Trucks

Decarbonization and electrification of long-haul trucks are notoriously difficult due to the high energy demand and limited gravimetric energy density of lithium-ion cells. In this study, we investigate the optimal deployment and operation of a grid-connected battery swapping station (BSS) for electric long-haul trucks as a mixed-integer optimization problem. We construct a model for reliably meeting customer energy needs while providing grid services, to demonstrate the business case and the operation of such a system. The impact of optimal sizing of the station is explored. A comparative...

The advantages and disadvantages of Battery Swapping Stations (BSS) for heavy-duty trucks are poorly understood, relative to Fast Charging Stations (FCS) systems. This study evaluates these two charging mechanisms for electric heavyduty trucks, aiming to compare the systems efficiency and identify their optimal design. A model was developed to address the planning and operation of BSS in a charging network, considering in-station batteries as assets for various services. We assess performance metrics including transportation efficiency and battery utilization efficiency. Our evaluation...

This article demonstrates the use of deep neural networks (NN) and deep reinforcement learning (deep-RL) for modeling and control of longitudinal heavy duty truck dynamics. Instead of explicit use of analytical model derived information or parameters about the truck, the deep NN model is fitted to data using a brief set of historical data collected from an arbitrary driving cycle. The deep model is used in this article to design a cruise controller for the truck using model-free deep-RL. The deep model and the control loop performances are demonstrated both using state-of-the-art...

This article proposes a perception model for enhancing the accuracy and stability of car-following control of a longitudinally automated truck. We applied a fusion-based tracking algorithm on measurements of a single preceding vehicle needed for car following control. This algorithm fuses two types of data, radar and LiDAR data, to obtain more accurate and robust longitudinal perception of the subject vehicle in various weather conditions. The filter's resulting signals are fed to the gap control algorithm at every tracking loop composed by a high-level gap control and lower acceleration...