Jeff Sterniak

Experiments and analysis of high cyclic variability at the operational limits of spark-assisted HCCI combustion

During combustion mode switches, between homogeneous charge compression ignition (HCCI) and spark ignition (SI) combustion, the engine will operate at throttled and stoichiometric conditions where high cyclic variability (CV) is typically observed. To analyze and eventually model the engine behavior at the high CV condition we perform measurements on a four-cylinder HCCI engine with negative valve overlap and describe a cycle-resolved analysis method that enables the characterization of cycle-to-cycle variations at such conditions. The dynamic behavior observed is characterized by the recycling of thermal and chemical energy between cycles. We quantify the cyclic exchange and relate it to the dynamic patterns that emerge from this high CV condition. We also clarify the contributions of the spark at these conditions, where advancing the spark can significantly reduce the variability. It is our conclusion that the dynamic patterns observed can be characterized by the cycle-resolved combustion efficiency as it is an essential non-linearity in the dynamic evolution.

Quantifying Cyclic Variability in a Multi-Cylinder HCCI Engine With High Residuals

Cyclic variability (CV) in lean HCCI combustion at the limits of operation is a known phenomenon, and this work aims at investigating the dominant effects for the cycle evolution at these conditions in a multi-cylinder engine. Experiments are performed in a four-cylinder engine at the operating limits at late phasing of lean HCCI operation with negative valve overlap (nvo). A combustion analysis method that estimates the unburned fuel mass on a per-cycle basis is applied on both main combustion and the nvo period revealing and quantifying the dominant effects for the cycle evolution at high CV. The interpretation of the results and comparisons with data from a single-cylinder engine indicate that, at high CV, the evolution of combustion phasing is dominated by low-order deterministic couplings similar to the single-cylinder behavior. Variations, such as in air flow and wall temperature, between cylinders strongly influence the level of CV but the evolution of the combustion phasing is governed by the interactions between engine cycles of the individual cylinders.Copyright

Particulate matter emission comparison of spark ignition direct injection (SIDI) and port fuel injection (PFI) operation of a boosted gasoline engine

Spark ignition direct injection (SIDI) gasoline engines, especially in downsized boosted engine platforms, are increasing their market share relative to port fuel injection (PFI) engines in U.S., European and Chinese vehicles due to better fuel economy by enabling higher compression ratios and higher specific power output. However, particulate matter (PM) emissions from engines are becoming a concern due to adverse human health and environment effects, and more stringent emission standards. To conduct a PM number and size comparison between SIDI and PFI systems, a 2.0 L boosted gasoline engine has been equipped and tested with both systems at different loads, air fuel ratios, spark timings, fuel pressures and injection timings for SIDI operation and loads, air fuel ratios and spark timings for PFI operation.Regardless of load, air fuel ratio, spark timing, fuel pressure, and injection timing, particle size distribution from SIDI and PFI is shown to be bimodal, exhibiting nucleation and accumulation mode particles. SIDI produces particle numbers that are an order of magnitude greater than PFI. Particle number can be reduced by retarding spark timing and operating the engine lean, both for SIDI and PFI operation. Increasing fuel injection pressure and optimizing injection timing with SIDI also reduces PM emissions. This study provides insight into the differences in PM emissions from boosted SIDI and PFI engines and an evaluation of PM reduction potential by varying engine operating parameters in boosted SIDI and PFI gasoline engines.Copyright

Particulate Matter Emission Comparison of Spark Ignition Direct Injection (SIDI) and Port Fuel Injection (PFI) Operation of a Boosted Gasoline Engine

