Search Results

A framework is proposed for model-based misfire detection in gasoline engines with dynamic skip fire by employing a novel control oriented engine model. The model-based techniques form compact description of plant behavior and have a number of well known benefits. The performance requirements and environment legislation resulted in a rigorous research on misfire detection due to which an extensive literature can be found for the problem of misfire detection in all-cylinder firing gasoline engines. Since there is no fix cylinder activation/de-activation sequence in dynamic skip fire engines. So, the problem of misfire detection in dynamic skip fire engines departs from its trivial nature. In the proposed framework, ‘cylinder skip sequence’ is also fed to the engine model along-with conventional engine inputs. The First Principle based Engine Model constructs the crankshaft angular speed fluctuation pattern for a given cylinder skip sequence.

Downsizing and boosting is currently the principal solution to reduce fuel consumption in automotive engines without penalizing the power output. A key challenge for controlling the boost pressure during highly transient operations lies in avoiding to operate the turbocharger compressor in its instability region, also known as surge. While this phenomenon is well known by control engineers, it is still difficult to accurately predict during transient operations. For this reason, the scientific community has directed considerable efforts to understand the phenomena leading to the onset of unstable behavior, principally through experimental investigations or high-fidelity CFD simulations. On the other hand, less emphasis has been placed on creating control-oriented models that adopt a physics-based (rather than data-driven) approach to predict the onset of instability phenomena.

Conventional silencers have extensively been used to attenuate airborne pressure pulsations in the breathing system of internal combustion engines, typically at low frequencies as dictated by the crankshaft speed. With the introduction of turbocharger compressors, however, particularly those with the ported shroud recirculating casing treatment, high-frequency tones on the order of 10 kHz have become a significant contributor to noise in the induction system. The elevated frequencies promote multi-dimensional wave propagation, rendering traditional silencing design methods invalid, as well as the standard techniques to assess silencer performance. The present study features a novel high-frequency silencer designed to target blade-pass frequency (BPF) noise at the inlet of turbocharger compressors. The concept uses an acoustic straightener to promote planar wave propagation across arrays of quarter-wave resonators, achieving a broadband attenuation.

The vibration isolation effectiveness of powertrain mount configurations is examined for electric vehicle application by considering the effect introduced by internal mount resonances. Unlike internal combustion engines where mounts are typically designed only for static support and low frequency dynamics, electric motors have higher excitation frequencies in a range where mount resonances often occur. The problem is first analytically formulated by considering a simple 3-dimensional powertrain system, and the vibration isolation effectiveness significantly deteriorates at the mount resonance(s). It is shown that by modifying the mount shape, the mount resonance(s) can be shifted while maintaining the same static rate, tuning the frequency away from any engine excitation or natural frequencies. Further, internal mount resonances are utilized to improve vibration isolation over a narrow frequency range, using non-identical mounts to split mount resonance peaks.

Topology optimization is a branch of structural optimization which solves an optimal material distribution problem. The resulting structural topology, for a given set of boundary conditions and constraints, has an optimal performance (e.g. minimum compliance). Conventional 3D topology optimization algorithms achieve quality optimized results; however, it is an extremely computationally intensive task which is, in general, impractical and computationally unachievable for real-world structural optimal design processes. Therefore, the current development of rapid topology optimization technology is experiencing a major drawback. To address the issues, a new approach is presented to utilize the powerful abilities of large deep learning models to replicate this design process for 3D structures. Adversarial models, primarily Wasserstein Generative Adversarial Networks (WGAN), are constructed which consist of 2 deep convolutional neural networks (CNN) namely, a discriminator and a generator.

Thoracic injury criteria have been previously developed to predict thoracic injury for vehicle occupants as a function of biomechanical response. Historically, biomechanical testing of post-mortem human surrogates (PMHS) for injury criteria development has primarily been focused on mid-sized males. Response targets and injury criteria for other demographics, including small females, have been determined by scaling values from mid-sized males. The objective of this study was to explore the applicability of scaled injury criteria to their representative population. Two PMHS were subjected to a side-impact loading condition which replicates a near-side, MDB-to-vehicle impact for the driver. This was accomplished using the Advanced Side Impact System, or ASIS, on a HYGE sled. The sled acceleration matched the acceleration profile of an impacted vehicle, while the four pneumatic cylinders of the ASIS produced realistic door intrusion.

