State of charge estimation of ultracapacitor  based on equivalent circuit model using  adaptive neuro-fuzzy inference system

Rizal Nurdiansyah; Novie Ayub Windarko; Renny Rakhmawati; Muhammad Abdul Haq; Rizal Nurdiansyah; Novie Ayub Windarko; Renny Rakhmawati; Muhammad Abdul Haq

doi:10.14203/j.mev.2022.v13.60-71

State of charge estimation of ultracapacitor based on equivalent circuit model using adaptive neuro-fuzzy inference system

Rizal Nurdiansyah, Novie Ayub Windarko, Renny Rakhmawati, Muhammad Abdul Haq

Abstract

Ultracapacitors have been attracting interest to apply as energy storage devices with advantages of fast charging capability, high power density, and long lifecycle. As a storage device, accurate monitoring is required to ensure and operate safely during the charge/discharge process. Therefore, high accuracy estimation of the state of charge (SOC) is needed to keep the Ultracapacitor working properly. This paper proposed SOC estimation using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The ANFIS is tested by comparing it to true SOC based on an equivalent circuit model. To find the best method, the ANFIS is modified and tested with various membership functions of triangular, trapezoidal, and gaussian. The results show that triangular membership is the best method due to its high accuracy. An experimental test is also conducted to verify simulation results. As an overall result, the triangular membership shows the best estimation. Simulation results show SOC estimation mean absolute percentage error (MAPE) is 0.70 % for charging and 0.83 % for discharging. Furthermore, experimental results show that MAPE of SOC estimation is 0.76 % for random current. The results of simulations and experimental tests show that ANFIS with a triangular membership function has the most reliable ability with a minimum error value in estimating the state of charge on the Ultracapacitor even under conditions of indeterminate random current.

10.14203/j.mev.2022.v13.60-71

Keywords

Ultracapacitors; state of charge; adaptive neuro-fuzzy inference system; energy storage devices; equivalent circuit model.

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