Spark ignition direct injection (SIDI) gasoline engines, especially in downsized boosted engine platforms, are increasing their market share relative to port fuel injection (PFI) engines in U.S., European and Chinese vehicles due to better fuel economy by enabling higher compression ratios and higher specific power output. However, particulate matter (PM) emissions from engines are becoming a concern due to adverse human health and environment effects, and more stringent emission standards. To conduct a PM number and size comparison between SIDI and PFI systems, a 2.0 L boosted gasoline engine has been equipped and tested with both systems at different loads, air fuel ratios, spark timings, fuel pressures and injection timings for SIDI operation and loads, air fuel ratios and spark timings for PFI operation.Regardless of load, air fuel ratio, spark timing, fuel pressure, and injection timing, particle size distribution from SIDI and PFI is shown to be bimodal, exhibiting nucleation and accumulation mode particles. SIDI produces particle numbers that are an order of magnitude greater than PFI. Particle number can be reduced by retarding spark timing and operating the engine lean, both for SIDI and PFI operation. Increasing fuel injection pressure and optimizing injection timing with SIDI also reduces PM emissions. This study provides insight into the differences in PM emissions from boosted SIDI and PFI engines and an evaluation of PM reduction potential by varying engine operating parameters in boosted SIDI and PFI gasoline engines.Copyright

Quantifying Cyclic Variability in a Multicylinder HCCI Engine With High Residuals

Cyclic variability (CV) in lean HCCI combustion at the limits of operation is a known phenomenon, and this work aims at investigating the dominant effects for the cycle evolution at these conditions in a multi-cylinder engine. Experiments are performed in a four-cylinder engine at the operating limits at late phasing of lean HCCI operation with negative valve overlap (nvo). A combustion analysis method that estimates the unburned fuel mass on a per-cycle basis is applied on both main combustion and the nvo period revealing and quantifying the dominant effects for the cycle evolution at high CV. The interpretation of the results and comparisons with data from a single-cylinder engine indicate that, at high CV, the evolution of combustion phasing is dominated by low-order deterministic couplings similar to the single-cylinder behavior. Variations, such as in air flow and wall temperature, between cylinders strongly influence the level of CV but the evolution of the combustion phasing is governed by the interactions between engine cycles of the individual cylinders.

Identification of the Dynamic Operating Envelope of HCCI Engines Using Class Imbalance Learning

Homogeneous charge compression ignition (HCCI) is a futuristic automotive engine technology that can significantly improve fuel economy and reduce emissions. HCCI engine operation is constrained by combustion instabilities, such as knock, ringing, misfires, high-variability combustion, and so on, and it becomes important to identify the operating envelope defined by these constraints for use in engine diagnostics and controller design. HCCI combustion is dominated by complex nonlinear dynamics, and a first-principle-based dynamic modeling of the operating envelope becomes intractable. In this paper, a machine learning approach is presented to identify the stable operating envelope of HCCI combustion, by learning directly from the experimental data. Stability is defined using thresholds on combustion features obtained from engine in-cylinder pressure measurements. This paper considers instabilities arising from engine misfire and high-variability combustion. A gasoline HCCI engine is used for generating stable and unstable data observations. Owing to an imbalance in class proportions in the data set, the models are developed both based on resampling the data set (by undersampling and oversampling) and based on a cost-sensitive learning method (by overweighting the minority class relative to the majority class observations). Support vector machines (SVMs) and recently developed extreme learning machines (ELM) are utilized for developing dynamic classifiers. The results compared against linear classification methods show that cost-sensitive nonlinear ELM and SVM classification algorithms are well suited for the problem. However, the SVM envelope model requires about 80% more parameters for an accuracy improvement of 3% compared with the ELM envelope model indicating that ELM models may be computationally suitable for the engine application. The proposed modeling approach shows that HCCI engine misfires and high-variability combustion can be predicted ahead of time, given the present values of available sensor measurements, making the models suitable for engine diagnostics and control applications.

A System Identification Framework for Modeling Complex Combustion Dynamics Using Support Vector Machines

Machine Learning is being widely applied to problems that are difficult to model using fundamental building blocks. However, the application of machine learning in powertrain modeling is not common because existing powertrain systems have been simple enough to model using simple physics. Also, black box models are yet to demonstrate sufficient robustness and stability features for widespread powertrain applications. However, with emergence of advanced technologies and complex systems in the automotive industry, obtaining a good physical model in a short time becomes a challenge and it becomes important to study alternatives. In this chapter, support vector machines (SVM) are used to obtain identification models for a gasoline homogeneous charge compression ignition (HCCI) engine. A machine learning framework is discussed that addresses several challenges for identification of the considered system that is nonlinear and whose region of stable operation is very narrow.

