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Title:
ARTIFICIAL-INTELLIGENCE-BASED SOC ESTIMATION METHOD FOR LITHIUM ION BATTERY SYSTEM
Document Type and Number:
WIPO Patent Application WO/2022/100229
Kind Code:
A1
Abstract:
An artificial-intelligence-based state of charge (SOC) estimation method for a lithium ion battery system. In the method, a relationship between charging segment data and the SOC of a battery system is established by means of deep learning, such that the SOC can be corrected in any stage during a charging process. SOC estimation during a discharging process is performed by using ampere-hour integration. By means of the provided estimation method, an adaptive update can be performed with a change in the working state of a battery system.

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Inventors:
XIONG RUI (CN)
TIAN JINPENG (CN)
DUAN YANZHOU (CN)
Application Number:
PCT/CN2021/116032
Publication Date:
May 19, 2022
Filing Date:
September 01, 2021
Export Citation:
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Assignee:
BEIJING INSTITUTE TECH (CN)
International Classes:
G01R31/378; G01R31/367
Foreign References:
CN112379272A2021-02-19
CN110058175A2019-07-26
CN109031147A2018-12-18
CN109633470A2019-04-16
CN107769335A2018-03-06
CN110673039A2020-01-10
Other References:
CHAORAN LI, FEI XIAO, YAXIANG FAN, XIN TANG, GUORUN YANG: "Joint Estimation of the State of Charge and the State of Health Based on Deep Learning for Lithium-ion Batteries", PROCEEDINGS OF THE CSEE, ZHONGGUO DIANJI GONGCHENG XUEHUI, CN, vol. 41, no. 2, 10 April 2020 (2020-04-10), CN , pages 681 - 692, XP055929392, ISSN: 0258-8013, DOI: 10.13334/j.0258-8013.pcsee.191867
YUEJIU ZHENG ET AL.: "Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles", JOURNAL OF POWER SOURCES, 22 December 2017 (2017-12-22), XP085322261
Attorney, Agent or Firm:
BEIJING CHENGHUI LAW FIRM (CN)
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