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Empirical models for engine-out oxides of Nitrogen (NOx) and smoke emissions have been developed for the purpose of minimizing transient emissions while maintaining transient response. Three major issues have been addressed: data acquisition, data processing and modeling method. Real and virtual transient parameters have been identified for acquisition. Accounting for the phase shift between transient engine events and transient emission measurements has been shown to be very important to the quality of model predictions. Several methods have been employed to account for the transient transport delays and sensor lags which constitute the phase shift. Finally several different empirical modeling methods have been used to determine the most suitable modeling method for transient emissions. These modeling methods include several kinds of neural networks, global regression and localized regression.

A control algorithm is developed for active/semi-active suspensions which can provide more comfort and better handling simultaneously. A weighting parameter is tuned online which is derived from two components - slow and fast adaptation to assign weights to comfort and handling. After establishing through simulations that the proposed adaptive control algorithm can demonstrate a performance better than some controllers in prior-art, it is implemented on an actual vehicle (Cadillac STS) which is equipped with MR dampers and several sensors. The vehicle is tested on smooth and rough roads and over speed bumps.

The analysis of nickel metal hydride (Ni-MH) battery performance is very important for automotive researchers and manufacturers. The performance of a battery can be described as a direct consequence of various chemical and physical phenomena taking place inside the container. In this paper, a physics-based model of a Ni-MH battery will be presented. To analyze its performance, the efficiency of the battery is chosen as the performance measure, which is defined as the ratio of the energy output from the battery and the energy input to the battery while charging. Parametric sensitivity analysis will be used to generate sensitivity information for the state variables of the model. The generated information will be used to showcase how sensitivity information can be used to identify unique model behavior and how it can be used to optimize the capacity of the battery. The results will be validated using a finite difference formulation.

Fuel efficiency for tractor/trailer combinations continues to be a key area of focus for manufacturers and suppliers in the commercial vehicle industry. Improved fuel economy of vehicles in transit can be achieved through reductions in aerodynamic drag, tire rolling resistance, and driveline losses. Fuel economy can also be increased by improving the efficiency of the thermal to mechanical energy conversion of the engine. One specific approach to improving the thermal efficiency of the engine is to implement a waste heat recovery (WHR) system that captures engine exhaust heat and converts this heat into useful mechanical power through use of a power fluid turbine expander. Several heat exchangers are required for this Rankine-based WHR system to collect and reject the waste heat before and after the turbine expander. The WHR condenser, which is the heat rejection component of this system, can be an additional part of the front-end cooling module.

The definition of the energy management strategy for a hybrid electric vehicle is a key element to ensure maximum energy efficiency. The ability to optimally manage the on-board energy sources, i.e., fuel and electricity, greatly affects the final energy consumption of hybrid powertrains. In the case of plug-in series-hybrid architectures, such as Range-Extender Electric Vehicles (REEVs), fuel efficiency optimization alone can result in a stressful operation of the range-extender engine with an excessively high number of start/stops. Nonetheless, reducing the number of start/stops can lead to long periods in which the engine is off, resulting in the after-treatment system temperature to drop and higher emissions to be produced at the next engine start.

This paper describes steady state, computationally rigorous, three-dimensional conjugate heat transfer 3D CFD analysis of an oil cooler. Thermal performance of an oil cooler is very significant from engine oil consumption, bearings performance etc. In an engine water jacket, coolant flows around and through the oil cooler making the flow three dimensional. Therefore, demanding the need of a 3D CFD analysis for capturing all the flow and heat transfer aspects and thereby accurate prediction of thermal performance. An oil cooler contains intricate turbulators in flow paths and have dimensions varying from as small as 0.25 mm to as large as 350 mm, therefore making the meshing and solution a formidable task. In current work an oil cooler with all the intricate details is modelled in a commercial CFD code. Objective is to develop a solution approach which can predict thermal performance of an oil cooler in an accurate way.

One field-returned DPF loaded with a high amount of ash is examined using experimental and modeling approaches. The ash-related design factors are collected by coupling the inspection results from terahertz spectroscopy with a calibrated DPF model. The obtained ash packing density, ash layer permeability and ash distribution profile are then used in the simulation to assess the ash impact on DPF backpressure and regeneration behaviors. The following features have been observed during the simulation: 1 The ash packing density, ash layer permeability and ash distribution profile should be collected at the same time to ensure the accurate prediction of ash impact on DPF backpressure. Missing one ash property could mislead the measurement of the other two parameters and thus affects the DPF backpressure estimation. 2 The ash buildup would gradually increase the frequency for the backpressure-based active soot regeneration.

The performance, life cycle cost, and safety of electric and hybrid electric vehicles (EVs and HEVs) depend strongly on their energy storage system. Advanced batteries such as lithium-ion (Li-ion) polymer batteries are quite viable options for storing energy in EVs and HEVs. In addition, thermal management is essential for achieving the desired performance and life cycle from a particular battery. Therefore, to design a thermal management system, a designer must study the thermal characteristics of batteries. The thermal characteristics that are needed include the surface temperature distribution, heat flux, and the heat generation from batteries under various charge/discharge profiles. Therefore, in the first part of the research, surface temperature distribution from a lithium-ion pouch cell (20Ah capacity) is studied under different discharge rates of 1C, 2C, 3C, and 4C.

In this paper, a consensus framework for cooperative parameter estimation within the vehicular network is presented. It is assumed that each vehicle is equipped with a dedicated short range communication (DSRC) device and connected to other vehicles. The improvement achieved by the consensus for parameter estimation in presence of sensor’s noise is studied, and the effects of network nodes and edges on the consensus performance is discussed. Finally, the simulation results of the introduced cooperative estimation algorithm for estimation of the unknown parameter of road condition is presented. It is shown that due to the faster dynamic of network communication, single agents’ estimation converges to the least square approximation of the unknown parameter properly.

