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Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints

Author

Listed:
  • Shun Xiang

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Guangdi Hu

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Ruisen Huang

    (School of Mechanical Engineering, Pusan National University, Busan 46241, Korea)

  • Feng Guo

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Pengkai Zhou

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

Abstract

The paper aims to realize a rapid online estimation of the state-of-power (SOP) with multiple constraints of a lithium-ion battery. Firstly, based on the improved first-order resistance-capacitance (RC) model with one-state hysteresis, a linear state-space battery model is built; then, using the dual extended Kalman filtering (DEKF) method, the battery parameters and states, including open-circuit voltage (OCV), are estimated. Secondly, by employing the estimated OCV as the observed value to build the second dual Kalman filters, the battery SOC is estimated. Thirdly, a novel rapid-calculating peak power/SOP method with multiple constraints is proposed in which, according to the bisection judgment method, the battery’s peak state is determined; then, one or two instantaneous peak powers are used to determine the peak power during T seconds. In addition, in the battery operating process, the actual constraint that the battery is under is analyzed specifically. Finally, three simplified versions of the Federal Urban Driving Schedule (SFUDS) with inserted pulse experiments are conducted to verify the effectiveness and accuracy of the proposed online SOP estimation method.

Suggested Citation

  • Shun Xiang & Guangdi Hu & Ruisen Huang & Feng Guo & Pengkai Zhou, 2018. "Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints," Energies, MDPI, vol. 11(2), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:283-:d:128520
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    References listed on IDEAS

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    Cited by:

    1. Li, Alan G. & Wang, Weizhong & West, Alan C. & Preindl, Matthias, 2022. "Health and performance diagnostics in Li-ion batteries with pulse-injection-aided machine learning," Applied Energy, Elsevier, vol. 315(C).
    2. Abraham Alem Kebede & Md Sazzad Hosen & Theodoros Kalogiannis & Henok Ayele Behabtu & Towfik Jemal & Joeri Van Mierlo & Thierry Coosemans & Maitane Berecibar, 2022. "Model Development for State-of-Power Estimation of Large-Capacity Nickel-Manganese-Cobalt Oxide-Based Lithium-Ion Cell Validated Using a Real-Life Profile," Energies, MDPI, vol. 15(18), pages 1-15, September.
    3. Ruifeng Zhang & Bizhong Xia & Baohua Li & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2018. "Study on the Characteristics of a High Capacity Nickel Manganese Cobalt Oxide (NMC) Lithium-Ion Battery—An Experimental Investigation," Energies, MDPI, vol. 11(9), pages 1-20, August.
    4. Xu Chen & Guangdi Hu & Feng Guo & Mengqi Ye & Jingyuan Huang, 2020. "Switched Energy Management Strategy for Fuel Cell Hybrid Vehicle Based on Switch Network," Energies, MDPI, vol. 13(1), pages 1-23, January.

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