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


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.


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

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C. Liu, Q. Li, and K. Wang, “State-of-charge estimation and remaining useful life prediction of supercapacitors,” Renew. Sustain. Energy Rev., vol. 150, p. 111408, Oct. 2021.

A. Afandi, B. Sumantri, and N. A. Windarko, “Estimation state of charge (SOC) of Ultracapacitor based on classical equivalent circuit using Extended Kalman Filter,” in 2020 International Electronics Symposium (IES), Surabaya, Indonesia, Sep. 2020, pp. 31–36.

P. Fornaro, P. Puleston, and P. Battaiotto, “On-line parameter estimation of a Lithium-Ion battery/supercapacitor storage system using filtering sliding mode differentiators,” J. Energy Storage, vol. 32, p. 101889, Dec. 2020.

K. Alobeidli and V. Khadkikar, “A new Ultracapacitor state of charge control concept to enhance battery lifespan of dual storage electric vehicles,” IEEE Trans. Veh. Technol., vol. 67, no. 11, pp. 10470–10481, Nov. 2018.

T. Rout, A. Chowdhury, M. K. Maharana, and S. Samal, “Analysis of energy management system for photovoltaic system with battery and supercapacitor using fuzzy logic controller,” in 2018 Technologies for Smart-City Energy Security and Power (ICSESP), Bhubaneswar, Mar. 2018, pp. 1–4.

A. Amin, K. Ismail, and A. Hapid, “Implementation of a LiFePO4 battery charger for cell balancing application,” J. Mechatron. Electr. Power Veh. Technol., vol. 9, no. 2, p. 81, Dec. 2018.

B. Traore, M. Doumiati, C. Morel, J.-C. Olivier, and O. Soumaoro, “Energy management strategy based on a new adaptive filtering algorithm for battery-ultracapacitor electric vehicles,” in 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), Kristiansand, Norway, Nov. 2020, pp. 392–396.

A. R. Al Tahtawi and A. S. Rohman, “Simple supercapacitor charging scheme of an electric vehicle on small-scale hardware simulator: a prototype development for education purpose,” J. Mechatron. Electr. Power Veh. Technol., vol. 7, no. 2, pp. 77–86, Dec. 2016.

H. Yang, “Estimation of supercapacitor charge capacity bounds considering charge redistribution,” IEEE Trans. Power Electron., vol. 33, no. 8, pp. 6980–6993, Aug. 2018.

Q. Zhu et al., “A new view of supercapacitors: Integrated supercapacitors,” Adv. Energy Mater., vol. 9, no. 36, p. 1901081, Sep. 2019.

H. Jiang, L. Xu, J. Li, Z. Hu, and M. Ouyang, “Energy management and component sizing for a fuel cell/battery/supercapacitor hybrid powertrain based on two-dimensional optimization algorithms,” Energy, vol. 177, pp. 386–396, Jun. 2019.

I. Jarraya, F. Masmoudi, M. H. Chabchoub, and H. Trabelsi, “An online state of charge estimation for Lithium-ion and supercapacitor in hybrid electric drive vehicle,” J. Energy Storage, vol. 26, p. 100946, Dec. 2019.

S. Hussain, M. U. Ali, G.-S. Park, S. H. Nengroo, M. A. Khan, and H.-J. Kim, “A real-time bi-adaptive controller-based energy management system for battery–supercapacitor hybrid electric vehicles,” Energies, vol. 12, no. 24, p. 4662, Dec. 2019.

C-T. Ma, “Design and implementation of a hybrid real-time state of charge estimation scheme for battery energy storage systems,” Processes, vol. 8, no. 1, p. 2, Dec. 2019.

H. Yang, “Application of Peukert’s law in supercapacitor discharge time prediction,” J. Energy Storage, vol. 22, pp. 98–105, Apr. 2019.

P. Venugopal, “State-of-charge estimation methods for li-ion batteries in electric vehicles,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 7, p. 11, 2019.

Poonam, K. Sharma, A. Arora, and S. K. Tripathi, “Review of supercapacitors: Materials and devices,” J. Energy Storage, vol. 21, pp. 801–825, Feb. 2019.

