首页|Study Results from University of Petroleum and Energy Studies Provide New Insights into Machine Learning (Analyzing Electric Vehicle Battery Health Performance Using Supervised Machine Learning)
Study Results from University of Petroleum and Energy Studies Provide New Insights into Machine Learning (Analyzing Electric Vehicle Battery Health Performance Using Supervised Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Current study results on Machine Learning have been published. According to news reportingoriginating from Dehradun, India, by NewsRx correspondents, research stated, “Lithium-ion batteries havinghigh energy and power densities, fast depleting cost, and multifaceted technological improvement lead to the first choice for electric transportation systems. A noble supervised K nearest neighbors (KNN),support vector regressor (SVR), decision tree (DT), and random forest (RF) regressors machine learningalgorithm are developed with different accuracy and usefulness for the state of health estimation from directmeasurable indices of voltage, current, and temperature without inherited electrochemical characteristicslike voltage hysteresis, aging, degradation level, operational, and environmental effect.”
DehradunIndiaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningUniversity of Petroleum and Energy Studies
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