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The reduction of automotive emissions is instrumental in the fight against air pollution and its impact on global warming. This realization has empowered governments around the world to mandate lower levels of vehicle emissions requiring the Original Equipment Manufacturers (OEMs) to implement advanced aftertreatment technologies in their applications. Achieving emission levels as low as SULEV30 or SULEV20 would have been impossible only a couple of decades ago, however, these lower levels of emissions are now a possibility through advanced control strategies and aftertreatment systems. As a part of this mandate to lower emissions, OEMs are also continuously monitoring the health and performance of their aftertreatment and control components. The implementation of On Board Diagnostics (OBD) ensures that control systems are functioning robustly and the emission levels are achieved and maintained to high mileages for the life of the vehicle.

Due to stringent environmental requirements, the vehicle under-hood and underbody temperatures have been steadily increasing. The increased temperatures affect components life and therefore, more thermal protection measures may be necessary. In this paper, we present an algorithm for estimation of automotive component life due to thermal effects through the vehicle life. Traditional approaches consider only the maximum temperature that a component will experience during severe driving maneuvers. However, that approach does not consider the time duration or frequency of exposure to temperature. We have envisioned a more realistic and science based approach to estimate component life based on vehicle duty cycles, component temperature profile, frequency and characteristics of material thermal degradation. In the proposed algorithm, a transient thermal analysis model provides the exhaust gas and exhaust surface temperatures for all exhaust system segments, and for any driving scenario.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

The application of Computational Fluid Dynamics (CFD) for three-dimensional (3D) combustion analysis coupled with detailed chemistry in engine development is hindered by its expensive computational cost. Chemistry computation may occupy as much as 90% of the total computational cost. In the present paper, a new two-step iso-octane combustion model was developed for spark-ignited (SI) engine to maximize computational efficiency while maintaining acceptable accuracy. Starting from the model constants of an existing global combustion model, the new model was developed using an approach based on sensitivity analysis to approximate the results of a reference skeletal mechanism. The present model involves only five species and two reactions and utilizes only one uniform set of model constants. The validation of the new model was performed using shock tube and real SI engine cases.

Maintaining the fuel temperature and fuel system components below certain values is an important design objective. Predicting these temperatures is therefore one of the key parts of the vehicle’s thermal management process. One of the physical processes affecting fuel tank temperature is fuel vaporization, which is controlled by the vapor pressure in the tank, fuel composition and fuel temperature. Models are developed to enable the computation of the fuel temperature, fuel vaporization rate in the tank, fuel temperatures along the fuel supply lines, and follow its path to the charcoal canister and into the engine intake. For diesel fuel systems where a fuel return line is used to return excess fluid back to the fuel tank, an energy balance will be considered to calculate the heat added from the high-pressure pump and vehicle under-hood and underbody.

In order to meet LEV III, EURO 6C and Beijing 6 emission levels, Original Equipment Manufacturers (OEMs) can potentially implement unique aftertreatment systems solutions which meet the varying legislated requirements. The availability of various washcoat substrates and PGM loading and ratio options, make selection of an optimum catalyst system challenging, time consuming and costly. Design for Six Sigma (DFSS) methodologies have been used in industry since the 1990s. One of the earliest applications was at Motorola where the methodology was applied to the design and production of a paging device which Consumer Reports called “virtually defect-proof”.[1] Since then, the methodology has evolved to not only encapsulate complicated “Variation Optimization” but also “Design Optimization” where multiple factors are in play. In this study, attempts are made to adapt the DFSS concept and methodology to identify and optimize a catalyst for diesel applications.

Spark ignition engines utilize catalytic converters to reform harmful exhaust gas emissions such as carbon monoxide, unburned hydrocarbons, and oxides of nitrogen into less harmful products. Aftertreatment devices require the use of expensive catalytic metals such as platinum, palladium, and rhodium. Meanwhile, tightening automotive emissions regulations globally necessitate the development of high-performance exhaust gas catalysts. So, automotive manufactures must balance maximizing catalyst performance while minimizing production costs. There are thousands of different recipes for catalytic converters, with each having a different effect on the various catalytic chemical reactions which impact the resultant tailpipe gas composition. In the development of catalytic converters, simulation models are often used to reduce the need for physical parts and testing, thus saving significant time and money.