H. Chaoui and H. Gualous, “Online lifetime estimation of supercapacitors,” IEEE Trans. Power Electron., vol. 32, no. 9, pp. 7199–7206, Sep. 2017.

L. Rozaqi, E. Rijanto, and S. Kanarachos, “Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study,” J. Mechatron. Electr. Power Veh. Technol., vol. 8, no. 1, pp. 40–49, Jul. 2017.

P. Saha, S. Dey, and M. Khanra, “Accurate estimation of state-of-charge of supercapacitor under uncertain leakage and open circuit voltage map,” J. Power Sources, vol. 434, p. 226696, Sep. 2019.

M. A. Awadallah and B. Venkatesh, “Accuracy improvement of SOC estimation in lithium-ion batteries,” J. Energy Storage, vol. 6, pp. 95–104, May 2016.

Z. Tao, G. Shaoting, L. Xin, and J. Jing, “SOC estimation scheme of super capacitor based on Calman filter,” in 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), Hefei, China, Jun. 2016, pp. 918–921.

J. Wang, L. Zhang, J. Mao, J. Zhou, and D. Xu, “Fractional order equivalent circuit model and SOC estimation of supercapacitors for use in HESS,” IEEE Access, vol. 7, pp. 52565–52572, 2019.

J. I. Hidalgo-Reyes, J. F. Gómez-Aguilar, R. F. Escobar-Jiménez, V. M. Alvarado-Martínez, and M. G. López-López, “Classical and fractional-order modeling of equivalent electrical circuits for supercapacitors and batteries, energy management strategies for hybrid systems and methods for the state of charge estimation: A state of the art review,” Microelectron. J., vol. 85, pp. 109–128, Mar. 2019.

L. Zhang, X. Hu, Z. Wang, F. Sun, and D. G. Dorrell, “A review of supercapacitor modeling, estimation, and applications: A control/management perspective,” Renew. Sustain. Energy Rev., vol. 81, pp. 1868–1878, Jan. 2018.

M. R. Djalal, H. Setiadi, and A. Imran, “Frequency stability improvement of micro hydro power system using hybrid SMES and CES based on Cuckoo search algorithm,” J. Mechatron. Electr. Power Veh. Technol., vol. 8, no. 2, pp. 76–84, Dec. 2017.

M. E. Şahin, F. Blaabjerg, and A. Sangwongwanich, “Modelling of supercapacitors based on simplified equivalent circuit,” CPSS Trans. Power Electron. Appl., vol. 6, no. 1, pp. 31–39, Mar. 2021.

M. Rif’an, F. Yusivar, and B. Kusumoputro, “Sensorless-BLDC motor speed control with ensemble Kalman filter and neural network,” J. Mechatron. Electr. Power Veh. Technol., vol. 10, no. 1, pp. 1–6, Dec. 2019.

K. Faqih, S. Sujito, S. Sendari, and F. S. Aziz, “Smart guided missile using accelerometer and gyroscope based on backpropagation neural network method for optimal control output feedback,” J. Mechatron. Electr. Power Veh. Technol., vol. 11, no. 2, pp. 55–63, Dec. 2020.

A. Soualhi et al., “Heath monitoring of capacitors and supercapacitors using the neo-fuzzy neural approach,” IEEE Trans. Ind. Inform., vol. 14, no. 1, pp. 24–34, Jan. 2018.

A. Fotouhi, D. J. Auger, K. Propp, and S. Longo, “Lithium–sulfur battery state-of-charge observability analysis and estimation,” IEEE Trans. Power Electron., vol. 33, no. 7, pp. 5847–5859, Jul. 2018.

K. V. Singh, H. O. Bansal, and D. Singh, “Hardware-in-the-loop implementation of ANFIS based adaptive SoC estimation of lithium-ion battery for hybrid vehicle applications,” J. Energy Storage, vol. 27, p. 101124, Feb. 2020.

H. Suryoatmojo, M. Ridwan, D. C. Riawan, E. Setijadi, and R. Mardiyanto, “Hybrid particle swarm optimization and recursive least square estimation based ANFIS multioutput for BLDC motor speed controller,” International Journal of Innovative Computing, Information and Control (ICIC), vol. 15, no. 3, June 2019.

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