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Study On State Of Health Estimation Of The Lithium-ion Battery

Posted on:2016-04-13Degree:MasterType:Thesis
Country:ChinaCandidate:J N ZhangFull Text:PDF
GTID:2272330467498733Subject:Pattern Recognition and Intelligent Systems
Abstract/Summary:PDF Full Text Request
As an energy carrier in the electric vehicle, how to estimate the battery state of health(SOH) accurately is one of the focus and core technologies in the area of electric vehicle.Accurate estimation of SOH can provide the battery’s state of health to the driver currently,and then guarantee the driver’s safety. In this paper, the following work has been done for theestimation of SOH:First, this paper introduces the development background of electric vehicles, elaboratesthe significance of researching SOH estimation methods and analyzes factors which havegreater influence on the SOH estimation. The paper summarizes the present researchsituation of the domestic and foreign battery SOH estimation methods. In addition, thecurrent internationally recognized definition of battery SOH and SOH estimation methodshave been comparatively analyzed and compared.The battery health assessment test and battery life test have been done to12Ahlithium-ion batteries. At different temperatures and storage voltage, we studied and analyzedin detail on the capacity characteristics and the internal resistance characteristics of thebattery by battery health assessment test for lithium-ion. Battery life test provided basic datato SOH estimation through a large number of cell experiments.Based on the data of lithium ion battery cycle life experiments, normolize voltage curveof battery, and validate accuracy of the voltage curve according to the relationship betweencapacity and voltage.Then select the reference curve and use neural network algorithm to fitcurve. Finally, estimate the SOH of the battery. Analysed by error and error distribution, itneeds to be optimized.Finally, in order to improve the accuracy of estimating battery SOH, this paper adds theadaptive control algorithm. Building the battery voltage curve model, and identifying theparameters of the model with the method of least-squares method in real time. The modelcontains a correction term, by constantly revising the reference curve, the correction term can match curves voltage under actual conditions real-time, to improve estimation accuracy.The experimental results show the feasibility, effectiveness and accuracy of adaptive controlalgorithm in the battery SOH estimation...
Keywords/Search Tags:the Lithium-ion battery, SOH Estimation, Voltage curve fitting, Adaptive algorithm
PDF Full Text Request
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