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YAO Fang,ZHANG Nan,HUANG Kai.Review of State Estimation and Life Prediction for Lithiumion Batteries[J].JOURNAL OF POWER SUPPLY,2020,18(3):175-183
Review of State Estimation and Life Prediction for Lithiumion Batteries
Received:March 05, 2018  Revised:October 08, 2019
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DOI:10.13234/j.issn.2095-2805.2020.3.175
Keywords:lithium ion-battery  estimation of state-of-charge and state-of-health  life prediction
Fund Project:The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)
        
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YAO Fang State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin , China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin , China
ZHANG Nan State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin , China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin , China
HUANG Kai State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin , China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin , China
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Abstract:
      Along with the wide applications of lithium-ion battery, its health management and life estimation have become a challenge and a research hotspot in many fields. An accurate battery state estimation and the prediction of remaining useful life(RUL) can enable users to timely obtain the battery information and update the failed battery to ensure the safe and efficient operation of the whole battery pack. On the basis of the above considerations, the research status of health management and life prediction for lithium-ion battery is analyzed. Specifically, the methods and application status of the prediction method for the RUL of lithium-ion batteries are summarized, including two key parts, i.e., the estimation of their state-of-charge and state-of-health, and the prediction of RUL. The advantages and disadvantages are summarized, and the development trend and research challenges in the future are also analyzed .
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