DESIGN BACK PROPAGATION NEURAL NETWORK (BPNN) – PID OF ACTIVE AIR SUSPENSION BASED ON HALF CAR MODEL AT PLUG-IN HYBRID ELECTRIC VEHICLE (PHEV)
A Plug-In Hybrid Electric Vehicle (PHEV) is a car with a combination of an electric motor and an internal combustion engine (ICE). The implementation of active air suspension in this research uses a half car model. Mathematical modeling is used to obtain system responses such as body displacement, b...
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University of Brawijaya
2025-06-01
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| Series: | Rekayasa Mesin |
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| Online Access: | https://rekayasamesin.ub.ac.id/index.php/rm/article/view/1919 |
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| author | Bambang Sampurno Kyla Anisa Windarta Mohammad Berel Toriki Liza Rusdiyana Dika Andini Suryandani |
| author_facet | Bambang Sampurno Kyla Anisa Windarta Mohammad Berel Toriki Liza Rusdiyana Dika Andini Suryandani |
| author_sort | Bambang Sampurno |
| collection | DOAJ |
| description | A Plug-In Hybrid Electric Vehicle (PHEV) is a car with a combination of an electric motor and an internal combustion engine (ICE). The implementation of active air suspension in this research uses a half car model. Mathematical modeling is used to obtain system responses such as body displacement, body acceleration, rear wheel displacement, and rear wheel acceleration using MATLAB software. There are 3 test modes, namely passive suspension, active suspension, and implementation using a neural network-based control system. Based on these 3 test modes in 3 conditions, the use of passive suspension for body displacement produces a maximum overshoot of 133% and a settling time of 2.15 seconds. Meanwhile, the active suspension produces 43.33% and a settling time of 0.7 seconds. When using a neural network, it produces 50% and a settling time of 2.14 seconds. Some while, the use of passive suspesion foor body acceleration produces a maximum overshoot of 133%, arms of 124,2, and a settling time of 2.15 seconds. Meanwhile, the active suspension produces maximum overshoot of 43.33% , arms of 2.92, and a settling time of 0.7 seconds. When using a neural network, it produces maximum overshoot of 50%, arms of 2.92 and a settling time of 2.14 seconds. |
| format | Article |
| id | doaj-art-aa8b37d66651445f84c3ccf3d3823f6c |
| institution | DOAJ |
| issn | 2338-1663 2477-6041 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | University of Brawijaya |
| record_format | Article |
| series | Rekayasa Mesin |
| spelling | doaj-art-aa8b37d66651445f84c3ccf3d3823f6c2025-08-20T03:19:17ZengUniversity of BrawijayaRekayasa Mesin2338-16632477-60412025-06-0116145546810.21776/jrm.v16i1.19191630DESIGN BACK PROPAGATION NEURAL NETWORK (BPNN) – PID OF ACTIVE AIR SUSPENSION BASED ON HALF CAR MODEL AT PLUG-IN HYBRID ELECTRIC VEHICLE (PHEV)Bambang Sampurno0Kyla Anisa Windarta1Mohammad Berel Toriki2Liza Rusdiyana3Dika Andini Suryandani4Industrial Mechanical Engineeering Department, Institut Teknologi Sepuluh Nopember, Kota Surabaya, Jawa Timur, INDONESIAIndustrial Mechanical Engineeering Department, Institut Teknologi Sepuluh Nopember, Kota Surabaya, Jawa Timur, INDONESIAIndustrial Mechanical Engineeering Department, Institut Teknologi Sepuluh Nopember, Kota Surabaya, Jawa Timur, INDONESIAIndustrial Mechanical Engineeering Department, Institut Teknologi Sepuluh Nopember, Kota Surabaya, Jawa Timur, INDONESIAIndustrial Mechanical Engineeering Department, Institut Teknologi Sepuluh Nopember, Kota Surabaya, Jawa Timur, INDONESIAA Plug-In Hybrid Electric Vehicle (PHEV) is a car with a combination of an electric motor and an internal combustion engine (ICE). The implementation of active air suspension in this research uses a half car model. Mathematical modeling is used to obtain system responses such as body displacement, body acceleration, rear wheel displacement, and rear wheel acceleration using MATLAB software. There are 3 test modes, namely passive suspension, active suspension, and implementation using a neural network-based control system. Based on these 3 test modes in 3 conditions, the use of passive suspension for body displacement produces a maximum overshoot of 133% and a settling time of 2.15 seconds. Meanwhile, the active suspension produces 43.33% and a settling time of 0.7 seconds. When using a neural network, it produces 50% and a settling time of 2.14 seconds. Some while, the use of passive suspesion foor body acceleration produces a maximum overshoot of 133%, arms of 124,2, and a settling time of 2.15 seconds. Meanwhile, the active suspension produces maximum overshoot of 43.33% , arms of 2.92, and a settling time of 0.7 seconds. When using a neural network, it produces maximum overshoot of 50%, arms of 2.92 and a settling time of 2.14 seconds.https://rekayasamesin.ub.ac.id/index.php/rm/article/view/1919phevactive suspensionneural networkpassive suspensionmatlab |
| spellingShingle | Bambang Sampurno Kyla Anisa Windarta Mohammad Berel Toriki Liza Rusdiyana Dika Andini Suryandani DESIGN BACK PROPAGATION NEURAL NETWORK (BPNN) – PID OF ACTIVE AIR SUSPENSION BASED ON HALF CAR MODEL AT PLUG-IN HYBRID ELECTRIC VEHICLE (PHEV) Rekayasa Mesin phev active suspension neural network passive suspension matlab |
| title | DESIGN BACK PROPAGATION NEURAL NETWORK (BPNN) – PID OF ACTIVE AIR SUSPENSION BASED ON HALF CAR MODEL AT PLUG-IN HYBRID ELECTRIC VEHICLE (PHEV) |
| title_full | DESIGN BACK PROPAGATION NEURAL NETWORK (BPNN) – PID OF ACTIVE AIR SUSPENSION BASED ON HALF CAR MODEL AT PLUG-IN HYBRID ELECTRIC VEHICLE (PHEV) |
| title_fullStr | DESIGN BACK PROPAGATION NEURAL NETWORK (BPNN) – PID OF ACTIVE AIR SUSPENSION BASED ON HALF CAR MODEL AT PLUG-IN HYBRID ELECTRIC VEHICLE (PHEV) |
| title_full_unstemmed | DESIGN BACK PROPAGATION NEURAL NETWORK (BPNN) – PID OF ACTIVE AIR SUSPENSION BASED ON HALF CAR MODEL AT PLUG-IN HYBRID ELECTRIC VEHICLE (PHEV) |
| title_short | DESIGN BACK PROPAGATION NEURAL NETWORK (BPNN) – PID OF ACTIVE AIR SUSPENSION BASED ON HALF CAR MODEL AT PLUG-IN HYBRID ELECTRIC VEHICLE (PHEV) |
| title_sort | design back propagation neural network bpnn pid of active air suspension based on half car model at plug in hybrid electric vehicle phev |
| topic | phev active suspension neural network passive suspension matlab |
| url | https://rekayasamesin.ub.ac.id/index.php/rm/article/view/1919 |
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