Search Results

Abstract Due to the increasing requirements for efficiency, the wide range of characteristics and the improved possibilities of modern development and production processes, compressors in turbochargers have become more individualized in order to adapt to the requirements of internal combustion engines. An understanding of the working mechanisms as well as an understanding of the way that losses occur in the flow allows a reduced development effort during the optimization process. This article presents three-dimensional (3D) Computational Fluid Dynamics (CFD) investigations of the loss mechanisms and quantitative calculations of individual losses. The 3D-CFD method used in this article will reduce the drawbacks of one-dimensional calculation as far as possible. For example, the twist of the blades is taken into account and the “discrete” method is used for loss calculation instead of the “average” method.

Abstract Regulations limiting GreenHouse Gases (GHG) from Heavy-Duty (HD) commercial vehicles in the United States (US) and European Union will phase in between the 2024 and 2030 model years. These mandates require efficiency improvements at both the engine and vehicle levels, with the most stringent reductions required in the heaviest vehicles used for long-haul applications. At the same time, a 90% reduction in oxides of nitrogen (NOx) will be required as part of new regulations from the California Air Resources Board. Any technologies applied to improve engine efficiency must therefore not come at the expense of increased NOx emissions. Research into advanced engine architectures and components has identified improved turbomachine efficiency as one of the largest potential contributors to engine efficiency improvement. However this comes at the cost of a reduced capability to drive high-pressure Exhaust Gas Recirculation (EGR).

Abstract Aiming to improve the handling performance of heavy tractor semi-trailer during turning or changing lanes at high speed, a hierarchical structure controller is proposed and a hardware-in-the-loop (HIL) test bench of the electronic pneumatic braking system is developed to validate the proposed controller. In the upper controller, a Kalman filter observer based on the heavy tractor semi-trailer dynamic model is used to estimate the yaw rates and sideslip angles of the tractor and trailer. Simultaneously, a sliding mode direct yaw moment controller is developed, which takes the estimated yaw rates and sideslip angles and the reference values calculated by the three-degrees-of-freedom dynamic model of the heavy tractor semi-trailer as the control inputs. In the lower controller, the additional yaw moments of tractor and trailer are transformed into corresponding wheel braking forces according to the current steering characteristics.

Abstract This article proposes a novel approach of a hybrid system of physics and data-driven modeling for accurately estimating the state of charge (SOC) of a battery. State of Charge (SOC) is a measure of the remaining battery capacity and plays a significant role in various vehicle applications like charger control and driving range predictions. Hence the accuracy of the SOC is a major area of interest in the automotive sector. The method proposed in this work takes the state-of-the-art practice of Kalman filter (KF) and merges it with intelligent capabilities of machine learning using neural networks (NNs). The proposed hybrid system comprises a physics-based battery model and a plurality of NNs eliminating the need for the conventional KF while retaining its features of the predictor-corrector mechanism of the variables to reduce the errors in estimation.

Abstract A kinematic modeling framework was established to predict status (position, displacement, velocity, acceleration, and shape) of a towing vehicle system with different driver inputs. This framework consists of three components: (1) a state space model to decide position and velocity for the vehicle system based on Newton’s second law; (2) an angular acceleration transferring model, which leads to a hypothesis that the each towed unit follows the same path as the towing vehicle; and (3) a polygon model to draw instantaneous polygons to envelop the entire system at any time point.

Abstract One of the key factors for accurate mass burn fraction and energy conversion point calculations is the accuracy of the compression ratio. The method presented in this article suggests a workflow that can be applied to determine or correct the compression ratio estimated geometrically or measured using liquid displacement. It is derived using the observation that, in a motored engine, the heat losses are symmetrical about a certain crank angle, which allows for the derivation of an expression for the clearance volume [1]. In this article, a workflow is implemented in real time, in a current production engine indicating system. The goal is to improve measurement data quality and stability for the energy conversion points calculated during measurement procedures. Experimental and simulation data is presented to highlight the benefits and improvement that can be achieved, especially at the start of combustion.

Abstract Turbocharging can provide a low cost means for increasing the power output and fuel economy of an internal combustion engine. Currently, turbocharging is common in multi-cylinder engines, but due to the inconsistent nature of intake air flow, it is not commonly used in single-cylinder engines. In this article, we propose a novel method for turbocharging single-cylinder, four-stroke engines. Our method adds an air capacitor-an additional volume in series with the intake manifold, between the turbocharger compressor and the engine intake-to buffer the output from the turbocharger compressor and deliver pressurized air during the intake stroke. We analyzed the theoretical feasibility of air capacitor-based turbocharging for a single-cylinder engine, focusing on fill time, optimal volume, density gain, and thermal effects due to adiabatic compression of the intake air.

