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A virtual diesel aftertreatment exhaust line is presented comprising DOC, DPF, SCR models and a unique Ammonia Oxidation catalyst model. All models are one dimensional models based on first principles. These models offer an attractive compromise between speed, accuracy and complexity for a variety of model applications: off-line simulation, control strategy development, Hardware in the Loop applications and model-based calibration. The implemented models are fast and suitable for real-time applications. Use of these virtual exhaust line models in a product development process has the potential of saving time and resources. The aftertreatment models are fitted based on specifically designed engine dynamometer experiments, which can be performed in a limited time frame. The effective test time required on a validated test setup is estimated on the order of 12 days in total. Specifically developed software tools facilitate the model fit process.

Cylinder pressure-based combustion control is widely introduced for passenger cars. Benefits include enhanced emission robustness to fuel quality variation, reduced fuel consumption due to more accurate (multi-pulse) fuel injection, and minimized after treatment size. In addition, it enables the introduction of advanced, high-efficient combustion concepts. The application in truck engines is foreseen, but challenges need to be overcome related to durability, increased system costs, and impact on the cylinder head. In this paper, a new single cylinder pressure sensor concept for heavy-duty Diesel engines is presented. Compared to previous studies, this work focuses on heavy-duty Diesel powertrains, which are characterized by a relatively flexible crank shaft in contrast to the existing passenger car applications.

Vibration and sound radiation characteristics of bead-stiffened panels are investigated. Rectangular panels with different bead configurations are considered. The attention is focused on various design parameters, such as orientation, depth, and periodicity, and their effects on equivalent bending stiffness, modal density, radiation efficiency and sound transmission. A combined FEA-SEA approach is used to determine the response characteristics of panels across a broad frequency range. The details of the beads are represented in fine-meshed FEA models. Based on predicted surface velocities, Rayleigh integral is evaluated numerically to calculate the sound pressure, sound power and then the radiation efficiency of beaded panels. Analytical results are confirmed by comparing them with experimental measurements. In the experiments, the modal densities of the panels are inferred from averaged mechanical conductance.

Regulations implemented by safety commissions throughout the world have resulted in extensive physical testing to protect the occupants during frontal impact events. Significant prototype and test costs aimed at optimizing structure and restraint systems are associated with meeting these regulations. To help reduce development costs, Computer Aided Engineering (CAE) is often applied. LS-DYNA [1] coupled with MADYMO [2] is widely used in crash and occupant safety simulation. An analysis technique which utilized a single model to design and optimize interiors (instrument panel, seats, visor, steering wheel, steering column) and restraints (airbag, seatbelts, retractor, pre-tensioner) was developed. The single model concept captures the global structural kinematics through minimal vehicle representation. Global vehicle modes such as pitch and roll can be represented by applying prescribed motion boundary conditions extracted from full vehicle models.

A predictive automatic gear shift system is currently under development. The system optimizes the gear shift process, taking the conditions of the road ahead into account, such that the fuel consumption is minimized. An essential part of the system is a module that predicts the vehicle speed dynamics: This calculates a speed trajectory, i.e. the most probable vehicle speed the driver will desire for the upcoming section of the route. In the paper the theoretical background for predicting the vehicle speed, and simulation results of the predictive shift algorithm are presented.

This paper presents a new approach to improve occupant response in a front impact event. Instead of designing a vehicle structure for maximum structural efficiency and safety and then engineer a restraint system for the vehicle, this paper proposes to use a systems approach. In this approach, the vehicle structural response during impact (i.e., pulse) and the restraint system are considered together in the optimization process. In this paper, the 35 mph front impact into a rigid barrier with belted occupants, which is the NHTSA NCAP test, will be used to demonstrate the proposed new approach.

In this paper, cradle design functional objectives are briefly reviewed and a durability development process is proposed focusing on the cradle loads, stress, strain, and fatigue life analysis. Based upon the proposed design process, sample isolated and non-isolated cradle finite element (FE) models for a uni-body sport utility vehicle (SUV) under different design phases are solved and correlated with laboratory bench and proving ground tests. The correlation results show that the applied cradle models can be used to accurately predict the critical stress spots and fatigue life under various loading conditions.

