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Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, possibly at the expense of increased tailpipe emissions due to multiple “cold” start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events may have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.

Vehicle stability is maintained by proper interactions between the driver and vehicle stability control system. While driver describes the desired target path by commanding steering angle and acceleration/deceleration rates, vehicle stability controller tends to stabilize higher dynamics of the vehicle by correcting longitudinal, lateral, and roll accelerations. In this paper, a finite-horizon optimal solution to vehicle stability control is introduced in the presence of driver's dynamical decision making structure. The proposed concept is inspired by Nash strategy for exactly known systems with more than two players, in which driver, commanding steering wheel angle, and vehicle stability controller, applying compensated yaw moment through differential braking strategy, are defined as the dynamic players of the 2-player differential linear quadratic game.

As batteries become a major component of numerous advanced vehicles, significant efforts have been allocated towards characterizing and estimating battery energy capability over the lifetime of a vehicle. Currently, battery State of Charge (SOC) is one of the primary values used for this characterization; however SOC usage has several issues when implemented in Electric Vehicle (EV), Hybrid Electric Vehicle (HEV), and Plug-In Hybrid Electric Vehicle (PHEV) systems. One of the main issues with reporting battery SOC as a characterization of battery energy capability is that it only gives a percentage of the energy available to the operator. SOC does not accurately represent the true capability or capacity of the battery, and thus fails to account for the impact to capability with respect to battery size, age, and recent operational history.

One seminal question that faces a vehicle's driver (either human or computer) is predicting the capability of the vehicle as it encounters upcoming terrain. A Performance Margin (PM) is defined in this work as the ratio of the required tractive effort to the available tractive effort for the front and rear respectively. This simple definition stems from and incorporates many traditional handling metrics and is robust in its scope of applicability. The PM is implemented in an Intervention Strategy demonstrating its use to avoid situations in which the vehicle exceeds its handling capabilities. Results from a design case study are presented to show the potential efficacy of developing a PM-based control system.

This experimental work focuses on defining the detailed morphology of secondary emission products derived from the combustion of cerium (Ce) fuel-borne catalyst (FBC) doped with diesel fuel. Cerium is often used to promote the oxidation of diesel particulates collected in diesel aftertreatment systems, such as diesel particulate filters (DPFs). However, it is suspected that the secondary products could be emitted from the vehicle tailpipe without being effectively filtered by the aftertreatment systems. In this work, these secondary emissions were identified by means of a high-resolution transmission electron microscope (TEM), and their properties were examined in terms of morphology and chemistry. In preparation for fuel doping, a cerium-based aliphatic organic compound solution was mixed with a low-sulfur (110 ppm) diesel fuel at 50 ppm in terms of weight concentration.

As part of an ongoing vehicle benchmarking effort at Argonne National Laboratory, four different power-split HEVs were tested on a chassis dynamometer to analyze their operational behavior and understand the control strategy and its relationship to the individual features of the vehicles tested. The controls that select the way in which engine operation matches best engine efficiency load points appears to have evolved From the Gen 1 to the Gen 2 Toyota Prius. The Ford Escape HEV and Lexus RX400h were also analyzed by using similar methods, although the data are not as extensive as those for the Prius hybrids. Whereas the Escape HEV appeared to operate in a manner similar to that of the Gen 1 Prius, the RX400h (with its relatively large engine) loads the engine with excess battery charge to keep it operating at higher power levels - apparently to improve overall efficiency.

Detailed diesel particulates morphology, nanostructures, fractal geometry, and nitrogen oxides (NOx) emissions were analyzed for five different test fuels in a 1.7-L, common-rail direct-injection diesel engine. The accurately formulated fuels permit the effects of sulfur, paraffins, aromatics, and naphthene concentrations to be determined. A novel thermophoretic sampling technique was used to collect particulates immediately after the exhaust valves. The morphology and nanostructures of particulate samples were examined, imaged with a high-resolution transmission electron microscope (HRTEM), and quantitatively analyzed with customized digital image processing/data acquisition systems. The results show that the particle sizes and the total mass of particulate matter (PM) emissions correlate most strongly with the fuels' aromatics and sulfur content.

