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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Transmisi: Jurnal Ilmiah Teknik Elektro Mechatronics, Electrical Power, and Vehicular Technology TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Electrical Technology UMY Techne : Jurnal Ilmiah Elektroteknika Buletin Ilmiah Sarjana Teknik Elektro IJITEE (International Journal of Information Technology and Electrical Engineering) International Journal of Quantitative Research and Modeling Automotive Experiences Jurnal Nasional Teknik Elektro dan Teknologi Informasi Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII

Adha Imam Cahyadi

Universitas Gadjah Mada

Author-ID : 2375714

Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

Published : 21 Documents Claim Missing Document

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Articles

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Effect of different core materials in very low voltage induction motors for electric vehicle Fransisco Danang Wijaya; Iftitah Imawati; Muhammad Yasirroni; Adha Imam Cahyadi
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 12, No 2 (2021)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2021.v12.95-103

The use of squirrel cage induction motor for electric vehicle (EV) has been increasingly popular than permanent magnet and brushless motors due to their independence on rare materials. However, its performance is significantly affected by the core materials. In this research, induction motors performance with various core materials (M19_24G, Arnon7, and nickel steel carpenter) are studied in very low voltage. Three phases, 50 Hz, 5 HP, 48 V induction motor were used as the propulsion force testbed applied for a golf cart EV. The aims are to identify loss distribution according to core materials and compare power density and cost. The design process firstly determines the motor specifications, then calculates the dimensions, windings, stator, and rotor slots using MATLAB. The parameters obtained are used as inputs to ANSYS Maxwell to calculate induction motor performance. Finally, the design simulations are carried out on RMxprt and 2D transient software to determine the loss characteristics of core materials. It is found that the stator winding dominates the loss distribution. Winding losses have accounted for 52-55 % of the total loss, followed by rotor winding losses around 25-27 % and losses in the core around 1-7 %. Based on the three materials tested, nickel steel carpenter and M19_24G attain the highest efficiency with 83.27 % and 83.10 %, respectively, while M19_24G and Arnon7 possess the highest power density with 0.37 kW/kg and 0.38 kW/kg whereas, in term of production cost, the Arnon7 is the lowest.

Cell Balancing On Three- Cell Lithium Polymer Batteries Connected In Series Erika Loniza; Johanes Andriano Situmorang; Adha Imam Cahyadi
Journal of Electrical Technology UMY Vol 1, No 3 (2017)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.1318

Electric vehicle becomes popular recently, particularly in Indonesia. One of the most important and crucial components in an electrical vehicle is the battery. BMS (Battery Management System) is a system to monitor and regulate the performance of the battery resulting in effective-efficient-durable performance. Usually, BMS is needed to prevent battery from system failure. One of the problems that normally happens in a multi-cell battery and causing system failure is voltage unbalance. In this study, the system is designed so it can monitor the voltage condition of the three battery’s cells in series circuit and manage to balances it. The process of balancing the value of the voltage at the battery cells is known as cell balancing. The method used in this study is by using passive shunt resistor balancing method. In this method, an electronic circuit is designed in order to balance the value of the voltage at the battery cells using resistors to remove excess voltage. The result shows that the electrical circuit is capable to balance the voltage of each cell. Moreover, the designed circuit is monitored by software so it can perform in flexible manner.

Current Sensorless Microcontroller-Based Battery Management System with SOC and Active Cell Balancing Muhammad Fikri Ardiansyah; Adha Imam Cahyadi; Oyas Wahyunggoro
International Journal of Quantitative Research and Modeling Vol 2, No 1 (2021)
Publisher : Research Collaboration Community (RCC)

Battery management system (BMS) has become an important research topic following the trend and development of the electric vehicle. Although research on Active Cell Balancing, SOC, and current estimation has been carried out, the previous work mostly focused on comparing and developing methods. In this research, we demonstrate the process of designing BMS hardware using a low-cost microcontroller and without using a current sensor. The SOC simulation results produce an RMSE of 0.0832% for the 100% -10% SOC-OCV curve, and the current estimation simulation produces an RMSE of 0.2576 A, while for testing using a 6-ohm pulse load, the RMSE error value is 0.3960 A. The Active Cell Balancing method was successfully performed in simulation with Simulink. Furthermore, our simulation and test results suggest that complex battery models and multiple SOC-OCV curves can be used for better current and OCV estimation results. Our experimental results are also useful to develop a guideline to design a microcontroller-based BMS.

