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Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

This paper presents a combined numerical and experimental investigation of the characteristics of spark discharge in a spark-ignition engine. The main objective of this work is to gain insights into the spark discharge process and early flame kernel development. Experiments were conducted in an inert medium within an optically accessible constant-volume combustion vessel. The cross-flow motion in the vessel was generated using a previously developed shrouded fan. Numerical modeling was based on an existing discharge model in the literature developed by Kim and Anderson. However, this model is applicable to a limited range of gas pressures and flow fields. Therefore, the original model was evaluated and improved to predict the behavior of spark discharge at pressurized conditions up to 45 bar and high-speed cross-flows up to 32 m/s. To accomplish this goal, a parametric study on the spark channel resistance was conducted.

In real-world automotive control, there are many constraints to be considered. In order to explicitly treat the constraints, we introduce a model-prediction-based algorithm called a reference governor (RG). The RG generates modified references so that predicted future variables in a closed-loop system satisfy their constraints. One merit of introducing the RG is that effort required in control development and calibration would be reduced. In the preceding research work by Nakada et al., only a single reference case was considered. However, it is difficult to extend the previous work to more complicated systems with multiple references such as the air path control of a diesel engine due to interference between the boosting and exhaust gas recirculation (EGR) systems. Moreover, in the air path control, multiple constraints need to be considered to ensure hardware limits. Hence, it is quite beneficial to cultivate RG methodologies to deal with multiple references and constraints.

Conventional CFD-based shape optimization technology that uses parametric shape modification and optimal solutions searching algorithms has the two problems: (1) outcome of optimized shapes depend on the selection of design parameters made by the designer, and (2) high computational costs. To resolve those problems, two innovative inverse analysis technologies based on the Adjoint Method were developed in previous study: surface geometry deformation sensitivity analysis to identify the locations to be modified, and topology optimization to generate an optimal shape for maximizing the cost function in the constrained design space. However, these technologies are only applicable to steady flows. Since most flows in a vehicle (such as engine in-cylinder flow) are transient, a practical technology for surface geometry sensitivity analysis has been developed based on the Transient Adjoint Method.

Computational fluid dynamics of gas-fueled large-bore spark ignition engines with pre-chamber ignition can speed up the design process of these engines provided that 1) the reliability of the results is not affected by poor meshing and 2) the time cost of the meshing process does not negatively compensate for the advantages of running a computer simulation. In this work a flame propagation model that runs with arbitrary hybrid meshes was developed and coupled with the KIVA4-MHI CFD solver, in order to address these aims. The solver follows the G-Equation level-set method for turbulent flame propagation by Tan and Reitz, and employs improved numerics to handle meshes featuring different cell types such as hexahedra, tetrahedra, square pyramids and triangular prisms. Detailed reaction kinetics from the SpeedCHEM solver are used to compute the non-equilibrium composition evolution downstream and upstream of the flame surface, where chemical equilibrium is instead assumed.

Many vehicles are currently equipped with active safety systems that can detect vulnerable road users like pedestrians and bicyclists, to mitigate associated conflicts with vehicles. With the advancements in technologies and algorithms, detailed motions of these targets, especially the limb motions, are being considered for improving the efficiency and reliability of object detection. Thus, it becomes important to understand these limb motions to support the design and evaluation of many vehicular safety systems. However in current literature, there is no agreement being reached on whether or not and how often these limbs move, especially at the most critical moments for potential crashes. In this study, a total of 832 pedestrian walking or cyclist biking cases were randomly selected from one large-scale naturalistic driving database containing 480,000 video segments with a total size of 94TB, and then the 832 video clips were analyzed focusing on their limb motions.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

Quietness is one of the most important characteristics for Hybrid Electric Vehicle quality. Reduction of the rattle noise caused by the torque fluctuation of an internal combustion engine can contribute to get a customer satisfaction. Toyota Hybrid System(THS) also has same requirement. Especially, the rattle noise during idling may happen discontinuously despite of periodical engine combustion excitation. It is necessary to study the mechanism and reduce the rattle noise. At lower engine torque range, decreasing the torsional damper’s stiffness can improve this condition as the manual transaxle done. However, the rattle noise can occur easily in conditions of relatively large torque spike inputs to the torsional system, such as the engine start/stop function of THS using the motor/generator in the transaxle.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

In this paper, a model-based linear estimator and a non-linear control law for an Fe-zeolite urea-selective catalytic reduction (SCR) catalyst for heavy duty diesel engine applications is presented. The novel aspect of this work is that the relevant species, NO, NO2 and NH3 are estimated and controlled independently. The ability to target NH3 slip is important not only to minimize urea consumption, but also to reduce this unregulated emission. Being able to discriminate between NO and NO2 is important for two reasons. First, recent Fe-zeolite catalyst studies suggest that NOx reduction is highly favored by the NO 2 based reactions. Second, NO2 is more toxic than NO to both the environment and human health. The estimator and control law are based on a 4-state model of the urea-SCR plant. A linearized version of the model is used for state estimation while the full nonlinear model is used for control design.

The development of an alternative oxygen reduction electrocatalyst to platinum based electrocatalysts is critical for practical use of the polymer electrolyte membrane fuel cell (PEMFC). Transition metal sulfide chalcogenides have recently been reported as a possible candidate for Pt replacement. Our work focused on chalcogenides composed of ruthenium, molybdenum, and sulfur (RuMoS). We elucidate the factors affecting electrocatalytic activity of carbon supported RuXMoY SZ catalyst. This was demonstrated through a correlation of oxygen reduction reaction (ORR) activity of the catalysts with structural changes resulting from designed changes in sulfur composition in the catalysts.

