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Lebesgue-Sampling-based Deep Learning for Battery Diagnosis and Prognosis
Reference #: 01603 The University of South Carolina is offering licensing opportunities for Lebesgue-Sampling-based Deep Learning for Battery Diagnosis and Prognosis Background: Accurate and efficient modeling of battery degradation is of great challenge and is becoming more and more complex for batteries in modern applications. Traditional degradation...
Published: 5/16/2023   |   Inventor(s): Bin Zhang, Guangxing Niu
Keywords(s): Deep belief network, Diagnosis and prognosis, Fault dynamic model, Lebesgue sampling, Lithium-ion battery, Particle filter, Uncertainty management
Category(s): Engineering and Physical Sciences, Energy