首页|Study Findings on Machine Learning Are Outlined in Reports from University of Pe rugia (A Guide To Equivalent Circuit Fitting for Impedance Analysis and Battery State Estimation)
Study Findings on Machine Learning Are Outlined in Reports from University of Pe rugia (A Guide To Equivalent Circuit Fitting for Impedance Analysis and Battery State Estimation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting originating from Perugia,Italy,by NewsR x correspondents,research stated,"In this study we define a comprehensive meth od for analyzing electrochemical impedance spectra of lithium batteries using eq uivalent circuit models,and for information extraction on state -of -charge and state -of -health from impedance data by means of machine learning methods. Est imation of circuit parameters typically implies a non -linear optimization probl em." Financial supporters for this research include European Union-Next Generation EU ,Mission Innovation program of Ministry of Environment and Energy Security (MAS E,ex-MITE) via the IEMAP project. Our news editors obtained a quote from the research from the University of Perug ia,"A detailed method for estimating initial values of the optimization algorit hm is described,emphasizing short computation times and efficient convergence t o global minimum. Parameters identifiability is investigated through an analysis of the injectivity of the model,Cramer-Rao lower bound,profile likelihood,an d sensitivity analysis. An exploratory data analysis is presented to estimate th e degree of correlation between impedance spectra (or circuit parameters) and ba ttery state -of -charge or state -of -health,prior to the implementation of any machine learning algorithm. A publicly available dataset of impedance spectra o f five lithium-polymer batteries is used to test the whole procedure."
PerugiaItalyEuropeCyborgsEmergin g TechnologiesMachine LearningUniversity of Perugia
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