The increasing integration of automation in transportation systems has heightened the need for intelligent safety mechanisms, particularly in urban environments where traffic accidents are prevalent. This study focuses on the design and implementation of an Autonomous Braking System for Micro Electric Autonomous Vehicles (MEAV) using Fuzzy Logic Control (FLC) with Tsukamoto and Mamdani inference methods. The system utilizes real-time inputs from LiDAR sensors and rotary encoders to detect obstacles and vehicle speed, respectively, and employs an electric rod actuator to apply braking pressure. A comparative analysis of the Tsukamoto and Mamdani methods is conducted, evaluating performance metrics such as target brake tracking, final distance to obstacle, and braking smoothness. Results indicate that the Mamdani method achieves shorter final obstacle distances-1.1523m compared to 1.3688m, suggesting more efficient braking, while both methods exhibit comparable tracking errors. Mamdani also have more smooth braking than Tsukamoto shown by the absolute differential of velocity- 0.05264 for Tsukamoto and 0.03971 for Mamdani. This study highlights the potential of fuzzy logic control in enhancing MEAV safety and provides insights for future improvements in autonomous braking technology.

Biogas from palm oil mill effluent (POME) is a promising fuel that has many advantages as an alternative fuel. The methane content in biogas derived from POME is up to 75% and can be used as an alternative fuel in an internal combustion engine. One of the technologies for utilizing biogas in compression ignition engines is the Diesel Dual-Fuel (DDF) technique due to the different characteristics of fuel and the impact on the environment due to significantly reducing emissions. This study aims to find the effect of biogas POME composition and energy ratio on the DDF engine’s performance and emissions. The simulations using AVL BOOST software were confirmed by experimental engine parameters. The modeling was conducted on the biogas energy ratio (20%, 40%, 60%, and 75% POME) and biogas POME composition (55% and 75% methane). The results showed that the fuel consumption of diesel fuel was reduced by up to 69%, and NOx and soot emissions were reduced by up to 92% and 80%, respectively, with dual-fuel mode operation. Meanwhile, the value of brake mean effective pressure (BMEP) and efficiency was reduced by up to 18%, volumetric efficiency decreased by up to 4%, the increase in brake specific energy consumption (BSEC) was up to 23%, and brake specific fuel consumption (BSFC) was up to 155%. The optimum of the engine’s performance and emission was 40% biogas ratio with 75% methane content.

End-to-end models for autonomous vehicles based on Convolutional Neural Networks (CNN) often have limitations in understanding the global context of a scene, which is crucial for safe and reliable trajectory planning. To address this problem, this research proposes GenoS SKGE-Swin, a new architecture that leverages the advantages of the Swin Transformer with a skip connection mechanism. This architecture is designed to effectively capture long-range spatial relationships and focuses on the waypoint prediction task as a safe and interpretable representation of motion planning. The model is evaluated offline using a real-world dataset and compared with a baseline architecture. The experimental results show that GenoS SKGE-Swin successfully achieves better performance compared Swin Transformer without skip connection mechanism evidenced by lower metric score. This model also demonstrates the highest performance on the semantic segmentation task and has the fastest inference latency. This research demonstrates that the integration of the Swin Transformer with skip connections significantly improves global context understanding, resulting in a better balance of performance and efficiency for autonomous vehicle trajectory prediction models.

The advancement of technology in the medical field has led to innovations in assistive devices, including wheelchairs, to enhance the mobility and independence of individuals with disabilities. This study investigates the use of electromyography (EMG) signals from hand muscles to control a wheelchair using the k-Nearest Neighbor (kNN) classification method. kNN is a classification algorithm that identifies objects based on the proximity of similar objects in the feature space. The wheelchair control process begins with the development of a kNN model trained on EMG signal data collected from five respondents over 30 seconds. The data was processed using feature extraction techniques, namely Mean Absolute Value (MAV) and Root Mean Square (RMS), to identify motion characteristics corresponding to five types of movement: forward, backward, right, left, and stop. The extracted features were classified using the kNN algorithm implemented on a Raspberry Pi 3. The classification results were then used to control the wheelchair through an Arduino UNO microcontroller connected to a BTS7960 motor driver. The study achieved an average accuracy of 96% with the MAV feature and 𝑘 = 3. Furthermore, combining MAV and RMS features significantly improved classification accuracy. The highest accuracy was obtained using the combination of MAV and RMS features with 𝑘 = 3, demonstrating the effectiveness of feature selection and parameter tuning in enhancing the system's performance.