Abstract Three-dimensional patterns representing crosshatched plateau-honed cylinder bores based on two-dimensional Fast Fourier Transform (FFT) of measured surfaces were generated and used to calculate pressure flow, shear-driven flow, and shear stress factors. Later, the flow and shear stress factors obtained by numerical simulations for various surface patterns were used to calculate lubricant film thickness and friction force between piston ring and cylinder bore contact in typical diesel engine conditions using a mixed lubrication model. The effects of various crosshatch honing angles, such as 30°, 45°, and 60°, and texture heights on engine friction losses, wear, and oil consumption were discussed in detail. It is observed from numerical results that lower lubricant film thickness values are generated with higher honing angles, particularly in mixed lubrication regime where lubricant film thickness is close to the roughness level, mainly due to lower resistance to pressure flow.

Abstract Due to the incoming phase out of fossil fuels from the market in order to reduce the carbon footprint of the automotive sector, hydrogen-fueled engines are candidate mid-term solution. Thanks to its properties, hydrogen promotes flames that poorly suffer from the quenching effects toward the engine walls. Thus, emphasis must be posed on the heat-up of the oil layer that wets the cylinder liner in hydrogen-fueled engines. It is known that motor oils are complex mixtures of a number of mainly heavy hydrocarbons (HCs); however, their composition is not known a priori. Simulation tools that can support the early development steps of those engines must be provided with oil composition and properties at operation-like conditions. The authors propose a statistical inference-based optimization approach for identifying oil surrogate multicomponent mixtures. The algorithm is implemented in Python and relies on the Bayesian optimization technique.

Abstract To solve the problem that it is difficult for drivers to control the vehicle at low speed, a new setting scheme of pedal map is proposed to ensure that the vehicle has the speed controllability in the full speed range. In this scheme, based on obtaining the maximum and minimum driving characteristics of the vehicle and the driving resistance characteristics of the vehicle, the pedal map is divided into a sensitive area and insensitive area. In the insensitive area, acceleration hysteresis is formed, which ensures that the throttle is slightly fluctuated and has good speed stability. At the same time, the sensitive area of the accelerator pedal is formed far away from the driving resistance curve to ensure that the vehicle has a great acceleration ability. To verify the effectiveness of the proposed scheme, the data of a commercial vehicle is selected for the design of the pedal map, and the driver-vehicle closed-loop test based on the driving simulator is conducted.

Abstract This article describes the real-time simulation of a tire model for the off-highway sector. The off-highway area is characterized by soft surfaces. The additional deformation of the ground can result in more complex interactions between the tires and ground than in the on-highway area. The basics for these relationships are explained using normal and shear stress models. Aspects such as elastic tires, sinking due to slip, and multipass are also described. It is explained how soft soil modeling is used by a height field model to calculate the deformations of the soil and the resulting tire forces. Particular emphasis is placed on the calculation time and the numerical stability. The implementation in an existing real-time-capable vehicle model is described, which is important to provide a comprehensive simulation solution. During the validation it could be shown that the implemented height field can correctly map the soft soil properties.

Abstract Numerous researchers are committed to finding solutions to the path planning problem of intelligence-based vehicles. How to select the appropriate algorithm for path planning has always been the topic of scholars. To analyze the advantages of existing path planning algorithms, the intelligence-based vehicle path planning algorithms are classified into conventional path planning methods, intelligent path planning methods, and reinforcement learning (RL) path planning methods. The currently popular RL path planning techniques are classified into two categories: model based and model free, which are more suitable for complex unknown environments. Model-based learning contains a policy iterative method and value iterative method. Model-free learning contains a time-difference algorithm, Q-learning algorithm, state-action-reward-state-action (SARSA) algorithm, and Monte Carlo (MC) algorithm.

Abstract In the past years, the automotive industry has been integrating multiple hardware in the vehicle to enable new features and applications. In particular automotive applications, it is important to monitor the actions and behaviors of drivers and passengers to promote their safety and track abnormal situations such as social disorders or crimes. These applications rely on multiple sensors that generate real-time data to be processed, and thus, they require adequate data acquisition and analysis systems. This article proposes a prototype to enable in-vehicle data acquisition and analysis based on the middleware framework Robot Operating System (ROS). The proposed prototype features two processing devices and enables synchronized audio and video acquisition, storage, and processing. It was assessed through the implementation of a live inference system consisting of a face detection algorithm from the data gathered from the cameras and the microphone.