Thermal management of lithium-Ion battery (LIB) has become very critical issue in recent years. One of the challenges for the design and packaging of the battery is to maintain the battery temperature within acceptable ranges and also reduce temperature gradients within the battery cells. Controlling the battery temperature is essential for the battery performance and the long-term battery life. Increased difference between battery cell temperatures can lead to non-uniform charging and non-uniform ageing of battery cells. The purpose of this paper is to investigate available technologies using heat pipes as a means of improving battery thermal management. Several studies have been conducted regarding the effect of heat pipes on battery temperature. However, in this paper we present a comprehensive study of heat pipes effects through transient analysis of a complete vehicle thermal model.

Downsized turbocharged gasoline direct injection (TGDI) engines with high specific power and torque can enable reduced fuel consumption in passenger vehicles while maintaining or even improving on the performance of larger naturally aspirated engines. However, high specific torque levels, especially at low speeds, can lead to abnormal combustion phenomena such as knock or Low-Speed Pre-Ignition (LSPI). LSPI, in particular, can limit further downsizing due to resulting and potentially damaging mega-knock events. Herein, we characterize the impacts of lubricant and fuel composition on LSPI frequency in a TGDI engine while specifically exploring the correlation between fuel composition, particulate emissions, and LSPI events. Our research shows that: (1) oil composition has a strong impact on LSPI frequency and that LSPI frequency can be reduced through a carefully focused approach to lubricant formulation.

There are a variety of test protocols associated with vehicle fuel economy and emissions testing. As a result, a number of test protocols currently exist to measure axle efficiency and spin loss. The intent of this technical paper is to describe a methodology that uses a singular axle efficiency and spin loss procedure. The data can then be used to predict the effects on vehicle FE and GHG for a specific class of vehicles via simulation. An accelerated break-in method using a comparable energy approach has been developed, and can be used to meet the break-in requirements of different vehicle emission test protocols. A “float to equilibrium” sump temperature approach has been used to produce instantaneous efficiency data, which can be used to more accurately predict vehicle FE and GHG, inclusive of Cold CO2. The “Float to Equilibrium” approach and “Fixed Sump Temperature” approach has been compared and discussed.

Advances in diesel engine and catalyst technologies have enabled light passenger vehicles in meeting the most stringent Tier 3/LEV III emission levels and durability requirements. The advancements in diesel aftertreatment catalyst technology have made catalysts more susceptible to low levels of impurities, typically referred to as poisons. Published studies over the last two decades, have shown a significant impact on the performance of catalysts, to the presence of sulfur and other inorganics in fuels and oils. The design of an aftertreatment system (ATS) typically sets limits for lubricant and fuel quality, specific to the geographical region and availability of certain level of regulated fuels. In this study, we investigate a real-world aged diesel vehicle which exhibited deterioration in tailpipe emissions, beyond levels targeted during engineering development.

The LEV IV FTP PM limit in the recently approved CARB ACC II regulations for passenger cars and light duty trucks will be 1 mg/mile starting in 2025. Gravimetric PM measurement at these levels is very challenging as the net mass of PM on the filter in full flow tunnel testing ranges between 8 to 32 micrograms depending on amount of dilution. This is approaching tunnel background levels which, in combination with filter handling, static charge removal and microbalance instability, compounds the uncertainty. One major source of the uncertainty at these low levels is the tunnel contamination resulting in high variability from test to test and cell to cell. This tunnel background is mostly HC artifact which cannot be easily controlled and can be significantly higher than the 5-μg CFR allowable correction limit in some test cells.

The Three Way Catalyst (TWC) is an effective pollutant conversion system widely used in current production vehicles to satisfy emissions regulations. A TWC’s conversion efficiency degrades over time due to chemical and/or thermal mechanisms causing the catalyst to age. This reduction in conversion efficiency must be accounted for to ensure full useful life emissions compliance. This paper presents an experimental study of the aging impact on TWC performance. Four TWCs differentiated by their age, given in terms of miles driven, were tested. It is shown that the dynamics of oxygen storage are substantially affected by aging of the TWC. A previously developed physics-based oxygen storage model [1] is subsequently used to incorporate the effect of aging on the total Oxygen Storage Capacity (OSC). Parameter identification results for the different age catalysts show that total oxygen storage capacity decreases substantially with aging and is insensitive to operating conditions.

