Search Results

Robustness testing of Advanced Driver Assistance Systems (ADAS) features is a crucial step in ensuring the safety and reliability of these systems. ADAS features include technologies like adaptive cruise control, lateral and longitudinal controls, automatic emergency braking, and more. These systems rely on various sensors, cameras, radar, lidar, and software algorithms to function effectively. Robustness testing aims to identify potential vulnerabilities and weaknesses in these systems under different conditions, ensuring they can handle unexpected scenarios and maintain their performance. Mileage accumulation is one of the validation methods for achieving robustness. It involves subjecting the systems to a wide variety of real-world driving conditions and driving scenarios to ensure the reliability, safety, and effectiveness of the ADAS features.

In recent decades, significant technological advances have made cruise control systems safer, more automated, and available in more driving scenarios. However, comparatively little progress has been made in optimizing vehicle efficiency while in cruise control. In this paper, two distinct strategies are proposed to deliver efficiency benefits in cruise control by leveraging flexibility around the driver’s requested set speed, and road information that is available on-board in many new vehicles. In today’s cruise control systems, substantial energy is wasted by rigidly controlling to a single set speed regardless of the terrain or road conditions. Introducing even a small allowable “error band” around the set speed can allow the propulsion system to operate in a pseudo-steady state manner across most terrain. As long as the vehicle can remain in the allowed speed window, it can maintain a roughly constant load, traveling slower up hills and faster down hills.

After-treatment sensors are used in the ECU feedback control to calibrate the engine operating parameters. Due to their contact with exhaust gases, especially NOx sensors are prone to soot deposition with a consequent decay of their performance. Several phenomena occur at the same time leading to sensor contamination: thermophoresis, unburnt hydrocarbons condensation and eddy diffusion of submicron particles. Conversely, soot combustion and shear forces may act in reducing soot deposition. This study proposes a predictive 3D-CFD model for the analysis of the development of soot deposition layer on the sensor surfaces. Alongside with the implementation of deposit and removal mechanisms, the effects on both thermal properties and shape of the surfaces are taken in account. The latter leads to obtain a more accurate and complete modelling of the phenomenon influencing the sensor overall performance.

Internal combustion engines (ICEs) exhaust emissions, particularly nitrogen oxides (NOx), have become a growing environmental and health concern. The biggest challenge for contemporary ICE industry is the development of clean ICEs, and the use of advanced design tools like Computational Fluid Dynamics (CFD) simulation is paramount to achieve this goal. In particular, the development of aftertreatment systems like Selective Catalyst Reduction (SCR) is a key step to reduce NOx emissions, and accurate and efficient CFD models are essential for its design and optimization. In this work, we propose a novel 3D-CFD methodology, which uses a Machine Learning (ML) approach as a surrogate model for the SCR catalyst chemistry, which aims to enhance accuracy of the simulations with a moderate computational cost. The ML approach is trained on a dataset generated from a set of 1D-CFD simulations of a single channel of an SCR catalyst.

Several predictive equations based on the chemical composition of gasoline have been shown to estimate the particulate emissions of light-duty, internal combustion engine (ICE) powered vehicles and are reviewed in this paper. Improvements to one of them, the PEISimDis equation are detailed herein. The PEISimDis predictive equation was developed by General Motor’s researchers in 2022 based on two laboratory gas chromatography (GC) analyses; Simulated Distillation (SimDis), ASTM D7096 and Detailed Hydrocarbon Analysis (DHA), ASTM D6730. The DHA method is a gas chromatography mass spectroscopy (GC/MS) methodology and provides the detailed speciation of the hundreds of hydrocarbon species within gasoline. A DHA’s aromatic species from carbon group seven through ten plus (C7 – C10+) can be used to calculate a Particulate Evaluation Index (PEI) of a gasoline, however this technique takes many hours to derive because of its long chromatography analysis time.

Hyper-elastic seals are extensively used in automotive applications for sealing various joints in assembly. They are also used in sealing battery packs. They are used in various sizes and shapes. Most of the gaskets used are solid gaskets. Hollow gaskets are also being used. Hollow gaskets typically have a fluid like air trapped inside. Analyzing these hollow gaskets also requires involving the physics of the fluid inside. The trapped fluid affects the performance of the gasket like contact pressure and width. Objective of this study is to analyze the hollow gasket performance including the effect of air trapped inside. The effect of air on performance of the hollow seal is also studied. Fluid Cavity capability in ABAQUS was selected after literature study to simulate the effect of trapped fluid (Air) on seal performance.

