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This paper presents an extension of our earlier work on Cummins Vehicle Mission Simulation (VMS) software. Previously, we presented VMS as a Windows based analysis tool to simulate vehicle missions quickly and to gauge, communicate, and improve the value proposition of Cummins engines to customers. Presenter Nagesh Belludi, Cummins Inc.

With increasing energy prices and concerns about the environmental impact of greenhouse gas (GHG) emissions, a growing number of national governments are putting emphasis on improving the energy efficiency of the equipment employed throughout their transportation systems. Within the U.S. transportation sector, energy use in commercial vehicles has been increasing at a faster rate than that of automobiles. A 23% increase in fuel consumption for the U.S. heavy duty truck segment is expected from 2009 to 2020. The heavy duty vehicle oil consumption is projected to grow while light duty vehicle (LDV) fuel consumption will eventually experience a decrease. By 2050, the oil consumption rate by LDVs is anticipated to decrease below 2009 levels due to CAFE standards and biofuel use. In contrast, the heavy duty oil consumption rate is anticipated to double. The increasing trend in oil consumption for heavy trucks is linked to the vitality, security, and growth of the U.S. and global economies.

The world-wide commercial vehicle industry is faced with numerous challenges to reduce oil consumption and greenhouse gases, meet stringent emissions regulations, provide customer value, and improve safety. This work focuses on the new U.S. regulation of greenhouse gas (GHG) emissions from commercial vehicles and diesel engines and the most likely technologies to meet future anticipated standards while improving transportation freight efficiency. In the U.S., EPA and NHTSA have issued a joint proposed GHG rule that sets limits for CO2 and other GHGs from pick-up trucks and vans, vocational vehicles, semi-tractors, and heavy duty diesel engines. This paper discusses and compares different technologies to meet GHG regulations for diesel engines based on considerations of cost, complexity, real-world fidelity, and environmental benefit.

The performance of an organic Rankine cycle (ORC) that recovers heat from the exhaust of a heavy-duty diesel engine was simulated. The work was an extension of a prior study that simulated the performance of an experimental ORC system developed and tested at Oak Ridge National laboratory (ORNL). The experimental data were used to set model parameters and validate the results of that simulation. For the current study the model was adapted to consider a 15 liter turbocharged engine versus the original 1.9 liter light-duty automotive turbodiesel studied by ORNL. Exhaust flow rate and temperature data for the heavy-duty engine were obtained from Southwest Research Institute (SwRI) for a range of steady-state engine speeds and loads without EGR. Because of the considerably higher exhaust gas flow rates of the heavy-duty engine, relative to the engine tested by ORNL, a different heat exchanger type was considered in order to keep exhaust pressure drop within practical bounds.

Cooled EGR continues to be a key technology to meet emission regulations, with EGR coolers performing a critical role in the EGR system. Designing EGR coolers that reliably manage thermal loads is a challenge with thermal fatigue being a top concern. The ability to estimate EGR cooler thermal fatigue life early in the product design and validation cycle allows for robust designs that meet engine component reliability requirements and customer expectations. This paper describes a process to create an EGR cooler thermal fatigue life model. Components which make up the EGR cooler have differing thermal responses, consequently conjugate transient CFD must be used to accurately model metal temperatures during heating and cooling cycles. Those metal temperatures are then imported into FEA software for structural analysis. Results from both the CFD and FEA are then used in a simplified numerical model to estimate the virtual strain of the EGR cooler.

A 1-dimensional analytic solution has been developed to evaluate the pressure drop and filtration performance of ceramic wall-flow partial diesel particulate filters (PFs). An axially resolved mathematical model for the static pressure and velocity profiles prevailing inside wall-flow filters, with such unique plugging configurations, is being proposed for the first time. So far, the PF models that have been developed are either iterative/numerical in nature [1], or based on commercial CFD packages [7]. In comparison, an analytic solution approach is a transparent and computationally inexpensive tool that is capable of accurately predicting trends as well as, offering explanations to fundamental performance behavior. The simple mathematical expressions that have been obtained facilitate rational decision-making when designing partial filters, and could also reduce the complexity of OBD logic necessary to control onboard filter performance.

Piston cooling nozzles/jets play several crucial roles in the power cylinder of an internal combustion engine. Primarily, they help with the thermal management of the piston and provide lubrication to the cylinder liner and the piston’s wrist pin. In order to evaluate the oil jet characteristics from various piston cooling nozzle (PCN) designs, a quantitative and objective process was developed. The PCN characterization began with a computational fluid dynamics (CFD) turbulent model to analyze the mean oil velocity and flow distribution at the nozzle exit/tip. Subsequently, the PCN was tested on a rig for a given oil temperature and pressure. A high-speed camera captured images at 2500 frames per second to observe the evolution of the oil stream as a function of distance from the nozzle exit. An algorithm comprised of standard digital image processing techniques was created to calculate the oil jet width and density.

