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All Journal IPTEK The Journal of Engineering IPTEK The Journal for Technology and Science Bulletin of Electrical Engineering and Informatics Rekayasa Mesin Jurnal Nasional Aplikasi Mekatronika, Otomasi dan Robot Industri (AMORI) Sewagati
Liza Rusdiyana
Institut Technologi Sepuluh Nopember, Surabaya
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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DESIGN BACK PROPAGATION NEURAL NETWORK (BPNN) – PID OF ACTIVE AIR SUSPENSION BASED ON HALF CAR MODEL AT PLUG-IN HYBRID ELECTRIC VEHICLE (PHEV) Sampurno, Bambang; Windarta, Kyla Anisa; Toriki, Mohammad Berel; Rusdiyana, Liza; Suryandani, Dika Andini
Jurnal Rekayasa Mesin Vol. 16 No. 1 (2025)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v16i1.1919

Abstract

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.
Co-Authors Andino Septian Andreyanto - Ardiyanti Pratama, Tania Atria Pradityana Bambang Sampurno, Bambang Bayu Adhi Saputro Budi Luwar Sanyoto Dedy Zul Hidayat Noor Dzakwan Shidqul Aziz Eddy Widiyono Fatan Odhi Dandha Heru Mirmanto M. Lukman Hakim Mahirul Mursid Mashuri Mashuri Mashuri Mashuri Muhammad Lukman Hakim Nanda Ela Sasmita Nur Husodo Rivai Wardhani Rizaldy Hakim Rizaldy Hakim Ash Shiddieqy Rizaldy Hakim Ash Shiddieqy Subiyanto, Hari Suhariyanto Suhariyanto Suhariyanto Suhariyanto Suhariyanto Suryandani, Dika Andini Toriki, Mohammad Berel WINARTO Winarto Winarto Windarta, Kyla Anisa