Advanced Online State-of-Health Prediction and Monitoring of Na-Ion Battery for Electric Vehicles Application
Na-ion batteries are growing interest due to their sustainability and low cost. A wide implementation in stationary applications, but also for short range transportation, is envisaged. This is further supported by the recent progress on Na-ion cells with increased energy density. To this regards, th...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Industry Applications |
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| Online Access: | https://ieeexplore.ieee.org/document/10834587/ |
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| author | D. Pelosi L. Trombetti F. Gallorini P. A. Ottaviano L. Barelli |
| author_facet | D. Pelosi L. Trombetti F. Gallorini P. A. Ottaviano L. Barelli |
| author_sort | D. Pelosi |
| collection | DOAJ |
| description | Na-ion batteries are growing interest due to their sustainability and low cost. A wide implementation in stationary applications, but also for short range transportation, is envisaged. This is further supported by the recent progress on Na-ion cells with increased energy density. To this regards, the development of procedures for real-time assessment of batteries state of health is of crucial relevance. The present paper provides an innovative procedure to assess sodium-ion battery capacity fading based on the application of discrete wavelet transform to voltage signals, acquired once a certain load pattern is applied at the battery terminals. The procedure development is provided through Na-ion cell aging test. During all the test battery capacity measurements are carried out. Root mean square error (RMSE) between assessed and measured values equals 1.18%. Moreover, during the aging test significant differences between performance evolution of Na-ion and NCR Li-ion cells are highlighted and discussed. |
| format | Article |
| id | doaj-art-29993bab32934555b0e5fdb43a1e2be4 |
| institution | Kabale University |
| issn | 2644-1241 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Industry Applications |
| spelling | doaj-art-29993bab32934555b0e5fdb43a1e2be42025-01-24T00:02:17ZengIEEEIEEE Open Journal of Industry Applications2644-12412025-01-016596810.1109/OJIA.2025.352772110834587Advanced Online State-of-Health Prediction and Monitoring of Na-Ion Battery for Electric Vehicles ApplicationD. Pelosi0https://orcid.org/0000-0003-4862-9302L. Trombetti1F. Gallorini2https://orcid.org/0000-0001-9047-8814P. A. Ottaviano3https://orcid.org/0000-0003-3353-540XL. Barelli4https://orcid.org/0000-0002-0177-3289Department of Engineering, University of Perugia, Perugia, ItalyDepartment of Engineering, University of Perugia, Perugia, ItalyVGA srl, Deruta (PG), ItalyVGA srl, Deruta (PG), ItalyDepartment of Engineering, University of Perugia, Perugia, ItalyNa-ion batteries are growing interest due to their sustainability and low cost. A wide implementation in stationary applications, but also for short range transportation, is envisaged. This is further supported by the recent progress on Na-ion cells with increased energy density. To this regards, the development of procedures for real-time assessment of batteries state of health is of crucial relevance. The present paper provides an innovative procedure to assess sodium-ion battery capacity fading based on the application of discrete wavelet transform to voltage signals, acquired once a certain load pattern is applied at the battery terminals. The procedure development is provided through Na-ion cell aging test. During all the test battery capacity measurements are carried out. Root mean square error (RMSE) between assessed and measured values equals 1.18%. Moreover, during the aging test significant differences between performance evolution of Na-ion and NCR Li-ion cells are highlighted and discussed.https://ieeexplore.ieee.org/document/10834587/Cycle agingdiscrete wavelet transformmultiresolution analysisNa-ion batterystate of health estimationtemperature effect |
| spellingShingle | D. Pelosi L. Trombetti F. Gallorini P. A. Ottaviano L. Barelli Advanced Online State-of-Health Prediction and Monitoring of Na-Ion Battery for Electric Vehicles Application IEEE Open Journal of Industry Applications Cycle aging discrete wavelet transform multiresolution analysis Na-ion battery state of health estimation temperature effect |
| title | Advanced Online State-of-Health Prediction and Monitoring of Na-Ion Battery for Electric Vehicles Application |
| title_full | Advanced Online State-of-Health Prediction and Monitoring of Na-Ion Battery for Electric Vehicles Application |
| title_fullStr | Advanced Online State-of-Health Prediction and Monitoring of Na-Ion Battery for Electric Vehicles Application |
| title_full_unstemmed | Advanced Online State-of-Health Prediction and Monitoring of Na-Ion Battery for Electric Vehicles Application |
| title_short | Advanced Online State-of-Health Prediction and Monitoring of Na-Ion Battery for Electric Vehicles Application |
| title_sort | advanced online state of health prediction and monitoring of na ion battery for electric vehicles application |
| topic | Cycle aging discrete wavelet transform multiresolution analysis Na-ion battery state of health estimation temperature effect |
| url | https://ieeexplore.ieee.org/document/10834587/ |
| work_keys_str_mv | AT dpelosi advancedonlinestateofhealthpredictionandmonitoringofnaionbatteryforelectricvehiclesapplication AT ltrombetti advancedonlinestateofhealthpredictionandmonitoringofnaionbatteryforelectricvehiclesapplication AT fgallorini advancedonlinestateofhealthpredictionandmonitoringofnaionbatteryforelectricvehiclesapplication AT paottaviano advancedonlinestateofhealthpredictionandmonitoringofnaionbatteryforelectricvehiclesapplication AT lbarelli advancedonlinestateofhealthpredictionandmonitoringofnaionbatteryforelectricvehiclesapplication |