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A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions

Author

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  • Lan-Rong Dung

    (Department of Electrical and Computer Engineering, National Chiao-Tung University, 1001 Ta-Hsueh Rd., Hsinchu 30010, Taiwan)

  • Hsiang-Fu Yuan

    (Institute of Electrical Control Engineering, National Chiao-Tung University, 1001 Ta-Hsueh Rd., Hsinchu 30010, Taiwan)

  • Jieh-Hwang Yen

    (Institute of Electrical Control Engineering, National Chiao-Tung University, 1001 Ta-Hsueh Rd., Hsinchu 30010, Taiwan)

  • Chien-Hua She

    (MiTAC International Corp., No. 1, R & D 2nd Road, Hsinchu Science-Based Industrial Park, Hsinchu 30010, Taiwan)

  • Ming-Han Lee

    (Department of Electrical and Computer Engineering, National Chiao-Tung University, 1001 Ta-Hsueh Rd., Hsinchu 30010, Taiwan)

Abstract

A new battery simulator based on a hybrid model is proposed in this paper for dynamic discharging behavior and runtime predictions in existing electronic simulation environments, e.g., PSIM, so it can help power circuit designers to develop and optimize their battery-powered electronic systems. The hybrid battery model combines a diffusion model and a switching overpotential model, which automatically switches overpotential resistance mode or overpotential voltage mode to accurately describe the voltage difference between battery electro-motive force (EMF) and terminal voltage. Therefore, this simulator can simply run in an electronic simulation software with less computational efforts and estimate battery performances by further considering nonlinear capacity effects. A linear extrapolation technique is adopted for extracting model parameters from constant current discharging tests, so the EMF hysteresis problem is avoided. For model validation, experiments and simulations in MATLAB and PSIM environments are conducted with six different profiles, including constant loads, an interrupted load, increasing and decreasing loads and a varying load. The results confirm the usefulness and accuracy of the proposed simulator. The behavior and runtime prediction errors can be as low as 3.1% and 1.2%, respectively.

Suggested Citation

  • Lan-Rong Dung & Hsiang-Fu Yuan & Jieh-Hwang Yen & Chien-Hua She & Ming-Han Lee, 2016. "A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions," Energies, MDPI, vol. 9(1), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:1:p:51-:d:62365
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    References listed on IDEAS

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    1. Bizhong Xia & Haiqing Wang & Mingwang Wang & Wei Sun & Zhihui Xu & Yongzhi Lai, 2015. "A New Method for State of Charge Estimation of Lithium-Ion Battery Based on Strong Tracking Cubature Kalman Filter," Energies, MDPI, vol. 8(12), pages 1-15, November.
    2. Jeongbin Lee & Jaeshin Yi & Chee Burm Shin & Seung Ho Yu & Won Il Cho, 2013. "Modeling the Effects of the Cathode Composition of a Lithium Iron Phosphate Battery on the Discharge Behavior," Energies, MDPI, vol. 6(11), pages 1-12, October.
    3. Saeed Sepasi & Leon R. Roose & Marc M. Matsuura, 2015. "Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation," Energies, MDPI, vol. 8(6), pages 1-17, June.
    4. Bizhong Xia & Haiqing Wang & Yong Tian & Mingwang Wang & Wei Sun & Zhihui Xu, 2015. "State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter," Energies, MDPI, vol. 8(6), pages 1-21, June.
    5. Kotub Uddin & Alessandro Picarelli & Christopher Lyness & Nigel Taylor & James Marco, 2014. "An Acausal Li-Ion Battery Pack Model for Automotive Applications," Energies, MDPI, vol. 7(9), pages 1-26, August.
    6. Jianping Gao & Yongzhi Zhang & Hongwen He, 2015. "A Real-Time Joint Estimator for Model Parameters and State of Charge of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 8(8), pages 1-19, August.
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    Cited by:

    1. Mazhar Abbas & Eung-sang Kim & Seul-ki Kim & Yun-su Kim, 2016. "Comparative Analysis of Battery Behavior with Different Modes of Discharge for Optimal Capacity Sizing and BMS Operation," Energies, MDPI, vol. 9(10), pages 1-19, October.

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