A hydrogen economy is an increasingly popular solution to lower global carbon dioxide emissions. Previous research has been focused on the economic conditions necessary for hydrogen to be cost competitive, which tends to neglect the effectiveness of greenhouse gas mitigation for the very solutions proposed. The holistic carbon footprint assessment of hydrogen production, distribution, and utilization methods, otherwise known as “well-to-wheels” carbon intensity, is critical to ensure the new hydrogen strategies proposed are effective in reducing global carbon emissions. When looking at these total carbon intensities, however, there is no single clear consensus regarding the pathway forward. When comparing the two fundamental technologies of steam methane reforming and electrolysis, there are different scenarios where either technology has a “greener” outcome.

Any young person who has taken delight in playing with toy slot cars knows that the world of racing and the world of electric cars has been intertwined for a long time. And anyone who has driven a modern performance electric vehicle knows that the instant acceleration, exhilarating speeds, and joy of driving of slot cars is reflected in these full sized “toys”, with the many more practical benefits that come from being full-sized and steerable. There is strong foreshadowing of a vibrant future for performance cars in some of the EV’s on the market now and in the near future, some offering “ludicrous” acceleration, and others storied nameplates with performance to match. The ease at which powerful electric drives can capably hurtle a massive vehicle around the track at high speeds, combined with the potential for the same electric drives to exert powerful regenerative braking, creates a very interesting situation for brake engineers.

Regulations have been established for the monitoring and reporting of greenhouse gas (GHG) emissions and fuel consumption from the transport sector. Low carbon fuels combined with new powertrain technologies have the potential to provide significant reductions in GHG emissions while decreasing the dependence on fossil fuel. In this study, a lean-burn ethanol-diesel dual-fuel combustion strategy has been used as means to improve upon the efficiency and emissions of a conventional diesel engine. Experiments have been performed on a 2.0 dm3 single cylinder heavy-duty engine equipped with port fuel injection of ethanol and a high-pressure common rail diesel injection system. Exhaust emissions and fuel consumption have been measured at a constant engine speed of 1200 rpm and various steady-state loads between 0.3 and 2.4 MPa net indicated mean effective pressure (IMEP).

The goal of this study was to generate exhaust fast gas data that could be used to identify phenomena that occur before, during, and after stochastic preignition (SPI), also called low-speed preignition (LSPI), events. Crank angle resolved measurement of exhaust hydrocarbons, NO, CO, and CO2 was performed under engine conditions prone to these events. Fuels and engine operating strategies were varied in an attempt to understand similarities and differences in SPI-related behavior that may occur between them. Several different uncommon (typically occurring in less than 1% of engine cycles) features of the fast gas data were identified, and the correlations between them and SPI events were explored. Although the thresholds used to define and identify these observations were arbitrary, they provided a practical means of identifying behavior in the fast gas data and correlating it to SPI occurrence.

Hood latches are provided with a secondary latch mechanism in order to restrain hoods in the event of an incomplete closing operation. It is important thus to understand the aerodynamically induced loading conditions the latch and hood will be subject to in order to design the hood and hood retention system to withstand those loads. In this paper a method of collecting load and displacement data from actual vehicles is presented, as well as an analysis of the results and the implications for hood and latch design.

Many countries including India have aggressively aimed to implement electric vehicles (EVs) usage from 2030 onwards. Companies such as General Motors, Uber, Waymo and Nissan etc. are exploring the realm of autonomous vehicles (AV) for use as taxis as early as 2019. Above facts logically arrive at the solution of Autonomous EVs as taxis. With the commitment towards enabling an all-electric future, there exists a need to provide suitable infrastructure for recharging. Major urban cities located by the shoreline such as New York, Hong Kong, Mumbai, Los Angeles etc. have been facing the space crunch, with real estate prices sky-rocketing exponentially. With this premise, the operating company would need a large amount of space to store their EVs for charging which attributes to a longer downtime. This brings a need for an economical charging location that has a reduced usage of urban infrastructure and energy consumption.

The paper analyzes the mechanism of automobile millimeter wave radar noise, this paper does not study radar noise from the angle of signal processing, but from the level of false detection and missed detection, at the same time, the noise mechanism is modeled and verified. Firstly, the purpose and significance of the research of radar vehicle noise are described, and then, we summarize and outline the macro phenomenon and the specific characteristics of the automobile millimeter wave radar noise.

