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Kuwait City, Kuwait

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Ho T.,University of Tasmania | Karri V.,Australian College of Kuwait
International Journal of Hydrogen Energy | Year: 2010

Many studies of renewable energy have shown hydrogen is one of the major green energy in the future. This has lead to the development of many automotive application of using hydrogen as a fuel especially in internal combustion engine. Nonetheless, there has been a slow growth and less knowledge details in building up the prototype and control methodology of the hydrogen internal combustion engine [1]. In this paper, The Toyota Corolla 4 cylinder, 1.8l engine running on petrol was systematically modified in such a way that it could be operated on either gasoline or hydrogen at the choice of the driver. Within the scope of this project, several ancillary instruments such as a new inlet manifold, hydrogen fuel injection, storage system and leak detection safety system were implemented. Attention is directed towards special characteristics related to the basic tuning of hydrogen engine such as: air to fuel ratio operating conditions, ignition timing and injection timing in terms of different engine speed and throttle position. Based on the experimental data, a suite of neural network models were tested to accurately predict the effect of different engine operating conditions (speed and throttle position) on the hydrogen powered car engine characteristics. Predictions were found to be ±3% to the experimental values for all of case studies. This work provided better understanding of the effect of hydrogen engine characteristic parameters on different engine operating conditions. © 2010 Published by Elsevier Ltd on behalf of Professor T. Nejat Veziroglu.

Yap W.K.,Charles Darwin University | Karri V.,Australian College of Kuwait
Applied Soft Computing Journal | Year: 2013

This paper presents a comparison of predictive models for the estimation of engine power and tailpipe emissions for a 4 kW gasoline scooter. This study forms a benchmark toward establishing an online emissions control and monitoring system to bring the emissions to within specific limits. Three emissions predictive models were investigated in this study; direct and series artificial neural network (ANN) models and a MATLAB dynamic model. The direct models takes variables lambda, throttle position, engine and vehicle speed to predict the engine power and the emissions CO, CO2 and HC. The series model first takes the mentioned input to predict the engine power and consequently using the engine power as the fifth input to predict the emissions. For the ANN models, two multilayered networks were compared and analyzed; the backpropagation (BP) and optimization layer-by-layer (OLL) algorithms. The predictive accuracy for each algorithm were compared and it was found that the OLL network is the most accurate with a maximum mean relative error (MRE) of 1.78% and 1.38% for the direct and series predictive model respectively. Comparative results showed that the series neural network model gives the most accurate predictions, with MRE of 0.63% and 0.47% for the engine power and emissions respectively. The series neural network model can be seen as generic virtual power and emissions sensors, substituting costly and cumbersome hardware. Simple obtainable process parameters together with the series neural network will contribute immensely in control and tuning of emissions for real-time vehicular applications. © 2012 Elsevier B.V.

Becker S.,University of Tasmania | Karri V.,Australian College of Kuwait
International Journal of Hydrogen Energy | Year: 2010

Predictive models were built using neural network based Adaptive Neuro-Fuzzy Inference Systems for hydrogen flow rate, electrolyzer system-efficiency and stack-efficiency respectively. A comprehensive experimental database forms the foundation for the predictive models. It is argued that, due to the high costs associated with the hydrogen measuring equipment; these reliable predictive models can be implemented as virtual sensors. These models can also be used on-line for monitoring and safety of hydrogen equipment. The quantitative accuracy of the predictive models is appraised using statistical techniques. These mathematical models are found to be reliable predictive tools with an excellent accuracy of ±3% compared with experimental values. The predictive nature of these models did not show any significant bias to either over prediction or under prediction. These predictive models, built on a sound mathematical and quantitative basis, can be seen as a step towards establishing hydrogen performance prediction models as generic virtual sensors for wider safety and monitoring applications. © 2010 Published by Elsevier Ltd on behalf of Professor T. Nejat Veziroglu.

Yap W.K.,Charles Darwin University | Karri V.,Australian College of Kuwait
Expert Systems with Applications | Year: 2012

This paper presents a two-stage emissions predictive model developed by investigating common feedforward neural network models. The first stage model involves predicting engine parameters power and tractive forces and the predicted parameters are used as inputs to the second stage model to predict the vehicle emissions. The following gasses were predicted from the tailpipe emissions for a scooter application; CO, CO2, HC and O2. Three feedforward neural network models were investigated and compared in this study; backpropagation, optimization layer-by-layer and radial basis function networks. Based on the experimental setup, the neural network models were trained and tested to accurately predict the effect of the engine operating conditions on the emissions by varying the number of hidden nodes. The selected optimization layer-by-layer network proved to be the most accurate and reliable predictive tool with prediction errors of ±5%. The effect of the engine operating conditions on the tailpipe emissions for a scooter is shown to display similar qualitative and quantitative trends between the simulated and the experimental data. This study provides a better understanding in effects of engine process parameters on tailpipe emissions for the scooter as well as for general vehicular applications. © 2011 Elsevier Ltd. All rights reserved.

Ho T.,University of Tasmania | Karri V.,Australian College of Kuwait
International Journal of Hydrogen Energy | Year: 2011

This paper presents the optimising control technique for a Toyota Corolla four-cylinder, 1.8-L hydrogen powered car. Based on the extensive experimental tuning data, statistical two stage models and calibration generation methodology are carried out, in which ignition timing, injection timing, injection duration and corresponding lambda value (indicate air to fuel ratio) are chosen as control variables while engine output torque and exhaust NOx emissions are chosen as performance index functions. The trade-off study is employed to optimise performance of hydrogen engine by considering different optimisation objectives at different engine operating states. Those engine operating states are defined by the throttle position and opening speed of throttle, except start and idle load states that need the auxiliary control parameters to be added in. Each value of ignition advance, lambda, injection duration and injection end angle are tested and the hydrogen engine is found to have good drivability and reliable on road optimisation. This work is a step towards establishing optimising control methodology of hydrogen powered car via application of advanced power train techniques while saving time, money and limiting damage for innovative hydrogen engine in early experimental fine tuning process. © 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights.

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