Wheel loader subsystems are multi-domain in nature, including controls, mechanisms, hydraulics, and thermal. This paper describes the process of developing a multi-domain simulation of a wheel loader. Working hydraulics, kinematics of the working tool, driveline, engine, and cooling system are modeled in LMS Imagine.Lab Amesim. Contacts between boom/bucket and bucket/ground are defined to constrain the movement of the bucket and boom. The wheel loader has four heat exchangers: charge air cooler, radiator, transmission oil cooler, and hydraulic oil cooler. Heat rejection from engine, energy losses from driveline, and hydraulic subsystem are inputs to the heat exchangers. 3D CFD modeling was done to calibrate airflows through heat exchangers in LMS Amesim. CFD modeling was done in ANSYS FLUENT® using a standard k - ε model with detailed fan and underhood geometry.

Commercial vehicle manufacturers face unique challenges for the development of vehicle electronics systems. For one, customers typically have unique requirements coupled with an expectation of high reliability. Vehicle electronics is often the enabler for customized features. Ensuring that the vehicle will perform as demanded and promised adds a degree of burden on the vehicle manufacturers. Furthermore, the verification and testing of a large number of unique electronic system configurations is very expensive and time-consuming. This paper will explore how Model-Based Design can be used to meet these challenges and provide a high degree of confidence for both the manufacturer and the customer that requirements have been met. It will discuss factors to consider to support configurability, approaches for defining a system architecture that facilitates reuse, and capabilities for modeling state-based systems.

This study analyzed the impact of transient and multi-mode engine conditions on emissions of dioxins and furans from a variety of diesel aftertreatment configurations. Exhaust aftertreatment systems included combinations of diesel oxidation catalyst, diesel particulate filter, and either Cu/zeolite or Fe/zeolite selective catalytic reduction catalyst. EPA method TO-9A was modified for proportional exhaust gas sampling, whereas EPA method 0023A was modified for raw exhaust gas sampling. Dioxin and furan emissions were first measured with modified method TO-9A during Federal Test Procedure transient cycles, but no toxic dioxins or furans were detected. Measurements were then taken with modified method 0023A during Ramped Mode Cycles-Supplemental Emissions Test experiments. Because more rigorous pre-cleaning and sample extraction procedures were used with this method and lower detection limits were achieved by the analytical laboratory, some dioxin and furan congeners were detected.

Real-world operation of diesel oxidation catalysts (DOCs), used in a variety of aftertreatment systems, subjects these catalysts to a large number of permanent and temporary deactivation mechanisms. These include thermal damage, induced by generating exotherm on the catalyst; exposure to various inorganic species contained in engine fluids; and the effects of soot and hydrocarbons, which can mask the catalyst in certain operating modes. While some of these deactivation mechanisms can be accurately simulated in the lab, others are specific to particular engine operation regimes. In this work, a set of DOCs, removed from prolonged service in the field, has been subjected to a detailed laboratory study. Samples obtained from various locations in these catalysts were used to characterize the extent and distribution of deactivation.

In August of 2011, the US Environmental Protection Agency issued new Green House Gas (GHG) emissions regulations for heavy duty vehicles. These regulations included new procedures for the evaluation of hybrid powertrains and vehicles. One of the hybrid options allows for the evaluation of an engine plus a hybrid transmission (a powertrain). For this type of testing, EPA has proposed simulating a vehicle following the hybrid vehicle test procedures, including the use of the vehicle cycles and the A to B comparison testing - as required for the full vehicle evaluation option. This paper proposes an alternative approach by defining a powertrain cycle. The powertrain cycle is based on the heavy duty engine emissions cycle - the transient FTP cycle. Simulation and test results are presented showing similar performance over the engine and vehicle cycles. This approach offers several advantages as compared to the procedure described in EPA's GHG rule.

The need to meet new regulatory requirements as well as customer expectations in terms of machine productivity, safety, maintenance and uptime, is driving a significant transformation from conventional hydraulic and mechanical systems to electro-hydraulic systems in the earth-moving and agricultural equipment industry. The ability to model and simulate such systems plays a key role in this transformation by allowing manufacturers to test whether the system meets requirements using virtual prototypes rather than physical prototypes. Modeling the electrical, electronic, mechanical, and hydraulic domains in the same modeling environment can significantly improve the product development process of such machines. This paper illustrates those benefits using the example of an electro-hydraulic implement system.

