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The emission of excessive quantities of NOx when the automobile air conditioner is turned on has received a fair amount of attention in recent years. Since NOx is a smog precursor, it is important to understand the reasons for this jump in emissions especially on hot sunny days when air conditioner usage is at a maximum. A simple thermodynamic model is used to demonstrate how the torque from a typical air conditioner compressor is mainly related to the ambient temperature. The compressor's on-off cycling patterns are also characterized. Since the compressor significantly loads the engine, it affects fuel economy and emissions. The key independent variable that we employ to represent engine load is fuel rate. The correlations between engine-out NOx emissions and fuel rate are shown for a number of light duty vehicles and trucks. From these, a physical model for engine-out NOx emissions (with and without air conditioning) is presented.

An experimental study is performed for homogeneous charge compression ignition (HCCI) combustion focusing on late phasing conditions with high cyclic variability (CV) approaching misfire. High CV limits the feasible operating range and the objective is to understand and quantify the dominating effects of the CV in order to enable controls for widening the operating range of HCCI. A combustion analysis method is developed for explaining the dynamic coupling in sequences of combustion cycles where important variables are residual gas temperature, combustion efficiency, heat release during re-compression, and unburned fuel mass. The results show that the unburned fuel mass carries over to the re-compression and to the next cycle creating a coupling between cycles, in addition to the well known temperature coupling, that is essential for understanding and predicting the HCCI behavior at lean conditions with high CV.

The emerging Variable Valve Timing (VVT) technology complicates the estimation of air flow rate because both intake and exhaust valve timings significantly affect engine's gas exchange and air flow rate. In this paper, we propose to use Artificial Neural Networks (ANN) to model the air flow rate through a 2.4 liter VVT engine with independent intake and exhaust camshaft phasers. The procedure for selecting the network architecture and size is combined with the appropriate training methodology to maximize accuracy and prevent overfitting. After completing the ANN training based on a large set of dynamometer test data, the multi-layer feedforward network demonstrates the ability to represent air flow rate accurately over a wide range of operating conditions. The ANN model is implemented in a vehicle with the same 2.4 L engine using a Rapid Prototype Controller.

There is a pressing need to develop accurate and robust approaches for predicting vehicle speed to enhance fuel economy/energy efficiency, drivability and safety of automotive vehicles. This paper details outcomes of research into various methods for the prediction of vehicle velocity. The focus is on short-term predictions over 1 to 10 second prediction horizon. Such short-term predictions can be integrated into a hybrid electric vehicle energy management strategy and have the potential to improve HEV energy efficiency. Several deterministic and stochastic models are considered in this paper for prediction of future vehicle velocity. Deterministic models include an Auto-Regressive Moving Average (ARMA) model, a Nonlinear Auto-Regressive with eXternal input (NARX) shallow neural network and a Long Short-Term Memory (LSTM) deep neural network. Stochastic models include a Markov Chain (MC) model and a Conditional Linear Gaussian (CLG) model.

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

This paper presents a simple method of using Voronoi partitions for estimating vehicle fuel economy from a limited set of engine operating conditions. While one of the overarching goals of engine research is to continually improve vehicle fuel economy, evaluating the impact of a change in engine operating efficiency on the resulting fuel economy is a non-trivial task and typically requires drive cycle simulations with experimental data or engine model predictions and a full suite of engine controllers over a wide range of engine speeds and loads. To avoid the cost of collecting such extensive data, proprietary methods exist to estimate fuel economy from a limited set of engine operating conditions. This study demonstrates the use of Voronoi partitions to cluster and quantize the fuel consumed along a complex trajectory in speed and load to generate fuel consumption estimates based on limited simulation or experimental results.