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When performing catalyst modeling and parameter tuning it is desirable that the experimental data contain both transient and stationary points and can be generated over a short period of time. Here a method of creating such concentration transients for a full scale engine rig system is presented. The paper describes a valuable approach for changing the composition of engine exhaust gas going to a DOC (or potentially any other device) by conditioning the exhaust gas with an additional upstream DOC and/or SCR. By controlling the urea injection and the DOC bypass a wide range of exhaust compositions, not possible by only controlling the engine, could be achieved. This will improve the possibilities for parameter estimation for the modeling of the DOC.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

System identification is an important aspect in model-based control design which is proven to be a cost-effective and time saving approach to improve the performance of hybrid electric vehicles (HEVs). This study focuses on modeling and parameter estimation of the longitudinal vehicle dynamics for Toyota Prius Plug-in Hybrid (PHEV) with power-split architecture. This model is needed to develop and evaluate various controllers, such as energy management system, adaptive cruise control, traction and driveline oscillation control. Particular emphasis is given to the driveline oscillations caused due to low damping present in PHEVs by incorporating flexibility in the half shaft and time lag in the tire model.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

The vision for the future automotive chassis is to interconnect the lateral, longitudinal, and vertical dynamics by separately controlling driving, braking, steering, and damping of each individual wheel. A major advantage of all wheel drive electric vehicles with four in-wheel motors is the possibility to control the torque and speed at each wheel independently. This paper proposes a traction controller for such a vehicle. It estimates the road's adhesion potential at each wheel and adjusts each motor voltage, such that the longitudinal slip is kept in an optimal range. For development and validation, a full vehicle model is designed in ADAMS/View software, in co-simulation with motor and control elements, modeled in MATLAB/Simulink.

The performance of a commercial Cu-zeolite SCR catalyst after differing degrees of hydrothermal aging (aged for 72 hours at 500, 700 and 800°C with 10% moisture balanced with air) was studied by spatially resolving different key reactions using gas-phase FTIR measurements. Gases were sampled along a channel at different positions and analyzed using FTIR, which overcomes the interference of water and nitrogen on ammonia concentration detection encountered in standard mass spectrometer-based spatial resolution measurements. The NO:NO₂ concentration ratio was changed so that the standard (NO:NO₂ = 1:0), fast (NO:NO₂ = 1:1) and NO₂ (NO:NO₂ = 0:1) SCR reactions could be investigated as a function of the catalyst's hydrothermal aging extent. In addition, the effects of hydrothermal aging on the activity of NH₃ and NO oxidation were also investigated. Hydrothermal aging had little effect on NO oxidation activity.

A predictive model of a specific vehicle was modeled in the system-level physical modeling tool, MapleSim, for performance and fuel consumption prediction of a full vehicle powertrain, driving a multi-body chassis model with tire models. The project also includes investigation into overall fuel efficiency and effect on vehicle handling for different drive cycles. The goals of this project were to investigate: 1) the relationships between the forces at tire/road interfaces during various drive cycles and the fuel efficiency of a vehicle, and 2) the interaction between the powertrain and the chassis of the vehicle. To accomplish these goals, a complete vehicle model was created in the lumped-parameter physical modeling tool, MapleSim. A great deal of effort has gone into using real parameters and to assure that some mathematical rigour has been employed in its development.

It is imperative to have accurate tire models when trying to control the trajectory of a vehicle. With the emergence of autonomous vehicles, it is more important than ever before to have models that predict how the vehicle will operate in any situation. Many different types of tire models have been developed and validated, including physics-based models such as brush models, black box models, finite element-based models, and empirical models driven by data such as the Magic Formula model. The latter is widely acknowledged to be one of the most accurate tire models available; however, collecting data for this model is not an easy task. Collecting data is often accomplished through rigorous testing in a dedicated facility. This is a long and expensive procedure which generally destroys many tires before a comprehensive data set is acquired. Using a Vehicle Measurement System (VMS), tires can be modeled through on-road data alone.

In vehicles equipped with conventional Electric Power Steering (EPS) systems, the steering effort felt by the driver can be unreasonably low when driving on slippery roads. This may lead inexperienced drivers to steer more than what is required in a turn and risk losing control of the vehicle. Thus, it is sensible for tire-road friction to be accounted for in the design of future EPS systems. This paper describes the design of an auxiliary EPS controller that manipulates torque delivery of current EPS systems by supplying its motor with a compensation current controlled by a fuzzy logic algorithm that considers tire-road friction among other factors. Moreover, a steering system model, a nonlinear vehicle dynamics model and a Dugoff tire model are developed in MATLAB/Simulink. Physical testing is conducted to validate the virtual model and confirm that steering torque decreases considerably on low friction roads.

The increasing severity in emission standards around the world has been accompanied by the development of more active, durable catalysts. With a view to investigating the effects of high thermal aging on the catalyst performance and structure, the relationships of washcoat composition, washcoat structure, and PGM location with respect to catalyst activity were clarified using a model gas test, as well as physical and chemical characterization methods. The influence of newly developed washcoat components and PGM location on catalyst performance are also demonstrated by engine bench tests. The results obtained in this study indicate the newly developed Pt/Rh catalyst techologies are appropriate for future applications in which the catalyst will be exposed to extremely high temperature and flowrates.

It has long been recognized that the key to achieving stringent emission standards such as ULEV is the control of cold-start hydrocarbons. This paper describes a new approach for achieving excellent cold-start hydrocarbon control. The most important component in the system is a catalyst that is highly active at ambient temperature for the exothermic CO oxidation reaction in an exhaust stream under net lean conditions. This catalyst has positive order kinetics with respect to CO for CO oxidation. Thus, as the concentration of CO in the exhaust is increased, the rate of this reaction is increased, resulting in a faster temperature rise over the catalyst.

