University of Naples Federico II | Baker Hughes | Italy
Dr. Raffaele Marotta is an accomplished researcher in vehicle dynamics, control systems, and AI-driven estimation, with proven academic and industrial impact. He earned his Ph.D. in Industrial Engineering (Mechatronics) with honors from the University of Naples Federico II, focusing on AI-enhanced vehicle dynamics. His career includes key roles at the Italian National Research Council (CNR), TU Ilmenau, Tenneco, ZF Group, and currently Baker Hughes, where he leads the development of advanced control algorithms for sustainable energy systems. He has contributed significantly to the European OWHEEL project, developing active chassis control and virtual sensing strategies. His research integrates Kalman filtering, neural networks, reinforcement learning, and digital twins into practical solutions for automotive and energy applications. He has published 22 documents, with 83 citations across 42 sources and an h-index of 6, reflecting strong scientific visibility and influence. His works, published in IEEE and SAE journals, include pioneering studies on wheel displacement estimation, traction force prediction, and vehicle mass estimation. International collaborations across Italy, Germany, Belgium, and Lithuania highlight his global network and impact. Recognized by Nova Talent’s top global talent network, he also mentors young engineers in STEM leadership programs. With his blend of theoretical innovation, experimental validation, and industrial application, Dr. Marotta stands out as a promising candidate for global research excellence awards.
Featured Publications
“Multi-output physically analyzed neural network for the prediction of tire–road interaction forces”
“Deep learning for the estimation of the longitudinal slip ratio”
“Estimation of the tire-road interaction forces by using Pacejka’s formulas with combined slips and camber angles”
“Active control of camber and toe angles to improve vehicle ride comfort”
“Improvement of traction force estimation in cornering through neural network”
“Camber angle estimation based on physical modelling and artificial intelligence”
“Electric vehicle corner architecture: driving comfort evaluation using objective metrics”
“A PID-Based Active Control of Camber Angles for Vehicle Ride Comfort Improvement”
“A strain-based estimation of tire-road forces through a supervised learning approach”
“On the prediction of the sideslip angle using dynamic neural networks”
“Neural Network-Based Virtual Measurement of Road Vehicle Wheel Displacements”
“Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator with Artificial Intelligence”
“On the measurement of unsprung mass displacement of road vehicles through a model-based virtual sensor”
“Model-Based Vehicle Mass Estimation for Enhanced Adaptive Cruise Control Performance”