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The incorporation of hydrogen fuel cells into heavy duty tactical mobility vehicles can bring about great opportunities in reducing the pollutant emissions of this kind of platforms (GVW > 30,000 kg). Furthermore the transportation of fuel to operational areas has become a key aspect for any deployment therefore optimal use of this resource is of paramount importance. Finally, it is also quite common for such platforms to serve additional purposes, besides freight delivery, such as powering external equipment (i.e. field hospitals or mobile artillery pieces). This work will describe the intelligent energy management system for a PEM Fuel Cell-Battery-Ultracapacitor Hybrid 8×8 heavy truck of the aforementioned weight class which also contemplates an internal electric/traction power generation unit. It will describe how the system optimizes the use of battery and hydrogen fuel energy while keeping system efficiency and performance at a maximum.

Energy management strategies greatly influence the power performance and fuel economy of series hybrid electric tracked bulldozers. In this paper, we present a procedure for the design of a power management strategy by defining a cost function, in this case, the minimization of the vehicle’s fuel consumption over a driving cycle. To explore the fuel-saving potential of a series hybrid electric tracked bulldozer, a dynamic programming (DP) algorithm is utilized to determine the optimal control actions for a series hybrid powertrain, and this can be the benchmark for the assessment of other control strategies. The results from comparing the DP strategy and the rule-based control strategy indicate that this procedure results in approximately a 7% improvement in fuel economy.

The work describes a concept application that aids a safety engineer to create a layup of equipment models by using an image scan of a schematic and a library of predefined standard component and their symbols. The approach uses image recognition techniques to identify the symbols within the scanned image of the schematic from a given library of symbols. Two recognition approaches are studied, one uses General Hough Transform; the other is based on pixel-level feature computation combining both structure and statistical features. The application allows the user to accept or edit the results of the recognition step and allows the user to define new components during the layup step. The tool then generates an output file that is compatible with a formal safety modeling tool. The identified symbols are associated to behavioral nodes from a model based safety tool.

Bus drivers are a group at risk of often suffering from musculoskeletal problems, such as low-back pain, while bus passengers on the last-row seats experience accelerations of high values. In this paper, the contribution of K-seat in decreasing the above concern is investigated with a detailed simulation study. The K-seat model, a seat with a suspension that functions according to the KDamper concept, which combines a negative stiffness element with a passive one, is benchmarked against the conventional passive seat (PS) in terms of comfort when applied to different bus users’ seats. More specifically, it is tested in the driver’s and two different passengers’ seats, one from the rear overhang and one from the middle part. For the benchmark shake, both are optimized by applying excitations that correspond to real intercity bus floor responses when it drives over a real road profile.

Increased production rates and cost reduction are affecting manufacturing in all mobility industry sectors. One enabling methodology that could achieve these goals in the burgeoning “Industry 4.0” environment is the optimized deterministic assembly (DA) approach. It always forms the same final structure and has a strong link to design-for-assembly and design-for-automation. The entire supply chain is considered, with drastic savings at the final assembly line level through recurring costs and lead-time reduction. Unsettled Technology Areas in Deterministic Assembly Approaches for Industry 4.0 examines the evolution of previous assembly principles that lead up to and enable the DA approach, related simulation methodologies, and undefined and unsolved links between these domains. Click here to access the full SAE EDGETM Research Report portfolio.

“Smart” refers to tools that are “specific, measurable, achievable, reasonable/realistic, and time bound.” Smart assembly tools are used in many industries, including automotive, aerospace, and space for measuring, inspecting, gauging, drilling, and installing all existing fastening systems. Inside the Industry 4.0 environment, these tools have a huge influence on Information and Communication Technology (ICT), assembly cost reduction, process control, and even the product and process quality. These four domains—and their undefined nature—are the focus of this SAE EDGE™ Research Report. The technical issues identified here need to be discussed, the goals clarifying the scope of the industry-wide need to be aligned, and the issues requiring standardization need prioritized. NOTE: SAE EDGE Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry.