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Life Cycle Assessment (LCA) is a methodology used to determine quantitatively the environmental impacts of a range of options. The environmental community has used LCA to study all of the impacts of a product over its life cycle. This analysis can help to prevent instances where a greater degree of environmental harm results when changes are made to products based on consideration of impacts in only part of the life cycle. This study applies the methodology to engine lubricants, and in particular chlorine limits in engine lubricant specifications. Concern that chlorine in lubricants might contribute to emissions from vehicle exhausts of polychlorinated dibenzo-p-dioxins (PCDD) and polychlorinated dibenzofurans (PCDF), collectively called PCDD/F, led to the introduction of chlorine limits in lubricant specifications. No direct evidence was available linking chlorine in lubricants to PCDD/F formation, but precautionary principles were used to set lubricant chlorine limits.

Increasingly stringent fuel economy and emissions regulations around the world have forced the further optimization of nearly all vehicle systems. Many technologies exist to improve fuel economy; however, only a smaller sub-set are commercially feasible due to the cost of implementation. One system that can provide a small but significant improvement in fuel economy is the lubrication system of an internal combustion engine. Benefits in fuel economy may be realized by the reduction of engine oil viscosity and the addition of friction modifying additives. In both cases, advanced engine oils allow for a reduction of engine friction. Because of differences in engine design and architecture, some engines respond more to changes in oil viscosity or friction modification than others. For example, an engine that is designed for an SAE 0W-16 oil may experience an increase in fuel economy if an SAE 0W-8 is used.

Downsized gasoline engines, coupled with gasoline direct injection (GDI) and turbocharging, have provided an effective means to meet both emissions standards and customers’ drivability expectations. As a result, these engines have become more and more common in the passenger vehicle marketplace over the past 10 years. To maximize fuel economy, these engines are commonly calibrated to operate at low speeds and high engine loads – well into the traditional ‘knock-limited’ region. Advanced engine controls and GDI have effectively suppressed knock and allowed the engines to operate in this high efficiency region more often than was historically possible. Unfortunately, many of these downsized, boosted engines have experienced a different type of uncontrolled combustion. This combustion occurs when the engine is operating under high load and low speed conditions and has been named Low Speed Pre-Ignition (LSPI). LSPI has shown to be very damaging to engine hardware.

Requirements to improve vehicle fuel economy continue to increase, spurred on by agreements such as the Kyoto Protocol. Lubricants can play a role in aiding fuel economy, as evidenced by the rise in the number of engine oil specifications that require fuel economy improvements. Part of this improvement is due to achieving suitable viscometric properties in the lubricant, but additional improvements can be made using friction modifier (FM) compounds. The use of FMs in lubricants is not new, with traditional approaches being oleochemical-based derivatives such as glycerol mono-oleate and molybdenum-based compounds. However, to achieve even greater improvements, new new friction modifying compounds are needed to help deliver the full potential required from next generation lubricants. This work looks at the potential improvements available from new FM technology over and above the traditional FM compounds.

In this work, a novel bearing test rig was used to evaluate the impact of oil viscoelasticity on friction torque and oil film thickness in a hydrodynamic journal bearing. The test rig used an electric motor to rotate a test journal, while a hydraulic actuator applied radial load to the connecting rod bearing. Lubrication of the journal bearing was accomplished via a series of axial and radial drillings in the test shaft and journal, replicating oil delivery in a conventional engine crankshaft. Journal bearing inserts from a commercial, medium duty diesel engine (Cummins ISB) were used. Oil film thickness was measured using high precision eddy current sensors. Oil film thickness measurements were taken at two locations, allowing for calculation of minimum oil film thickness. A high-precision, in-line torque meter was used to measure friction torque. Four test oils were prepared and evaluated.

Fuel economy is not an absolute attribute, but is highly dependent on the method used to evaluate it. In this work, two test methods are used to evaluate the differences in fuel economy brought about by changes in engine oil viscosity grade and additive chemistry. The two test methods include a chassis dynamometer vehicle test and an engine dynamometer test. The vehicle testing was conducted using the Federal Test Procedure (FTP) testing protocol while the engine dynamometer test uses the proposed American Society for Testing and Materials (ASTM) Sequence VIE fuel economy improvement 1 (FEI1) testing methodology. In an effort to improve agreement between the two testing methods, the same model engine was used in both test methods, the General Motors (GM) 3.6 L V6 (used in the 2012 model year Chevrolet™ Malibu™ engine). Within the lubricant industry, this choice of engine is reinforced because it has been selected for use in the proposed Sequence VIE fuel economy test.

1Increasing adoption of downsized, boosted, spark-ignition engines has improved vehicle fuel economy, and continued improvement is desirable to reduce carbon emissions in the near-term. However, this strategy is limited by damaging preignition events which can cause hardware failure. Research to date has shed light on various contributing factors related to fuel and lubricant properties as well as calibration strategies, but the causal factors behind an individual preignition cycle remain elusive. If actionable precursors could be identified, mitigation through active control strategies would be possible. This paper uses artificial neural networks to search for identifiable precursors in the cylinder pressure data from a large real-world data set containing many preignition cycles. It is found that while follow-up preignition cycles in clusters can be readily predicted, the initial preignition cycle is not predictable based on features of the cylinder pressure.