Search Results

Low temperature combustion (LTC) modes are among the advanced combustion technologies which offer thermal efficiencies comparable to conventional diesel combustion and produce ultra-low NOx and particulate matter (PM) emissions. However, combustion timing control, excessive pressure rise rate and high cyclic variations are the common challenges encountered by the LTC modes. These challenges can be addressed by developing model-based control framework for the LTC engine. In the current study, in-cylinder pressure data for dual-fuel LTC engine operation is analyzed for 636 different operating conditions and the heat release rate (HRR) traces are classified into three distinct classes based on their distinct shapes. These classes are named as Type-1, Type-2 and Type-3, respectively.

A potential route to reduce CO2 emissions from heavy-duty trucks is to combine low-carbon fuels and a hybrid-electric powertrain to maximize overall efficiency. A hybrid electric powertrain can reduce the peak power required from the internal combustion engine, leading to opportunities to reduce the engine size but still meet vehicle performance requirements. Although engine downsizing in the light-duty sector can offer significant fuel economy savings mainly due to increased part-load efficiency, its benefits and downsides in heavy-duty engines are less clear. As there has been limited published research in this area to date, there is a lack of a standardized engine downsizing procedure.

A diesel piloted natural gas engine's performance varies depending on operating conditions and has performed best under medium to high loads. It can often equal or better the fuel conversion efficiency of a diesel-only engine in this operating range. This paper presents a study performed on a multi-cylinder Cummins ISB 6.7L diesel engine converted to run stoichiometric natural gas/diesel micro-pilot combustion with a maximum diesel contribution of 10%. This study systematically quantifies and ranks the sensitivity of control factors on combustion and performance while operating at medium loads. The effects of combustion control parameters, including the pilot start of injection, pilot injection pressure, pilot injection quantity, exhaust gas recirculation, and global equivalence ratio, were tested using a design of experiments orthogonal matrix approach.

The development and calibration of modern combustion engines is challenging in the area of continuously tightening emission limits and the necessity for meeting real driving emissions regulations. A focus is on the knowledge of the internal engine processes and the determination of pollutants formations in order to predict the engine emissions. A physical model-based development provides an insight into hardly measurable phenomena properties and is robust against changing input data. With increasing modeling depth the required computing capacities increase. As an alternative to physical modeling, data-driven machine learning methods can be used to enable high-performance modeling accuracy. However, these are dependent on the learned data. To combine the performance and robustness of both types of modeling a hybrid application of data-driven and physical models is developed in this paper as a grey box model for the exhaust emission prediction of a commercial vehicle diesel engine.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

A physics-based control-oriented model is developed to dynamically predict cycle-to-cycle combustion timing in transient fueling conditions for Homogeneous Charge Compression Ignition (HCCI) engines. The model simulates the engine cycle from the intake stroke to the exhaust stroke and includes the thermal coupling dynamics caused by the residual gases from one cycle to the next cycle. A residual gas model, a modified knock integral model, a fuel burn rate model, and thermodynamic models for the gas state in combustion and exhaust strokes are incorporated to simulate the engine cycle. The gas exchange process, generated work and completeness of combustion are predicted using semi-empirical correlations. The resulting model is parameterized for the combustion of Primary Reference Fuel (PRF) blends using 5703 simulations from a detailed thermo-kinetic model. Semi-empirical correlations in the model are parameterized using the experimental data obtained from a single-cylinder engine.