Abstract Autonomous vehicle technology has become an unprecedented trend in the development of the automobile industry, which can ensure highly efficient use of resources, effectively improve the driving experience, and greatly reduces the driver’s burden. As one of the key technologies of autonomous vehicles, path planning has an important impact on the practical applications of autonomous vehicles. Planning a proper and efficient path is a prerequisite, which can improve the driving experience of autonomous vehicles. Therefore, in-depth research and development on applications of AI technology in path planning definitely have significant value in academic research. In this article, we will introduce a variety of path planning approaches for autonomous vehicles. We summarize the attributes of these path planning algorithms; simultaneously, we analyze the improvements to these algorithms. Then, we have a preliminary discussion on the applications in vehicle positioning and navigation.

Abstract Particulates are a major source of emission from diesel engine. They consist of particles of carbon, sulfates, oil, fuel, and water. These constituents are measured by filtering a sample diluted in a partial- or full-flow tunnel and weighing them. It is a general trend for measuring particulate matter (PM) on cycle basis. But 1-D simulation needs complete PM 3-D contour map considering all engine operating region. It is very tedious work for generating PM on each steady-state point on engine test bed. Hence, Filter smoke meter or opacimeter measurements can be used for estimating PM. Filter smoke meters measured the light reflected from a filter paper through which a known volume of exhaust gas was passed. Opacity meters measure light absorbed by a standard column of exhaust. Both equipments measure visible black smoke comparatively at lower expenditure cost. They are designed to control measurement noise, resolution and repeatability with acceptable accuracy level.

Abstract In order to meet upcoming emission targets, an increasing number of ships using Liquefied Natural Gas (LNG) as fuel have been put into service. In this context, many shipowners are particularly interested in the dual-fuel (DF) large-engine technology, which enables ships to operate with both gaseous and conventional liquid fuels. The use of different combustion principles in DF engines requires a layout of the base engine with a relatively low compression ratio (CR) for the gas mode to prevent unstable combustion (knocking). However, this layout leads to disadvantages in the Diesel operation mode, which requires a higher CR for optimal fuel efficiency. Therefore, a two-stage variable compression ratio (VCR) system is a technology particularly suitable for DF engines. It allows to reduce fuel costs by approximately 5.5%.

Abstract Improving the aerodynamics of heavy trucks is an important consideration in the strive for more energy-efficient vehicles. Cooling drag is one part of the total aerodynamic resistance acting on a vehicle, which arises as a consequence of air flowing through the grille area, the heat exchangers, and the irregular under-hood area. Today cooling packages of heavy trucks are dimensioned for a critical cooling case, typically when the vehicle is driving fully laden, at low speed up a steep hill. However, for long-haul trucks, mostly operating at highway speeds on mostly level roads, it may not be necessary to have all the cooling airflow from an open-grille configuration. It can therefore be desirable for fuel consumption purposes, to shut off the entire cooling airflow, or a portion of it, under certain driving conditions dictated by the cooling demands. In Europe, most trucks operating on the roads are of cab-over-engine type, as a consequence of the length legislations present.

Abstract A tire is one of the most essential parts of a vehicle that not only helps to provide stability but also helps in isolating shocks from the road surface as well as giving longitudinal and lateral powers for vehicle acceleration, steering, and braking. We are familiar with the tire as a pneumatic tire, but with recent developments, a new category of tires called non-pneumatic tires (NPT) has recently come to light. The unique design equipped for these tires provides them with several advantages over the conventional pneumatic tires, which include the elimination of tire blowouts and elimination of punctures. Since rotating tires are known to contribute up to one-third of the vehicle’s total aerodynamic force [5] and also because of the vast advantages provided by the NPT.

Abstract The advancement in vision sensors and embedded technology created the opportunity in autonomous vehicles to look ahead in the future to avoid potential obstacles and steep regions to reach the target location as soon as possible and yet maintain vehicle safety from rollover. The present work focuses on developing a nonlinear model predictive controller (NMPC) for a high-speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles, and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in the MATLAB environment. The CasADi tool provides a platform to formulate the NMPC problem using symbolic expressions, which is an easy and efficient way of solving the optimization problem. In the present work, the vehicle lateral dynamics are modeled using the Pacejka nonlinear tire model.

Abstract Direct dual fuel stratification (DDFS) strategy benefits the advantages of the RCCI and PPC strategies simultaneously. DDFS has improved control over the heat release rate, by injecting a considerable amount of fuel near TDC, compared to RCCI. In addition, the third injection (near TDC) is diffusion-limited. Consequently, piston bowl geometry directly affects the formation of emissions. The modified piston geometry was developed and optimized for RCCI by previous scholars. Since all DDFS experimental tests were performed with the modified piston profile, the other piston profiles need to be investigated for this strategy. In this article, first, a comparative study between the three conventional piston profiles, including the modified, stock, and scaled pistons, was performed. Afterward, the gasoline injector position was shifted to the head cylinder center for the stock piston. NOX emissions were improved; however, soot was increased slightly.