Online Battery Parameter And Open Circuit Voltage (OCV) Estimation Using Recursive Least Square (RLS) Harmoko Harmoko; Dani Prasetyo; Sigit Agung Widayat; Lora Khaula Amifia; Bobby Rian Dewangga; Adha Imam Cahyadi; Oyas Wahyunggoro
Techné : Jurnal Ilmiah Elektroteknika Vol. 15 No. 01 (2016)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

After decades, the battery usage has been widespread for many applications, especially in the field of Electric Vehicle (EV). The battery is a very important component in the EV. Because the battery as the primary power source replacement of the fossil fuel. Therefore, the condition of the batteries should be always in good condition. To prevent failure of the battery for battery management system (BMS) is needed. BMS is a system to regulate the use of the battery and protects the battery from the failure of the battery supply. Many factors can be monitored at BMS, one of which is a State of Charge (SOC). SOC determination is directly related to the estimated OCV (Open Circuit Voltage). The accuracy of the estimation algorithms depend on the accuracy of the model selection to describe the dynamic characteristics of the battery. This study begins with the selection of the right model (fig.1, fig.2, fig.3) for estimating OCV. Selection of appropriate model using RLS algorithm for estimate the battery terminal voltage. Parameter that reference for determining the selection of the model is the max, min, mean, RMSE, mean RMSE of the error. Later models have been used to estimate the OCV. The result based on this research shows that modeling with n = 1 is the best result to be used in model parameter estimation and OCV battery in term of the smaller max, min, mean, rmse error. This research also show us that RLS algorithm can be estimate the parameters of the batery, OCV (fig.4), and terminal voltage of the battery with an error less than 0.1%

Parameter Identification of Nonlinear System on Combustion Engine Based MVEM using PEM Trigas Badmianto; Eka Firmansyah; Adha Imam Cahyadi
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 1, No 4 (2017): December 2017
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

In four-stroke engine injection system, often called spark ignition (SI) engine, the air-fuel ratio (AFR) is taken from the measurement of lambda sensor in the exhaust. This sensor does not directly describe how much AFR in the combustion chamber due to the large transport delay. Therefore, the lambda sensor is used only as a feedback in AFR control "correction", not as the "main" control. The purpose of this research is to identify the parameters of the non-linear system in SI engines to produce AFR estimator. The AFR estimator is expected to be used as a feedback of the main "AFR" control system. The process of identifying the parameters using the Gauss-Newton method, due to its rapid computation to Achieve convergence, is based on prediction error minimization (PEM). The models of AFR estimator is an open-loop system without a universal exhaust gas oxygen (UEGO) sensors as feedback, called a virtual AFR sensor. The high price of UEGO sensors makes the virtual AFR sensor can be a practical solution to be applied in AFR control. The model in this research is based on the mean value engine models (MVEM) with some modifications. The research dataset was taken from a Hyundai Verna 2002 with the additional UEGO type of lambda sensors. The throttle opening angle (input) is played by stepping on the gas pedal and the signal to the injector (input) is set to a certain quantity to produce the AFR (output) value read by the UEGO sensor. This research produces an open loop estimator model or AFR virtual sensors with normalized root mean square error (NRMSE) = 0.06831 = 6.831%.