In the last decade, the use of Driver-in-the-Loop (DiL) simulators has significantly increased in research, product development, and motorsports. To be used as a verification tool in research, simulators must show a level of correlation with real-world driving for the chosen use case. This study aims to assess the validity of a low-cost, limited travel Vehicle Dynamics Driver-in-Loop (VDDiL) simulator by comparing on-road and simulated driving data using a statistical evaluation of longitudinal and lateral metrics. The process determines if the simulator is appropriate for verifying control strategies and optimization algorithms for longitudinal vehicle dynamics and evaluates consistency in the chosen metrics. A validation process explaining the experiments, choice of metrics, and analysis tools used to perform a validation study from the perspective of the longitudinal vehicle model is shown in this study.

RESULTS of tests performed on a modified type F-4 CFR engine show that precombustion reactions in both the fired and motored engine gave the same carbonyl products. The maximum specific yields of these carbonyls were similar for a given fuel compressed with comparable pressure-time-temperature histories in both motored- and fired-engine tests. As the motored engine seems to duplicate precombustion reactions occurring in a fired engine under normal operating conditions, the authors of this paper conclude that the motored engine, offering ease of control and sampling, is a convenient and valid tool for combustion research.

The automotive industry is striving to adopt model-based engine design and optimization procedures to reduce development time and costs. In this scenario, first-principles gas dynamic models predicting the mass, energy and momentum transport in the engine air path system with high accuracy and low computation effort are extremely important today for performance prediction, optimization and cylinder charge estimation and control. This paper presents a comparative study of two different modeling approaches to predict the one-dimensional unsteady compressible flow in the engine air path system. The first approach is based on a quasi-3D finite volume method, which relies on a geometrical reconstruction of the calculation domain using networks of zero-dimensional elements. The second approach is based on a model-order reduction procedure that projects the nonlinear hyperbolic partial differential equations describing the 1D unsteady flow in engine manifolds onto a predefined basis.

The EcoCAR 2: Plugging into the Future team at the Ohio State University is designing a Parallel-Series Plug-in Hybrid Electric Vehicle capable of 50 miles of all-electric range. The vehicle features a 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes made possible by a 1.8-L ethanol (E85) engine and 6-speed automated manual transmission. This vehicle is designed to drastically reduce fuel consumption, with a utility factor weighted fuel economy of 75 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This report details the rigorous design process followed by the Ohio State team during Year 1 of the competition. The design process includes identifying the team customer's needs and wants, selecting an overall vehicle architecture and completing detailed design work on the mechanical, electrical and control systems. This effort was made possible through support from the U.S.

This study examines clutch-damper subsystem dynamics under transient excitation and validates predictions using a new laboratory experiment (which is the subject of a companion paper). The proposed models include multi-staged stiffness and hysteresis elements as well as spline nonlinearities. Several example cases such as two high (or low) hysteresis clutches in series with a pre-damper are considered. First, detailed multi-degree of freedom nonlinear models are constructed, and their time domain predictions are validated by analogous measurements. Second, key damping sources that affect transient events are identified and appropriate models or parameters are selected or justified. Finally, torque impulses are evaluated using metrics, and their effects on driveline dynamics are quantified. Dynamic interactions between clutch-damper and spline backlash nonlinearities are briefly discussed.

With the advancement of the autonomous vehicle development, the different possibilities of improving fuel economy have increased significantly by changing the driver or powertrain response under different traffic conditions. Development of new fuel-efficient driving strategies requires extensive experiments and simulations in traffic. In this paper, a fuel efficiency simulator environment with existing simulator software such as Simulink, Vissim, Sumo, and CarSim is developed in order to reduce the overall effort required for developing new fuel-efficient algorithms. The simulation environment is created by combining a mid-sized sedan MATLAB-Simulink powertrain model with a realistic microscopic traffic simulation program. To simulate the traffic realistically, real roads from urban and highway sections are modeled in the simulator with different traffic densities.

Backlash is the movement between the gear teeth that allows them to mate without binding. Backlash can cause large torque fluctuations in vehicle powertrains when the input torque changes direction. These fluctuations cause a jerk and shuddering, which negatively affects drive quality. Input torque frequently changes direction in electric vehicles due to regenerative braking. Limiting zero crossings is an option for better drive quality; however, this leads to decreased vehicle efficiency. Because of this, modulating the torque through the backlash region is preferred, yet, if done poorly, it can result in sluggish torque response. This paper proposes a torque-shaping algorithm for an electric motor and gear/differential system to reduce backlash in electric vehicles. The control algorithm modulates the commanded torque’s rate of change based on the vehicle speed and zero-crossing torque.

