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Study On State Of Health Estimation Algorithm For Lithium Power Battery Used On Pure Electric Vehicle

Posted on:2013-05-21Degree:MasterType:Thesis
Country:ChinaCandidate:W J XuFull Text:PDF
GTID:2232330371985905Subject:Vehicle Engineering
Abstract/Summary:PDF Full Text Request
With the energy crisis and environmental pollution becoming a serious problem,the development of pure electric vehicle is getting highly attention by governments.Battery as the power source of pure electric vehicle, its performance directlyinfluences the performance index of pure electric vehicle. In order to meet nationalregulatory requirements, and to ensure the safe and reliable operation of pure electricvehicles, it is necessary to monitor the power battery health state—the State ofHealth(SOH).This article comes from the development of science and technology plan projectby Science and Technology Department of Jilin Province. This project is “TheDevelopment of Pure Electric High-Grade Business Bus”. Jilin University commitsto research and develop electric vehicle control system, braking energy recoverysystem and power battery monitoring system. In this paper, regard the company’sindependent development of pure electric bus remote monitoring system as platform,and regard the company’s CCQ6750pure electric bus as the research object. Then,according to the Henan Huanyu220Ah power Lithium-ion battery, carried out theresearch of the SOH estimation method. The results of Single battery test, groupbattery test and the real vehicle testing show that the proposed estimationmethod can meet the operational requirements of pure electric bus and providesmonitoring measures for the safe and reliable operation of electric vehicle.The main research content includes the following sections.(1) Analyzed the basic structure and working principle of the power Lithium-ionbattery; analyzed the reasons for capacity fading of power Lithium-ion battery fromtwo aspects, battery manufacturing technology and battery process of using. In theaspect of battery manufacturing process, analyzed some technological requirementsduring the manufacturing process, as well as the impact of these requirements on thebattery capacity; In the battery use process aspect, analyzed the influence on batterycapacity when the battery has the following circumstances, such as overcharge,self-discharge, electrode unstable, electrolyte decomposition and etc.. (2)According to the battery model used in this research vehicle, thisarticle made the Huanyu220Ah single battery charge and discharge cycling test, so asto get the battery charge, discharge and cycle characteristics. Based on the actualusage of pure electric vehicles, determined to estimate SOH in the electric vehiclecharging. First, this paper normalized the charge voltage curve of the monomer cycleand selected a benchmark curve, and then used the BP neural network to fitthe benchmark curve, and finally used the fitting curve to estimate battery SOH.When the battery is using, the change of environment temperature and the internalresistance of will influence its capacity, so it is need to improve the methods describedabove.(3) In order to improve the SOH estimation accuracy of power Lithium-ionbattery, the adaptive method was increased on the basis of original estimationmethod, because of mainly considering an important factor——the battery internalresistance. First, this article established the voltage curve model, and then usedthe recursive least squares method for model identification andtook the recognition results back to the model, and finally determined the curve modelof charging voltage. In order to improve the SOH estimation accuracy, it was carriedout once model identification per battery charge. In addition, according to the cyclecharacteristics of battery and the current battery SOH, the remaining battery cyclesestimated.(4) This paper verify the voltage curve fitting method by group battery test, aswell as verify based adaptive voltage curve fitting method by the real vehicle test. Thetest results showed that based adaptive voltage curve fitting method had a betteraccuracy for SOH estimation. Thus, finally the based adaptive voltage curve fittingmethod was chosen SOH actual estimation. By the technology of DDE the King Viewand MATLAB can be connected. Using remote monitoring system, the SOHestimation of electric vehicle power group battery is realized as the electric vehiclecharging. Also provide an effective control measures for the safe and reliableoperation of electric vehicle.
Keywords/Search Tags:SOH (State of Health), Lithium iron phosphate battery, Adaptive, Remotemonitoring system
PDF Full Text Request
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