The conversion design strategy, and emissions and performance results for a dedicated propane, vapour injected, 1995 Dodge Dakota truck are reported. Data is obtained from the University of Waterloo entry in the 1997 Propane Vehicle Challenge. A key feature of the design strategy is its focus on testing and emissions while preserving low engine speed power for drivability. Major changes to the Dakota truck included the following: installation of a custom shaped fuel tank, inclusion of a fuel temperature control module, addition of a vaporizer and a fuel delivery metering unit, installation of a custom vapour distribution manifold, addition of an equivalence ratio electronic controller, inclusion of a wide range oxygen sensor, addition of an exhaust gas recirculation cooler and installation of thermal insulation on the exhaust system. A competition provided natural gas catalyst was used.

A fuel cell hybrid powertrain design is implemented and optimized by the University of Waterloo Alternative Fuels Team for the ChallengeX competition. A comprehensive set of bench-top and in-vehicle validation results are used to generate accurate fuel cell vehicle models for SIL/HIL control strategy testing and tuning. The vehicle is brought to a “99% buy-off” level of production readiness, and a detailed crashworthiness analysis is performed. The vehicle performance is compared to Vehicle Technical Specifications (VTS).

The molecular filtration of sulfur components in ultra low sulfur diesel (ULSD) fuel is described. A comprehensive screening of potential sulfur removal chemistries has yielded a sorbent which has the capability to efficiently remove organo-sulfur components in ULSD fuel. This sorbent has been used to treat ULSD fuel on a heavy duty engine equipped with NOx adsorber after-treatment technology and has been shown to lengthen the time between desulfation steps for the NOx adsorber. The fuel properties, cetane number and aromatics content, etc., have not been changed by the removal of the sulfur in the fuel with the exception of the lubricity which is reduced.

In this study, a finite element software application (SORPAS®) is used to simulate the effect of pulsing on the expected weld thermal cycle during resistance spot welding (RSW). The predicted local cooling rates are used in combination with experimental observation to study the effect pulsing has on the microstructure and mechanical properties of Zn-coated DP600 AHSS (1.2mm thick) spot welds. Experimental observation of the weld microstructure was obtained by metallographic procedures and mechanical properties were determined by tensile shear testing. Microstructural changes in the weld metal and heat affect zone (HAZ) were characterized with respect to process parameters.

The implementations of the Tier 2 and LEVII emission levels require fast catalyst light-off and fast closed loop control through high-speed engine management. The paper describes the development of innovative catalyst designs. During the development thermal and mechanical boundary conditions were collected and component tests conducted on test rigs to identify the emission and durability performance. The products were evaluated on a Super Imposed Test Setup (SIT) where thermal and mechanical loads are applied to the test piece simultanously and results are compared to accelerated vehicle power train endurance runs. The newly developed light-off catalyst with Perforated Foil Technology (PE) showed superior emission light-off characteristic and robustness.

Porous silicon as gas/chemical sensing material has been widely investigated in recent years. In this paper, the humidity sensing property of n-type porous silicon with ordered structure is studied for the first time. The ordered porous silicon used in this experiment has uniform pore size, pore shape and distribution. Both the membrane and closed bottom samples were studied. The resistance change of the porous silicon was measured. A 22-28% decrease of resistance was observed when relative humidity was changed from 1% to 100%. Both the response time and the recovery time were within 10 minutes, and 90% of the response can be reached in 6 minutes for the PS membrane sample. The possible sensing mechanism and future work are also discussed in this paper.

Traditional electrode materials for lithium-ion storage cells are typically crystalline layered structures such as metal oxides, and graphitic carbons. These materials power billions of portable electronic devices in today's society. However, large-scale, high-capacity storage devices capable of powering hybrid electric vehicles (HEV″s) or their plug-in versions (PHEV's) have much more demanding requirements with respect to safety, cost, and the power they must deliver. Recently, nanostructured solid state materials, which are comprised of two more compositional or structural phases, have been found to show exciting possibilities to meet these criteria.

The SNR-technique is a new NOx aftertreatment system for lean burn gasoline and diesel applications. The objective of SNR is NOx removal from lean exhaust gas by NOx adsorption and subsequent selective external recirculation and decomposition of NOx in the combustion process. The SNR-project is composed of two major parts. Firstly the development of NOx adsorbents which are able to store large quantities of NOx in lean exhaust gas, and secondly the NOx decomposition by the combustion process. Emphasis of this paper is the investigation of NOx reduction in the combustion process, including experimental investigation and numerical simulation. The NOx decomposition process has been proven in diesel and lean-burn gasoline engines. Depending on the type of engine NOx-conversion rates up to 90 % have been observed. Regarding the complete SNR-system, including the efficiency of the adsorbing material and the NOx decomposition by the combustion, a NOx removal of more than 50% is achievable.

Selective NOx recirculation (SNR), involving adsorption, selective external recirculation and decomposition of the NOx by the combustion process, is itself a promising technique to abate NOx emissions. Three types of materials containing Ba: barium aluminate, barium tin perovskite and barium Y-zeolites have been developed to adsorb NOx under lean-burn or Diesel conditions, with or without the presence of S02. All these materials adsorb NO2 selectively (lean-burn conditions), and store it as nitrate/nitrite species. The desorption takes place by decomposition of these species at higher temperatures. Nitrate formation implies also sulfate formation in the presence of SO2 and SO3, while the NO2/SO2 competition governs the poisoning of such catalysts.