Qingning Zhang

A review of parallel and series turbocharging for the diesel engine

Several turbocharger units can be used for engine boosting in series or parallel arrangements in which they are phased in and out according to the operating conditions of the engine. This technology has the potential to facilitate downsizing of automotive engines in order to yield benefits in terms of their transient performance, the fuel consumption and emissions output. This review investigates the benefits and drawbacks of series and parallel turbocharging arrangements. Since the effectiveness of using the boosting technology crucially depends on the control scheme applied, developments in the modelling and control approaches used in single-stage, series and parallel turbocharging are also examined. In comparison with single-stage turbocharging, using several turbochargers in series or parallel can provide a faster transient response without compromising the fuel consumption, while also having the potential to provide higher boost pressures. Novel non-linear and robust control approaches have demonstrated improvements in performance and robustness over traditional approaches used in commercial engine control relying on separate control loops for the different engine variables.

A Research on Waste-Gated Turbine Performance Under Unsteady Flow Condition

Turbochargers are key components of engine air-paths that must be carefully considered during the development process. The combination of fluid, mechanical, and thermal phenomenon make the turbocharger a highly dynamic and nonlinear modeling challenge. The aim of this study is to quantify the dynamic response of the turbocharger system across a frequency spectrum from 0.003 Hz to 500 Hz, i.e., for exhaust gas pulsation in steady state, load steps, and cold start drive cycles, to validate the assumption of quasi-steady assumptions for particular modeling problems. A waste-gated turbine was modeled using the dual orifice approach, a lumped capacitance heat transfer model, and novel, physics-based pneumatic actuator mechanism model. Each submodel has been validated individually against the experimental measurements. The turbine inlet pressure and temperature and the waste-gate actuator pressure were perturbed across the full frequency range both individually and simultaneously in separate numerical investigations. The dynamic responses of turbine housing temperature, turbocharger rotor speed, waste-gate opening, mass flow, and gas temperatures/pressures were all investigated. The mass flow parameter exhibits significant dynamic behavior above 100 Hz, illustrating that the quasi-steady assumption is invalid in this frequency range. The waste-gate actuator system showed quasi-steady behavior below 10 Hz, while the mechanical inertia of the turbine attenuated fluctuations in shaft speed for frequencies between 0.1 and 10 Hz. The thermal inertia of the turbocharger housing meant that housing temperature variations were supressed at frequencies above 0.01 Hz. The results have been used to illustrate the importance of model parameters for three transient simulation scenarios (cold start, load step, and pulsating exhaust flow).

Experimental and analytical investigation of implementing a ball bearing turbocharger on a production diesel engine

Ball bearing turbocharger technology has started to be adopted for mass-production engines due to the potential benefit in transient performance and fuel consumption. Compared to the conventional journal bearing, the low friction of the ball bearing allows the turbocharger to accelerate faster so that the engine can be supplied with boost pressure more quickly following a transient torque request and under steady state offers reduced engine back pressure, which can reduce engine fuel consumption. In this study, the benefits of using a ball bearing turbocharger compared to a conventional journal bearing turbocharger were identified first in simulation and then validated in a back to back comparison of two otherwise identical turbochargers through extensive experimental analysis.

Predicting the Nitrogen Oxides Emissions of a Diesel Engine using Neural Networks

Nitrogen oxides emissions are an important aspect of engine design and calibration due to increasingly strict legislation. As a consequence, accurate modeling of nitrogen oxides emissions from Diesel engines could play a crucial role during the design and development phases of vehicle powertrain systems. A key step in future engine calibration will be the need to capture the nonlinear behavior of the engine with respect to nitrogen oxides emissions within a rapid-calculating mathematical model. These models will then be used in optimization routines or on-board control features. In this paper, an artificial neural network structure incorporating a number of engine variables as inputs including torque, speed, oil temperature and variables related to fuel injection is developed as a method of predicting the production of nitrogen oxides based on measured test data. A multi-layer perceptron model is identified and validated using data from dynamometry tests. The model predicts exhaust nitrogen oxide concentrations under different engine conditions with satisfactory accuracy. The developed neural network model has potential applications in real-time control aimed at reducing nitrogen oxides emission levels.

Control Strategy Study of the Series Sequential Turbocharging Using 1-D Simulation

The series sequential turbocharging arrangement is a promising technology to turbocharge downsized diesel engines to higher ratings with combined benefits in transient performance, fuel efficiency and emissions reduction. However, the control of such a complex charging system proves to be problematic. Without a robust control strategy, the potential of series sequential turbocharging will not be fully exploited. Worse still, a badly constructed control strategy could have serious negative impact on the turbocharger/engine system. In this paper, several control strategies of the series sequential turbocharging system were proposed and implemented in simulations.Copyright

An empirical model for the carbon dioxide emissions of a diesel engine

Carbon dioxide emissions from vehicles are a particular focus and challenge for automotive designers and manufacturers due to increasingly stringent engine emissions legislation. In addition to the potential environmental impacts, the rate of carbon dioxide production is strongly indicative of the efficiency and therefore fuel economy of an engine at its different operating conditions. In this paper, a neural network model is developed in order to predict the carbon dioxide production rate from a number of engine variables including engine speed, torque, temperature and parameters controlling fuel injection timing. The model structure accurately predicts the rate of carbon dioxide production and has applications in future efficiency and emissions optimisation during engine design and also in online engine control.