Experimental Study on the Performance of Controllers for the Hydrogen Gas Production Demanded by an Internal Combustion Engine
This work presents the design and application of two control techniques—a model predictive control (MPC) and a proportional integral derivative control (PID), both in combination with a multilayer perceptron neural network—to produce hydrogen gas on-demand, in order to use it as...
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doaj-b0ee026c0b5e4c3a99c7e46b1c6dd8402020-11-24T23:33:40ZengMDPI AGEnergies1996-10732018-08-01118215710.3390/en11082157en11082157Experimental Study on the Performance of Controllers for the Hydrogen Gas Production Demanded by an Internal Combustion EngineMarisol Cervantes-Bobadilla0Ricardo Fabricio Escobar-Jiménez1José Francisco Gómez-Aguilar2Jarniel García-Morales3Víctor Hugo Olivares-Peregrino4Posgrado del Tecnológico Nacional de México/Centro Nacional de Investigación y Desarrollo Tecnológico. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos 62324, MexicoTecnológico Nacional de México/Centro Nacional de Investigación y Desarrollo Tecnológico. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos 62324, MexicoCONACyT-Tecnológico Nacional de México/CENIDET. Interior Internado Palmira S/N, Col. Palmira C.P.62490, Cuernavaca, Morelos 63324, MexicoTecnológico Nacional de México/Centro Nacional de Investigación y Desarrollo Tecnológico. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos 62324, MexicoTecnológico Nacional de México/Centro Nacional de Investigación y Desarrollo Tecnológico. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos 62324, MexicoThis work presents the design and application of two control techniques—a model predictive control (MPC) and a proportional integral derivative control (PID), both in combination with a multilayer perceptron neural network—to produce hydrogen gas on-demand, in order to use it as an additive in a spark ignition internal combustion engine. For the design of the controllers, a control-oriented model, identified with the Hammerstein technique, was used. For the implementation of both controllers, only 1% of the overall air entering through the throttle valve reacted with hydrogen gas, allowing maintenance of the hydrogen–air stoichiometric ratio at 34.3 and the air–gasoline ratio at 14.6. Experimental results showed that the average settling time of the MPC controller was 1 s faster than the settling time of the PID controller. Additionally, MPC presented better reference tracking, error rates and standard deviation of 1.03 × 10 − 7 and 1.06 × 10 − 14 , and had a greater insensitivity to measurement noise, resulting in greater robustness to disturbances. Finally, with the use of hydrogen as an additive to gasoline, there was an improvement in thermal and combustion efficiency of 4% and 0.6%, respectively, and an increase in power of 545 W, translating into a reduction of fossil fuel use.http://www.mdpi.com/1996-1073/11/8/2157hydrogen production controldigital PIDmodel predictive control |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Marisol Cervantes-Bobadilla Ricardo Fabricio Escobar-Jiménez José Francisco Gómez-Aguilar Jarniel García-Morales Víctor Hugo Olivares-Peregrino |
spellingShingle |
Marisol Cervantes-Bobadilla Ricardo Fabricio Escobar-Jiménez José Francisco Gómez-Aguilar Jarniel García-Morales Víctor Hugo Olivares-Peregrino Experimental Study on the Performance of Controllers for the Hydrogen Gas Production Demanded by an Internal Combustion Engine Energies hydrogen production control digital PID model predictive control |
author_facet |
Marisol Cervantes-Bobadilla Ricardo Fabricio Escobar-Jiménez José Francisco Gómez-Aguilar Jarniel García-Morales Víctor Hugo Olivares-Peregrino |
author_sort |
Marisol Cervantes-Bobadilla |
title |
Experimental Study on the Performance of Controllers for the Hydrogen Gas Production Demanded by an Internal Combustion Engine |
title_short |
Experimental Study on the Performance of Controllers for the Hydrogen Gas Production Demanded by an Internal Combustion Engine |
title_full |
Experimental Study on the Performance of Controllers for the Hydrogen Gas Production Demanded by an Internal Combustion Engine |
title_fullStr |
Experimental Study on the Performance of Controllers for the Hydrogen Gas Production Demanded by an Internal Combustion Engine |
title_full_unstemmed |
Experimental Study on the Performance of Controllers for the Hydrogen Gas Production Demanded by an Internal Combustion Engine |
title_sort |
experimental study on the performance of controllers for the hydrogen gas production demanded by an internal combustion engine |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2018-08-01 |
description |
This work presents the design and application of two control techniques—a model predictive control (MPC) and a proportional integral derivative control (PID), both in combination with a multilayer perceptron neural network—to produce hydrogen gas on-demand, in order to use it as an additive in a spark ignition internal combustion engine. For the design of the controllers, a control-oriented model, identified with the Hammerstein technique, was used. For the implementation of both controllers, only 1% of the overall air entering through the throttle valve reacted with hydrogen gas, allowing maintenance of the hydrogen–air stoichiometric ratio at 34.3 and the air–gasoline ratio at 14.6. Experimental results showed that the average settling time of the MPC controller was 1 s faster than the settling time of the PID controller. Additionally, MPC presented better reference tracking, error rates and standard deviation of 1.03 × 10 − 7 and 1.06 × 10 − 14 , and had a greater insensitivity to measurement noise, resulting in greater robustness to disturbances. Finally, with the use of hydrogen as an additive to gasoline, there was an improvement in thermal and combustion efficiency of 4% and 0.6%, respectively, and an increase in power of 545 W, translating into a reduction of fossil fuel use. |
topic |
hydrogen production control digital PID model predictive control |
url |
http://www.mdpi.com/1996-1073/11/8/2157 |
work_keys_str_mv |
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