首页|Reports on Support Vector Machines Findings from Hebei University Provide New In sights (State-of-health Estimation of Lithium-ion Batteries Using a Kernel Suppo rt Vector Machine Tuned By a New Nonlinear Gray Wolf Algorithm)
Reports on Support Vector Machines Findings from Hebei University Provide New In sights (State-of-health Estimation of Lithium-ion Batteries Using a Kernel Suppo rt Vector Machine Tuned By a New Nonlinear Gray Wolf Algorithm)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews - Research findings on Support Vector Machines are discussed in a new report. According to newsreporting originating in Baoding, P eople’s Republic of China, by NewsRx journalists, research stated, “Thecomputer -aided estimation of battery state of health (SOH) has been regarded as an activ e field of energymanagement because of the high demand for electric vehicles an d consumer electronics. In this study,a new data-driven model is proposed for t he capacity prediction and online monitoring of lithium-ionbatteries, which is formulated based on a kernel support vector machine (KSVM) and a nonlinear GrayWolf Optimization (NGWO) to capture the health information in electrochemical im pedance spectroscopy(EIS) data.”
BaodingPeople’s Republic of ChinaAsi aAlgorithmsElectrochemical Impedance SpectroscopyEmerging TechnologiesMa chine LearningSupport Vector MachinesVector MachinesHebei University
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