This paper describes the development vehicle cycles based on heavy duty engine test cycles for emissions certification. In the commercial vehicle and industrial equipment markets, emissions are evaluated using engine test cycles. For the on-highway market in the United States, these cycles include the transient heavy duty engine FTP test, and the steady state heavy duty engine SET test. Evaluation of engine only emissions is a practical approach given the diversity of applications, small volumes, and lack of vertical integration in the commercial vehicle market. However certain vehicle and powertrain characteristics can contribute significantly to fuel consumption and emissions. A number of approaches have been proposed to evaluate vehicle performance, and all of these vehicle evaluation methodologies require the selection of a vehicle cycle.

Using Model-Based Design, engineers model complex systems and simulate them on their desktop environment for analysis and design purposes. Model-Based Design supports a wide variety of C/C++ code generation applications that include stand-alone simulation, rapid control prototyping, hardware-in-the-loop testing, and production or embedded code deployment. Many of these code generation scenarios impose different requirements on the generated code. Stand-alone simulations usually need to run fast, for parameter sweep or Monte Carlo studies, but do not need to execute in true hard real-time. Hardware-in-the-loop tests by definition use engine control unit (ECU) component hardware that requires a hard real-time execution environment to protect the physical devices. Code generated for production ECUs must satisfy hard real-time, efficiency, legacy code, and other requirements involving verification and validation efforts.

The increased adoption of electronic controls in off-highway machines increases the complexity of typical machine systems and stresses the traditional process used to develop these machines. To address this issue design engineers are turning from the traditional design methods to Model-Based Design. By using models in the early design stages, engineers can create executable specifications that enable them to immediately validate and verify specifications against the requirements. These models also allow the machine designer to evaluate the complex interactions between mechanics, hydraulics, electronics and other physical phenomena and thereby detect design errors earlier when the cost to fix them is less. This paper presents a model-based approach for developing off-highway equipment machine systems. A dynamic model of the machine and the electro-hydraulic implement and propulsion system is developed and used to verify the overall machine behavior.

The alternator provides power to vehicle electrical loads with the battery, and its maximum current depends on various factors such as electrical load, engine speed, thermal condition, and other variables. Above all, thermal effects make alternator simulations more complicated. For example statically similar conditions may show different results according to the temperature variation for each alternator operation. This paper proposes a two-stage statistically-based model structure which separates dynamic thermal effects from steady state performance. The method was validated by experiments and shows good predictive performance, suitable for use in test reduction.

In previous publications, Singh et al. [1, 2] have shown that direct integration of CFD with a detailed chemistry auto-ignition model (KIVA-CHEMKIN) performs reasonably well for predicting combustion, emissions, and flame structure for stratified diesel engine operation. In this publication, it is shown that the same model fails to predict combustion for partially premixed dual-fuel engines. In general, models that account for chemistry alone, greatly under-predict cylinder pressure. This is shown to be due to the inability of such models to simulate a propagating flame, which is the major source of heat release in partially premixed dual-fuel engines, under certain operating conditions. To extend the range of the existing model, a level-set-based, hybrid, auto-ignition/flame-propagation (KIVA-CHEMKIN-G) model is proposed, validated and applied for both stratified diesel engine and partially premixed dual-fuel engine operation.

Electrical system capacity determination for conventional vehicles can be expensive due to repetitive empirical vehicle-level testing. Electrical system modeling and simulation have been proposed to reduce the amount of physical testing necessary for component selection [1, 2]. To add value to electrical system component selection, the electrical system simulation models must regard the electrical system as a whole [1]. Electrical system simulations are heavily dependent on the battery sub-model, which is the most complex component to simulate. Methods for modeling the battery are typically unclear, difficult, time consuming, and expensive. A simple, fast, and effective equivalent circuit model structure for lead-acid batteries was implemented to facilitate the battery model part of the system model. The equivalent circuit model has been described in detail. Additionally, tools and processes for estimating the battery parameters from laboratory data were implemented.

Model-Based Design has become a standard in the automotive industry. In addition to the well-documented advantages that come from modeling control algorithms, [1,2,3,4] modeling plants can lead to more robust designs. Plant modeling enables engineers to test a controller with multiple plant parameters, and to simulate nominal or ideal values. Modeling variable physical parameters provides a better representation of what can be expected in production. Monte Carlo analysis is a standard method of simulating variability that occurs in real physical parameters. Automotive companies use Monte Carlo testing to ensure high quality, robust designs. Due to time and resource constraints, engineers often examine only a limited number of key parameters rather than an entire set. This leaves the design vulnerable to problems caused by missing the full potential impact of parameters that were unvaried during testing.