The Reactivity Controlled Compression Ignition concept for dual-fuel engines has multiple challenges of which some can be overcome using Variable Valve Actuation approaches. For various fuel combinations, the engine research community has shown that running dual-fuel engines in RCCI mode, improves thermal efficiency and results in ultra-low engine-out nitrous oxides and soot. However, stable RCCI combustion is limited to a certain load range, depending on available hardware. At low loads, the combustion efficiency can drop significantly, whereas at high loads, the maximum in-cylinder pressure can easily exceed the engine design limit. In this paper, three VVA measures to increase load range, improve combustion efficiency, and perform thermal management are presented. Simulation results are used to demonstrate the potential of these VVA measures for a heavy-duty engine running on natural gas and diesel.

Complying to both the increasingly stringent pollutant emissions as well as (future) GHG emission legislation - with increased focus on in-use real-world emissions - puts a great challenge to the engine/aftertreatment control development process. Control system complexity, calibration and validation effort has increased dramatically over the past decade. A trend that is likely to continue considering the next steps in emission and GHG emission legislation. Control-oriented engine models are valuable tools for efficient development of engine monitoring and control systems. Furthermore, these (predictive) engine models are more and more used as part of control algorithms to ensure legislation compliant and optimized performance over the system lifetime. For these engine models, it is essential that simulation and prediction of system variables during non-nominal engine operation, such as non-standard ambient conditions, is well captured.

It is becoming increasingly difficult to obtain good repeatability from running lab vehicle correlation testing, since vehicle variability is so significant at the Low ULEV and SULEV emissions levels. These new emission standards are becoming so stringent that it makes it very difficult to distinguish whether a problem is a result of vehicle variability, test cell sampling or the analytical system. A vehicle exhaust emission simulator (VEES) developed by Horiba, can simulate emissions from low emitting gasoline vehicles by producing tailpipe flow rates containing emissions constituents ( HC, CH4, CO, NOx, CO2 ) injected at the tailpipe flow stream via mass flow controllers.

The emerging Variable Valve Timing (VVT) technology complicates the estimation of air flow rate because both intake and exhaust valve timings significantly affect engine's gas exchange and air flow rate. In this paper, we propose to use Artificial Neural Networks (ANN) to model the air flow rate through a 2.4 liter VVT engine with independent intake and exhaust camshaft phasers. The procedure for selecting the network architecture and size is combined with the appropriate training methodology to maximize accuracy and prevent overfitting. After completing the ANN training based on a large set of dynamometer test data, the multi-layer feedforward network demonstrates the ability to represent air flow rate accurately over a wide range of operating conditions. The ANN model is implemented in a vehicle with the same 2.4 L engine using a Rapid Prototype Controller.

A traction test machine, developed for evaluation of traction-CVT fluids for the automotive consortium, USCAR, provides precision traction measurements to stresses up to 4 GPa. The high stress machine, WAMhs, provides an elliptical contact between AISI 52100 steel roller and disc specimens. Machine stiffness and positioning technology offer precision control of linear slip, sideslip and spin. A USCAR traction test methodology includes entrainment velocities from 2 to 10 m/sec and temperatures from -20°C to 140°C. The purpose of the USCAR machine and test methodology is to encourage traction fluid development and to establish a common testing approach for fluid qualification. The machine utilizes custom software, which provides flexibility to conduct comprehensive traction fluid evaluations.

Accurate motion prediction can be used to evaluate vibrations at seat track and steering wheel. This paper presents the prediction and correlation of truck frame motion from wheel force transducer (WFT) measurements. It is assumed that the method can be used to predict vibrations at seat track and steering wheel for unibody vehicles. Two durability events were used for calculation. WFT measurements were used as inputs applied on frame from suspension. Frame loads were then used as inputs to calculate frame motions using a FEA approach. The predicted frame motions are represented by four exhaust hangers and they are compared with measured motions of the same locations. The correlations include displacement, velocity, and acceleration. It is shown that good correlations are obtained in velocity and displacement. Acceleration shows bigger differences than velocity and displacement.

This paper introduces a reliable method to calculate body mount loads from wheel-force-transducer (WFT) measurements on framed vehicles. The method would significantly reduce time and cost in vehicle development process. The prediction method includes two parts: Hybrid Load Analysis (HLA) that has been used by DaimlerChrysler Corporation and Body Mount Load Analysis (BMLA) that is introduced by this paper for the first time. The method is validated on a body-on-frame SUV and a pickup truck through one proving ground events. The example shown in this paper is for a SUV and one of the most severe events. In HLA, the loads at suspension-to-frame attachments are calculated from spindle loads measured by WFT. In BMLA, body mount loads were calculated using outputs of HLA with detailed finite-element-modeled frame and body. The loads are compared with measured body mount loads. The comparisons are conducted in range, standard deviation (S.D.), and fatigue pseudo-damage.

