10 citations · 14 across the 10 of their papers we have counts for
11 papers
Quadratic Surrogate Attractor for Particle Swarm Optimization
Maurizio Clemente, Marcello Canova
This paper presents a particle swarm optimization algorithm that leverages surrogate modeling to replace the conventional global best solution with the minimum of an n-dimensional…
Eco-Driving Control of Connected and Automated Vehicles using Neural Network based Rollout
Jacob Paugh, Zhaoxuan Zhu, Shobhit Gupta +2
Connected and autonomous vehicles have the potential to minimize energy consumption by optimizing the vehicle velocity and powertrain dynamics with Vehicle-to-Everything info en ro…
Data-driven Driver Model for Speed Advisory Systems in Partially Automated Vehicles
Olivia Jacome, Shobhit Gupta, Stephanie Stockar +1
Vehicle control algorithms exploiting connectivity and automation, such as Connected and Automated Vehicles (CAVs) or Advanced Driver Assistance Systems (ADAS), have the opportunit…
Model Predictive Control for Automotive Climate Control Systems via Value Function Approximation
Dennis Kibalama, Yuxing Liu, Stephanie Stockar +1
Among the auxiliary loads in light-duty vehicles, the air conditioning system is the single largest energy consumer. For electrified vehicles, the impact of heating and cooling loa…
Real-time Eco-Driving Control in Electrified Connected and Autonomous Vehicles using Approximate Dynamic Programming
Shreshta Rajakumar Deshpande, Shobhit Gupta, Abhishek Gupta +1
Connected and Automated Vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driv…
Eco-Driving of Connected and Autonomous Vehicles with Sequence-to-Sequence Prediction of Target Vehicle Velocity
Shobhit Gupta, Marcello Canova
The Eco-Driving control problem seeks to perform fuel efficient speed planning for a Connected and Autonomous Vehicle (CAV) that can exploit information available from advanced map…
Safe Model-based Off-policy Reinforcement Learning for Eco-Driving in Connected and Automated Hybrid Electric Vehicles
Zhaoxuan Zhu, Nicola Pivaro, Shobhit Gupta +2
Connected and Automated Hybrid Electric Vehicles have the potential to reduce fuel consumption and travel time in real-world driving conditions. The eco-driving problem seeks to de…
A GPU Implementation of a Look-Ahead Optimal Controller for Eco-Driving Based on Dynamic Programming
Zhaoxuan Zhu, Shobhit Gupta, Nicola Pivaro +2
Predictive energy management of Connected and Automated Vehicles (CAVs), in particular those with multiple power sources, has the potential to significantly improve energy savings…
An Algorithm to Warm Start Perturbed (WASP) Constrained Dynamic Programs
Abhishek Gupta, Shreshta Rajakumar Deshpande, Marcello Canova
Receding horizon optimal control problems compute the solution at each time step to operate the system on a near-optimal path. However, in many practical cases, the boundary condit…
A Deep Reinforcement Learning Framework for Eco-driving in Connected and Automated Hybrid Electric Vehicles
Zhaoxuan Zhu, Shobhit Gupta, Abhishek Gupta +1
Connected and Automated Vehicles (CAVs), in particular those with multiple power sources, have the potential to significantly reduce fuel consumption and travel time in real-world…
Integrated Approximate Dynamic Programming and Equivalent Consumption Minimization Strategy for Eco-Driving in a Connected and Automated Vehicle
Shreshta Rajakumar Deshpande, Daniel Jung, Marcello Canova
This paper focuses on the velocity planning and energy management problems for Connected and Automated Vehicles (CAVs) with hybrid electric powertrains. The eco-driving problem is…