Tesla (United States)

With the increasing application demands placed on high-voltage power modules, the insulation performance must be correspondingly enhanced. In particular, insulation failures and breakdowns initiated by partial discharge (PD) within the module are categorically intolerable. Therefore, making a comprehensive mechanistic understanding of module PD and the derivation of quantitative insulation-design guidelines indispensable. In this work, a chain-equivalent circuit model is established to rigorously analyze the electric-field–driven PD mechanisms in high-voltage power modules for the first time, fully elucidating the factors that distort the field at the triple point (TP). Under conditions dominated by the dielectric properties of the packaging materials, the partial-discharge inception voltage (PDIV) at TP scales proportionally with the square roots of ceramic thickness, package-material thickness, and their respective relative permittivity, while scaling inversely with the square root of the ceramic’s permittivity. The accuracy of the predicted correlations between structural-material parameters and PDIV are experimentally confirmed. Based on the chain- equivalent circuit model, an optimized structural– material parameter matching scheme is rapidly derived. Then a systematic insulation-design workflow is devised. The results of this study provide precise, quantitative guidance for the insulation design and material selection of high-voltage power-module substrates.

With the confluence of Wide Bandgap (WBG) power semiconductor device’s superior characteristics, trends of high efficiency, low volume and weight, and cost-effective solution for power electronics systems, the demands for the advanced power packaging are growing rapidly. Laterally conducting Gallium Nitride/Aluminum Gallium Nitride (GaN/AlGaN) heterojunction transistor is one of the WBG power devices and holds fast-switching characteristics enabling high efficiency and small form factor. To not limit the fast-switching advantages, an ultra-low inductance interconnection configuration for a half-bridge power module is proposed. This configuration includes an optimal power device orientation, package metalized trace layout, and a heterogeneously integrated power interposer (HIPI) using ultra-thin dielectrics. Ultra-low inductance is achieved by parallel-plate multi-loop interconnection which facilitates a significant magnetic field cancellation compared with the state-of-the-art single-loop structure.

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Federated Learning (FL) has pioneered the idea of "share wisdom not raw data" to enable collaborative learning over decentralized data. FL achieves this goal by averaging model parameters instead of centralizing data. However, representing "wisdom" in the form of model parameters has its own limitations including the requirement for uniform model architectures across clients and communication overhead proportional to model size. In this work we introduce Co-Dream a framework for representing "wisdom" in data space instead of model parameters. Here, clients collaboratively optimize random inputs based on their locally trained models and aggregate gradients of their inputs. Our proposed approach overcomes the aforementioned limitations and comes with additional benefits such as adaptive optimization and interpretable representation of knowledge. We empirically demonstrate the effectiveness of Co-Dream and compare its performance with existing techniques.

This paper presents a hybrid CNN-LSTM model integrated with GMAC for intrusion detection and secure communication in IoT networks. The system uses Yule-Simon Distribution-Based Lyrebird Optimization Algorithm for feature selection that can achieve the highest anomaly detection accuracy at minimal computational cost in real-time applications. The proposed model uses GMAC with simultaneous encryption and authentication and the CNN-LSTM hybrid for the detection of intrusion in IoT traffic data. Hence, the detection accuracy and the processing time is much better compared to the traditional methods. System detection accuracy at 99.10% was achieved with a latency of 980ms; thus, there is robust performance with minimal overhead of computation. This advanced approach improves the security of IoT and offers an effective and scalable solution for smart city and industrial IoT networks, providing high accuracy, low latency, and strong encryption.

Electrolyte motion in commercial Li-ion batteries has become an important topic as researchers seek to understand patterns of degradation that occur in large-format cells. Recent work has linked the motion of excess electrolyte to Li plating on the anode of large-format cells after repeated fast charging - an effect known as electrolyte motion induced salt inhomogeneity (EMSI). Mapping the distribution and flow patterns of electrolyte in the cell is critical to understanding these phenomena and predicting the patterns of Li plating that can result. In this work, we used time-resolved, synchrotron computed tomography (CT) to directly image the flow of electrolyte in two commercial 18650 cells during cycling, with one cell containing SiOx in the negative electrode and the other containing only graphite. The former cell shows significantly more electrolyte “pumping” during charge and discharge as well as asymmetric redistribution of salt along the jelly roll after hundreds of cycles. The results yield new insights into how electrolyte motion and its effects are influenced by the composition, geometry, and orientation of the cell.