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This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing.

This project is one component of a broader effort whose ultimate goal is to provide CFD-based tools that can be used to optimize the design of urea SCR NOx aftertreatment systems for heavy-duty diesel engines. Here the focus is on predicting the distributions of key chemical species (ammonia, in particular) at the inlet to the catalysts. Two aspects of the physical models have been emphasized: the multi-phase models, and the gas-phase chemistry models. A hierarchy of four simplified geometric configurations has been used for model development and parametric studies, and to establish the appropriate level of physical modeling and numerical fidelity required. The resulting physical and numerical parameters then have been used to model a production SCR system. Initial quantitative comparisons with experimental measurements are encouraging.

This paper presents a review of the core functions and decisions of transportation and traffic management in the automobile industry. Its purpose is to present the concepts within a revised framework which is set in a logistical channels context. Using this approach it easier to see the interdependence which is necessary between shippers and carriers that handle raw materials, component parts, and finished products.

One promising method for reducing fuel consumption and emissions, particularly in heavy duty trucks, is platooning. As the distance between vehicles decreases, the following vehicles will experience less aerodynamic drag on the front of the vehicle. However, reducing the velocity of the air contacting the front of the vehicle could have adverse effects on the temperature of the engine. To compensate for this effect, the energy consumption of the engine cooling system might increase, ultimately limiting the overall improvements obtained with platooning. Understanding the coupling between drag reduction and engine cooling load requirement is key for successfully implementing platooning strategies. Additionally, in a Connected and Automated Vehicle (CAV) environment, where information of the future engine load becomes available, the operation of the cooling system can be optimized in order to achieve the maximum fuel consumption reduction.

Optical soot pyrometry is a mature experimental technique that has been applied to a broad range of combustion systems for measuring soot temperature and concentration. Even though the method is widely used and well documented, the line of sight nature of the technique makes the interpretation of its results challenging. Notably, gradients in temperature and soot concentration along the line of sight or across the field of view can introduce significant levels of uncertainty in the results. This paper presents a numerical study where the signal from the experimental two-color pyrometry technique in a marine diesel engine reference experiment is reconstructed employing computational fluid dynamics (CFD) and a detailed Line-by-Line (LBL) spectral radiation model. The analysis is aimed at qualitatively supporting interpretability of experimental observations.