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A laboratory study was performed to assess the effects of sulfur poisoning and desulfation temperature on the NO conversion of a LNT+(Cu/SCR) in-situ system. Four LNT+(Cu/SCR) systems were aged for 4.5 hours without sulfur at 600, 700, 750, and 800°C using A/F ratio modulations to represent 23K miles of desulfations at different temperatures. NO conversion tests were performed on the LNT alone and on the LNT+SCR system using a 60 s lean/5 s rich cycle. The catalysts were then sulfur-poisoned at 400°C and desulfated four times and re-evaluated on the 60/5 tests. This test sequence was repeated 3 more times to represent 100K miles of desulfations. After simulating 23K miles of desulfations, the Cu-based SCR catalysts improved the NO conversion of the LNT at low temperatures (e.g., 300°C), although the benefit decreased as the desulfation temperature increased from 600°C to 800°C.

Effects of fuel cell material properties on water management were numerically investigated using Volume of Fluid (VOF) method in the FLUENT. The results show that the channel surface wettability is an important design variable for both serpentine and interdigitated flow channel configurations. In a serpentine air flow channel, hydrophilic surfaces could benefit the reactant transport to reaction sites by facilitating water transport along channel edges or on channel surfaces; however, the hydrophilic surfaces would also introduce significantly pressure drop as a penalty. For interdigitated air flow channel design, it is observable that liquid water exists only in the outlet channel; it is also observable that water distribution inside GDL is uneven due to the pressure distribution caused by interdigitated structure. An in-situ water measurement method, neutron imaging technique, was used to investigate the water behavior in a PEM fuel cell.

Gasoline turbocharged direct injection (GTDI) engines, such as EcoBoost™ from Ford, are becoming established as a high value technology solution to improve passenger car and light truck fuel economy. Due to their high specific performance and excellent low-speed torque, improved fuel economy can be realized due to downsizing and downspeeding without sacrificing performance and driveability while meeting the most stringent future emissions standards with an inexpensive three-way catalyst. A logical and synergistic extension of the EcoBoost™ strategy is the use of E85 (approximately 85% ethanol and 15% gasoline) for knock mitigation. Direct injection of E85 is very effective in suppressing knock due to ethanol's high heat of vaporization - which increases the charge cooling benefit of direct injection - and inherently high octane rating. As a result, higher boost levels can be achieved while maintaining optimal combustion phasing giving high thermal efficiency.

This study extends research previously reported from our laboratory [SAE 2009-01-0285] on diesel NOx control utilizing a new generation of Lean NOx Trap (LNT) plus in-situ Selective Catalytic Reduction (SCR) catalyst systems. Key findings from this work include 1) evidence for a “non-ammonia” reduction pathway over the SCR catalyst (in addition to the conventional ammonia pathway), 2) high NOx conversions utilizing LNT formulations with substantially lower platinum group metal (PGM) loadings than utilized in earlier systems, 3) ability of the downstream SCR catalyst to maintain high overall system NOx efficiency with aged LNTs, and 4) effectiveness of both Cu- and Fe-zeolite SCR formulations to enhance overall system NOx efficiency. FTP NOx conversion efficiencies in excess of 95% were obtained on two light-duty vehicle platforms with lab-aged catalyst systems, thus showing potential of the LNT+SCR approach for achieving the lowest U.S. emissions standards

Pareto optimal map concept has been applied to the optimization of the vehicle system control (VSC) strategy for a power-split hybrid electric vehicle (HEV) system. The methodology relies on an inner-loop optimization process to define Pareto maps of the best engine and electric motor/generator operating points given wheel power demand, vehicle speed, and battery power. Selected levels of model fidelity, from simple to very detailed, can be used to generate the Pareto maps. Optimal control is achieved by applying Pontryagin's minimum principle which is based on minimization of the Hamiltonian comprised of the rate of fuel consumption and a co-state variable multiplied by the rate of change of battery SOC. The approach delivers optimal control for lowest fuel consumption over a drive cycle while accounting for all critical vehicle operating constraints, e.g. battery charge balance and power limits, and engine speed and torque limits.

The compressive behavior of lithium-iron phosphate battery cells is investigated by conducting in-plane constrained compression tests and out-of-plane compression tests of representative volume element (RVE) specimens. The results for cell RVE specimens under in-plane constrained compression tests without pre-strains and with pre-strains in the out-of-plane direction indicate that the load carrying capacity is characterized by the buckling of cell specimens. As the pre-strain increases, the nominal compressive stress-strain curve becomes higher. The nominal stress-strain curves in the out-of-plane direction were also obtained and used to determine the elastic moduli for the elastic buckling analyses of the cell components in the cell RVE specimens with different pre-strains. Based on the elastic buckling analyses for a beam with different lateral constraints due to different pre-strains in the out-of-plane direction, the number of half waves and the buckling stresses were obtained.