DC Motor Speed Control Using Hybrid PID-Fuzzy with ITAE Polynomial Initiation Hari Wibawa; Oyas Wahyunggoro; Adha Imam Cahyadi
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 3, No 1 (2019): March 2019
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

DC motors are widely applied in industrial sector, especiallyprocesses of automation and robotics. Given its role in the sector, DC motor operation needs to be optimized. One of optimization steps is controlling speed using several control methods, for example conventional PID methods, PID Ziegler Nichols, PID based on ITAE polynomials, and Hybrid PID-Fuzzy. From these methods, Hybrid PID-Fuzzy was chosen as a method to be proposed in this paper because it can anticipate shortcomings of PID controllers and fuzzy controllers so as to produce system responses that are fast and adaptive to errors. This paper aimed to design a Hybrid PID-Fuzzy system based on ITAE polynomials (Hybrid-ITAE), to analyze its performance parameters values, and tp compare Hybrid-ITAE performance with conventional PID method. Working parameters being reviewed include overshoot, rise time, settling time, and ITAE. First of all, JGA25-370 DC motor was modeled in a form of a third order transfer function equation. Based on the transfer function, PID parameters were calculated using PID Output Feedback and ITAE polynomial methods. The best ITAE polynomial PID controllers were then be combined with a Fuzzy Logic Controller to form a Hybrid-ITAE system. Simulation and experimental stages were carried out in two conditions, namely no load and loaded. Simulation and experimental results showed that Hybrid-ITAE (l = 0.85) was the best controller for no-load simulation conditions. For loaded simulation Hybrid-ITAE (l=1) was a better controller. In no-loads experiment, the best controller was Hybrid PID-Ziegler Nichols, while for loaded condition the best controller was Hybrid PID-Ziegler Nichols.

Simulasi ADCRC (Active Disturbance Rejection Controller) dan kendali PD pada Model Cavity Siklotron DECY 13 Agus Dwiatmaja; Adha Imam Cahyadi; Prapto Nugroho
Retii Prosiding Seminar Nasional ReTII ke-11 2016
Publisher : Institut Teknologi Nasional Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Percepatan partikel pada Siklotron DECY 13 ditentukan oleh sistem radio frekuensi sebagai pemercepat, siklotron akan bekerja optimum pada kondisi tune frequency. Gangguan-gangguan yang terjadit pada sistem maupun luar sistem akan mempengaruhi parameter siklotron, salah satunya adalah nilai kapasitansi cavity. Nilai kapasintansi yang berubah menyebabkan tidak terjadi resonansi pada cavity. Diperlukan sistem kendali untuk dapat mengendalikan tuning frekuensi sistem dengan cara meredam gangguan (disturbance) . Metode ADRC (Active Disturbance Rejection Controller) yang diterapkan pada parameter lain dipilih untuk mengatasi hal tersebut, selain itu metode PD juga diterapkan sebagai pembanding. Pemodelan dan simulasi mewakili nilai-nilai parameter yang sebenarnya. Hasil simulasi menunjukkan ADRC memberikan tanggapan kendali yang lebih baik dari sisi settling time dan over shoot, daripada kendali PD, dan akan mengalami optimasi ketika ADRC didukiung dengan kendali Proposional Derivatif (PD). Kata Kunci: disturbance, kendali, optimasi, radio, frekuensi

Kendali Inverted Pendulum: Studi Perbandingan dari Kendali Konvensional ke Reinforcement Learning Ahmad Ataka; Andreas Sandiwan; Hilton Tnunay; Dzuhri Radityo Utomo; Adha Imam Cahyadi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i3.7065

The rise of deep reinforcement learning in recent years has led to its usage in solving various challenging problems, such as chess and Go games. However, despite its recent success in solving highly complex problems, a question arises on whether this class of method is best employed to solve control problems in general, such as driverless cars, mobile robot control, or industrial manipulator control. This paper presents a comparative study between various classes of control algorithms and reinforcement learning in controlling an inverted pendulum system to evaluate the performance of reinforcement learning in a control problem. A test was performed to test the performance of root locus-based control, state compensator control, proportional-derivative (PD) control, and a reinforcement learning method, namely the proximal policy optimization (PPO), to control an inverted pendulum on a cart. The performances of the transient responses (such as overshoot, peak time, and settling time) and the steady-state responses (namely steady-state error and the total energy) were compared. It is found that when given a sufficient amount of training, the reinforcement learning algorithm was able to produce a comparable solution to its control algorithm counterparts despite not knowing anything about the system’s properties. Therefore, it is best used to control plants with little to no information regarding the model where testing a particular policy is easy and safe. It is also recommended for a system with a clear objective function.