With rapid increase of vehicles on the road, safety concerns have become increasingly prominent. Since the leading cause of many traffic accidents is known to be by human drivers, developing autonomous vehicles is considered to be an effective approach to solve the problems above. Although trajectory tracking plays one of the most important roles on autonomous driving, handling the coupling between trajectory-tracking control and ESP under certain driving scenarios remains to be challenging. This paper focuses on trajectory-tracking control considering the role of ESP. A vehicle model is developed with two degrees of freedom, including vehicle lateral, and yaw motions. Based on the proposed model, the vehicle trajectory is separated into both longitudinal and lateral motion. The coupling effect of the vehicle and ESP is analyzed in the paper. The lateral trajectory-tracking algorithm is developed based on the preview follower theory.

Interior permanent magnet machines are being widely used in hybrid vehicles owing to their compact size and high power density. Vehicle level application requires the motor to operate at high speed beyond the base speed of the motor. This is accomplished through flux weakening control. Nonfunctioning of inverter switches and/or gate driver circuit during flux weakening could give rise to a potential fault scenario called Un-Controlled Generation (UCG). This paper gives a detailed background of UCG and its impact on the high voltage and propulsion systems. In further sections the details related to modelling and analysis of UCG will be discussed. Finally, the paper will conclude with simulation results and comparison of the results with motor dynamometer test data.

As one of many pack-level battery simulation approaches developed within the General Motors-led Computer-Aided Engineering of Automotive Batteries (CAEBAT) Phase 1 project, the system approach treats the entire battery pack as a dynamic system which includes multiple engineering disciplines for simulation. It is the most efficient approach of all the CAEBAT battery pack-level approaches in terms of computational time and resources. This paper reports the application of the system approach for a 24-cell liquid-cooled prototype battery pack. It also summarizes the verification of the approach by comparing the simulation results with the measurement data. The results using the system approach are found to have a very good agreement with the measurements.

The Chevrolet Volt is an electric vehicle (EV) with extended-range (ER) that is capable of operation on battery power alone, and on power generated by an on-board gasoline engine after depletion of the battery charge. For 2016, GM has developed the next generation of the Volt vehicle and “Voltec” propulsion system. Building on the experience of the first generation Volt, the second generation targeted improved all-electric range, improved charge sustaining fuel economy, and improved performance. All of this was to be accomplished while maintaining the EV character of the first generation Volt which customers clearly valued. This paper describes the next generation “Voltec” system and the realized improvements in efficiency and performance. The features of the propulsion system components, including energy storage, transaxle, electric motors and power electronics, on-board charging, and engine are described and compared with the previous generation.

Electromagnetic compatibility (EMC) is becoming more important in power converters and motor drives as seen in hybrid electric vehicles (HEV) to achieve higher reliability of the vehicle and its components. Electromagnetic interference (EMI) of the electronic components for a vehicle are evaluated and validated at a component-level test bench; however, it is sometimes observed that the EMI level of the components can be changed in a vehicle-level test due to differences in the vehicle's configuration (cable routing, connecting location etc.). In this presentation, a vehicle-level EMC simulation methodology is introduced to estimate radiated emissions from a vehicle. The comparison between the simulation and measurement results is also presented and discussed.

Carbon, hydrogen and oxygen are major elements in vehicle fuels. Knowledge of fuels elemental composition is helpful in addressing its performance characteristics. Carbon, hydrogen and oxygen composition is an important parameter in engine calibration affecting vehicle performance, emissions and fuel economy. Biodiesel, a fuel comprised of mono-alkyl esters of long-chain fatty acids also known as Fatty Acid Methyl Esters(FAME), derived from vegetable oils or animal fats, has become an important commercial marketplace automotive fuel in the United States (US) and around the world over last few years. FAME biodiesels have many chemical and physical property differences compared to conventional petroleum based diesel fuels. Also, the properties of biodiesel vary based on the feedstock chosen for biodiesel production. One of the key differences between petroleum diesel fuels and biodiesel is the oxygen content.

The intention of this paper is to discuss the importance of analysis of some electrical parameters in order to design analog circuits in electronic modules, including automotive ones. Today, the challenge is to have devices which consume less power, high performance and higher integration density, so that it explains why such analysis is crucial to achieve better performances and meet the desired results.