Predictive Approach for Age and RUL Estimation of Lithium-Ion Batteries in EVs based on Capacitance

Electric Vehicles (EVs) have garnered tremendous popularity due to their eco-friendly characteristics. Nevertheless, the performance and durability of EV batteries can be influenced by factors such as usage patterns, charging behaviors, and temperature. Timely monitoring of Lithium-ion battery (LIB) health is vital to ensure reliability, safety, and longevity, thereby necessitating timely replacement. This paper presents a secure and efficient approach by leveraging capacitance measurement at 1 kHz as an alternative to Electrochemical Impedance Spectroscopy (EIS). This method provides valuable insights into the battery’s internal processes and enables accurate estimation of its remaining lifespan. By monitoring capacitance, potential issues that contribute to premature deterioration can be identified, enabling proactive replacement after a specific number of charge and discharge cycles. Such proactive maintenance reduces the risk of sudden battery failures and optimizes overall lifespan. Through the use of a non-destructive method to predict the battery’s age and Remaining Useful Life (RUL), this paper makes a significant contribution to the accurate assessment of LIBs’ age and RUL, benefiting battery management and energy storage systems. The experimental findings serve as evidence for the effectiveness of the proposed methodology.