Ali Uysal
Papers
3
Total Citations
38
H-Index
3
About
Ali Uysal is a researcher working at the intersection of advanced structural dynamics and intelligent energy systems. His work is defined by a dual focus: enhancing the performance of industrial robotic systems through smart vibration control, and pushing the boundaries of battery state-of-charge (SOC) estimation using cutting-edge deep learning. In a key contribution to manufacturing, Uysal developed a hybrid vibration control strategy for an industrial CFRP composite robot-manipulator system, employing a reduced order model to achieve superior precision—work that has garnered 17 citations. Simultaneously, he is making significant strides in energy storage technology. His most cited paper (16 citations) introduces a novel GWO-BiLSTM method, which optimizes hyper-parameters using the Grey Wolf Optimizer to dramatically improve the accuracy of lithium polymer battery SOC estimation. This work, alongside a related study, demonstrates his commitment to solving the practical challenges of real-time battery management. By bridging the gap between mechanical design and AI-driven energy analytics, Uysal’s research offers tangible solutions for more efficient, reliable, and intelligent mechatronic systems.
Research Focus
Key Achievements
Top Papers
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