Obstacle/Object inference tasks in autonomous driving systems are crucial for ensuring vehicle safety and operational efficiency. Traditional object detection models, while effective, often exhibit limitations in handling complex and dynamic driving scenarios, particularly inaccurate obstacle detection and estimating the distance to obstacles to provide timely warnings. To address these challenges, state-of-the-art deep learning frameworks such as YOLOv8 and Detectron2, have shown promising advancements in object detection accuracy and distance estimation. However, the comparative performance of these frameworks in terms of key evaluation metrics such as Precision, Recall, and F1 Score remains underexplored. Here, we present a comprehensive analysis of YOLOv8 and Detectron2, for obstacle inference and distance estimation tasks in autonomous driving systems. Our results indicate that YOLOv8 achieves a Precision of 88%, Recall of 88%, and F1 Score of 88%, demonstrating robust and balanced detection performance. In contrast, Detectron2 achieves a slightly higher Precision of 89% but lower Recall and F1 Score at 84% and 86%, respectively, indicating a trade-off between detection accuracy and sensitivity. The proposed distance estimation algorithm has MAE of about 80 cm. These findings underscore the nuanced performance characteristics of YOLOv8 and Detectron2 when deployed in autonomous driving environments, providing valuable insights for selecting appropriate frameworks based on specific detection and distance estimation priorities.

The electrification of the two-wheel vehicle segment is an important strategy for decarbonising the transportation sector. This study aimed to assess the hybridisation of gasoline motorcycles with battery electric systems as an option for decarbonisation. A gasoline motorcycle that had been converted to a hybrid motorcycle was evaluated in several aspects: energy consumption, greenhouse gas (GHG) emission, and cost of energy. The vehicle was tested under the United Nations economic commission for europe (UNECE) Regulation No.40 and compared to a battery electric motorcycle. The test in internal combustion engine (ICE) mode consumed 233.31 Wh/km of specific energy, emitted 60.69 gCO2/km and cost 1.65 US-cent/km on average. The test in hybrid mode consumed specific energy at 6 % higher and 4 % lower specific energy consumption than ICE, thus not improving the carbon dioxide (CO2) emission and operating cost. In electric battery mode, energy consumption was saved by 83 %, with 35 % lower CO2 and 74 % cost savings. The battery electric motorcycle runs more efficiently with 88 % lower energy consumption, 53.8 % lower CO2 and saved cost by 82 %. If the hybrid controller is improved in future development, it could lower specific energy consumption by 41.7 %, reduce CO2 by 11.2 % and save cost by 35.7 %.

With the benefit of reduced exhaust emissions, electric vehicles provide a substitute for conventional vehicles. Nevertheless, the battery capacity of electric cars is still constrained, which limits their driving range. Technology has been created to increase the electric vehicle's restricted range in order to solve this issue. A range extender is a portable electrical generator that only kicks on when the battery runs low or is not fully charged. In this study, the working performance and effectiveness of the range extender are examined following modifications to the throttle and control system. The prototype, which is powered by a 3500-watt BLDC motor with a speed of approximately 3500 rpm and an operating voltage of approximately 48V to 60V, was used in two states of experimentation: no load and track load. The results demonstrate how the range extender vehicle's performance is impacted by the suggested throttle control system. The engine can deliver up to 3kW of power with the suggested throttle controller on the name plate. The system managed to deliver 2.93 kW of power without experiencing a major voltage loss or engine rpm while climbing a 2.1% sloped road.

In advancing electric vehicle technology, the braking system is a vital component with significant impacts on driver safety features. Numerous factors, such as high speeds, adverse weather conditions, human mistakes, and system malfunctions, play a role in the large number of vehicle accidents that happen each year. This has prompted vehicle manufacturers to develop advanced braking systems with the objective of reducing the likelihood of such incidents. The objective of this research is to implement a plugging braking system in electric vehicles, given the importance of the combination of electric and mechanical braking torque in ensuring the safe stopping of a vehicle. The methodology employed involved plugging brake modeling of a BLDC motor, the experiments of plugging brake, and the utilization of PWM as a regulator of plugging brake. The findings demonstrate that an increase in the PWM duty cycle of 58% (150), 78% (200), 98% (250) and 100% (255 full) can reduce the time required to stop the motor up to 2 seconds from the initial speed of 500 rpm, thereby underscoring the significance of PWM duty cycle variation in altering the braking current and thus the braking force.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Muhammad Khristamto Aditya Wardana, Bambang Wahono, Mulia Pratama, Yanuandri Putrasari, Arifin Nur, Achmad Praptijanto, Ahmad Dimyani, Suherman, Ocktaeck Lim; Study on super-hydrophobic pattern to increase the NH3 adsorption and desorption in SCR systems. AIP Conf. Proc. 28 February 2024; 3003 (1): 020012. https://doi.org/10.1063/5.0186240 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