Accounting for combustion mode switch dynamics and fuel penalties in drive cycle fuel economy

A methodology is introduced to analyze the drive cycle fuel economy of a vehicle equipped with a multimode combustion engine, utilizing commercial cam phasers and two-step cam profile switching. The analysis is based on a longitudinal vehicle model with manual transmission. The engine model employs a finite state machine describing mode switches between two distinct combustion modes, namely, spark-ignited and homogeneous charge compression ignition. Preliminary combustion mode switch experiments were used to parameterize the model. The influence of mode switch fuel penalties on drive cycle fuel economy was quantified through application of the model to the federal test procedure (FTP-75), the highway fuel economy test (HWFET) and the US06 supplemental federal test procedure. The mode switches were analyzed individually in terms of their benefit on fuel economy and a distinction was made between harmful and beneficial mode switches. Mode switches were defined as harmful whether their fuel penalty is greater than the benefits originating from spending time in the subsequent homogeneous charge compression ignition mode. A parametric study was conducted to investigate the impact of harmful mode switches on fuel economy as a function of the fuel penalties during the switch. In the case of high fuel penalties, supervisory control becomes an important tool for minimizing the number of harmful mode switches. One possible supervisory strategy discussed is a smoothing strategy, in which a mode switch is delayed by introducing a dwell time.

Fuel Economy of a Multimode Combustion Engine With Three-Way Catalytic Converter

Highly diluted, low temperature homogeneous charge compression ignition (HCCI) combustion leads to ultralow levels of engine-out NOx emissions. A standard drive cycle, however, would require switches between HCCI and spark-ignited (SI) combustion modes. In this paper we quantify the efficiency benefits of such a multimode combustion engine, when emission constraints are to be met with a three-way catalytic converter (TWC). The TWC needs unoccupied oxygen storage sites in order to achieve acceptable performance. The lean exhaust gas during HCCI operation, however, fills the oxygen storage and leads to a drop in NOx conversion efficiency. If levels of tailpipe NOx become unacceptable, a mode switch to a fuel rich combustion mode is necessary in order to deplete the oxygen storage and restore TWC efficiency. The resulting lean-rich cycling leads to a penalty in fuel economy. Another form of penalty originates from the lower combustion efficiency during a combustion mode switch itself. In order to evaluate the impact on fuel economy of those penalties, a finite state model for combustion mode switches is combined with a longitudinal vehicle model and a phenomenological TWC model, focused on oxygen storage. The aftertreatment model is calibrated using combustion mode switch experiments from lean HCCI to rich spark-assisted HCCI (SA-HCCI) and back. Fuel and emission maps acquired in steady-state experiments are used. Different depletion strategies are compared in terms of their influence on drive cycle fuel economy and NOx emissions. It is shown that even an aggressive lean-rich cycling strategy will marginally satisfy the cumulated tailpipe NOx emission standards under warmed-up conditions. More notably, the cycling leads to substantial fuel penalties that negate most of HCCI’s efficiency benefits. [DOI: 10.1115/1.4028885]

A low-order adaptive engine model for SI–HCCI mode transition control applications with cam switching strategies

This article presents a low-order engine model to support model-based control development for mode transitions between spark ignition (SI) and homogeneous charge compression ignition (HCCI) combustion modes in gasoline engines. The modeling methodology focuses on cam switching mode transition strategies wherein the mode is abruptly changed between SI and recompression HCCI via a switch of the cam lift and phasing. The model is parameterized to a wide range of steady-state data which are selected to include conditions pertinent to cam switching mode transitions. An additional HCCI combustion model parameter is augmented and tuned based on transient data from SI to HCCI mode transitions where the conditions can be significantly outside any contained in the baseline steady-state parameterization. An adaptation routine is given which allows transient data be assimilated in online operation to update the augmented parameter and improve SI–HCCI transition predictions. With the baseline steady-state parameterization and augmented mode transition parameter, the model is shown to reproduce both steady-state data and transient performance output time histories from SI–HCCI transitions with considerable accuracy.