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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.

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.

Carefully controlled intake valve deposit (IVD) cleanup testing is found to be an effective method for differentiating the effect of the deposit control additives on combustion chamber deposits (CCD). The IVD buildup procedure produces a consistent initial level of CCD that the cleanup additive, the additive of interest, continues to build on until the end of the cleanup test. This “end of cleanup” CCD is found to be as repeatable and differentiable a measurement as tests run under the more common “keep clean” type operation. While IVD cleanup testing induces a mid-test disturbance in the form of the end of buildup measurement, it aligns well with two key CCD protocols in terms of the higher additive treat rates used and the extended total test length. In an analysis of results from IVD cleanup tests run using four different engine/vehicle procedures on seven different additives, several findings stood out.

Diesel fuel is a blend of various middle distillate components separated at the refinery. The composition and characteristics of the diesel fuel blend changes daily if not hourly because of normal process variation, changing refinery processing conditions, changing crude oil diet or changing diesel fuel and kerosene market conditions. Regardless of the situation going on at the refinery or the market, the resultant diesel fuel must consistently meet established cloud point specifications. To consistently meet the cloud point specifications, refiners are forced to blend their diesel fuels in such a way that the resultant blend is always on the low side of the cloud point specification even when the refining process adversely changes the fuel characteristics. This practice has the effect of producing several degrees of cloud point “giveaway” when the refinery is not experiencing adverse swings in product quality.

The Engine Oil Industry Base Oil Interchange (BOI) and Viscosity Grade Read Across (VGRA) guidelines developed by the American Petroleum Institute (API) provide a means to significantly reduce the time to market for current technology oils. The guidelines also allow conversion of a fraction of the millions of dollars spent each year on engine testing in pursuit of API engine oil licensing into research testing and the development of fundamental knowledge. In the past, guidelines have been developed based upon a general assessment of minimal engine test data. Recently, however, regression models have been used to assess Base Oil and Viscosity Grade effects. The use of statistical regression models and Virtual Tests in determining effects to establish BOI and VGRA has several advantages. These advantages, demonstrated through an example and a case study, include volume of data and breadth of data.

The operating conditions of a typical motorcycle are considerably different than those of a typical passenger car and thus require an oil capable of handling the unique demands. One primary difference, wet clutch lubrication, is already addressed by the current JASO four-stroke motorcycle engine oil specification (JASO T 903:2011). Another challenge for the oil is gear box lubrication, which may be addressed in part with the addition of a gear protection test in a future revision to the JASO specification. A third major difference between a motorcycle oil and passenger car oil is the more severe conditions an oil is subjected to within a motorcycle engine, due to higher temperatures, engine speeds and power densities. Scooters, utilizing a transmission not lubricated by the crankcase oil, also place higher demands on an engine oil, once again due to higher temperatures, engine speeds and power densities.

One of the key functions of lubricating oil additives in diesel engines is to control oil thickening caused by soot accumulation. Over the last several years, it has become apparent that the composition of the base oil used within the lubricant plays an extremely important role in the oil thickening phenomenon. In particular, oil thickening observed in the Mack T-8 test is significantly affected by the aromatic content of the base oil. We have found that the Mack T-8 thickening phenomenon is associated with high electrical activity, i.e., engine drain oils which exhibit high levels of viscosity increase show significantly higher conductivities. These findings suggest that electrical interactions are involved in soot-induced oil thickening.

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.

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.

In API engine oil licensing, a candidate oil must meet the performance requirements of category defined engine tests. The reason for the engine tests is to assess the capability of the candidate oil in field performance. Unfortunately, due to the time consuming and expensive nature of most engine tests, a candidate oil is typically run only once or twice in an attempt to meet the performance requirements. Given that the results from most engine tests have large amounts of variability, the assessment of the candidate oil in several tests, although adequate, is obviously not perfect or inexpensive. The Virtual Engine Test is a process in which the time and expense of category defined engine tests may be reduced while maintaining, or even improving, the assessment of the candidate oil capability.

Historically, vehicle fluid condition has been monitored by measuring miles driven or hours operated. Many current vehicles have more sophisticated monitoring methods that use additional variables such as fuel consumption, engine temperature and engine revolutions to predict fluid condition. None of these monitoring means, however, actually measures a fluid property to determine condition, and that is about to change. New sensors and diagnostic systems are being developed that allow real time measurement of some lubricant physical and/or chemical properties and interpret the results in order to recommend oil change intervals and maximize performance. Many of these new sensors use electrochemical or acoustic wave technologies. This paper examines the use of these two technologies to determine engine oil condition and focuses on the effects of lubricant chemistry on interpreting the results.

