Shengyi Wang

OverviewAI-generated summary

Shengyi Wang's research focuses on the optimization of energy systems and manufacturing processes, with a strong emphasis on machine learning applications. His work includes developing advanced algorithms for the real-time voltage regulation of unbalanced low-voltage networks that incorporate significant photovoltaic penetration, and optimizing energy storage systems through deep reinforcement learning. Wang also investigates methods for improving selective laser melting (SLM) processes, such as in-situ porosity classification using coaxial monitoring and image processing, and multi-objective process parameter optimization using ensemble metamodels. His research extends to state-of-charge estimation for lithium-ion batteries using quantum-inspired optimization techniques and exploring learning models for electric power grids, including stochastic synchronous learning for electric vehicle aggregators. Wang has authored or co-authored 67 publications, accumulating over 1,020 citations, and holds an h-index of 13.

Metrics

• h-index: 13
• Publications: 67
• Citations: 1,020