To meet future stringent emission regulations such as Euro6, the design and control of diesel exhaust after-treatment systems will become more complex in order to ensure their optimum operation over time. Moreover, because of the strong pressure for CO₂ emissions reduction, the average exhaust temperature is expected to decrease, posing significant challenges on exhaust after-treatment. Diesel Oxidation Catalysts (DOCs) are already widely used to reduce CO and hydrocarbons (HC) from diesel engine emissions. In addition, DOC is also used to control the NO₂/NOx ratio and to generate the exothermic reactions necessary for the thermal regeneration of Diesel Particulate Filter (DPF) and NOx Storage and Reduction catalysts (NSR). The expected temperature decrease of diesel exhaust will adversely affect the CO and unburned hydrocarbons (UHC) conversion efficiency of the catalysts. Therefore, the development cost for the design and control of new DOCs is increasing.

This paper considers an application of reference governor (RG) to automotive diesel aftertreatment temperature control. Recently, regulations on vehicle emissions have become more stringent, and engine hardware and software are expected to be more complicated. It is getting more difficult to guarantee constraints in control systems as well as good control performance. Among model-based control methods that can directly treat constraints, this paper focuses on the RG, which has recently attracted a lot of attention as one method of model prediction-based control. In the RG, references in tracking control are modified based on future prediction so that the predicted outputs in a closed-loop system satisfy the constraints. This paper proposes an online RG algorithm, taking account of the real-time implementation on engine embedded controllers.

The details of Di-Air, a new NOx reduction system using continuous short pulse injections of hydrocarbons (HC) in front of a NOx storage and reduction (NSR) catalyst, have already been reported. This paper describes further studies into the deNOx mechanism, mainly from the standpoint of the contribution of HC and intermediates. In the process of a preliminary survey regarding HC oxidation behavior at the moment of injection, it was found that HC have unique advantages as a reductant. The addition of HC lead to the reduction or metallization of platinum group metals (PGM) while keeping the overall gas atmosphere in a lean state due to adsorbed HC. This causes local O₂ inhibition and generates reductive intermediate species such as R-NCO. Therefore, the specific benefits of HC were analyzed from the viewpoints of 1) the impact on the PGM state, 2) the characterization of intermediate species, and 3) Di-Air performance compared to other reductants.

For a luxury sedan, quietness is a major selling point, and a hybrid luxury sedan is expected to be especially quiet. Therefore, in the development of the hybrid luxury sedan, every possible effort is needed to reduce the hybrid system noise in order to ensure a level of quietness far superior to that of an ordinary gasoline-powered vehicle. In addition, the noise and vibration phenomena that are particular to vehicles with longitudinal power trains require special reduction technologies. This paper first describes the superior quietness of hybrid luxury vehicles in comparison with ordinary gasoline-powered vehicles. This paper then addresses the development issues of vibration during engine starting, engine booming noise, and motor noise, explaining the mechanisms by which they are generated and the technologies employed to reduce them.

The Diesel Particulate and NOx Reduction System (DPNR) is an effective technology as a diesel after-treatment system, which can reduce particulate matter (PM) and nitrogen oxides (NOx) simultaneously. However, it requires desulfurization control since the DPNR catalyst is poisoned by sulfur components in the exhaust gas from the fuel and lubricant. Desulfurization control causes some degree of fuel penalty and thermal deterioration of the DPNR catalyst because it requires control of rich air fuel ratio and high temperature simultaneously. In this paper, we investigated a new system with a sulfur trap catalyst which can trap sulfur components included in the exhaust gas as sulfates (Sulfur trap DPNR). In this system, desulfurization control is not performed because the sulfur poisoning of the DPNR catalyst is drastically suppressed by the sulfur trap catalyst. In the present DPNR, periodic desulfurization control is required.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

An obstacle recognition algorithm for the Pre-Crash Safety system has been newly developed with a stereo vision system and a millimeter wave radar with additional functions. This algorithm uses the merits of both the millimeter wave radar and the stereo vision system, and has two main features. One feature utilizes the merits of the stereo vision system detection with the detection results from the millimeter wave radar allowing for a more detailed horizontal position and width of the obstacle. This enables the equipment to operate at an earlier stage according to how well the relationship between the vehicle and the obstacle is understood. Another feature fuses detection from the millimeter wave radar and the stereo vision system. This system has succeeded in enhancing the detection performance of pedestrians who have been more difficult to detect than reflective objects such as cars.

A new HC adsorber system was developed to achieve California SULEV emission standards for a V8 5.0-liter engine application (i.e. LS600hL). A HC adsorber system was first released on 2001 PZEV Prius (1.5-liter engine) in U.S.A. For the 5.0L application the substrate volume of both catalyst and adsorber had to be enlarged for a large volume displacement. Prius-type adsorber system could not be adopted for LS600hL because of the problems of installation. So, a new constructional adsorber was proposed. However the increase of gas flow into the adsorber substrate was a problem for desorption. The gas flow into the adsorber substrate was found to be controllable by the specification adjustment of the “throat” and “retainer” parts of adsorber system. Thus the rapid desorption was successfully reduced, and the HC adsorber system achieved a 50% reduction of HC emission.

In order to enhance the catalytic performance of the NOx Storage-Reduction Catalyst (NSR Catalyst), the sulfur tolerance of the NSR catalyst was improved by developing new support and NOx storage materials. The support material was developed by nano-particle mixing of ZrO2-TiO2 and Al2O3 in order to increase the Al2O3-TiO2 interface and to prevent the ZrO2-TiO2 phase from sintering. A Ba-Ti oxide composite material was also developed as a new NOx storage material containing highly dispersed Ba. It was confirmed that the sulfur tolerance and activity of the developed NSR catalyst are superior to that of the conventional one.