F
Farhang Motallebiaraghi
Researcher at Western Michigan University
Publications - 7
Citations - 40
Farhang Motallebiaraghi is an academic researcher from Western Michigan University. The author has contributed to research in topics: Computer science & Energy management. The author has an hindex of 2, co-authored 5 publications receiving 11 citations.
Papers
More filters
Proceedings ArticleDOI
Vehicle Velocity Prediction Using Artificial Neural Network and Effect of Real World Signals on Prediction Window
Tushar Gaikwad,Aaron Rabinowitz,Farhang Motallebiaraghi,Thomas H. Bradley,Zachary D. Asher,Alvis Fong,Rick Meyer +6 more
TL;DR: This research shows that the lowest Mean Absolute Error of future velocity prediction is with a fully inclusive dataset in 10-second velocity prediction windows, and has demonstrated that the LSTM neural network used for velocity prediction can be implemented in real-time using an NVIDIA DRIVE PX2.
Proceedings ArticleDOI
High-Fidelity Modeling of Light-Duty Vehicle Emission and Fuel Economy Using Deep Neural Networks
Farhang Motallebiaraghi,Aaron Rabinowitz,Shantanu H. Jathar,Alvis Fong,Zachary D. Asher,Thomas H. Bradley +5 more
TL;DR: Preliminary results show that the deep neural network’s performance consistently improves when given datasets with more input variables, potentially indicating improved usability for researchers compared to shallow and basic neural networks.
Journal ArticleDOI
Mobility Energy Productivity Evaluation of Prediction-Based Vehicle Powertrain Control Combined with Optimal Traffic Management
Farhang Motallebiaraghi,Kaisen Yao,Aaron Rabinowitz,Christopher G. Hoehne,Venu M Garikapati,Jacob Holden,Eric Wood,Suren Chen,Zachary D. Asher,Thomas H. Bradley +9 more
TL;DR: This research aims to integrate previously developed and published research on Predictive Optimal Energy Management Strategies (POEMS) and Intelligent Traffic Systems (ITS), to address the need for quantifying improvement in system efficiency resulting from simultaneous vehicle and network optimization.
Journal ArticleDOI
Autonomous Eco-Driving with Traffic Light and Lead Vehicle Constraints: An Application of Best Constrained Interpolation
Yara Hazem Mahmoud,Nicholas E. Brown,Farhang Motallebiaraghi,Melinda E. Koelling,Richard T. Meyer,Zachary D. Asher,Assen Dontchev,Ilya Kolmanovsky +7 more
TL;DR: In this article, the authors demonstrate the connection between Eco-Driving and best interpolation in the strip, which is a problem in approximation theory and optimal control, and generate optimal Eco-driving trajectories that can be driven with an autonomous system and evaluate them using conventional, hybrid electric, and fully electric vehicle models from FASTSim software.