Fossil fuels such as natural gas used in engines still play the most important role worldwide despite such measures as the German energy transition which however is also exacerbating climate change as a result of carbon dioxide emissions. One way of reducing carbon dioxide emissions is the choice of energy sources and with it a more favourable chemical composition. Natural gas, for instance, which consist mainly of methane, has the highest hydrogen to carbon ratio of all hydrocarbons, which means that carbon dioxide emissions can be reduced by up to 35% when replacing diesel with natural gas. Although natural gas engines show an overall low CO2 and pollutant emissions level, methane slip due to incomplete combustion occurs, causing methane emissions with a more than 20 higher global warming potential than CO2.
The issue of greenhouse gas (GHG) emissions from the transportation sector is widely acknowledged. Recent years have witnessed a push towards the electrification of cars, with many considering it the optimal solution to address this problem. However, the substantial battery packs utilized in electric vehicles contribute to a considerable embedded ecological footprint. Research has highlighted that, depending on the vehicle's size, tens or even hundreds of thousands of kilometers are required to offset this environmental burden. Human-powered vehicles (HPVs), thanks to their smaller size, are inherently much cleaner means of transportation, yet their limited speed impedes widespread adoption for mid-range and long-range trips, favoring cars, especially in rural areas. This paper addresses the challenge of HPV speed, limited by their low input power and non-optimal distribution of the resistive forces.
Let’s start with the uncomfortable truth, climate change is happening, and the automotive industrial network is one of the main industries contributing to greenhouse gas emissions. SKF is an energy intensive business – directly using energy, mainly in the form of electricity and gas, in its operations around the world. In addition, SKF utilizes materials, predominantly steel, and services which can be energy and carbon intensive – such as transports and raw material in production and processing. The combined impact of these direct and indirect energy uses (scope 1, 2 and 3 upstream) generates an excess of over two million metric tons of CO2e per year. This figure would however be significantly higher were it not for the actions SKF has taken to reduce both energy and carbon intensity. In 2000, we were one of the first companies to actually start to report and set climate targets.
A GE Aviation Systems report documents that the National Oceanic and Atmospheric Administration (NOAA) provided weather forecast data has a bias of 15 knots and a standard deviation of 13.3 knots for the 40 flights considered for the research. It also had a 0.47 bias in the temperature with a standard deviation of 0.27. The temperature errors are not as significant as the wind. There is a potential opportunity to reduce the operational cost by improving the weather forecast. The flight management system (FMS) currently uses the weather forecast, available before takeoff, to identify an optimized flight path with minimum operational costs depending on the selected speed mode. Such a flight plan could be optimum for a shorter flight because these flight path planning algorithms are very less susceptible to the accuracy of the weather forecast.
Modern advances in the technical developments of Advanced Driver Assistance Systems (ADAS) have elevated autonomous vehicle operations to a new height. Vehicles equipped with sensor-based ADAS have been positively contributing to safer roads. As the automotive industry strives for SAE Level 5 full driving autonomy, challenges inevitably arise to ensure ADAS performance and reliability in all driving scenarios, especially in adverse weather conditions. In adverse weather driving conditions, ADAS sensors such as optical cameras and LiDARs suffer performance degradation, leading to inaccuracy and inability to provide crucial environmental information for object detection. Currently, the difficulty to simulate realistic and dynamic adverse weather scenarios experienced by vehicles in a controlled environment becomes one of the challenges that hinders further ADAS development.
With the increasing demand for Battery Electric Vehicles (BEVs) capable of extended mileage, optimizing their efficiency has become paramount for manufacturers. However, the challenge lies in balancing the need for climate control within the cabin and precise thermal regulation of the battery, which can significantly reduce a vehicle's driving range, often leading to energy consumption exceeding 50% under severe weather conditions. To address these critical concerns, this study embarks on a comprehensive exploration of the impact of weather conditions on energy consumption and range for the 2019 Nissan Leaf Plus. The primary objective of this research is to enhance the understanding of thermal management for BEVs by introducing a sophisticated thermal management system model, along with detailed thermal models for both the battery and the cabin.
