28 citations · 75 across the 11 of their papers we have counts for
11 papers
Coupled Routing and Charge Schedule Optimization of Electrified Delivery Truck Fleets: Feasibility Analyses
Yared Tadesse Wendimagegnehu, Beshah Ayalew, Andrej Ivanco +1
Electrifying truck fleets has the potential to improve energy efficiency and reduce carbon emissions from the freight transportation sector. However, the range limitations and subs…
Optimal control barrier functions for RL based safe powertrain control
Habtamu Hailemichael, Beshah Ayalew, Andrej Ivanco
Reinforcement learning (RL) can improve control performance by seeking to learn optimal control policies in the end-use environment for vehicles and other systems. To accomplish th…
Combined film and pulse heating of lithium ion batteries to improve performance in low ambient temperature
Habtamu Hailemichael, Beshah Ayalew
Low ambient temperatures significantly reduce Lithium ion batteries' (LIBs') charge/discharge power and energy capacity, and cause rapid degradation through lithium plating. These…
Shared learning of powertrain control policies for vehicle fleets
Lindsey Kerbel, Beshah Ayalew, Andrej Ivanco
Emerging data-driven approaches, such as deep reinforcement learning (DRL), aim at on-the-field learning of powertrain control policies that optimize fuel economy and other perform…
Safe Reinforcement Learning for an Energy-Efficient Driver Assistance System
Habtamu Hailemichael, Beshah Ayalew, Lindsey Kerbel +2
Reinforcement learning (RL)-based driver assistance systems seek to improve fuel consumption via continual improvement of powertrain control actions considering experiential data f…
Safety Filtering for Reinforcement Learning-based Adaptive Cruise Control
Habtamu Hailemichael, Beshah Ayalew, Lindsey Kerbel +2
Reinforcement learning (RL)-based adaptive cruise control systems (ACC) that learn and adapt to road, traffic and vehicle conditions are attractive for enhancing vehicle energy eff…
Probabilistic Constraint Tightening Techniques for Trajectory Planning with Predictive Control
Nathan Goulet, Qian Wang, Beshah Ayalew
In order for automated mobile vehicles to navigate in the real world with minimal collision risks, it is necessary for their planning algorithms to consider uncertainties from meas…
Distributed Maneuver Planning with Connected and Automated Vehicles for Boosting Traffic Efficiency
Nathan Goulet, Beshah Ayalew
Connected and automated vehicles (CAVs) have the potential to improve traffic throughput and achieve a more efficient utilization of the available roadway infrastructure. They also…
Residual Policy Learning for Powertrain Control
Lindsey Kerbel, Beshah Ayalew, Andrej Ivanco +1
Eco-driving strategies have been shown to provide significant reductions in fuel consumption. This paper outlines an active driver assistance approach that uses a residual policy l…
Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning
Lindsey Kerbel, Beshah Ayalew, Andrej Ivanco +1
With the growing need to reduce energy consumption and greenhouse gas emissions, Eco-driving strategies provide a significant opportunity for additional fuel savings on top of othe…
Energy and Flow Effects of Optimal Automated Driving in Mixed Traffic: Vehicle-in-the-Loop Experimental Results
Tyler Ard, Longxiang Guo, Robert Austin Dollar +5
This paper experimentally demonstrates the effectiveness of an anticipative car-following algorithm in reducing energy use of gasoline engine and electric Connected and Automated V…