Abstract
Aimed at improving SOC estimation accuracy, speed and robust of battery on electric vehicle, SOC estimation method based on adaptive particle filter is proposed. 1-order RC and lag model, 2-order RC and lag model, 3-order RC and lag model are built. Particle Swarm algorithm is used to search optimal parameters. Considering calculation and model accuracy, 1-order lag model is chosen. Traditional particle filter principle is analyzed. State estimation is a substitute to observation equation, and observation estimation is gotten. Observation noise variance is adjusted adaptively through observation error. Verification by simulation, convergence speed and robust of adaptive particle filter are superior to traditional algorithm when SOC original error is large. Besides, SOC estimation accuracy and stability is superior to traditional algorithm obviously.
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Funding
This study is sponsored by Hubei Provincial Natural Science Foundation of China (2018CFC863, 2019CFC837); China Postdoctoral Science Foundation (2019M661913, 2018M642181); National Science Foundation of China (61906076) should be modified to National Science Foundation of China (52002215, 61906076); Natural Science Foundation of Jiangsu Province (BK20190853); JITRI Suzhou Automotive Research Institute Project (CEC20190404); the Research Project of Hubei Provincial Department of Education (Q20182905); the Scientific Research Project of Huanggang Normal University.
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Jiabao Tao, Chuan Sun wrote the first draft of the manuscript. All authors edited the manuscript and approved the final version. All authors made substantial contributions to conception and design, analysis, and interpretation of data, and critical review of the manuscript. Jiabao Tao secured funding, interpretation of the results, and wrote the manuscript. Chuan Sun carried out the design of the study, performed the statistical analyses, and the revision of the manuscript. Dunyao Zhu performed statistical analyses of mediation, and helped rewrite the manuscript substantially during the revision process. Yuli Ma helped get the funding for the study, advised on SOC estimation methods, and was involved in drafting the manuscript. Haibo Li, Yicheng Li and Tingxuan Xu helped with literature review and revising the manuscript. Duanfeng Chu have been involved in revising the manuscript critically for important intellectual content.
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Jiabao Tao, Zhu, D., Sun, C. et al. A Novel Method of SOC Estimation for Electric Vehicle Based on Adaptive Particle Filter. Aut. Control Comp. Sci. 54, 412–422 (2020). https://doi.org/10.3103/S0146411620050089
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DOI: https://doi.org/10.3103/S0146411620050089