Digital design
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The Digital Design page serves as a hub for resources exploring the cutting-edge tools and technologies reshaping electric vehicle development. With access to reports, scientific papers, and case studies, this section highlights the growing role of virtual simulations, digital twin models, and advanced software in the design process. Whether you're researching how digital tools are accelerating design iterations or improving product quality, these materials provide essential information to support innovation in the EV digital design landscape.
FMEA 2.0: Machine Learning Applications in Smart Microgrid Risk Assessment
Modern Smart Grids are complex systems incorporating physical components like distributed energy resources and storage, along with cyber components for advanced control, networking, and monitoring. This study proposes an integrated methodology for risk prioritization and failure mode classification into low, moderate and high-risk faults using Grey Relational Analysis (GRA) together with Failure Mode and Effects Analysis (FMEA) and Deep Learning algorithms. The results demonstrate that, especially in complex systems like Smart Microgrids, the proposed method more accurately captures the coupling relationships between failure modes compared to the conventional FMEA method.
Consultants in Smart Grid Safety and Reliability, Cyber-Physical System Engineers, Energy System Developers, Government Policy Makers in Energy Infrastructure, Reliability Engineers, Risk Management Specialists, Smart Grid Technology Researchers
Clustering Algorithms, Decision Support Systems, Heuristic Algorithms, Knowledge Based Systems, Machine Learning Algorithms, RHODaS, Smart Grids
Link:
ieeexplore.ieee.org