Restrictions on track testing, combined with advances in technology, have contributed to an increased dependence on sensors and data acquisition for diagnosing problems and improving performance in motorsports vehicles. This dependence has created a new set of challenges for race engineers to collect quality data from a vehicle at the track. Successful 7- or 8-post shaker rig testing is highly dependent on the quality of the data acquired at the track. An improperly configured data acquisition system can actually be worse than a faulty sensor. This paper highlights a few of the most common problems in motorsports data acquisition: aliasing and sample rate selection. The effects of these problems are described for typical suspension sensors such as accelerometers, shock potentiometers, load cells, and laser ride height sensors. An experimental case study is presented to explain the implications of these problems.

This paper covers the development of a closed loop transaxle synchronization algorithm which was a key deliverable in the control system design for the L3 Enigma, a Battery Dominant Hybrid Electric Vehicle. Background information is provided to help the reader understand the history that lead to this unique solution of the input and output shaft synchronizing that typically takes place in a manual vehicle transmission or transaxle when shifting into a gear from another or into a gear from neutral when at speed. The algorithm stability is discussed as it applies to system stability and how stability impacts the speed at which a shift can take place. Results are simulated in The MathWorks Simulink programming environment and show how traction motor technology can be used to efficiently solve what is often a machine design issue. The vehicle test bed to which this research is applied is a parallel biodiesel hybrid electric vehicle called the Enigma.

Model-based control system design improves quality, shortens development time, lowers engineering cost, and reduces rework. Evaluating a control system's performance, functionality, and robustness in a simulation environment avoids the time and expense of developing hardware and software for each design iteration. Simulating the performance of a design can be straightforward (though sometimes tedious, depending on the complexity of the system being developed) with mathematical models for the hardware components of the system (plant models) and control algorithms for embedded controllers. This paper describes a software tool and a methodology that not only allows a complete system simulation to be performed early in the product design cycle, but also greatly facilitates the construction of the model by automatically connecting the components and subsystems that comprise it.

Many of today's automotive control system simulation tools are suitable for simulation, but they provide rather limited support for model building and management. Setting up a simulation model requires more than writing down state equations and running them on a computer. The role of a model library is to manage the models of physical components of the system and allow users to share and easily reuse them. In this paper, we describe how modern software techniques can be used to support modeling and design activities; the objective is to provide better system models in less time by assembling these system models in a “plug-and-play” architecture. With the introduction of hybrid electric vehicles, the number of components that can populate a model has increased considerably, and more components translate into more possible drivetrain configurations. To address these needs, we explain how users can simulate a large number of drivetrain configurations.

The physical and morphological properties of the particulate matter emitted from a 1.7-liter light-duty diesel engine were characterized by observing its evolution in size and fractal geometry along the exhaust system. A common-rail direct-injection diesel engine, the exhaust system of which was equipped with a turbocharger, EGR, and two oxidation catalysts, was powered with a California low-sulfur diesel fuel at various engine-operating conditions. A unique thermophoretic sampling system, a high-resolution transmission electron microscope (TEM), and customized image processing/data acquisition systems were key instruments that were used for the collection of particulate matter, subsequent imaging of particle morphology, and detailed analysis of particle dimensions and fractal geometry, respectively. The measurements were carried out at four different positions along the exhaust pipe.

The performance of five different skyhook control methods is studied experimentally, using a quarter-car rig. The control methods that are analyzed include: skyhook control, groundhook control, hybrid control, displacement skyhook, and relative displacement skyhook. Upon evaluating the performance of each method in frequency domain for various control conditions, they are compared with each other as well as with passive damping. The results indicate that no one control method outperforms other control methods at both the sprung and unsprung mass natural frequencies. Each method can perform better than the other control methods in some respect. Hybrid control, however, comes close to providing the best compromise between different dynamic demands on a primary suspension. The results indicate that hybrid control can offer benefits to both the sprung and unsprung mass with control gain settings that provide equal contributions from skyhook control and groundhook control.

Hybrid electric vehicles (HEVs) may have key fuel economy and emissions advantages over current conventional vehicles, but they have drawbacks such as frequent engine starts that can slow down market penetration of HEVs. First, the hydrocarbon emissions due to the numerous engine starts would make newly developed HEV powertrains even more demanding on the emission control system. Second, frequent starts may make the engine deteriorate quickly. This study is an attempt to gain a better understanding of the engine start characteristics of two limited-production HEVs (Toyota Prius and Honda Insight). Using fast-response (5 ms) hydrocarbon and NO (nitric oxide) analyzers, the transient emissions were measured in the engine exhaust ports during cold and hot engine starts. On the basis of the experimental findings, several recommendations were made to improve performance and emissions of future HEVs.

