首页|New Data from University of the Chinese Academy of Sciences Illuminate Findings in Machine Learning (A Comparative Study On State-of-charge Estimation for Lithi um-rich Manganese-based Battery Based On Bayesian Filtering and Machine Learning ...)
New Data from University of the Chinese Academy of Sciences Illuminate Findings in Machine Learning (A Comparative Study On State-of-charge Estimation for Lithi um-rich Manganese-based Battery Based On Bayesian Filtering and Machine Learning ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “In this paper, a comparative study on the state of charge (SOC) estimation of the lithium-rich manganese-ba sed battery (LRMB) has been conducted by systematically considering the equivale nt circuit model (ECM), aging state and algorithms. The results show that the first-order RC model combined with the Extended Kalman filter (EKF) is more suitable for the SOC estimation of the LRMB when the battery decays less severely.”
BeijingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningUniversity of the Chinese A cademy of Sciences
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