Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural Networks
Lithium-titanate-oxide batteries can reduce the long charging time of electric vehicles by offering fast charging capabilities. However, high charging currents require an accurate estimation of battery internal state to prevent early aging of the battery and dangerous situations. An accurate algorit...
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MDPI AG
2025-06-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/11/6/235 |
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| author | Riccardo Di Dio Roberto Di Rienzo Gianluca Aurilio Davide Cavaliere Roberto Saletti |
| author_facet | Riccardo Di Dio Roberto Di Rienzo Gianluca Aurilio Davide Cavaliere Roberto Saletti |
| author_sort | Riccardo Di Dio |
| collection | DOAJ |
| description | Lithium-titanate-oxide batteries can reduce the long charging time of electric vehicles by offering fast charging capabilities. However, high charging currents require an accurate estimation of battery internal state to prevent early aging of the battery and dangerous situations. An accurate algorithm based on neural networks for the co-estimation of state of charge, internal resistance, and capacity state of health is proposed in this work. The algorithm is trained with synthetic data generated by an electric vehicle simulation platform running seven different standard driving cycles at various settings. The algorithm is then validated using an additional standard driving cycle, achieving, for state of charge, internal resistance, and capacity state of health, a root mean square error lower than 2%, 80 μΩ, and 2.9%, and a mean absolute percentage error lower than 3.4%, 4.4%, and 3.3%, respectively. The results obtained and the comparison with literature works indicate that the co-estimation algorithm proposed is able to estimate the considered quantities with very good accuracy. |
| format | Article |
| id | doaj-art-83d74bc1e2dc4a718c215b3f29ee5be1 |
| institution | Kabale University |
| issn | 2313-0105 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-83d74bc1e2dc4a718c215b3f29ee5be12025-08-20T03:27:02ZengMDPI AGBatteries2313-01052025-06-0111623510.3390/batteries11060235Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural NetworksRiccardo Di Dio0Roberto Di Rienzo1Gianluca Aurilio2Davide Cavaliere3Roberto Saletti4Marelli Europe S.p.A., Via del Timavo 33, 40131 Bologna, ItalyDipartimento di Ingegneria dell’Informazione, University of Pisa, Via Caruso 16, 56122 Pisa, ItalyMarelli Europe S.p.A., Via del Timavo 33, 40131 Bologna, ItalyMarelli Corporation, Kodama Plant, 540-7 Kyoei, Kodama-cho, Honjo-City 367-0206, Saitama, JapanDipartimento di Ingegneria dell’Informazione, University of Pisa, Via Caruso 16, 56122 Pisa, ItalyLithium-titanate-oxide batteries can reduce the long charging time of electric vehicles by offering fast charging capabilities. However, high charging currents require an accurate estimation of battery internal state to prevent early aging of the battery and dangerous situations. An accurate algorithm based on neural networks for the co-estimation of state of charge, internal resistance, and capacity state of health is proposed in this work. The algorithm is trained with synthetic data generated by an electric vehicle simulation platform running seven different standard driving cycles at various settings. The algorithm is then validated using an additional standard driving cycle, achieving, for state of charge, internal resistance, and capacity state of health, a root mean square error lower than 2%, 80 μΩ, and 2.9%, and a mean absolute percentage error lower than 3.4%, 4.4%, and 3.3%, respectively. The results obtained and the comparison with literature works indicate that the co-estimation algorithm proposed is able to estimate the considered quantities with very good accuracy.https://www.mdpi.com/2313-0105/11/6/235lithium titanate oxidebattery management systemco-estimation algorithmneural networksstate of chargeinternal resistance |
| spellingShingle | Riccardo Di Dio Roberto Di Rienzo Gianluca Aurilio Davide Cavaliere Roberto Saletti Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural Networks Batteries lithium titanate oxide battery management system co-estimation algorithm neural networks state of charge internal resistance |
| title | Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural Networks |
| title_full | Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural Networks |
| title_fullStr | Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural Networks |
| title_full_unstemmed | Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural Networks |
| title_short | Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural Networks |
| title_sort | advanced algorithm for soc internal resistance and soh co estimation of lithium titanate oxide batteries using neural networks |
| topic | lithium titanate oxide battery management system co-estimation algorithm neural networks state of charge internal resistance |
| url | https://www.mdpi.com/2313-0105/11/6/235 |
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