Low Pass Filter as Energy Management for Hybrid Energy Storage of Electric Vehicle: A Survey Maghfiroh, Hari; Wahyunggoro, Oyas; Cahyadi, Adha Imam
Automotive Experiences Vol 6 No 3 (2023)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.9398

The transportation sector contributes up to 35% of carbon dioxide pollution. Electric Vehicles (EVs) offer a pollution-free alternative but face a crucial challenge in their battery-based Energy Storage System (ESS). The solution to the battery issues is combining it with other ESS with high power density called a Hybrid Energy Storage System (HESS). Energy Management Strategy (EMS) is used to distribute the power demand in the HESS. Low Pass Filters (LPFs) are one type of EMS that can be used to ensure the smooth flow of power between different energy storage elements. This article focuses on the pivotal role of Low Pass Filters (LPFs) within HESS for EVs, facilitating seamless power flow. The novelty lies in the comprehensive review of LPFs in this context, shedding light on their impact on energy management. Four LPF architecture classes are discussed: fixed cut-off, optimal cut-off, adaptive cut-off, and combination, referencing prior research. Additionally, a critical examination of challenges and limitations is provided, offering insights for researchers and practitioners.

Inverse kinematic solution and singularity avoidance using a deep deterministic policy gradient approach Surriani, Atikah; Wahyunggoro, Oyas; Imam Cahyadi, Adha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2999-3009

The robotic arm emerges as a subject of paramount significance within the industrial landscape, particularly in addressing the complexities of its kinematics. A significant research challenge lies in resolving the inverse kinematics of multiple degree of freedom (M-DOF) robotic arms. The inverse kinematics of M-DOF robotic arms pose a challenging problem to resolve, thus it involves consideration of singularities which affect the arm robot movement. This study aims a novel approach utilizing deep reinforcement learning (DRL) to tackle the inverse kinematic problem of the 6-DOF PUMA manipulator as a representative case within the M-DOF manipulator. The research employs Jacobian matrix for the kinematics system that can solve the singularity, and deep deterministic policy gradient (DDPG) as the kinematics solver. This chosen technique offers enhancing speed and ensuring stability. The results of inverse kinematic solution using DDPG were experimentally validated on a 6-DOF PUMA arm robot. The DDPG successfully solves inverse kinematic solution and avoids the singularity with 1,000 episodes and yielding a commendable total reward of 1,018.

Co-Authors Afrisal, Hadha Agus Dwiatmaja Ahmad Ataka Awwalur Rizqi, Ahmad Ataka Andreas Sandiwan Anugrah Persada Bobby Rian Dewangga Bwo Ren Ke Dani Prasetyo Dzuhri Radityo Utomo Eka Firmansyah Erika Loniza Fransisco Danang Wijaya Ginting, Almido Almido Haryanto Hanifah Rahmi Fajrin, Hanifah Rahmi Hari Maghfiroh Hari Wibawa Harmoko Harmoko Hilton Tnunay iftitah imawati Indah Soesanti Iswanto Iswanto Johanes Andriano Situmorang Kuo Lung Lian Lasmadi Lasmadi Lora Khaula Amifia Maghfiroh, Hari Muhammad Fikri Ardiansyah Muhammad Yasirroni Ng Khin Hooi Nia Maharani Raharja Oyas Wahyunggoro Prapto Nugroho Prayadi Sulistyanto Risanuri Hidayat Rubiyah Yusof Samiadji Herdjunanto Sigit Agung Widayat Surriani, Atikah Trigas Badmianto Yoshio Yamamoto

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