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Increasingly complex hybrid electric vehicle (HEV) powertrains are being developed to address the growing stringency of emissions regulations, fuel economy standards and drivability/performance requirements. Early in their design process, it is desirable to rapidly evaluate powertrain architectures and components using simulation models before committing to costly physical prototyping. However, HEV powertrains have multiple controlled degrees of freedom, such as power split, engine on/off and gear selection, the operation of which needs to be pre-determined before a meaningful performance evaluation can be carried out. In this paper, we describe a multistage mixed integer trajectory optimization methodology that allows design engineers to rapidly perform performance analyses of complex powertrains. The methodology can generate optimal input signals for both continuous (engine torque or motor power) and discrete (engine on/off or gear selection) degrees of freedom for a given scenario.

The objective of this study was to optimize the occupant restraint systems for a light tactical vehicle in frontal crashes. A combination of sled testing and computational modeling were performed to find the optimal seatbelt and airbag designs for protecting occupants represented by three size of ATDs and two military gear configurations. This study started with 20 sled frontal crash tests to setup the baseline performance of existing seatbelts, which have been presented previously; followed by parametric computational simulations to find the best combinations of seatbelt and airbag designs for different sizes of ATDs and military gear configurations involving both driver and passengers. Then 12 sled tests were conducted with the simulation-recommended restraint designs. The test results were further used to validate the models. Another series of computational simulations and 4 sled tests were performed to fine-tune the optimal restraint design solutions.

The interior layout of passenger cars and light trucks is substantially aided by SAE occupant packaging tools, which include the SAE J941 eyellipse, SAE J287 reach curves, and the seating accommodation model in SAE J4004. Most of these tools were developed based on posture and position data from drivers, although an eyellipse and head contour are available for fixed-seat passenger positions. This paper reviews the current SAE occupant packaging tools and related industry practice in the context of current concepts for highly automated vehicles, considering SAE levels 4 and 5. Concepts that have driver controls for occasional use and vehicles with no driver controls are examined. Gaps in the current knowledge and tools are reviewed to establish priorities for research and development of new standards and recommended practices.

Driver upper-extremity postures and activities were manually coded in 9856 video frames from 165 drivers in 100 vehicles that were instrumented with interior cameras as part of the Connected Vehicle Safety Pilot Model Deployment study. Drivers had left, right, and both hands on the steering wheel in 64%, 46%, and 28%, respectively, of frames in which the hand placements could be determined. The driver’s left elbow was in contact with the door or armrest in 18% of frames, and the driver’s right elbow was contacting the center console armrest in 29% of frames. Men were more likely than women to use both the left and right armrests. Women had approximately the same percentage of armrest use across vehicles, but men’s usage differed widely, suggesting that armrest design may influence whether people of different statures can use the armrests comfortably.

Motion sickness in road vehicles may become an increasingly important problem as automation transforms drivers into passengers. Motion sickness could be mitigated through control of the vehicle motion dynamics, design of the interior environment, and other interventions. However, a lack of a definitive etiology of motion sickness challenges the design of automated vehicles (AVs) to address motion sickness susceptibility effectively. Few motion sickness studies have been conducted in naturalistic road-vehicle environments; instead, most research has been performed in driving simulators or on motion platforms that produce prescribed motion profiles. To address this gap, a vehicle-based experimental platform using a midsize sedan was developed to quantify motion sickness in road vehicles. A scripted, continuous drive consisting of a series of frequent 90-degree turns, braking, and lane changes were conducted on a closed track.

As we move towards the world of autonomous vehicles it becomes increasingly important to integrate several chassis control systems to provide the desired vehicle stability without mutual interference. The principles for integration proposed in existing technical literature are majorly centralized which are not only computationally expensive but does not fit the current supplier based OEM business model. An Automotive OEM brings multiple suppliers on-board for developing the Active Safety systems considering several factors such as cost, quality, time, ease of business etc. When these systems are put together in the vehicle they may interfere with each other’s function. Decoupling their function results in a need of heavy calibration causing performance trade-offs and loss in development time.

The goal of this paper is to explore the complete set of single mode hybrid electric powertrain designs that can be generated with one and two planetary gearsets (PGs). Contrary to an automated design exploration approach, an analytically-based manual method is developed to identify all unique design modes for each hybrid electric powertrain architecture (parallel, series, power-split) that can be created with two planetary gearsets, one engine, one vehicle output shaft, two electric machines, and at most two brake clutches. Feasible design modes are generated according to a procedure that provably covers the entire design space.