Online parameter identification for real-time supercapacitor performance estimation in automotive applications

• We developed a real-time adaptive process for supercapacitor performance-estimation.
• The dynamic model described accurately the SC behavior under real-use tests.
• Adaptive RLS algorithm with a time-variant forgetting factor for parameter identification.
• On-line identified internal resistance served as a state of health (SOH) indicator.
• Results from the adaptive SOH indicator are compared to IEC standard and EIS methods.

This paper focuses on synthesizing a real-time adaptive process for supercapacitor performance estimation using a dynamic model describing the SC behavior which can vary within each experiment. We develop a simple and linear-recursive model that proved its efficiency regarding the comparison between simulation results and real data from power cycling tests. Based on a recursive least squared algorithm with a time-variant forgetting factor, the on-line estimation of the dynamic supercapacitor-model parameters, mainly the internal resistance, served as a state of health indicator. Model shows very good performances since the maximum relative modeling error do not exceed 3%. Results from state of health indicator are compared to those issued from IEC standard and electrochemical impedance spectroscopy methods.