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Optimization of a Fuzzy-Logic-Control-Based Five-Stage Battery Charger Using a Fuzzy-Based Taguchi Method

Author

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  • Chun-Liang Liu

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, EE-105-1 #No.43, Sec. 4, Keelung Rd., Da'an Dist., Taipei City 10600, Taiwan)

  • Yi-Shun Chiu

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, EE-105-1 #No.43, Sec. 4, Keelung Rd., Da'an Dist., Taipei City 10600, Taiwan)

  • Yi-Hua Liu

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, EE-105-1 #No.43, Sec. 4, Keelung Rd., Da'an Dist., Taipei City 10600, Taiwan)

  • Yeh-Hsiang Ho

    (Electric Energy Technology Division Power Electronics Department , Industrial Technology Research Institute, Rm#841, Bldg. 51, No. 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 31040, Taiwan)

  • Shu-Syuan Huang

    (Electric Energy Technology Division Power Electronics Department , Industrial Technology Research Institute, Rm#841, Bldg. 51, No. 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 31040, Taiwan)

Abstract

Lithium ion (Li-ion) batteries have been widely used in various kinds of applications, including consumer electronics, green energy systems and electrical vehicles. Since the charging method has a significant influence on the performance and lifetime of Li-ion batteries, an intelligent charging algorithm which can properly determine the charging current is essential. In this study, a fuzzy-logic-control-based (FLC-based) five-stage Li-ion battery charger is proposed. The proposed charger takes the temperature rise and the gradient of temperature rise of battery into account, and adjusts the charging current accordingly. To further improve the performance of the proposed FLC, the fuzzy-based Taguchi method is utilized to determine the optimal output membership functions (MFs). Comparing with the conventional constant current-constant voltage (CC-CV) method, the charging time, charging efficiency, average temperature rise and the obtained cycle life of the Li-ion battery are improved by about 58.3%, 1.65%, 26.7% and 59.3%, respectively.

Suggested Citation

  • Chun-Liang Liu & Yi-Shun Chiu & Yi-Hua Liu & Yeh-Hsiang Ho & Shu-Syuan Huang, 2013. "Optimization of a Fuzzy-Logic-Control-Based Five-Stage Battery Charger Using a Fuzzy-Based Taguchi Method," Energies, MDPI, vol. 6(7), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:7:p:3528-3547:d:27273
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    References listed on IDEAS

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    7. Qian Zhang & Kin Keung Lai & Dongxiao Niu & Qiang Wang & Xuebin Zhang, 2012. "A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power," Energies, MDPI, vol. 5(9), pages 1-18, September.
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    2. Edison Banguero & Antonio Correcher & Ángel Pérez-Navarro & Francisco Morant & Andrés Aristizabal, 2018. "A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems," Energies, MDPI, vol. 11(4), pages 1-15, April.
    3. Román-Ramírez, L.A. & Marco, J., 2022. "Design of experiments applied to lithium-ion batteries: A literature review," Applied Energy, Elsevier, vol. 320(C).
    4. Jaw-Kuen Shiau & Min-Yi Lee & Yu-Chen Wei & Bo-Chih Chen, 2014. "Circuit Simulation for Solar Power Maximum Power Point Tracking with Different Buck-Boost Converter Topologies," Energies, MDPI, vol. 7(8), pages 1-20, August.
    5. 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.

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