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).

In this work, an alternative method is proposed and validated for quantifying the axial performance of a state-of-the-art Cu zeolite SCR catalyst. Catalyst cores of a standard length, with varying lengths of wash-coated regions were used to axially resolve the functional performance of the SCR catalyst. This proposed method was validated by quantifying the catalyst entrance and exit effects, as well as the effect of non-uniform wash-coat loading densities. This method is less susceptible to some of the complications highlighted in the previous studies, such as flow uniformity between channels, as well as radiative heating effects, since the product gases are sampled across the entire monolith cross-section rather than through a single catalyst channel. The specific catalyst functions quantified include: NO and NH₃ oxidation, NH₃ storage capacity, as well as NOx conversion efficiency.

In this study we investigated the interaction of short- and long-chain hydrocarbons (HCs), represented by propene (C₃H₆) and n-dodecane (n-C₁₂H₂₆), respectively, with a state-of-the-art small-pore Cu-Zeolite SCR catalyst. By varying HC adsorption conditions, we determined that physisorption was the primary mechanism for some minor HC storage at low temperatures (≺ 200°C), while chemical transformation was involved in more substantial HC storage at higher temperatures (200-400°C). The latter was evidenced by the oxygen-dependent and thermally activated nature of the storage process, and further confirmed by the carbon-rich composition of the deposits. The nature of HC-derived deposits of different origins and amounts was further probed using the standard SCR reaction at kinetically challenging conditions (at 200°C), as well by ammonia adsorption/desorption experiments.

Mitigation of ammonia slip from SCR system is critical to meeting the evolving NH₃ emission standards, while achieving maximum NOx conversion efficiency. Ammonia slip catalysts (ASC) are expected to balance high activity, required to oxidize ammonia across a broad range of operating conditions, with high selectivity of converting NH₃ to N₂, thus avoiding such undesirable byproducts as NOx or N₂O. In this work, new insights into the behavior of an advanced ammonia slip catalyst have been developed by using accelerated progressive catalyst aging as a tool for catalyst property interrogation. The overall behavior was deconstructed to several underlying functions, and referenced to an active but non-selective NH₃ oxidation function of a diesel oxidation catalyst (DOC) and to the highly selective but minimally active NH₃ oxidation function of an SCR catalyst.

For renewable fuels to displace petroleum, they must be compatible with emissions control devices. Pure biodiesel contains up to 5 ppm Na + K and 5 ppm Ca + Mg metals, which have the potential to degrade diesel emissions control systems. This study aims to address these concerns, identify deactivation mechanisms, and determine if a lower limit is needed. Accelerated aging of a production exhaust system was conducted on an engine test stand over 1001 h using 20% biodiesel blended into ultra-low sulfur diesel (B20) doped with 14 ppm Na. This Na level is equivalent to exposure to Na at the uppermost expected B100 value in a B20 blend for the system full-useful life. During the study, NOx emissions exceeded the engine certification limit of 0.33 g/bhp-hr before the 435,000-mile requirement.

A simple 1-dimensional filter model, with symmetric and asymmetric channels, has been developed to investigate the fundamental behavior and performance of ceramic partial diesel particulate filters (PFs). The governing equations of mass and momentum are similar to those of a full DPF [7, 15]. A standard DPF with the plugs at its inlet face removed has been referred to as a ‘rear-plugged PF’ while, one with the plugs at the outlet face removed has been referred to as a ‘front-plugged PF’ in the present study. Removal of some of the plugs from a standard ceramic DPF reduces the (i) overall pressure drop (ΔP) across the filter, (ii) filtration efficiency (FE) of the DPF, and (iii) manufacturing cost. Partial filters stand a high chance of being deployed in diesel exhaust after-treatment systems for the emerging markets (Brazil, Russia, India, China) that follow Euro 4 emission regulations.

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 this contribution, nuanced changes of a commercial Cu-SSZ-13 catalyst with hydrothermal aging, which have not been previously reported, as well as their corresponding impact on SCR functions, are described. In particular, a sample of Cu-SSZ-13 was progressively aged between 550 to 900°C and the changes of performance in NH3 storage, oxidation functionality and NOx conversion of the catalyst were measured after hydrothermal exposure at each temperature. The catalysts thus aged were further characterized by NH3-TPD, XRD and DRIFTS techniques for structural changes. Based on the corresponding performance and structural characteristics, three different regimes of hydrothermal aging were identified, and tentatively as assigned to “mild”, “severe” and “extreme” aging. Progressive hydrothermal aging up to 750°C decreased NOx conversion to a small degree, as well as NH3 storage and oxidation functions.

In this work fuel dilution of engine oil, and the impact of biodiesel fuel on dilution, were examined. New emissions requirements have driven the adoption of a range of aftertreatment systems for diesel engines. These aftertreatment devices in many cases have specific requirements for exhaust composition and temperature. Meeting these requirements can lead to fuel dilution of the engine oil. Measurement of fuel dilution of engine oil can be challenging, and in this study a new strategy for utilizing Fourier Transform Infrared Spectroscopy (FTIR) was examined. A synthetic component of aviation oil, pentaerythritol ester (PE), was found to be a very useful tracer for measuring dilution with ultra low sulfur diesel (ULSD), but not useful for measuring dilution with B20. Fuel dilution and evaporation rates were measured for both ULSD and for a blend of biodiesel and ULSD (B20).

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.