Transmission mounts are usually tested as an assembly and typically only translational stiffnesses are provided. The torsional stiffness of the assembly is traditionally estimated based on experience in load simulation and analysis. This paper presents a procedure to estimate the torsional stiffness of the transmission mount assembly by using the test data. The effects of the torsional stiffness on the simulation results are also discussed.

Increasing efforts to minimize global warming has led to regulation of greenhouse gas (GHG) emissions of automotive applications. The US is frontrunner regarding implementation of GHG related legislation with the introduction of GHG phase 1 and phase 2, ultimately targeting a 40% fuel consumption reduction in 2027 compared to 2010 on vehicle level. More specific, engines are required to reduce CO2 emissions by 6% compared to GHG phase 1 levels. Next to the GHG emission legislation, more stringent legislation is anticipated in the US to further reduce NOx emissions: a further 90% reduction is targeted as soon as 2024 compared to 2010 standard. Meeting these anticipated ultra-low NOx standards within the GHG phase 2 constraints on CO2 poses a great challenge. This paper presents an overview of the main challenges and key aspects regarding meeting ultra-low NOx requirements within the constraints on CO2 and N2O set by GHG phase 2 regulations.

Self-learning energy management is a promising concept, which optimizes real-world system performance by automated, on-line adaptation of control settings. In this work, the potential of self-learning capabilities related to optimization is studied for energy management in Plug-in Hybrid Electric Vehicles (PHEV). These vehicles are of great interest for the transport sector, since they combine high fuel efficiency with last mile full-electric driving. We focus on a specific use case: PHEV operation through future Zero Emission (ZE) zones of cities. As a first step towards self-learning control, we introduce a novel, adaptive supervisory controller that combines modular energy and emission management (MEEM) and deals with varying constraints and system uncertainty. This optimal control strategy is based on Pontryagin’s Minimum Principle and maximizes overall energy efficiency.

The operation of a dual fuel combustion engine using combustion mode-switching offers the benefit of higher thermal efficiency compared to single-mode operation. For various fuel combinations, the engine research community has shown that running dual fuel engines in Reactivity Controlled Compression Ignition (RCCI) mode, is a feasible way to further improve thermal efficiency compared to Conventional Dual Fuel (CDF) operation of the same engine. In RCCI combustion, also ultra-low engine-out NOx and soot emissions have been reported. Depending on available hardware, however, stable RCCI combustion is limited to a certain load range and operating conditions. Therefore, mode-switching is a promising way to implement RCCI in practice on short term. In this paper, a model-based development approach for a dual fuel mode-switching controller is presented. Simulation results demonstrate the potential of this controller for a heavy-duty engine running on natural gas and diesel.

For natural gas (NG)-diesel RCCI, a multi-zonal, detailed chemistry modeling approach is presented. This dual fuel combustion process requires further understanding of the ignition and combustion processes to maximize thermal efficiency and minimize (partially) unburned fuel emissions. The introduction of two fuels with different physical and chemical properties makes the combustion process complicated and challenging to model. In this study, a multi-zone approach is applied to NG-diesel RCCI combustion in a heavy-duty engine. Auto-ignition chemistry is believed to be the key process in RCCI. Starting from a multi-zone model that can describe auto-ignition dominated processes, such as HCCI and PCCI, this model is adapted by including reaction mechanisms for natural gas and NOx and by improving the in-cylinder pressure prediction. The model is validated using NG-diesel RCCI measurements that are performed on a 6 cylinder heavy-duty engine.

It is often necessary to measure three-axis displacement of a deforming or moving part in static or dynamic impact tests. A point moving in the three-dimensional space can be monitored and measured using three string-pots or other distance measuring devices with a methodology developed here. A numerical algorithm along with required equations are shown and discussed. The algorithm was applied as an example to static seat pull test and compared to results from film analysis. The application with string pots is useful especially when the point of concern gets hidden or blocked by other parts disabling the photogrammetry technology.