The design of a performance hybrid electric vehicle includes a wide range of architecture possibilities. A large part of the design process is identifying reasonable vehicle architectures and vehicle performance capabilities. The Ohio State University EcoCAR 3 team designed a plug-in hybrid electric vehicle (PHEV) post-transmission parallel 2016 Chevrolet Camaro. With the end-goal of reducing the environmental impact of the vehicle, the Ohio State Camaro has been designed with a 44-mile all-electric range. It also features an 18.9 kWh Li-ion energy storage system, a 119 kW 2.0L GDI I4 engine that runs on 85% ethanol (E85) fuel, a 5-speed automated manual transmission, and a 150 kW peak electric machine. This report details the design and modeling process followed by the Ohio State team during Year 1 of the competition. The process included researching the customer needs of the vehicle, determining team design goals, initial modeling, and selecting a vehicle architecture.

Evaluating the safety of Autonomous Vehicles (AV) is a challenging problem, especially in traffic conditions involving dynamic interactions. A thorough evaluation of the vehicle’s decisions at all possible critical scenarios is necessary for estimating and validating its safety. However, predicting the response of the vehicle to dynamic traffic conditions can be the first step in the complex problem of understanding vehicle’s behavior. This predicted response of the vehicle can be used in validating vehicle’s safety. In this paper, models based on Machine Learning were explored for predicting and classifying driver’s response. The Naturalistic Driving Study dataset (NDS), which is part of the Strategic Highway Research Program-2 (SHRP2) was used for training and validating these Machine Learning models.

The effect of aerodynamically induced pre-swirl on the acoustic and performance characteristics of an automotive centrifugal compressor is studied experimentally on a steady-flow turbocharger facility. Accompanying flow separation, broadband noise is generated as the flow rate of the compressor is reduced and the incidence angle of the flow relative to the leading edge of the inducer blades increases. By incorporating an air jet upstream of the inducer, a tangential (swirl) component of velocity is added to the incoming flow, which improves the incidence angle particularly at low to mid-flow rates. Experimental data for a configuration with a swirl jet is then compared to a baseline with no swirl. The induced jet is shown to improve the surge line over the baseline configuration at all rotational speeds examined, while restricting the maximum flow rate. At high flow rates, the swirl jet increases the compressor inlet noise levels over a wide frequency range.

The current work demonstrates effective suppression of compression system surge instabilities by installing a variable cross-sectional flow area restriction within the inlet duct of a turbocharger centrifugal compressor operating on a bench-top facility. This restriction couples with the compressor, similar to stages in a multi-stage turbomachine, where the effective pressure ratio is the product of those for the restriction and compressor. During experiments at constant compressor rotational speed, the compressor is stable over the negatively sloped portion of the pressure ratio vs. flow rate characteristics, so the restriction is eliminated within this operating region to preserve compressor performance. At low flow rates, the slope of the compressor alone characteristics reaches a positive value, and the unrestricted compression system enters mild surge. Further reduction of flow rate with the unrestricted compressor inlet results in a sudden transition to deep surge instabilities.

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing.

In this paper, a new algorithm for the off-line estimation of wet clutch friction parameters is proposed for automotive transmissions, motivated by the usefulness of such an algorithm for diagnosing the condition of the clutch and transmission fluid in service. We assume that clutch pressure is measured, which is the case in dual clutch transmissions (DCT). The estimation algorithm uses measured rotational speeds and estimated accelerations at the input and output sides of a clutch, measured clutch pressures, and a simplified dynamic model of clutch friction to estimate the viscous and contact components of clutch friction torque. Coefficient of friction data is generated using the contact friction torque. A Stribeck friction model is fit to the data, and parameters in the model are then calculated by applying linear least squares estimation.

Downsizing and turbocharging are today considered an effective way to reduce CO2 emissions in automotive gasoline engines, especially for the European and US markets. In the broad field of research and development for engine boosting systems, the instability phenomenon of surge has gathered considerable interest in recent years, as the main limiting factor to high performance boosting and boost pressure control. To this extent, developing an in-depth knowledge of the surge dynamics and on the phenomena governing the transition from stable to unstable operation can provide very valuable information for the design of the intake system and boost pressure control algorithms, allowing optimal boost pressure without compromising the transient response.