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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.

Motorcycle OEMs faced with stringent global fuel economy and emission regulations are being forced to develop new hardware and emissions control technologies to remain compliant. Motorcycle oils have become an enabling technology for the development of smaller, more efficient engines operating at higher power density. Many OEMs have therefore become reliant on lubricants to not only provide enhanced durability under more extreme operating conditions, but to also provide fuel economy benefits through reduced energy losses. Unlike passenger car oils that only lubricate the engine, motorcycle oils must lubricate both the engine and the drive train. These additional requirements place different performance demands versus a crankcase lubricant. The drive train includes highly loaded gears that are exposed to high pressures, in turn requiring higher levels of oil film strength and antiwear system durability.

Multigrade automotive lubricants contain polymeric viscosity modifiers which enable the oil to provide adequate hydrodynamic lubrication at high temperatures and good starting/pumping performance at low temperatures. Under operating conditions in engines, transmissions and gear boxes, polymeric additives undergo both temporary and permanent viscosity loss. The former is caused by flow orientation and the latter by molecular chain scission. Whatever the mechanism, original equipment manufacturers are interested in maintaining a minimum level of hydrodynamic viscosity from oil change to oil change. This is often expressed as a “stay-in-grade” requirement. Commercial viscosity modifiers (VM) span a wide range of chemistries and molecular architectures.

The tightening legislation in the on-road heavy-duty diesel area means that pollution control systems will soon be widely introduced on such engines. A number of different aftertreatment systems are currently being considered to meet the incoming legislation, including Diesel Particulate Filters (DPF), Diesel Oxidation Catalysts (DOC) and Selective Catalytic Reduction (SCR) systems. Relatively little is known about the interactions between lubricant-derived species and such aftertreatment systems. This paper describes the results of an experimental program carried out to investigate these interactions within DPF, DOC and SCR systems on a state-of-the-art 9 litre engine. The influence of lubricant composition and lube oil ash level was investigated on the different catalyst systems. In order to reduce costs and to speed up testing, test oil was dosed into the fuel. Tests without dosing lubricant into the fuel were also run.

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.