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Browse AllAbstract Real-world driving data is an invaluable asset for several types of transportation research, including emissions estimation, vehicle control development, and public infrastructure planning. Traditional methods of real-world driving data collection use expensive GPS-based data logging equipment which provide advanced capabilities but may increase complexity, cost, and setup time. This paper focuses on using the Google Maps application available for smartphones due to the potential to scale-up real-world driving data logging. Samples of the potential data processing and information that can be gathered by such a logging methodology is presented. Specifically, two months of Google Maps driving data logged by a rural Michigan resident on their smartphone may provide insights on their driving range, duration, and geographic area of coverage (AOC) to guide them on future vehicle purchase decisions. Aggregating such statistics from crowd-sourcing real-world driving data via Google
The automotive industry faces ongoing challenges in reducing vehicle mass and carbon emissions while ensuring structural integrity. Traditional design approaches often fail to address these issues comprehensively. This paper explores the application of generative design (GD) to optimize critical automotive components, specifically focusing on reducing mass and in turn carbon emissions. GD builds upon traditional topology optimization by employing iterative method using MELS approach to refine designs providing multiple alternative designs to choose from. MELS (Modified Extensible Lattice Sequence) specifically is used to equally spread-out points (designs) in a space by minimizing clumps and empty spaces. This property of MELS makes lattice sequences an excellent space filling DOE scheme. GD leverages the design of experiments (DOE) to vary key design variables systematically to generate and consider many potential design concepts for a given problem. It also uses artificial
Automotive seating systems have become increasingly sophisticated, providing consumers with more flexible configurations and comfort functionalities. Traditional power seating, which relied on a few motors to adjust the seat position, has evolved into more technically advanced reconfigurable systems equipped with additional feedback sensors and actuators. These advancements include features such as Easy Entry, Zero Gravity, Stadium Swivel, IP Nesting, Auto Lumbar/Bolster Adjustment and Power Long Rails. All the features indicate that the overall control of seating systems now resembles robotic arm control or multi-body control, involving numerous coordinated movements. In this paper, we propose a novel control strategy for the coordinated speed control of multiple motors. Unlike traditional seating controls, which typically use direct switches or open-loop systems, we introduce a feedback approach that incorporates Kalman-filter-based speed estimation using raw signals directly from