Ambient temperature is a very critical and sensitive use condition for electric vehicles (EVs), so it is imperative to ensure the maintenance of suitable temperatures during both usage and parking. This is particularly important in regions characterized by prolonged exposure to unfavorable temperature conditions. In such cases, it becomes necessary to implement insulation measures within parking facilities and allocate energy resources to sustain a desired temperature level. However, the availability of non-renewable energy sources is finite, necessitating further research to promote the sustainable and efficient utilization of energy. Consequently, the provision of affordable energy with minimal emissions assumes significant importance. Solar energy is a renewable and environmentally friendly source of energy that is widely available. However, the effectiveness of utilizing solar energy is influenced by various factors, such as the time of day and weather conditions.
For safe driving function, signs must be visible. Sign luminance is one of the primary factors that determine the visibility of a sign. In daytime conditions, sign luminance is a function of the ambient lighting. In night-time conditions however, sign luminance is a function of the retro-reflectivity of the sign material, the illumination provided by the vehicle headlamps, and the relative locations of the vehicle, sign, and driver. Traffic sign visibility at night is largely determined by sign luminance. Virtual simulation approach is used for analyzing the sign board visibility. Among several factors which affect the visibility of traffic signs at night, headlamp installation position from ground, distance between two lamps & eye position of driver are considered for analyzing the sign board visibility.
The potential blinding of Advanced Driver Assistance Systems (ADAS) sensors due to contamination poses a notable threat to autonomous vehicles. These sensors' performance can be compromised by diverse sources such as dust, water, or snow. However, our investigation concentrates primarily on snow-related contamination, a frequent occurrence during winter. The accumulation of snow and ice significantly hampers the operational efficacy of autonomous vehicles. Over the years, a series of field tests and wind tunnel experiments have been conducted to analyze the mechanisms of snow interaction and soiling patterns on vehicles and bluff bodies. Notably distinctive patterns of soiling have been identified across multiple areas of these structures. The central challenge revolves around constructing an accurate model to predict snow buildup on vehicles.
ISO 26262-1:2018 defines the fault tolerant time interval (FTTI) as the minimum time span from the occurrence of a fault within an electrical / electronic system to a possible occurrence of a hazardous event. FTTI provides a time limit within which compliant vehicle safety mechanisms must detect and react to faults capable of posing risk of harm to persons. This makes FTTI a vital safety characteristic for system design. Common automotive industry practice accommodates recording fault times of occurrence. However, defining the time of hazardous event onset seems more subjective. This paper presents a novel method to define hazardous event onset more objectively. The method introduces the Streetscope Collision Hazard Measure (SHMTM) and a refined approach to hazardous event classification. SHM inputs kinematic factors such as proximity, relative speed, and acceleration as well as environmental characteristics like traffic patterns, visibility, and road conditions.
The author has developed UV based photocatalytic air purification system (Mathur, 2021, 2023) that can eliminate all pathogens from the cabin air including COVID-19. In this study, the focus is to determine the risk of infection due to pathogens/germs in the cabin of an automobile. Author has determined the risk of infection by using Wells-Riley model and the conducted CFD analysis to determine propagation of virus in cabin.: 1. Cabin Volume & Number of Occupants (Wells-Riley Model in OSA mode): (i) Cabin volume from: Small Sedan, Large Sedan and a SUV; with 4 occupants (males & females); Number of infector 1; Air flowrate (m3/min); (ii) A 15-seater minibus - with 10 occupants(males); Number of infectors 1 & 2; Air flowrate (m3/min) 2.
SLAM (Simultaneous Localization and Mapping) plays a key role in autonomous driving. Recently, 4D Radar has attracted widespread attention because it breaks through the limitations of 3D millimeter wave radar and can simultaneously detect the distance, velocity, horizontal azimuth and elevation azimuth of the target with high resolution. However, there are few studies on 4D Radar in SLAM. In this paper, a 4D Radar SLAM method based on pose graph is proposed. The RANSAC (Random Sample Consensus) method is used to eliminate the dynamic obstacle points from a single scan, and the ego-motion velocity is estimated from the static point cloud. A 4D Radar velocity factor is constructed in GTSAM to receive the estimated velocity in a single scan as a measurement and directly integrated into the pose graph. The 4D Radar point clouds of consecutive frames are matched as the odometry factor.
