ion battery (LIB) models are crucial for realizing an accurate state estimation in battery management systems (BMSs). Recently, the fractional-order equivalent circuit models (FOMs), as a frequency-domain modeling approach, offer distinct advantages for constructing high-pr...
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MDPI AG
2025-06-01
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| author | Xinyuan Wei Longxing Wu Chunhui Liu Zhiyuan Si Xing Shu Heng Li |
| author_facet | Xinyuan Wei Longxing Wu Chunhui Liu Zhiyuan Si Xing Shu Heng Li |
| author_sort | Xinyuan Wei |
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| description | The high-fidelity lithium-ion battery (LIB) models are crucial for realizing an accurate state estimation in battery management systems (BMSs). Recently, the fractional-order equivalent circuit models (FOMs), as a frequency-domain modeling approach, offer distinct advantages for constructing high-precision battery models in field of electric vehicles. However, the quantitative evaluations and adaptability of these models under different driving cycle datasets are still lacking and challenging. For this reason, comparative evaluations of different FOMs using a novel drive cycle dataset of a battery was carried out in this paper. First, three typical FOMs were initially established and the particle swarm optimization algorithm was then employed to identify model parameters. Complementarily, the efficiency and accuracy of the offline identification for three typical FOMs are also discussed. Subsequently, the terminal voltages of these different FOMs were investigated and evaluated under dynamic operating conditions. Results demonstrate that the FOM-W model exhibits the highest superiority in simulation accuracy, achieving a mean absolute error (MAE) of 9.2 mV and root mean square error (RMSE) of 19.1 mV under Highway Fuel Economy Test conditions. Finally, the accuracy verification of the FOM-W model under two other different dynamic operating conditions has also been thoroughly investigated, and it could still maintain a RMSE and MAE below 21 mV, which indicates its strong adaptability and generalization compared with other FOMs. Conclusions drawn from this paper can further guide the selection of battery models to achieve reliable state estimations of BMS. |
| format | Article |
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| institution | DOAJ |
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| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-52e76dac3a1a480f8f47adee8e1c1b7f2025-08-20T03:07:58ZengMDPI AGFractal and Fractional2504-31102025-06-019742910.3390/fractalfract9070429Comparative Evaluation of Fractional-Order Models for Lithium-Ion Batteries Response to Novel Drive Cycle DatasetXinyuan Wei0Longxing Wu1Chunhui Liu2Zhiyuan Si3Xing Shu4Heng Li5College of Intelligent Manufacturing, Anhui Science and Technology University, Chuzhou 233100, ChinaCollege of Intelligent Manufacturing, Anhui Science and Technology University, Chuzhou 233100, ChinaCollege of Intelligent Manufacturing, Anhui Science and Technology University, Chuzhou 233100, ChinaCollege of Intelligent Manufacturing, Anhui Science and Technology University, Chuzhou 233100, ChinaKey Laboratory of Advanced Manufacture Technology for Automobile Parts (Chongqing University of Technology), Ministry of Education, Chongqing 40050, ChinaSchool of Electronic Information, Central South University, Changsha 410075, ChinaThe high-fidelity lithium-ion battery (LIB) models are crucial for realizing an accurate state estimation in battery management systems (BMSs). Recently, the fractional-order equivalent circuit models (FOMs), as a frequency-domain modeling approach, offer distinct advantages for constructing high-precision battery models in field of electric vehicles. However, the quantitative evaluations and adaptability of these models under different driving cycle datasets are still lacking and challenging. For this reason, comparative evaluations of different FOMs using a novel drive cycle dataset of a battery was carried out in this paper. First, three typical FOMs were initially established and the particle swarm optimization algorithm was then employed to identify model parameters. Complementarily, the efficiency and accuracy of the offline identification for three typical FOMs are also discussed. Subsequently, the terminal voltages of these different FOMs were investigated and evaluated under dynamic operating conditions. Results demonstrate that the FOM-W model exhibits the highest superiority in simulation accuracy, achieving a mean absolute error (MAE) of 9.2 mV and root mean square error (RMSE) of 19.1 mV under Highway Fuel Economy Test conditions. Finally, the accuracy verification of the FOM-W model under two other different dynamic operating conditions has also been thoroughly investigated, and it could still maintain a RMSE and MAE below 21 mV, which indicates its strong adaptability and generalization compared with other FOMs. Conclusions drawn from this paper can further guide the selection of battery models to achieve reliable state estimations of BMS.https://www.mdpi.com/2504-3110/9/7/429lithium-ion batteryfractional-order modelparameter identificationbattery management system |
| spellingShingle | Xinyuan Wei Longxing Wu Chunhui Liu Zhiyuan Si Xing Shu Heng Li Comparative Evaluation of Fractional-Order Models for Lithium-Ion Batteries Response to Novel Drive Cycle Dataset Fractal and Fractional lithium-ion battery fractional-order model parameter identification battery management system |
| title | Comparative Evaluation of Fractional-Order Models for Lithium-Ion Batteries Response to Novel Drive Cycle Dataset |
| title_full | Comparative Evaluation of Fractional-Order Models for Lithium-Ion Batteries Response to Novel Drive Cycle Dataset |
| title_fullStr | Comparative Evaluation of Fractional-Order Models for Lithium-Ion Batteries Response to Novel Drive Cycle Dataset |
| title_full_unstemmed | Comparative Evaluation of Fractional-Order Models for Lithium-Ion Batteries Response to Novel Drive Cycle Dataset |
| title_short | Comparative Evaluation of Fractional-Order Models for Lithium-Ion Batteries Response to Novel Drive Cycle Dataset |
| title_sort | comparative evaluation of fractional order models for lithium ion batteries response to novel drive cycle dataset |
| topic | lithium-ion battery fractional-order model parameter identification battery management system |
| url | https://www.mdpi.com/2504-3110/9/7/429 |
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