230000000875 corresponding Effects 0.000 claims description 9.238000006731 degradation reaction Methods 0.000 claims abstract description 70.
230000004059 degradation Effects 0.000 claims abstract description 70. 230000015556 catabolic process Effects 0.000 claims abstract description 70. 238000010998 test method Methods 0.000 title claims abstract description 17. 238000010438 heat treatment Methods 0.000 title claims abstract description 160. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.) Filing date Publication date Priority to CN201910806603.3A priority Critical patent/CN110470992B/en Priority to CN201910806603.3 priority Application filed by Shell Internationale Research Maatschappij BV filed Critical Shell Internationale Research Maatschappij BV Priority to PCT/EP2020/073981 priority patent/WO2021037984A1/en Publication of CA3151496A1 publication Critical patent/CA3151496A1/en Status Pending legal-status Critical Current Links Original Assignee Shell Internationale Research Maatschappij BV Priority date (The priority date is an assumption and is not a legal conclusion. Shell Internationale Research Maatschappij BV Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.) Inventor Yudi QIN Languang LU Yalun Li Minggao Ouyang Jianqiu LI Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.) Pending Application number CA3151496A Other languages French ( fr) Google Patents Durability test method and system and data table generation method for battery pulsed heating Google Patents CA3151496A1 - Durability test method and system and data table generation method for battery pulsed heating A LiFePO 4 battery is tested in the test station for 37 weeks to verify the validation of the proposed method.CA3151496A1 - Durability test method and system and data table generation method for battery pulsed heating 
Afterwards, SVM is able to establish the optimal SOH estimator on the basis of the optimal feature combination and the battery SOH. In order to find the most effective feature for SOH estimation, all the possible combinations of the features are investigated and compared. After applying the short term current pulse test (few seconds), the keen points and the slopes in the voltage response curve are selected as the potential candidate features. The benefit of the proposed method is that the features come from the short-term test, which is much convenient to be obtained in real applications. Since the terminal voltage measured at the same condition varies with the battery aging process, the features for SOH estimation are extracted from the voltage response under a specific current pulse test. Utilizing the features from the terminal voltage response of the Li-ion battery under current pulse test, a new method is proposed in this paper by using the Support Vector Machine (SVM) technique for accurately estimating the battery SOH. State of Health (SOH) of Lithium-ion (Li-ion) battery plays a pivotal role in the reliability and safety of the Battery Energy Storage System (BESS) in the power system.
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