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Accurate lithium-ion battery modeling with inverse repeat binary sequence for electric vehicle applications

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  • Zhu, Rui
  • Duan, Bin
  • Zhang, Chenghui
  • Gong, Sizhao

Abstract

The attribute of battery current excitation signal significantly influences the battery model parameter identification accuracy. However, currently the studies mainly focus on the selection of battery models and the improvement of algorithm, and overlook the influence of excitation signal. More importantly, the conventional excitation signals, which are unsuited to the processes that subjected to the nonlinear effects, can lead to poor estimation accuracy of model parameters. Therefore, this paper proposes a novel excitation signal design method called inverse repeat binary sequence (IRBS). The theoretical analysis shows that the antisymmetric characteristic of IRBS can overcome the adverse effects of the direct current component and even-order nonlinearities for parameter estimation. Then, the design parameters of the excitation signal are determined for real application by analysing the single-sided amplitude spectrum of the typical battery test loading profiles of electric vehicles, and model parameters are estimated by means of particle swarm optimization algorithm. Finally, the experimental results of different temperatures based on the LiNiMnCoO2 lithium-ion battery validate that IRBS is feasible, and has the higher accuracy than three commonly used excitation signal design methods.

Suggested Citation

  • Zhu, Rui & Duan, Bin & Zhang, Chenghui & Gong, Sizhao, 2019. "Accurate lithium-ion battery modeling with inverse repeat binary sequence for electric vehicle applications," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:251:y:2019:i:c:111
    DOI: 10.1016/j.apenergy.2019.113339
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    References listed on IDEAS

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    2. Modawy Adam Ali Abdalla & Wang Min & Omer Abbaker Ahmed Mohammed, 2020. "Two-Stage Energy Management Strategy of EV and PV Integrated Smart Home to Minimize Electricity Cost and Flatten Power Load Profile," Energies, MDPI, vol. 13(23), pages 1-18, December.
    3. Kang, Yongzhe & Duan, Bin & Zhou, Zhongkai & Shang, Yunlong & Zhang, Chenghui, 2020. "Online multi-fault detection and diagnosis for battery packs in electric vehicles," Applied Energy, Elsevier, vol. 259(C).
    4. Zhu, Rui & Duan, Bin & Zhang, Junming & Zhang, Qi & Zhang, Chenghui, 2020. "Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter," Applied Energy, Elsevier, vol. 277(C).
    5. Maheshwari, Arpit & Paterakis, Nikolaos G. & Santarelli, Massimo & Gibescu, Madeleine, 2020. "Optimizing the operation of energy storage using a non-linear lithium-ion battery degradation model," Applied Energy, Elsevier, vol. 261(C).
    6. Li, Shuangqi & He, Hongwen & Zhao, Pengfei & Cheng, Shuang, 2022. "Data cleaning and restoring method for vehicle battery big data platform," Applied Energy, Elsevier, vol. 320(C).
    7. Li, Shuangqi & He, Hongwen & Zhao, Pengfei & Cheng, Shuang, 2022. "Health-Conscious vehicle battery state estimation based on deep transfer learning," Applied Energy, Elsevier, vol. 316(C).
    8. Li, Changlong & Cui, Naxin & Wang, Chunyu & Zhang, Chenghui, 2021. "Reduced-order electrochemical model for lithium-ion battery with domain decomposition and polynomial approximation methods," Energy, Elsevier, vol. 221(C).
    9. Shaofei Qu & Yongzhe Kang & Pingwei Gu & Chenghui Zhang & Bin Duan, 2019. "A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis," Energies, MDPI, vol. 12(17), pages 1-11, August.

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