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A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control

Author

Listed:
  • Muhammad Umair Ali

    (School of Electrical Engineering, Pusan National University, Busandaehak-ro 63 Beon-gil 2, Busan 46241, Korea)

  • Sarvar Hussain Nengroo

    (School of Electrical Engineering, Pusan National University, Busandaehak-ro 63 Beon-gil 2, Busan 46241, Korea)

  • Muhamad Adil Khan

    (School of Electrical Engineering, Pusan National University, Busandaehak-ro 63 Beon-gil 2, Busan 46241, Korea)

  • Kamran Zeb

    (School of Electrical Engineering, Pusan National University, Busandaehak-ro 63 Beon-gil 2, Busan 46241, Korea
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Muhammad Ahmad Kamran

    (Department of Cogno-Mechatronics Engineering, Pusan National University, Busandaehak-ro 63 Beon-gil 2, Busan 46241, Korea)

  • Hee-Je Kim

    (School of Electrical Engineering, Pusan National University, Busandaehak-ro 63 Beon-gil 2, Busan 46241, Korea)

Abstract

The lithium-ion battery has high energy and power density, long life cycle, low toxicity, low discharge rate, more reliability, and better efficiency compared to other batteries. On the other hand, the issue of a reduction in charging time of the lithium-ion battery is still a bottleneck for the commercialization of electric vehicles (EVs). Therefore, an approach to charge lithium-ion batteries at a faster rate is needed. This paper proposes an efficient, real-time, fast-charging methodology of lithium-ion batteries. Fuzzy logic was adopted to drive the charging current trajectory. A temperature control unit was also implemented to evade the effects of fast charging on the aging mechanism. The proposed method of charging also protects the battery from overvoltage and overheating. Extensive testing and comprehensive analysis were conducted to examine the proposed charging technique. The results show that the proposed charging strategy favors a full battery recharging in 9.76% less time than the conventional constant-current–constant-voltage (CC/CV) method. The strategy charges the battery at a 99.26% state of charge (SOC) without significant degradation. The entire scheme was implemented in real time, using Arduino interfaced with MATLAB TM Simulink. This decrease in charging time assists in the fast charging of cell phones and notebooks and in the large-scale deployment of EVs.

Suggested Citation

  • Muhammad Umair Ali & Sarvar Hussain Nengroo & Muhamad Adil Khan & Kamran Zeb & Muhammad Ahmad Kamran & Hee-Je Kim, 2018. "A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control," Energies, MDPI, vol. 11(5), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1122-:d:144211
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    References listed on IDEAS

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    Cited by:

    1. Guangwei Chen & Zhitao Liu & Hongye Su, 2020. "An Optimal Fast-Charging Strategy for Lithium-Ion Batteries via an Electrochemical–Thermal Model with Intercalation-Induced Stresses and Film Growth," Energies, MDPI, vol. 13(9), pages 1-16, May.
    2. Haitao Min & Boshi Wang & Weiyi Sun & Zhaopu Zhang & Yuanbin Yu & Yanzhou Zhang, 2020. "Research on the Combined Control Strategy of Low Temperature Charging and Heating of Lithium-Ion Power Battery Based on Adaptive Fuzzy Control," Energies, MDPI, vol. 13(7), pages 1-21, April.
    3. Omer Faruk Goksu & Ahmet Yigit Arabul & Revna Acar Vural, 2020. "Low Voltage Battery Management System with Internal Adaptive Charger and Fuzzy Logic Controller," Energies, MDPI, vol. 13(9), pages 1-15, May.
    4. Sadam Hussain & Muhammad Umair Ali & Gwan-Soo Park & Sarvar Hussain Nengroo & Muhammad Adil Khan & Hee-Je Kim, 2019. "A Real-Time Bi-Adaptive Controller-Based Energy Management System for Battery–Supercapacitor Hybrid Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-24, December.
    5. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    6. Yeong-Jun Choi & Hwa-Rang Cha & Sang-Min Jung & Rae-Young Kim, 2018. "An Integrated Current-Voltage Compensator Design Method for Stable Constant Voltage and Current Source Operation of LLC Resonant Converters," Energies, MDPI, vol. 11(6), pages 1-18, May.
    7. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Gwan-Soo Park & Hee-Je Kim, 2019. "Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features," Energies, MDPI, vol. 12(22), pages 1-14, November.
    8. Basit Ali & Muhammad Waseem Ashraf & Shahzadi Tayyaba, 2019. "Simulation, Fuzzy Analysis and Development of ZnO Nanostructure-based Piezoelectric MEMS Energy Harvester," Energies, MDPI, vol. 12(5), pages 1-15, February.
    9. Muhammad Umair Ali & Muhammad Ahmad Kamran & Pandiyan Sathish Kumar & Himanshu & Sarvar Hussain Nengroo & Muhammad Adil Khan & Altaf Hussain & Hee-Je Kim, 2018. "An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method," Energies, MDPI, vol. 11(11), pages 1-19, October.

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