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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Open Journal of Industry Applications |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10834587/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2644-1241 |