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The concept of a structural index, λ, is developed using the simple example of a hollow beam with a cantilever load case. It is assumed that when performing material substitution, only the thickness, t, is changed. It is shown that the stiffness, K, of the beam can be defined as a function of tλ, that 1 ≤ λ ≤ 3, and that λ can be used to predict the weight savings from material substitution where stiffness is held constant. It is then demonstrated that λ can be used to predict the weight savings from material substitution in the more complex cases of the joints of a light truck cab.

Although the cost-saving and good environmental impacts are the benefits that make Electric Vehicles (EVs) popular, these advantages are significantly influenced by the cost of battery replacement over the vehicle lifetime. After several charging and discharging cycles, the battery is subjected to energy and power degradation which affects the performance and efficiency of the vehicle. In addition to battery replacement cost, the electricity cost being paid by drivers is another key factor in selecting the EVs. An Energy Management System (EMS) with Model Predictive Control-based (MPC) algorithm is presented for a specific case of heavy-duty EV. Such EV draws its energy from the grid via catenary in addition to the on-board battery. Dynamic model of the vehicle will be defined by State Space Equations (SSE).

This paper reports on the efforts of the initial phase of the IMPACT program to define the underlying structural theory behind selecting the proper material(s) to reduce weight in the most efficient, cost-effective manner. Following this initial phase, the IMPACT program will proceed to design and build, optimized, proprietary, full vehicle platform prototypes that achieve up to a 25 percent weight reduction total without compromising any customer-driven vehicle attributes. Most importantly, the materials and technologies selected must be implementation ready for high volume, low cost, dual-use applications. The purpose of the initial phase and an in-depth discussion on which material properties should most influence material selection are presented.

Recent developments in the area of autonomous vehicle navigation have emphasized algorithm development for the characterization of LiDAR 3D point-cloud data. The LiDAR sensor data provides a detailed understanding of the environment surrounding the vehicle for safe navigation. However, LiDAR point cloud datasets need point-level labels which require a significant amount of annotation effort. We present a framework which generates simulated labeled point cloud data. The simulated LiDAR data was generated by a physics-based platform, the Mississippi State University Autonomous Vehicle Simulator (MAVS). In this work, we use the simulation framework and labeled LiDAR data to develop and test algorithms for autonomous ground vehicle off-road navigation. The MAVS framework generates 3D point clouds for off-road environments that include trails and trees.

Methanol is one of the most promising fuels for the decarbonization of the off-road and transportation sectors. Although methanol is typically seen as an alternative fuel for spark ignition engines, mixing-controlled compression ignition (MCCI) combustion is typically preferred in most off-road and medium-and heavy-duty applications due to its high reliability, durability and high-efficiency. In this paper, the potential of using ignition enhancers to enable methanol MCCI combustion was investigated. Methanol was blended with 2-ethylhexyl nitrate (EHN) and experiments were performed in a single-cylinder production-like diesel research engine, which has a displacement volume of 0.83 L and compression ratio of 16:1. The effect of EHN has been evaluated with three different levels (3%vol, 5%vol, and 7%vol) under low- and part-load conditions. The injection timing has been swept to find the stable injection window for each EHN level and load.