This paper focuses on the conjugate heat transfer analysis of an I4 engine, and discusses optimization of the coolant passages in engine coolant jackets. Direct Optimization approach integrates an optimizer with the numerical solver. This method of optimization is compared with a metamodel-based optimization in which a metamodel is generated to aid in finding an optimal design. The direct optimization and metamodel approaches are compared in terms of their accuracy, and execution time.

As automotive emissions become more stringent, accurate control of three-way catalyst temperature is increasingly important for maintaining high levels of conversion efficiency as well as preventing damage to the catalyst. A real-time catalyst temperature model provides critical information to the engine control system. In order to improve emissions and ensure regulatory compliance over a wide range of speed-load conditions, it is desirable to use modelled catalyst temperature as the primary input to catalyst efficiency control strategies. This requirement creates a challenge for traditional empirical models designed for component protection at high speed-load conditions. Simulation results show that a physics aligned model can estimate temperature in all operating conditions, including: cold-start, extended idle, engine shutdown, stop-start events, deceleration fuel shut-off, as well as traditional high load and part load points.

The performance and durability of the lead acid battery is highly dependent on the internal battery temperature. The changes in internal battery temperatures are caused by several factors including internal heat generation and external heat transfer from the vehicle under-hood environment. Internal heat generation depends on the battery charging strategy and electric loading. External heat transfer effects are caused by customer duty cycle, vehicle under-hood components and under-hood ambient air. During soak conditions, the ambient temperature can have significant effect on battery temperature after a long drive for example. Therefore, the temperature rise in a lead-acid battery must be controlled to improve its performance and durability. In this paper a thermal model for lead-acid battery is developed which integrates both internal and external factors along with customer duty cycle to predict battery temperature at various driving conditions.

Every passing year automotive engineers are challenged to attain higher fuel economy and improved emission targets. One widely used approach is to use Cooled Exhaust Gas Recirculation (CEGR) to meet these objectives. Apart from reducing emissions and improving fuel economy, CEGR also plays a significant role in knock mitigation in spark ignited gasoline engines. Generally, CEGR is introduced into the intake manifold in SI gasoline engine. Even though the benefits of using CEGR are significant, they can be easily negated by the uneven CEGR flow distribution between the cylinders, which can result in combustion instability. This paper describes the application of co-simulation between one and three dimensional tools to accurately predict the distribution of CEGR to the cylinders and the effect of its distribution on engine performance.

The original Table 2 includes incorrect data under the "Primary Energy" column for "multi-strike", "continuous" and "plasma" system types.

In this paper we investigate the application of time constant to estimate the external heat transfer coefficient (h) around specific vehicle components. Using this approach, a test sample in the form of a steel plate is placed around the component of interest. A step change is applied to air temperature surrounding the sample. The response of the sample temperature can be analyzed and the heat transfer coefficient can therefore be calculated. Several test samples were installed at several locations in the vehicle under-hood and underbody. A series of vehicle tests were designed to measure the time constant around each component at various vehicle speeds. A correlation between estimated heat transfer coefficients and vehicle speed was generated. The developed correlations and the measured component ambient temperatures can be readily used as input for thermal simulation tools. This approach can be very helpful whenever CFD resources may not be available.

As European and Chinese tailpipe emission regulations for gasoline light-duty vehicles impose particulate number limits, automotive manufacturers have begun equipping some vehicles with a gasoline particulate filter (GPF). Increased understanding of how soot morphology, reactivity, and GPF loading affect GPF filtration and regeneration characteristics is necessary for advancing GPF performance. This study investigates the impacts of morphology, reactivity, and filter soot loading on GPF filtration and regeneration. Soot morphology and reactivity are varied through changes in fuel injection parameters, known to affect soot formation conditions. Changes in morphology and reactivity are confirmed through analysis using a transmission electron microscope (TEM) and a thermogravimetric analyzer (TGA) respectively.