文章基本信息

标题：Learning physics based models of Lithium-ion Batteries
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作者：Maricela Best Mckay ; Brian Wetton ; R. Bhushan Gopaluni 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：3
页码：97-102
DOI：10.1016/j.ifacol.2021.08.225
语种：English
出版社：Elsevier
摘要：AbstractLithium-ion (Li-ion) batteries are increasingly pervasive and important in daily life. We present a surrogate modeling approach that uses synthetic data generated by an electrochemical model to approximate Li-ion battery dynamics using a Deep Neural Network. Elechtrochemical models are needed to describe high current operation but are computationally costly. As an initial study, we prototype our approach for the Single Particle Model. We use a battery use-cycle and observations of concentrations and voltages to predict future battery behavior. Given only the use cycle and knowledge that the battery is fully charged, the surrogate model can accurately forecast observations of Li-ion concentrations and voltages for the entire use cycle, as well as give a window of time for battery depletion.
关键词：KeywordsTime series modellingNonlinear model reductionApplication of power electronicsOptimal operationcontrol of power systemsReal time simulationdispatchingModelingsimulation of power systems