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State of charge estimation based on a simplified electrochemical model for a single LiCoO2 battery and battery pack

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  • Li, Junfu
  • Wang, Lixin
  • Lyu, Chao
  • Pecht, Michael

Abstract

Accurate battery state of charge (SOC) estimation can contribute to a reasonable charging/discharging strategy for battery management systems (BMSs). It can also prevent severe damage to the battery (pack) caused by over-charging or over-discharging. This work develops a battery SOC estimation method based on a simplified electrochemical model. Simulated validation under dynamic current loads at room temperature showed a maximum SOC error of less than 2.37% within the whole range for a single cell. The developed method can reach a balance between estimation accuracy and computational cost, with average iterative calculation time of about 0.05 ms. A charging/discharging control strategy for battery packs with deep charging depth and fast speed has also been developed, and it can help identify the “weakest’’ cell according to the definition of battery pack SOC. Statistical results show that the SOC average absolute error (AAE) at two constant discharge C-rates ranged from 0.44% to 1.65%. Analysis and assessment of the accuracy and robustness of the developed method for single cells and battery packs indicate that the SOC estimation accuracy is acceptable and shows potential for applications in BMSs.

Suggested Citation

  • Li, Junfu & Wang, Lixin & Lyu, Chao & Pecht, Michael, 2017. "State of charge estimation based on a simplified electrochemical model for a single LiCoO2 battery and battery pack," Energy, Elsevier, vol. 133(C), pages 572-583.
    • Handle: RePEc:eee:energy:v:133:y:2017:i:c:p:572-583
    DOI: 10.1016/j.energy.2017.05.158
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    Citations

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    Cited by:

    1. Chen, Liping & Wu, Xiaobo & Lopes, António M. & Yin, Lisheng & Li, Penghua, 2022. "Adaptive state-of-charge estimation of lithium-ion batteries based on square-root unscented Kalman filter," Energy, Elsevier, vol. 252(C).
    2. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    3. Shi, Man & He, Hongwen & Li, Jianwei & Han, Mo & Jia, Chunchun, 2021. "Multi-objective tradeoff optimization of predictive adaptive cruising control for autonomous electric buses: A cyber-physical-energy system approach," Applied Energy, Elsevier, vol. 300(C).
    4. Li, Junfu & Wang, Lixin & Lyu, Chao & Wang, Dafang & Pecht, Michael, 2019. "Parameter updating method of a simplified first principles-thermal coupling model for lithium-ion batteries," Applied Energy, Elsevier, vol. 256(C).
    5. Liao, Peng & Tang, Tie-Qiao & Liu, Ronghui & Huang, Hai-Jun, 2021. "An eco-driving strategy for electric vehicle based on the powertrain," Applied Energy, Elsevier, vol. 302(C).
    6. Bi, Jun & Wang, Yongxing & Sai, Qiuyue & Ding, Cong, 2019. "Estimating remaining driving range of battery electric vehicles based on real-world data: A case study of Beijing, China," Energy, Elsevier, vol. 169(C), pages 833-843.
    7. 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.
    8. Tian, Yong & Huang, Zhijia & Li, Xiaoyu & Tian, Jindong, 2022. "Parallel-connected battery module modeling based on physical characteristics in multiple domains and heterogeneous characteristic analysis," Energy, Elsevier, vol. 239(PB).
    9. Fan, Guodong & Li, Xiaoyu & Zhang, Ruigang, 2021. "Global Sensitivity Analysis on Temperature-Dependent Parameters of A Reduced-Order Electrochemical Model And Robust State-of-Charge Estimation at Different Temperatures," Energy, Elsevier, vol. 223(C).
    10. Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2017. "A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique," Energy, Elsevier, vol. 141(C), pages 1402-1415.
    11. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2022. "Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model," Energy, Elsevier, vol. 240(C).
    12. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    13. Shen, Yanqing, 2018. "Improved chaos genetic algorithm based state of charge determination for lithium batteries in electric vehicles," Energy, Elsevier, vol. 152(C), pages 576-585.
    14. 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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