Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle
The design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation of hybrid electric vehicles (HEVs) are proposed in this article. The proposed intelligent controller is adopted based on the enhancement of a model predictive controller (MPC) by an artificial neu...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/4/971 |
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| author | Mohamed El-Sayed M. Essa Joseph Victor W. Lotfy M. Essam K. Abd-Elwahed Khaled Rabie Basem M. ElHalawany Mahmoud Elsisi |
| author_facet | Mohamed El-Sayed M. Essa Joseph Victor W. Lotfy M. Essam K. Abd-Elwahed Khaled Rabie Basem M. ElHalawany Mahmoud Elsisi |
| author_sort | Mohamed El-Sayed M. Essa |
| collection | DOAJ |
| description | The design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation of hybrid electric vehicles (HEVs) are proposed in this article. The proposed intelligent controller is adopted based on the enhancement of a model predictive controller (MPC) by an artificial neural network (ANN) approach. The MPC-based ANN (NNMPC) is proposed to control the speed of HEVs for a simulation system model and experimental HIL test systems. The HIL is established to assess the performance of the NNMPC to control the velocity of HEVs in an experimental environment. The real-time environment of HIL is implemented through a low-cost approach such as the integration of an Arduino Mega 2560 and a host Lenovo PC with a Core i7 @ 3.4 GHz processor. The NNMPC is compared with a proportional–integral (PI) controller, a classical MPC, and two different settings of the ANN methodology to verify the efficiency of the proposed intelligent NNMPC. The obtained results show a distinct behavior of the proposed NNMPC to control the speed of HEVs with good performance based on the distinct transient response, minimum error steady state, and system robustness against parameter perturbation. |
| first_indexed | 2024-03-11T08:54:24Z |
| format | Article |
| id | doaj.art-e858e56705194484ab4ec125b1386951 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T08:54:24Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e858e56705194484ab4ec125b13869512023-11-16T20:13:02ZengMDPI AGElectronics2079-92922023-02-0112497110.3390/electronics12040971Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric VehicleMohamed El-Sayed M. Essa0Joseph Victor W. Lotfy1M. Essam K. Abd-Elwahed2Khaled Rabie3Basem M. ElHalawany4Mahmoud Elsisi5Electrical Power and Machines Department, Institute of Aviation Engineering and Technology (I.A.E.T), Egyptian Aviation Academy, Imbab Airport, Giza 12815, EgyptElectronics and Communication Department, Institute of Aviation Engineering and Technology (I.A.E.T), Egyptian Aviation Academy, Imbab Airport, Giza 12815, EgyptElectronics and Communication Department, Institute of Aviation Engineering and Technology (I.A.E.T), Egyptian Aviation Academy, Imbab Airport, Giza 12815, EgyptDepartment of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UKDepartment of Electronics and Communication Engineering, Kuwait College of Science and Technology, Kuwait City 13133, KuwaitDepartment of Electrical Engineering, Faculty of Engineering (Shoubra), Benha University, Cairo 11629, EgyptThe design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation of hybrid electric vehicles (HEVs) are proposed in this article. The proposed intelligent controller is adopted based on the enhancement of a model predictive controller (MPC) by an artificial neural network (ANN) approach. The MPC-based ANN (NNMPC) is proposed to control the speed of HEVs for a simulation system model and experimental HIL test systems. The HIL is established to assess the performance of the NNMPC to control the velocity of HEVs in an experimental environment. The real-time environment of HIL is implemented through a low-cost approach such as the integration of an Arduino Mega 2560 and a host Lenovo PC with a Core i7 @ 3.4 GHz processor. The NNMPC is compared with a proportional–integral (PI) controller, a classical MPC, and two different settings of the ANN methodology to verify the efficiency of the proposed intelligent NNMPC. The obtained results show a distinct behavior of the proposed NNMPC to control the speed of HEVs with good performance based on the distinct transient response, minimum error steady state, and system robustness against parameter perturbation.https://www.mdpi.com/2079-9292/12/4/971hybrid electric vehiclemodel predictive controlartificial intelligencehardware in the loop |
| spellingShingle | Mohamed El-Sayed M. Essa Joseph Victor W. Lotfy M. Essam K. Abd-Elwahed Khaled Rabie Basem M. ElHalawany Mahmoud Elsisi Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle Electronics hybrid electric vehicle model predictive control artificial intelligence hardware in the loop |
| title | Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle |
| title_full | Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle |
| title_fullStr | Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle |
| title_full_unstemmed | Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle |
| title_short | Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle |
| title_sort | low cost hardware in the loop for intelligent neural predictive control of hybrid electric vehicle |
| topic | hybrid electric vehicle model predictive control artificial intelligence hardware in the loop |
| url | https://www.mdpi.com/2079-9292/12/4/971 |
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