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Vehicle certification data are used to study the effectiveness of the major powertrain technologies used by car manufacturers to reduce fuel consumption. Methods for differentiating vehicles effectively were developed by leveraging theoretical models of engine and vehicle fuel consumption. One approach normalizes by displacement per unit distance, which puts both fuel used and vehicle work in mean effective pressure units, and is useful when comparing engine technologies. The other normalizes by engine rated power, a customer-relevant output metric. The normalized work/power is proportional to weight/power, the most fundamental performance metric. Certification data for 2016 and 2017 U.S. vehicles with different powertrain technologies are compared to baseline vehicles with port fuel injection (PFI) naturally aspirated engines and six-speed automatic transmissions.

In 1999, the first generation NOx sensor from NGK Spark Plug, Co., Ltd. was commercialized for use in gasoline LNT NOx after-treatment systems [ 1 ]. Since then, as emissions regulations and OBD requirements have become more stringent, the demand for a high-accuracy NOx sensor with fast light-off has increased, particularly for diesel after-treatment systems. To meet such market demands, NGK Spark Plug, Co., Ltd. has developed, in collaboration with Ford Motor Company, a second generation NOx sensor.

In order to detect degradation of engine oil lubricant, bench testing along with a number of diesel-powered Ford trucks were instruments and tested. The purpose of the bench testing was primarily to determine performance aspects such as repeatability, hysteresis effects and so on. Vehicle testing was conducted by designing and installing a separate oil reservoir along with a circulation system which was mounted in the vicinity of the oil pan. An innovative oil sensor was directly installed on the reservoir which can measure five (5) independent oil parameters (viscosity, density, permittivity, conductance, temperature). In addition, the concept is capable of detecting the oil level continuously during normal engine operation. The sensing system consists of an ultrasonic transducer for the oil level detection as well as a Tuning Fork mechanical resonator for the oil condition measurement.

In this paper a new knock control algorithm is developed based on a stochastic interpretation of the knock signal and on a control objective specified as a certain percentage of knocking cycles. Unlike previous ‘stochastic’ knock controllers, the new algorithm does not average or low pass filter the knock intensity signal and the transient response of the controller is consequently much faster. The performance of the new controller is compared in detail with the response of a traditional deterministic controller using a simple but effective knock simulation tool. The results show that the new controller is able to operate at a more advanced mean spark angle and that there is much less cyclic variance about this mean. The transient response to excess knocking events is as fast, or faster, than the conventional controller, though the rate of recovery from overly retarded conditions is slower.

A wavelet-based technique for reducing the impulsive character of sound recordings is presented. The amount of impulsive content removed may be adjusted by varying a statistical threshold. The technique is validated for a diesel idle sound-quality application. The wavelet-based modification produces a substantial decrease in impulsive character as verified by an objective sound-quality metric for engine “ticking”. Informal subjective assessment of the modified results found them to be realistic and free from artifacts. The procedure is expected to be useful for sound-quality simulation and target-setting for diesel powertrain noise and other automotive sounds containing both impulsive and non-impulsive content.

Recent applied mathematics research on the properties of the invertible shift-invariant discrete wavelet transform has produced new ways to visualize, separate, and synthesize impulsive sounds, such as thuds, slaps, taps, knocks, and rattles. These new methods can be used to examine the joint time-frequency characteristics of a sound, to select individual components based on their time-frequency localization, to quantify the components, and to synthesize new sounds from the selected components. The new tools will be presented in a non-mathematical way illustrated by two real-life sound quality problems, extracting the impulsive components of a windshield wiper sound, and analyzing a door closing-induced rattle.

Scalograms based on shift-invariant orthonormal wavelet transforms can be used to analyze impulsive and transient sounds in the presence of more stationary sound backgrounds, such as wind noise or drivetrain noise. The visual threshold of detection for impulsive features on the scalogram (signal energy content vs. time and frequency,) is shown to be similar to the audible threshold of detection of the human auditory system for the corresponding impulsive sounds. Two examples of impulsive sounds in a realistic automotive sound background are presented: automotive interior rattle in a vehicle passenger compartment, and spark knock recorded in an engine compartment.

A device consisting of a mass-loaded cantilever and a striker block mounted on an electromagnetic shaker can be used to create a wide variety of mechanical rattle sounds. The construction of the rattle mechanism is described and illustrated with diagrams. Examples of the different qualities of rattles that can be produced are presented as plots of microphone waveforms. The applications of such a device are primarily in support of the development, testing, and evaluation of rattle detection systems based on human experts or computers.

One important design goal for spark-ignited engines is to minimize cyclic variability. A small amount of cyclic variability (slow burns) can produce undesirable engine vibrations. A larger amount of cyclic variability (incomplete burns) leads to increased hydrocarbon consumption/emissions. Recent studies have reported deterministic patterns in cyclic variability under extremely lean (misfiring) operating conditions. The present work is directed toward more realistic non-misfiring conditions. Production engine test results suggest that deterministic patterns in cyclic variability are the consequence of incomplete combustion, hence control algorithms based on the occurrence of these patterns are not expected to be of significant practical value.

Transient automotive sounds often possess a complex internal structure resulting from one or more impacts combined with mechanical and acoustic cavity resonances. This structure can be revealed by a new technique for obtaining translation-invariant scalograms from orthogonal discrete wavelet transforms. These scalograms are particularly well suited to the visualization of complex sound transients which span a wide dynamic range in time (ms to s) and frequency (∼100Hz to ∼10kHz). As examples, scalograms and spectrograms of door latch closing events from a variety of automotive platforms are discussed and compared in light of the subjective rankings of the sounds.

Acoustic standing waves are excited in internal combustion chambers by both normal combustion and autoignition. The energy in these acoustic modes can be transmitted through the engine block and radiated as high-frequency engine noise. Using finite-element models of two different (four-valve and two-valve) production engine combustion chambers, the mode shapes and relative frequencies of the in-cylinder volume acoustic modes are calculated as a function of crank angle. The model is validated by comparison to spectrograms of experimental time-sampled waveforms (from flush-mounted cylinder pressure sensors and accelerometers) from these two typical production spark-ignited engines.

Spark knock pressure oscillations can be detected by a cylinder pressure transducer or by an accelerometer mounted on the engine block. Accelerometer-based detection is lower cost but is affected by extraneous mechanical vibrations and the frequency response of the engine block and accelerometer. The knock oscillation frequency changes during the expansion stroke because the chamber geometry is changing due to the piston motion and the burned gases are cooling. Spectrogram analysis shows the time-dependent frequency content of the pressure and acceleration signals, revealing characteristic signatures of knock and mechanical vibrations. Illustrative spectrograms are presented which yield physical insight into accelerometer-based knock detection.

The frequency of the pressure oscillations caused by spark knock depends on the temperature-dependent speed of sound in the combustion gases. Engine dynamometer tests showed a 6.5% (390 Hz) reduction in the knock fundamental frequency as the air/fuel ratio was swept from 13:1 to 20:1. Engine cycle simulation model predictions of maximum burned gas temperatures correlate well with the data. A robust knock detection system must be insensitive to the range of burned gas temperature (frequency of pressure oscillations) that will be encountered with a particular engine control system operating under the expected range of fuels and environmental conditions.