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Novel Modulation Strategy for a CLC Resonant Dual Active Bridge
W. L. Malan,D. M. Vilathgamuwa,G. R. Walker,D. J. Thrimawithana,U. K. Madawala 전력전자학회 2015 ICPE(ISPE)논문집 Vol.2015 No.6
This paper proposes a novel modulation strategy for a phase controlled Capacitor-Inductor- Capacitor (CLC) Resonant Dual Active Bridge (RDAB). The proposed modulation strategy improves the soft turnon, Zero-Current-Switching (ZCS) and Zero-Voltage-Switching (ZVS) range of the converter while only minimally increasing the required reactive currents in the ac link. A mathematical analysis of the proposed modulation scheme is presented along with a theoretical loss comparison between several modulation strategies. The proposed modulation strategy was implemented and the experimental results are presented.
Recurrent Neural Network to Estimate Intake Manifold O₂ Concentration in a Diesel Engine
Loris Ventura,Stefano A. Malan 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10
Emission regulations are becoming more and more stringent, especially on NOX pollutants, making diesel engines with their embedded control systems more and more complex. To ensure a correct and clean engine functioning, all the control strategies related to aftertreatment, fuel injection and air-path have to exploit or target the intake manifold O₂ concentration. The O₂ concentration is strictly related to engine-out NOX emissions and an accurate model, to be implemented in emission control systems, is essential. The paper addresses the modeling of the intake O₂ concentration in a turbocharged diesel engine by means of a Recurrent Neural Network with simulation focus and fed with four inputs. The inputs are engine load, engine speed and the position of Exhaust Gas Recirculation and Variable Geometry Turbochargers valves. Training and validation data are generated using the engine simulation tool GT-Power implementing a detailed model of the engine while the training procedure is performed in MATLAB environment through NNSYSID toolbox. The performances of the obtained model are satisfactory in different tests and the model is able to account for the engine nonlinearities during transients.
Air path and combustion controls coordination in diesel engine
Loris Ventura,Stefano A. Malan 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
The tightening of the diesel pollutants emissions regulations has made the performances obtainable from steady-state map controls, commonly employed in Internal Combustion Engine (ICE) management, unsatisfactory. To overcome these performance limitations, control systems have to cope with the engine transient operation conditions, coupling between its subsystem dynamics, and the trade-off between different requirements to efficiently manage the engine. The work demonstrates the deployment of a reference generator that coordinates the air path and combustion control systems of a turbocharged diesel engine for heavy-duty applications. The control system coordinator is based on neural networks and allows to exploit the best performance of the two control systems. The key idea is to generate air path targets, intake O₂ concentration and Intake MAnifold Pressure (IMAP), coherent with the ones of the combustion control system, engine load and engine-out Nitrogen Oxides (NOx). In this way, the air path control system provides the global conditions for the correct functioning of the engine, while, in cooperation, the combustion control will react to fast changes in the engine operating state and compensate for the remaining deviations with respect to load and NOx targets. Reference generator networks are suitable for further real-time implementation on rapid-prototyping hardware and their performance was overall good.
NLQR Control of High Pressure EGR in Diesel Engine
Loris Ventura,Stefano A. Malan 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10
The tightening of the diesel pollutant emission regulations has made the performances obtainable from steady-state map controls, commonly employed in Internal Combustion Engine management, unsatisfactory. To overcome these limitations a NonLinear Quadratic Regulator (NLQR) system for the High Pressure Exhaust Gas Recirculation (HP-EGR) loop of a turbocharged diesel engine has been developed to control the intake O₂ concentration and the Intake MAnifold Pressure (IMAP). This model-based control approach exploits the prediction of two dynamic Recurrent Neural Networks (RNN) to compute the command actions for the HP-EGR valve and VGT (Variable Geometry Turbocharger) rack position. Engine speed, engine load, HP-EGR and VGT valves positions together with the intake O₂ concentration and IMAP feedbacks are the inputs used by the RNN to compute the predictions. In order to select the next HP-EGR and VGT control actions the effect of different command combinations, retrieved from a discretized action space, are evaluated through a quadratic objective function to be minimized. Two different transient profiles have been used to test the designed control system against the steady-state map approach. The developed control system has shown a satisfactory performance improvement over the map control. Therefore it is suitable for the subsequent assessment on the real engine.