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Battery state estimation methods and management system under vehicle–cloud collaboration: A Survey

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  • Mei, Peng
  • Karimi, Hamid Reza
  • Xie, Jiale
  • Chen, Fei
  • Ou, Lei
  • Yang, Shichun
  • Huang, Cong

Abstract

With the development of new energy vehicles, EVs have received ever-increasing research attention as an essential strategic orientation for the world to face climate change and energy issues. EVs have significant energy-saving and emission-reduction advantages, but power battery state estimation accuracy has always been a bottleneck restricting its promotion. Centered on power battery cloud management and control methodology, this work systematically examines the development of battery cloud models, formulates battery life and safety management strategies, and investigates the integration of cloud management technology within advanced electronic and electrical architectures. Firstly, the overall framework of the device–cloud fusion technology is introduced. Secondly, aiming at the complex problem of power battery state estimation, the models and fusion estimation methods of the cloud and vehicle battery models are summarized. Then, the joint estimation method is outlined for the power battery states, including the state of charge and state of health. Finally, a viable cloud-based management solution is elucidated through a comprehensive comparison and analysis of the current battery management technologies’ strengths and limitations. This offers a theoretical framework for advancing power battery cloud management and control technology.

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  • Mei, Peng & Karimi, Hamid Reza & Xie, Jiale & Chen, Fei & Ou, Lei & Yang, Shichun & Huang, Cong, 2024. "Battery state estimation methods and management system under vehicle–cloud collaboration: A Survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:rensus:v:206:y:2024:i:c:s1364032124005835
    DOI: 10.1016/j.rser.2024.114857
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    as
    1. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    2. Li, Weihan & Cao, Decheng & Jöst, Dominik & Ringbeck, Florian & Kuipers, Matthias & Frie, Fabian & Sauer, Dirk Uwe, 2020. "Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries," Applied Energy, Elsevier, vol. 269(C).
    3. Xie, Jiale & Xu, Jingfan & Wei, Zhongbao & Li, Xiaoyu, 2023. "Fault isolating and grading for li-ion battery packs based on pseudo images and convolutional neural network," Energy, Elsevier, vol. 263(PD).
    4. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    5. 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).
    6. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    7. Shu, Xing & Shen, Jiangwei & Chen, Zheng & Zhang, Yuanjian & Liu, Yonggang & Lin, Yan, 2022. "Remaining capacity estimation for lithium-ion batteries via co-operation of multi-machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    8. Zhang, Qisong & Yang, Lin & Guo, Wenchao & Qiang, Jiaxi & Peng, Cheng & Li, Qinyi & Deng, Zhongwei, 2022. "A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system," Energy, Elsevier, vol. 241(C).
    9. Miaomiao Zeng & Peng Zhang & Yang Yang & Changjun Xie & Ying Shi, 2019. "SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm," Energies, MDPI, vol. 12(16), pages 1-15, August.
    10. Wang, Limei & Pan, Chaofeng & Liu, Liang & Cheng, Yong & Zhao, Xiuliang, 2016. "On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis," Applied Energy, Elsevier, vol. 168(C), pages 465-472.
    11. Song, Yuchen & Liu, Datong & Liao, Haitao & Peng, Yu, 2020. "A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 261(C).
    12. Lin, Cheng & Mu, Hao & Xiong, Rui & Shen, Weixiang, 2016. "A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm," Applied Energy, Elsevier, vol. 166(C), pages 76-83.
    13. 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.
    14. Yu, Hanqing & Zhang, Lisheng & Wang, Wentao & Li, Shen & Chen, Siyan & Yang, Shichun & Li, Junfu & Liu, Xinhua, 2023. "State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteries," Energy, Elsevier, vol. 278(C).
    15. Lin, Cheng & Mu, Hao & Xiong, Rui & Cao, Jiayi, 2017. "Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: State-of-energy," Applied Energy, Elsevier, vol. 194(C), pages 560-568.
    16. 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).
    17. Yang, Fangfang & Zhang, Shaohui & Li, Weihua & Miao, Qiang, 2020. "State-of-charge estimation of lithium-ion batteries using LSTM and UKF," Energy, Elsevier, vol. 201(C).
    18. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
    19. Bai, Guangxing & Su, Yunsheng & Rahman, Maliha Maisha & Wang, Zequn, 2023. "Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    20. Lingling Li & Pengchong Wang & Kuei-Hsiang Chao & Yatong Zhou & Yang Xie, 2016. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-13, September.
    21. Bizhong Xia & Zhen Sun & Ruifeng Zhang & Deyu Cui & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2017. "A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries," Energies, MDPI, vol. 10(8), pages 1, August.
    22. Ning, Bo & Cao, Binggang & Wang, Bin & Zou, Zhongyue, 2018. "Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online," Energy, Elsevier, vol. 153(C), pages 732-742.
    23. Gu, Xubo & Bai, Hanyu & Cui, Xiaofan & Zhu, Juner & Zhuang, Weichao & Li, Zhaojian & Hu, Xiaosong & Song, Ziyou, 2024. "Challenges and opportunities for second-life batteries: Key technologies and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    24. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    25. Khaleghi, Sahar & Hosen, Md Sazzad & Van Mierlo, Joeri & Berecibar, Maitane, 2024. "Towards machine-learning driven prognostics and health management of Li-ion batteries. A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    26. Xiangdong Sun & Jingrun Ji & Biying Ren & Chenxue Xie & Dan Yan, 2019. "Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery," Energies, MDPI, vol. 12(12), pages 1-15, June.
    27. Hanlei Sun & Jianrui Sun & Kun Zhao & Licheng Wang & Kai Wang & Mohammad Yaghoub Abdollahzadeh Jamalabadi, 2022. "Data-Driven ICA-Bi-LSTM-Combined Lithium Battery SOH Estimation," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, March.
    28. He, HongWen & Zhang, YongZhi & Xiong, Rui & Wang, Chun, 2015. "A novel Gaussian model based battery state estimation approach: State-of-Energy," Applied Energy, Elsevier, vol. 151(C), pages 41-48.
    29. Xiong, Rui & Sun, Fengchun & He, Hongwen & Nguyen, Trong Duy, 2013. "A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles," Energy, Elsevier, vol. 63(C), pages 295-308.
    30. Hu, Xiaosong & Deng, Zhongwei & Lin, Xianke & Xie, Yi & Teodorescu, Remus, 2021. "Research directions for next-generation battery management solutions in automotive applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    31. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    32. Lei Zhang & Zhenpo Wang & Fengchun Sun & David G. Dorrell, 2014. "Online Parameter Identification of Ultracapacitor Models Using the Extended Kalman Filter," Energies, MDPI, vol. 7(5), pages 1-14, May.
    33. Huang, Huanyang & Meng, Jinhao & Wang, Yuhong & Feng, Fei & Cai, Lei & Peng, Jichang & Liu, Tianqi, 2022. "A comprehensively optimized lithium-ion battery state-of-health estimator based on Local Coulomb Counting Curve," Applied Energy, Elsevier, vol. 322(C).
    34. Dmitry V. Pelegov & Jean-Jacques Chanaron, 2022. "Electric Car Market Analysis Using Open Data: Sales, Volatility Assessment, and Forecasting," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    35. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
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