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Bo Sun
Researcher at Beihang University
Publications - 166
Citations - 1589
Bo Sun is an academic researcher from Beihang University. The author has contributed to research in topics: Reliability (statistics) & Computer science. The author has an hindex of 15, co-authored 156 publications receiving 984 citations. Previous affiliations of Bo Sun include Guangdong University of Technology & Delft University of Technology.
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Thermal Management on IGBT Power Electronic Devices and Modules
TL;DR: A quick and efficient evaluation judgment for the thermal management of the IGBTs depended on the requirements on the junction-to-case thermal resistance and equivalent heat transfer coefficient of the test samples is proposed.
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Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries
TL;DR: A one-dimensional convolution neural network (1D CNN)-based method that takes random segments of charging curves as inputs to perform capacity estimation for lithium-ion batteries is presented and it is proved that the proposed method is feasible to provide accurate estimations on capacity degradation for both kinds of batteries.
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PoF-Simulation-Assisted Reliability Prediction for Electrolytic Capacitor in LED Drivers
TL;DR: A physics-of-failure (PoF)-based reliability prediction methodology is developed for LED drivers to consider the temperature change of electrolytic capacitor and results in an overestimated driver lifetime by more than 38% for the selected case study.
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A reliability design method for a lithium-ion battery pack considering the thermal disequilibrium in electric vehicles
TL;DR: The reliability of battery packs of different redundant cell numbers and configurations does not monotonically increase with the number of redundant cells for the thermal disequilibrium effects, and the reliability of a 6 × 5 parallel-series configuration is the optimal system structure.
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Agent-based restoration approach for reliability with load balancing on smart grids
TL;DR: A modified restoration strategy based on reinforcement learning, namely, the wolf pack algorithm (WPA), is proposed under the multi-agent framework and communication architecture to optimize the reliability of a system in the restoration process, considering load balancing as a constraint.