In the evolving landscape of intelligent vehicular perception, mounting emphasis has been bestowed upon detecting road-based obstacles and entities. Yet, the monitoring of road typology and surface uniformity, pivotal for safeguarding vehicular operations and enhancing driving ambience, remains relatively under-addressed. Accurate real-time assessments of these parameters can significantly augment kinematic vehicle control, bolster braking efficacy, elevate fuel economy, and refine the driving experience, while furnishing robust technical scaffolding for sophisticated automated driving systems. However, current technical approaches are fraught with numerous limitations. Direct image classification or segmenting fixed regions can inadvertently capture non-road entities, introducing erroneous judgments exacerbated by extraneous object interference.
Emissions and fuel economy certification testing for vehicles is carried out on a chassis dynamometer using standard test procedures. The vehicle coastdown method (SAE J2263) used to experimentally measure the road load of a vehicle for certification testing is a time-consuming procedure considering the high number of distinct variants of a vehicle family produced by an automaker today. Moreover, test-to-test repeatability is compromised by environmental conditions: wind, pressure, temperature, track surface condition, etc., while vehicle shape, driveline type, transmission type, etc. are some factors that lead to vehicle-to-vehicle variation. Controlled lab tests are employed to determine individual road load components: tire rolling resistance (SAE J2452), aerodynamic drag (wind tunnels), and driveline parasitic loss (dynamometer in a driveline friction measurement lab). These individual components are added to obtain a road load model to be applied on a chassis dynamometer.
Amid rising demands for fuel efficiency and emissions reduction, enhancing the thermal efficiency of gasoline engines has become imperative, which requires higher efficiency combustion strategies and integrated optimized design to maximize the work output from fuel. In gasoline engines, both increasing the compression ratio and using lean burn mode improve the thermal efficiency effectively. Although there is limited scope for increasing the compression ratio due to the higher sensitivity to knocking, especially under stoichiometric conditions, reduced sensitivity could be got with leaner mixture fill into cylinder, which can further increase the specific heat ratio and thermal efficiency. However, realizing the efficiency benefits of lean burn in gasoline engines necessitates overcoming critical challenges like ensuring robust ignition process and accelerating burning rates to achieve short, stable combustion durations.
It is widely recognized that internal combustion engines (ICE) are needed for global transport for years to come, however, demands on ICE fuel efficiency, emissions, cost, and performance are extremely challenging. Gasoline compression ignition (GCI) is one approach to achieving demanding efficiency and emissions targets. At Aramco Research Center-Detroit, an advanced, multi-cylinder GCI engine was designed and built using the latest combustion system, engine controls, and lean aftertreatment. The combustion system uses Aramco's PPCI-diffusion process for ultra-low NOx and smoke. A P2 48V mild hybrid system was integrated on the engine for braking energy recovery and improved cold starts. For robust low-load operation, a 2-step valvetrain system was used for exhaust rebreathing. The fuel injection system was a modified diesel system with high injection rate and 2000 bar pressure rating.
The proliferation of electric vehicles (EVs) is making big transition in the automotive industry, promising reduced greenhouse gas emissions and improved energy efficiency. The architectural configurations and power distribution strategies necessitate the optimization of their drivability performance, all-electric ranges, and overall efficiency. This paper reports the efforts of the University of California at Riverside (UCR) EcoCAR team in EV architecture selection to match the EcoCAR EV Challenge theme of shared mobility for disadvantaged communities. The UCR EcoCAR team conducted a comprehensive analysis of various EV architectures (including rear-wheel drive, front-wheel drive, and all-wheel drive) and motor parameters, considering a spectrum of targeted vehicle technology specifications such as acceleration and braking performance, fuel economy, and cargo/passenger capacity.
Recently, as part of the purpose of improving fuel efficiency and cost reduction of eco-friendly vehicles, the R-gearless system has been applied in TMED (P)HEV system It is necessary to develop a separate backward driving method as the reverse gear is removed, so backward driving can be enabled by using the e-Motor system in TMED (P)HEV system. However, backward driving with e-Motor is limited as partial failure of high-voltage system in R-gearless system Here we show that, it is possible to improve the backward driving problems by applying new fail-safe strategy. In the event of a high voltage battery system failure, the backward driving is available by using e-Motor with constant voltage control by HSG as we proposed in this paper. So feed-forward compensation of variable constant voltage control enables to secure more active output power within limited HSG output power.