Browse Topic: Human Factors and Ergonomics
This paper explores the application of a modeled torque converter in the real-time control of a hybrid electric powertrain. The study aims to determine the optimal gear selection and engine speed target required to meet driver demands. It also delves into the concept of torque converter input inertia compensation, particularly during open, open-to-close, and close-to-open states. The primary objective is to achieve the intended driver torque while minimizing torque sag and bumps during these transitions. This approach ensures improved powertrain response and maintains system integrity within the operational limits of the battery, motors, and engine.
Abstract 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
This paper introduces a novel approach to optimize battery power usage and optimal engine torque for Axle disconnect device engagement under power constrained scenarios for range extended hybrid vehicles. Range extended hybrid architecture provides benefits of BEV architecture and relief the range anxiety that BEV drivers often have. The Axle disconnect device helps improve the efficiency of the battery power usage when it is disconnected and provides better drivability and performance to fulfill driver demand when it is connected [1]. Under power constraint scenario, the disconnect device engagement could take too long or eventually fail to engage and result in degradation for drivability and vehicle level performance. This novel approach is utilizing the engine to either generate more power to spin up the disconnect motor faster under discharge limited case or generate less power to allow the disconnect motor to spin down under charge limited case. The effectiveness of this approach
E-mobility is revolutionizing the automotive industry by improving energy-efficiency, lowering CO2 and non-exhaust emissions, innovating driving and propulsion technologies, redefining the hardware-software-ratio in the vehicle development, facilitating new business models, and transforming the market circumstances for electric vehicles (EVs) in passenger mobility and freight transportation. Ongoing R&D action is leading to an uptake of affordable and more energy-efficient EVs for the public at large through the development of innovative and user-centric solutions, optimized system concepts and components sizing, and increased passenger safety. Moreover, technological EV optimizations and investigations on thermal and energy management systems as well as the modularization of multiple EV functionalities result in driving range maximization, driving comfort improvement, and greater user-centricity. This paper presents the latest advancements of multiple EU-funded research projects under
The study analyzed data from on-road drives with a pre-production Level 2 (L2) partial automation system using a sample of 27 drivers ranging from 21 to 75 years of age. The system provides continuous automatic lateral and longitudinal control but requires the driver to remain attentive and intervene when necessary. The L2 system was equipped with a Driving Monitoring System (DMS) that issued escalating alerts to remind the driver to pay attention or take over when needed. During the 14-month study period, drivers completed 354,768 miles of travel with the L2 system engaged, totaling 5,913 trips. The results of the study showed that drivers were highly responsive to attention reminders and takeover alerts, with high compliance rates and quick response times. Importantly, there was no evidence of habituation to these alerts over time. These findings support the effectiveness of the system's DMS and alert HMI (Human-Machine Interface) strategy in promoting the proper use of the system
Plasticized polyvinyl chloride (PVC) has many applications in automotive industry including electrical harnesses, door handles, seat and head rest covers, and instrument panel (IP) and other interior trim. In IP applications, the PVC skin plays a critical role in passenger airbag deployment (PAB) by tearing along the scored edge of the PAB door and allowing the door to open and the airbag to inflate to protect the occupant. As part of the IP, the PVC skin may be exposed to elevated temperatures and ultraviolet (UV) radiation during the years of the vehicle life cycle which can affect the PVC material properties over time and potentially influence the kinematics of the airbag deployment. Chemical and thermal aging of plasticized PVC materials have been studied in the past, yet no information is found on how the aging affects mechanical properties at high rates of loading typical for airbag deployment events. This paper compares mechanical properties of the virgin PVC-based IP skin
Drivers present diverse landscapes with their distinct personalities, preferences, and driving habits influenced by many factors. Though drivers' behavior is highly variable, they can exhibit clear patterns that make sorting them into one category or another possible. Discrete segmentation provides an effective way to categorize and address the differences in driving style. The segmentation approach offers many benefits, including simplification, measurement, proven methodology, customization, and safety. Numerous studies have investigated driving style classification using real-world vehicle data. These studies employed various methods to identify and categorize distinct driving patterns, including naturalist differences in driving and field operational tests. This paper presents a novel hybrid approach for segmenting driver behavior based on their driving patterns. We leverage vehicle acceleration data to create granular driver segments by combining event and trip-based methodologies
The research activity aims at defining specific Operational Design Domains (ODDs) representative of Italian traffic environments. The paper focuses on the human-machine interaction in Automated Driving (AD), with a focus on take-over scenarios. The study, part of the European/Italian project “Interaction of Humans with Level 4 AVs in an Italian Environment - HL4IT”, describes suitable methods to investigate the effect of the Take-Over Request (TOR) on the human driver’s psychophysiological response. The DriSMI dynamic driving simulator at Politecnico di Milano has been used to analyse three different take-over situations. Participants are required to regain control of the vehicle, after a take-over request, and to navigate through a urban, suburban and highway scenario. The psychophysiological characterization of the drivers, through psychological questionnaires and physiological measures, allows for analyzing human factors in automated vehicles interactions and for contributing to
High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured
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