The work presented here is the second of two papers investigating the KA24E engine test. The first paper characterized the KA24E engine in terms of the physical and chemical operating environment it presents to lubricants. The authors investigated oil degradation and wear mechanisms, and examined the differences between the KA24E and the Sequence VE engine tests. It was shown that while the KA24E does not degrade the lubricant to the extent that occurs in the Sequence VE, wear could be a serious problem if oils are poorly formulated. This second paper examines the wear response of the KA24E to formulation variables. A statistically designed matrix demonstrated that the KA24E is sensitive to levels of secondary zinc dialkyldithiophosphate (ZDP), dispersant and calcium sulfonate detergent. This matrix also showed that the KA24E appears to have good repeatability for well formulated oils and is a reasonable replacement for the wear component of the Sequence VE.

The Nissan KA24E engine test is designated to replace the Ford Sequence VE engine test as the low temperature valve train wear requirement for ILSAC (International Lubricant Standardization and Approval Committee) GF-3. The KA24E (recently designated the Sequence IV A) represents much of the current world-wide material and design technology while retaining the sliding cam/follower contact found in earlier engine designs. The work presented here is the first of two reports. In this first report, the physical and chemical environment the KA24E engine presents a lubricant is characterized and compared to those of the Sequence VE engine. Valve train materials and wear modes are investigated and described. Although chemical analysis of drain oils indicate the KA24E procedure does not degrade the lubricant to the extent seen in the Sequence VE test, valve train wear appears to proceed in a similar manner in both tests.

Throughout the evolution of the automobile, passenger car motor oils have been developed to address issues of wear, corrosion, deposit formation, friction, and viscosity stability. As a result, the internal combustion engines are now developed with the expectation that the lubricants to be used in them will deliver certain performance attributes. Metallurgies, clearances, and built-in stresses are all chosen with certain expectations from the lubricant. A family of chemicals that has been universally used in formulating passenger car motor oils is zinc dithiophosphates (ZDPs). ZDPs are extremely effective at protecting highly stressed valve train components against wear failure, especially in engine designs with a sliding contact between cams and followers. While ZDPs' benefits on wear control are universally accepted, ZDPs have been identified as the source of phosphorus, which deactivates noble metal aftertreatment systems.

Global emission legislations for diesel engines are becoming increasingly stringent. While the exhaust gas composition requirements for prior iterations of emission legislation could be met with improvements in the engine's combustion process, the next issue of European, North American and Japanese emission limits greater than 2005 will require more rigorous measures, mainly employment of exhaust gas aftertreatment systems. As a result, many American diesel OEMs are considering NOx adsorbers as a means to achieve 2007+ emission standards. Since the efficacy of a NOx adsorber over its lifetime is significantly affected by sulfur (“sulfur poisoning”), forthcoming reductions in diesel fuel sulfur (down to 15 ppm), have raised industry concerns regarding compatibility and possible poisoning effects of sulfur from the lubricant.

This study describes the use of Quantitative Structure Activity Relationships (QSAR) to develop predictive models for non-acidic Lubricity agents. The work demonstrates the importance of separating certain chemical families to give better and more robust equations rather than grouping a whole data set together. These models can then be used as important tools in further development work by predicting activities of new compounds before actual synthesis/testing.

The development of new transmission designs continues to affect the vehicle market. Continuously variable transmissions (CVTs) remain one of the more recent designs that impact the vehicle market. A desire for high belt-pulley capacity has driven studies concentrating on metal-on-metal (M/M) friction as a function of the CVT fluid. This paper describes the statistical techniques used to optimize the fluid friction as a function of additive components in a bench-scale, three-element test rig.

Two major trends can be identified for powertrain control in the next decade. The legislation will more and more focus on in-use emissions. Together with the global trend to reduce the CO₂ emissions, this will lead to an integral drive train approach. To develop and validate this integral drive train approach, the need for a new chapter in powertrain testing arises. The climatic-altitude chamber, suited for heavy vehicles, serves a wide variety of testing needs. Ambient temperature can be controlled between -45°C and +55°C and ambient pressure can be reduced up to a level found at an altitude to 4000 meters. The chamber's dynamometers enable transient testing of heavy-duty engines and vehicles and the chamber is equipped with a comprehensive array of emission measurement capabilities, working under extreme conditions.

The impact of comfort is becoming increasingly important. On one hand, manufacturers use comfort to distinguish their products from their competitors. On the other hand, more cars than ever are used professionally. The prolonged sitting in automotive conditions of professional drivers introduced new physical complaints, resulting in high social costs. However, the cause of these complaints is not well understood. The use of virtual testing tools can contribute to both speeding up and reducing the costs of the development process of new more comfortable cars and the research in the causes of the new complaints. Vibration loading has often been identified as a source of discomfort. In literature, several human models developed for prediction of human resonance behaviour in vibrations were described. In most of these human body models, the muscles are represented in a simplified way.