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A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

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  • Liu, Guangming
  • Ouyang, Minggao
  • Lu, Languang
  • Li, Jianqiu
  • Hua, Jianfeng

Abstract

In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy (ERDE) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available ERDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the ERDE directly to the current state of charge (SOC). To enhance the ERDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the ERDE prediction horizon, and the ERDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different ERDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the ERDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online ERDE prediction. The correlation of SOC estimation and ERDE calculation is then discussed to illustrate the importance of an accurate ERDE method in real-world applications.

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  • Liu, Guangming & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Hua, Jianfeng, 2015. "A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications," Applied Energy, Elsevier, vol. 149(C), pages 297-314.
    • Handle: RePEc:eee:appene:v:149:y:2015:i:c:p:297-314
    DOI: 10.1016/j.apenergy.2015.03.110
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    3. Zhihang Zhang & Languang Lu & Yalun Li & Hewu Wang & Minggao Ouyang, 2023. "Accurate Remaining Available Energy Estimation of LiFePO 4 Battery in Dynamic Frequency Regulation for EVs with Thermal-Electric-Hysteresis Model," Energies, MDPI, vol. 16(13), pages 1-28, July.
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    8. Farmann, Alexander & Sauer, Dirk Uwe, 2018. "Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 225(C), pages 1102-1122.
    9. Scioletti, Michael S. & Goodman, Johanna K. & Kohl, Paul A. & Newman, Alexandra M., 2016. "A physics-based integer-linear battery modeling paradigm," Applied Energy, Elsevier, vol. 176(C), pages 245-257.
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    12. Ye Yang & Zhongfu Tan & Yilong Ren, 2020. "Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles," Sustainability, MDPI, vol. 12(8), pages 1-19, April.
    13. Qiaohua Fang & Xuezhe Wei & Haifeng Dai, 2019. "A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency," Energies, MDPI, vol. 12(6), pages 1-24, March.
    14. Blaifi, S. & Moulahoum, S. & Colak, I. & Merrouche, W., 2016. "An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications," Applied Energy, Elsevier, vol. 169(C), pages 888-898.
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    16. Jiang, Zhanhong & Liu, Chao & Akintayo, Adedotun & Henze, Gregor P. & Sarkar, Soumik, 2017. "Energy prediction using spatiotemporal pattern networks," Applied Energy, Elsevier, vol. 206(C), pages 1022-1039.
    17. Wang, Yujie & Chen, Zonghai & Zhang, Chenbin, 2017. "On-line remaining energy prediction: A case study in embedded battery management system," Applied Energy, Elsevier, vol. 194(C), pages 688-695.
    18. Wang, Shunli & Shang, Liping & Li, Zhanfeng & Deng, Hu & Li, Jianchao, 2016. "Online dynamic equalization adjustment of high-power lithium-ion battery packs based on the state of balance estimation," Applied Energy, Elsevier, vol. 166(C), pages 44-58.
    19. Sun, Tao & Xu, Yuwen & Feng, Lihong & Xu, Bowen & Chen, Dizuo & Zhang, Fang & Han, Xuebing & Zhao, Lihui & Zheng, Yuejiu, 2022. "A vehicle-cloud collaboration strategy for remaining driving range estimation based on online traffic route information and future operation condition prediction," Energy, Elsevier, vol. 248(C).
    20. An, Fulai & Zhang, Weige & Sun, Bingxiang & Jiang, Jiuchun & Fan, Xinyuan, 2023. "A novel battery pack inconsistency model and influence degree analysis of inconsistency on output energy," Energy, Elsevier, vol. 271(C).
    21. Xie, Yanping & Zhao, Hongbin & Cheng, Hongwei & Hu, Chenji & Fang, Wenying & Fang, Jianhui & Xu, Jiaqiang & Chen, Zhongwei, 2016. "Facile large-scale synthesis of core–shell structured sulfur@polypyrrole composite and its application in lithium–sulfur batteries with high energy density," Applied Energy, Elsevier, vol. 175(C), pages 522-528.
    22. Xiaopeng Tang & Ke Yao & Boyang Liu & Wengui Hu & Furong Gao, 2018. "Long-Term Battery Voltage, Power, and Surface Temperature Prediction Using a Model-Based Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-16, January.
    23. Mahmoudzadeh Andwari, Amin & Pesiridis, Apostolos & Rajoo, Srithar & Martinez-Botas, Ricardo & Esfahanian, Vahid, 2017. "A review of Battery Electric Vehicle technology and readiness levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 414-430.

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