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Tag: Mobility
  • Cold Regions Vehicle Start: Next-Generation Lithium-Ion Battery Technologies for Stryker Vehicles

    Abstract: Operating vehicles in extremely cold environments is a significant problem for not only the public but also the military. The Department of Defense has encountered issues when trying to reliably cold start large, heavy-duty military vehicles, specifically the M1126 Stryker Combat Vehicle, in cold regions. As noted in previous work, the issue stems from the current battery technology’s limited temperature range. This current project utilized the protocol established in the previous phase to evaluate next-generation lithium-ion battery technologies for use in cold regions. Selected battery technologies met necessary military specifications for use in large military combat vehicles and were evaluated using a mechanical load system developed in previous work to simulate the starting of a Stryker engine. This work also evaluated the performance of the existing battery technology of a Stryker under Alaskan winter temperatures, which will verify the accuracy of the simulated cold room testing on the mechanical load system. The results of the tests showed that while the system was able to reliably operate down to −20°C, the battery management system encountered challenges at the lower end of the temperature range. This technology has a potential to reliably support cold regions operations but needs further evaluation.
  • Characterizing Snow Surface Properties Using Airborne Hyperspectral Imagery for Autonomous Winter Mobility

    Abstract: With changing conditions in northern climates it is crucial for the United States to have assured mobility in these high-latitude regions. Winter terrain conditions adversely affect vehicle mobility and, as such, they must be accurately characterized to ensure mission success. Previous studies have attempted to remotely characterize snow properties using varied sensors. However, these studies have primarily used satellite-based products that provide coarse spatial and temporal resolution, which is unsuitable for autonomous mobility. Our work employs the use of an Unmanned Aerial Vehicle (UAV) mounted hyperspectral camera in tandem with machine learning frameworks to predict snow surface properties at finer scales. Several machine learning models were trained using hyperspectral imagery in tandem with in-situ snow measurements. The results indicate that random forest and k-nearest neighbors models had the lowest Mean Absolute Error for all surface snow properties. A Pearson correlation matrix showed that density, grain size, and moisture content all had a significant positive correlation to one another. Mechanically, density and grain size had a slightly positive correlation to compressive strength, while moisture had a much weaker negative correlation. This work provides preliminary insight into the efficacy of using hyperspectral imagery for characterizing snow properties for autonomous vehicle mobility.
  • PUBLICATION NOTICE: Seasonal Effects on Vehicle Mobility: High-Latitude Case Study

    Abstract: Seasonality plays a key role in altering the terrain of many military operating environments. Since seasonality has such a large impact on the terrain, it needs to be properly accounted for in vehicle dynamics models. This work outlines a variety of static and dynamic seasonal terrain conditions and their impacts on vehicle mobility in an austere region of Europe. Overall the vehicles performed the best in the dry season condition. The thaw season condition had the most drastic impact on mobility with all but the heavy tracked vehicle being almost completely NOGO in the region. Overall, the heavy tracked vehicle had the best performance in all terrain conditions. These results highlight the importance of incorporating seasonal impacts on terrain into NRMM or any vehicle dynamics model. Future work will focus on collecting more data to improve the empirical relationships between vehicles and seasonal terrain conditions, thereby allowing for more accurate speed predictions.
  • PUBLICATION NOTICE: A Comparison of Frost Depth Estimates from Ground Observations and Modelling Using Measured Values and Reanalysis Data for Vehicle Mobility 

    Abstract: Frozen soils can withstand heavy vehicle loads and provide major maneuver corridors in locations where the soils are otherwise too weak to support the loading conditions. Vehicle mobility models require input of the ground conditions to assess seasonal traffickability. Increasingly, measured air temperatures from weather station locations are becoming more widespread, however they lack a global gridded coverage. Similarly, ground profile measurements, such as soil temperature and moisture, are significant inputs to estimate depths of frost. New data products, such as gridded reanalysis data provides weather and soil data on a gridded global scale. This study compared frost depths determined from measured soil temperatures at stations in North Dakota and Minnesota with frost depths determined from soil temperatures from NASA’s Modern Era Retrospective Analysis for Research Application Version 2 (MERRA-2). The objectives of the study were to evaluate the usefulness of the MERRA-2 data to provide estimates of frost depth, and to determine the accuracy of estimated frost depths from modelling using either measured air temperatures or reanalysis air temperature data. To estimate the maximum frost depth a one-dimensional decoupled heat and moisture flow model was used. Differences in estimated frost depth resulted from modelling when compared to the measured soil temperatures. These differences are likely due to the influence of a snow layer. The properties of the snow layer play an important role in estimating the depth of frost. Improved material properties of the snow layer are needed to more accurately estimate the depth of ground freezing.
  • PUBLICATION NOTICE: Improved Vehicle Mobility by Using Terrain Surfacing Systems

    Abstract: Even for military vehicles designed with superior off-road capabilities, problematic soil conditions can impede mobility, particularly when many vehicles need to traverse the same path. Loose sands with little shear strength or wet silts or clays with little bearing capacity can deform rapidly under traffic. U.S. Army Engineer Research and Development Center researchers conducted field testing over several terrain conditions to measure performance of terrain surfacing systems designed to improve vehicle mobility. Soil conditions included poorly-graded sand, medium-strength silt, weak marsh, and two different slope conditions. Five different terrain surfacing, or matting systems, were tested that included four commercial variants and one U.S. government design. All testing took place at the ERDC Ground Vehicle Terrain Surfacing Test Facility in Vicksburg, Mississippi. Military test vehicles included a Marine Tactical Vehicle Replacement, Common Bridge Transporter, and M1 Abrams tank. Results from the testing showed that all matting systems provided notable improvement in the number of allowable vehicle passes over soft sands. Results varied for the different systems over weaker soils, with performance improved for those matting systems having thicker and stiffer panels. However, improved performance among matting systems came with a sacrifice of increased logistical burden. Data presented here-in include detailed site characteristics and soil deformation as a function of traffic.