A thermophoretic particulate sampling device was used to investigate the detailed morphology and microstructure of diesel particulates at various engine-operating conditions. A 75 HP Caterpillar single-cylinder direct-injection diesel engine was operated to sample particulate matter from the high-temperature exhaust stream. The morphology and microstructure of the collected diesel particulates were analyzed using a high-resolution transmission electron microscope and subsequent image processing/data acquisition system. The analysis revealed that spherical primary particles were agglomerated together to form large aggregate clusters for most of engine speed and load conditions. Measured primary particle sizes ranged from 34.4 to 28.5 nm at various engine-operating conditions. The smaller primary particles observed at high engine-operating conditions were believed to be caused by particle oxidation at the high combustion temperature.

Tire-pavement interaction noise (TPIN) is a dominant source for passenger cars and trucks above 40 km/h and 70 km/h, respectively. TPIN is mainly generated from the interaction between the tire and the pavement. In this paper, twenty-two passenger car radial (PCR) tires of the same size (16 in. radius) but with different tread patterns were tested on a non-porous asphalt pavement. For each tire, the noise data were collected using an on-board sound intensity (OBSI) system at five speeds in the range from 45 to 65 mph (from 72 to 105 km/h). The OBSI system used an optical sensor to record a once-per-revolution signal to monitor the vehicle speed. This signal was also used to perform order tracking analysis to break down the total tire noise into two components: tread pattern-related noise and non-tread pattern-related noise.

This paper focuses on stability of automotive supervisory control systems (ASCSs). It serves to introduce the concept of stability with respect to an entire ASCS. The realm of ASCSs is categorized and a brief description of pre-existing classical methods of stability analysis is presented. With the concept then having been fully introduced, an approach to evaluating stability of a key category of ASCS, the rule-based deterministic ASCS, is presented. This approach, cited from unrelated modern literature concerning stability of deterministic finite state machines, is novel in that its original target research area was not specifically automotive engineering.

Detailed characteristics of morphology, nanostructures, and sizes were analyzed for particulate matter (PM) emissions from low-temperature combustion (LTC) modes of a single-cylinder, light-duty diesel engine. The LTC engines have been widely studied in an effort to achieve high combustion efficiency and low exhaust emissions. Recent reports indicate that the number of nucleation mode particles increased in a broad engine operating range, which implies a negative impact on future PM emissions regulations in terms of the nanoparticle number. However, the size measurement of solid carbon particles by commercial instruments is indeed controversial due to the contribution of volatile organics to small nanoparticles. In this work, an LTC engine was operated with various biofuel blends, such as blends (B20) of soy bean oil (soy methyl ester, SME20) and palm oil (palm methyl ester, PME20), as well as an ultra-low-sulfur diesel fuel.

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is participating in the 2011-2014 EcoCAR 2 competition in which the team is tasked with re-engineering the powertrain of a GM donated vehicle. The primary goals of the competition are to reduce well to wheels (WTW) petroleum energy use (PEU) and reduce WTW greenhouse gas (GHG) and criteria emissions while maintaining performance, safety, and consumer acceptability. To meet these goals HEVT has designed a series parallel plug-in hybrid electric vehicle (PHEV) with multiple modes of operation. This paper will first cover development of the control system architecture with a dual CAN bus structure to meet the requirements of the vehicle architecture. Next an online optimization control strategy to minimize fuel consumption will be developed. A simple vehicle plant model will then be used for software-in-the-loop (SIL) testing to improve fuel economy.

Heavy-vehicle event data recorders (HVEDRs) provide a source of temporal vehicle data just prior to, during, and for a short period after, an event. In the 1990s, heavy-vehicle (HV) engine manufacturers expanded the capabilities of engine control units (ECU) and engine control modules (ECM) to include the ability to record and store small amounts of parametric vehicle data. This advanced capability has had a significant impact on vehicle safety by helping law enforcement, engineers, and researchers reconstruct events of a vehicle crash and understand the details surrounding that vehicle crash. Today, EDR technologies have been incorporated into a wide range of heavy vehicle (HV) safety systems (e.g., crash mitigation systems, air bag control systems, and behavioral monitoring systems). However, the adoption of EDR technologies has not been uniform across all classes of HVs or their associated vehicle systems.