Electric vehicles are receiving considerable attention because they offer a more efficient and sustainable transportation alternative compared to conventional fossil-fuel powered vehicles. Since the battery pack represents the primary energy storage component in an electric vehicle powertrain, it requires accurate monitoring and control. In order to effectively estimate the battery pack critical parameters such as the battery state of charge (SOC), state of health (SOH), and remaining capacity, a high-fidelity battery model is needed as part of a robust SOC estimation strategy. As the battery degrades, model parameters significantly change, and this model needs to account for all operating conditions throughout the battery's lifespan. For effective battery management system design, it is critical that the physical model adapts to parameter changes due to aging.

This paper proposes an approach that characterizes a driver's driving behavior and style in real-time during car-following drives. It uses an online learning of the evolving Takagi-Sugeno fuzzy model combined with the Markov model. The inputs fed into the proposed algorithm are from the measured signals of on-board sensors equipped with current vehicles, including the relative distance sensors for Adaptive Cruise Control feature and the accelerometer for Electronic Stability Control feature. The approach is verified using data collected using a test vehicle from several car-following test trips. The effectiveness of the proposed approach has been shown in the paper.

Due to magnesium alloy's poor weldability, other joining techniques such as laser assisted self-piercing rivet (LSPR) are used for joining magnesium alloys. This research investigates the fatigue performance of LSPR for magnesium alloys including AZ31 and AM60. Tensile-shear and coach peel specimens for AZ31 and AM60 were fabricated and tested for understanding joint fatigue performance. A structural stress - life (S-N) method was used to develop the fatigue parameters from load-life test results. In order to validate this approach, test results from multijoint specimens were compared with the predicted fatigue results of these specimens using the structural stress method. The fatigue results predicted using the structural stress method correlate well with the test results.

Aftertreatment system design involves multiple tradeoffs between engine performance, fuel economy, regulatory emission levels, packaging, and cost. Selection of the best design solution (or “architecture”) is often based on an assumption that inherent catalyst activity is unaffected by location within the system. However, this study acknowledges that catalyst activity can be significantly impacted by location in the system as a result of varying thermal exposure, and this in turn can impact the selection of an optimum system architecture. Vehicle experiments with catalysts aged over a range of mild to moderate to severe thermal conditions that accurately reflect select locations on a vehicle were conducted on a chassis dynamometer. The vehicle test data indicated CO and NOx could be minimized with a catalyst placed in an intermediate location.

Exhaust pressures (P3) are hard parameters to measure and can be readily estimated, the cost of the sensors and the temperature in the exhaust system makes the implementation of an exhaust pressure sensor in a vehicle control system a costly endeavor. The contention with measured P3 is the accuracy required for proper engine and vehicle control can sometimes exceed the accuracy specification of market available sensors and existing models. A turbine inlet exhaust pressure observer model based on isentropic expansion and heat transfer across a turbocharger turbine was developed and investigated in this paper. The model uses 4 main components; an open loop P3 orifice flow model, a model of isentropic expansion across the turbine, a turbine and pipe heat transfer models and an integrator with the deviation in the downstream turbine outlet parameter.

Diesel NOx emissions control utilizing combined Lean NOx Trap (LNT) and so-called passive or in-situ Selective Catalytic Reduction (SCR) catalyst technologies (i.e. with reductant species generated by the LNT) has been the subject of several previous papers from our laboratory [ 1 - 2 ]. The present study focuses on hydrocarbon (HC) emissions control via the same LNT+SCR catalyst technology under FTP driving conditions. HC emissions control can be as challenging as NOx control under both current and future federal and California/Green State emission standards. However, as with NOx control, the combined LNT+SCR approach offers advantages for HC emission control over LNT-only aftertreatment. The incremental conversion obtained with the SCR catalyst is shown, both on the basis of vehicle and laboratory tests, to result primarily from HC adsorbed on the SCR catalyst during rich LNT purges that reacts during subsequent lean engine operation.

This paper proposes an approach to use ABS wheel speed sensor signals together with other vehicle state information from a brake control module to detect an unbalanced tire or tires in real-time. The proposed approach consists of two-stage algorithms that mix a qualitative method using band-pass filtering with a quantitative parameter identification using conditional least squares. This two-stage approach can improve the robustness of tire imbalance or imbalances. The proposed approach is verified through vehicle testing and the test results show the effectiveness of the approach.