The prediction of agents' future trajectory is a essential task in supporting advanced driver-assistance systems (ADAS). It plays an even more crucial role in making safe decisions for autonomous driving (AD). The current prevalent trajectory prediction methods heavily take high definition maps (HD maps) as the prior knowledge. Although the surrounding environment information provided by HD maps improves the accuracy of trajectory prediction, the high cost and legal restrictions of HD maps limit their widespread use. Moreover, due to object occlusion, limited field of view, and other reasons, the historical trajectory of the target agent is often incomplete. This limitation reduces the accuracy of trajectory prediction. Therefore, this paper proposes ETSA-Pred, a mapless trajectory prediction model with enhanced temporal modeling and spatial self attention.
Ergonomics plays an important role in automobile design to achieve optimal compatibility between occupants and vehicle components. The overall goal is to ensure that the vehicle design accommodates the target customer group, who come in varied sizes, preferences and tastes. Headroom is one such metric that not only influences accommodation rate but also conveys a visual perception on how spacious the vehicle is. An adequate headroom is necessary for a good seating comfort and a relaxed driving experience. Headroom is intensely discussed in magazine tests and is a key deciding factor in purchase of a car. SAE J1100 defines a set of measurements and standard procedures for motor vehicle dimensions. H61, W27, W35, H35 and W38 are some of the standard dimensions that relate to headroom and head clearances. While developing the vehicle architecture in the early design phase, it is customary to specify targets for various ergonomic attributes and arrive at the above-mentioned dimensions.
Reliable information pertaining to position, velocity, and attitude is essential for automated driving. However, LiDAR-based method and camera-based method may be easily affected by bad weather such as rain, snow, and fog. The 4D millimeter-wave radar (x, y, z, doppler) with all-weather performance and high resolution has attracted more interest. However, there are few positioning algorithms based on 4D millimeter-wave radar (4D Radar). This paper proposes a new tightly-coupled 4D Radar/IMU/Vehicle Dynamics system under factor graph framework, termed as RIO-Vehicle, to achieve reliable precision pose estimation. This integration system fuses wheel speed sensor measurements with IMU and 4D Radar measurements. To bolster the accuracy of vehicle dynamic constraints, the paper introduces dynamics pre-integration based on the vehicle dynamics model.
Electric vehicles (EV) present distinctive challenges compared to ICE (internal combustion engine) powered counterparts. Cabin heating and air-conditioning stands out among them, especially cabin heating in cold weather, owing to its outsized effect on drivable range of the vehicle. Efficient management of cabin thermal system has the potential to improve vehicle range without compromising passenger comfort. A method to reduce cabin thermal energy consumption by effectively managing solar load on the vehicle is proposed in this work. The methodology utilizes connectivity and mapping data to predict the solar load over a horizon. Typically, the solar load is treated as an unmeasured external disturbance which is compensated with control. It can however be treated as an estimated quantity with potential to offset energy use. The solar load prediction, coupled with a passenger thermal comfort model, enables energy use optimization over a route.
Focused on the permanent magnet synchronous motor (PMSM) used in electric vehicle, this paper proposes an online insulation testing method based on voltage injection under high-temperature and high-humidity conditions. The effect of constant humidity and temperature on the insulation performance has been also studied. Firstly, the high-voltage insulation structure and principle of PMSM are analyzed,while an electrical insulation testing method considered constant humidity and temperature is proposed. Finally,a temperature and humidity experimental cycling test is carried out on a certain prototype PMSM, taking heat conduction and radiation models, water vapor, and partial discharge into account. The results show that the electrical insulation performance of the motor under constant humidity and temperature operation environment exhibits a decreasing trend. This study can provide theoretical and practical references for the reliable durability design of PMSM.