Advanced Online State-of-Health Prediction and Monitoring of Na-Ion Battery for Electric Vehicles Application

Abstract

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.