Torque controls for the engine and electric motors in a Powersplit HEV are keys to the success of balancing fuel economy, driveability, and battery power control. The electric variable transmission (EVT) offers an opportunity to let the engine operate at system-optimal fuel efficient points independently of any load. Existing work shows such a benefit can be realized through a decentralized control structure that translates the driver inputs to independent engine torque and speed control. However, our study shows that the decentralized control structures have a fundamental limitation that arises from the nonminimum phase (NMP) zero in the transfer function from the driver power command to the generator torque change rate, and thus not only is it difficult to obtain smooth generator torque but also it can cause violations on battery power limits during transients. Additionally, it adversely affects the driveability due to the generator torque transients reflected at the ring gear.

In an attempt to predict the responses of side crash pressure sensors, the Corpuscular Particle Method (CPM) was adopted and enhanced in this research. Acceleration-based crash sensors have traditionally been used extensively in automotive industry to determine the air bag firing time in the event of a vehicle accident. The prediction of crash pulses obtained from the acceleration-based crash sensors by using computer simulations has been very challenging due to the high frequency and noisy responses obtained from the sensors, especially those installed in crash zones. As a result, the sensor algorithm developments for acceleration-based sensors are largely based on prototype testing. With the latest advancement in the crash sensor technology, side crash pressure sensors have emerged recently and are gradually replacing acceleration-based sensor for side impact applications.

An Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this study to predict the responses of side crash pressure sensors in an attempt to assist pressure sensor algorithm development by using computer simulations. Acceleration-based crash sensors have traditionally been used to deploy restraint devises (e.g., airbags, air curtains, and seat belts) in vehicle crashes. The crash pulses recorded by acceleration-based crash sensors usually exhibit high frequency and noisy responses depending on the vehicle's structural design. As a result, it is very challenging to predict the responses of acceleration-based crash sensors by using computer simulations, especially those installed in crush zones. Therefore, the sensor algorithm developments for acceleration-based sensors are mostly based on physical testing.

As automakers strategize approaches to sustainable vehicle technologies, alternative powertrains must be considered to reduce future fleet vehicle emissions and improve energy security. These alternative vehicles include different fuels and electrification. The ultimate for on-road CO2 reductions is a zero emission vehicle, which can be achieved by either a hydrogen fuel cell or battery electric vehicle. These vehicles would also require a renewable energy source to provide their propulsion energy in order to achieve maximum sustainability for both CO2 reduction and energy security. Renewable energy sources such as wind or solar result in heat or electricity that needs to be generated into an energy carrier such as hydrogen or stored in a battery. When examining these options based strictly on the efficiency path, previous analysis have concluded fuel cell vehicles may not be an appropriate suitability strategy in comparison to battery electric vehicles.

Internal combustion (IC) engines fueled by hydrogen are among the most efficient means of converting chemical energy to mechanical work. The exhaust has near-zero carbon-based emissions, and the engines can be operated in a manner in which pollutants are minimal. In addition, in automotive applications, hydrogen engines have the potential for efficiencies higher than fuel cells.[1] In addition, hydrogen engines are likely to have a small increase in engine costs compared to conventionally fueled engines. However, there are challenges to using hydrogen in IC engines. In particular, efficient combustion of hydrogen in engines produces nitrogen oxides (NOx) that generally cannot be treated with conventional three-way catalysts. This work presents the results of experiments which consider changes in direct injection hydrogen engine design to improve engine performance, consisting primarily of engine efficiency and NOx emissions.

With a goal to help develop pressure sensor calibration and deployment algorithms using computer simulations, an Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this research to predict the responses of side crash pressure sensors for unitized vehicles. For occupant protection, acceleration-based crash sensors have been used in the automotive industry to deploy restraint devices when vehicle crashes occur. With improvements in the crash sensor technology, pressure sensors that detect pressure changes in door cavities have been developed recently for vehicle crash safety applications. Instead of using acceleration (or deceleration) in the acceleration-based crash sensors, the pressure sensors utilize pressure change in a door structure to determine the deployment of restraint devices. The crash pulses recorded by the acceleration-based crash sensors usually exhibit high frequency and noisy responses.

The Reverse Braking Assist (RBA) feature is designed to automatically activate full braking in a backing vehicle. When this feature activates, a backing vehicle is suddenly stopped or may slide to a stop. During this process, an understanding of the driver's behavior may be useful in the design of an appropriate human-machine-interface (HMI) for the RBA. Several experimental studies were done to examine driver behavior in response to an unexpected and automatic braking event while backing [1]. Two of these studies are reported in this paper. A 7-passenger Crossover Utility Vehicle was fitted with a rear-view camera, a center-stack mounted LCD screen, and ancillary recording devices. In the first study, an object was suddenly placed in the path of a backing vehicle. The backing vehicle came to a sudden and complete stop. The visual image of the backing path on the LCD prominently showed that an obstacle was present in the backing path of the vehicle.