Adaptive State-of-Energy Estimation of Supercapacitor Using Fractional Calculus

The state of energy (SOE) is a critical performance indicator for energy optimization and management in supercapacitor (SC) based energy storage systems. For an accurate SOE estimation, the supercapacitor model must be highly adaptable to account for changes in its electrochemical dynamics. In practical applications, the capacitance and the equivalent series resistance (ESR) may drift from their rated values, which in turn changes the internal states of SC. Accurate SOE estimation depends on precise internal voltage and current estimation to identify energy losses within the supercapacitor. In this regard, this work suggests an adaptive SOE estimation algorithm that uses a fractional order model (FOM) and a fractional order extended Kalman filter (EKF) for online estimation of model parameters and internal states. Experiments are conducted on a commercially available 5F Maxwell SC to identify the FOM and check the performance of the proposed scheme. Additionally, the suggested method is compared with the integer order approach and two existing energy estimation methods.