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 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. 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.
Authors
- A. Ottaviano (ORCID: https://orcid.org/0000-0003-3353-540X)
- Dario Pelosi (ORCID: https://orcid.org/0000-0003-4862-9302)
- Lorenzo Trombetti
- Linda Barelli (ORCID: https://orcid.org/0000-0002-0177-3289)
- Federico Gallorini (ORCID: https://orcid.org/0000-0001-9047-8814)
Institutions
- University of Perugia (IT)
Publication Details
- Journal
- IEEE Open Journal of Industry Applications
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/ojia.2025.3527721
- Citations
- 3
- Primary Topic
- Advanced Battery Technologies Research
- Type
- article
- Field-Weighted Citation Impact
- 2.05