Air pollution remains a big issue in many countries. One form of air pollution comes from the use of fossil fuels as the primary fuel in the power generating and transportation sectors. Diesel engines are employed in a variety of industries due to their dependability, durability, and efficiency. Enhancing the availability of oxygen within the combustion chamber is one technique for reducing exhaust gas emissions and optimizing diesel engine combustion. The aim of this study is to investigate how oxygen enrichment in diesel engines with diesel fuel and biodiesel affects their performance and emissions. The modeling in this research was carried out using AVL BOOST version 2011 software based on experimental results of the YANMAR TF 155 R-DI diesel engine at 1200 rpm with and without oxygen enrichment. Modeling was performed based on the baseline parameter of a diesel engine with gradual loads at 50%, 75%, and 100%. The oxygen concentration was increased to 30.6%, 37.8%, 45%, and 54% by mass. The results show an increase in the maximum heat release rate (HRR) and the mass fraction burned (MFB) up to 90% for both fuels. The peak heat release rate of biodiesel shifts around 6 J/degree and the brake-specific fuel consumption (BSFC) is up to 0.0035 kg/kWh higher than that of diesel fuel. When compared to diesel fuel, the thermal efficiency and BSFC of biodiesel usage are around 0.3% and 0.028 kg/kWh, respectively. NOx emissions increase due to higher combustion temperatures and more oxygen availability. Biodiesel emits 50% less NOx than diesel fuel, presumably due to a lower combustion temperature. As a result, while high-concentration oxygen enrichment improves combustion and lowers soot emissions, it raises NOx emissions. Soot emissions were reduced as a result of the enhanced combustion process, while NOx emissions rose due to higher combustion temperatures and increased oxygen availability.

The rise of Advanced Driver Assistance Systems (ADAS) has emphasized the need for technologies such as Automatic Emergency Braking (AEB) and Adaptive Cruise Control, both of which require brake activation without driver input. Since 2016, automotive manufacturers, particularly in the U.S., have been incorporating AEB technologies in response to safety demands. Bosch's iBooster, an electromechanical brake booster, has gained prominence for its role in autopilot and braking systems, notably in Tesla's Model S and X. The iBooster system can generate full braking pressure in just 120 milliseconds and allows for electronic control while still maintaining manual override capabilities. Tesla's iBooster Gen1, used in its autopilot system, operates via a Controlled Area Network (CAN), though the exact method is not publicly available. Honda has also adopted the iBooster Gen1 in the 2018 CR-V, utilizing it for the Brake Assist feature, although details on Honda's CAN network usage remain limited. This paper explores reverse engineering of the iBooster to uncover key hardware and software technologies. The reverse engineering operation entails altering the system's control loop, capturing the pedal sensor signal, and replicating this signal using an Arduino. Additionally, it includes designing circuit schematics and PCBs. The results demonstrate the success of this reverse engineering approach, which uses a work cycle and Pulse Width Modulation (PWM) to interact with the iBooster Gen1. The method produced identical responses to the iBooster with PWM values set at 30%, 40%, and 50%.

Pemanfaatan kontrol injeksi berperan penting dalam mendukung penggunaan bahan bakar alternatif guna mencapai target net zero emission. Perbaikan performa dan monitoring kondisi mesin berupa kontrol injeksi modern dan canggih dilengkapi dengan banyak sensor dan sistem monitoring yang memungkinkan penggunaan bahan bakar yang lebih efisien dan respons yang lebih cepat terhadap perubahan kondisi mesin, ruang bakar, atau komposisi bahan bakar dan udara dapat mewujudkan kendaraan dengan mesin konvensional namun ramah lingkungan yang mendukung program net zero emission. Hal ini penting dalam mengurangi jejak karbon transportasi dan mencapai target keseluruhan net zero emission. Orasi ini berfokus pada pembahasan terkait perkembangan teknologi sistem bahan bakar khususnya pemanfaatan kontrol injeksi untuk aplikasi bahan bakar alternatif dalam mendukung program net zero emission. Naskah ini dibagi kedalam lima Bab dan diawali perkembangan teknologi system bahan bakar untuk efisiensi tinggi dan rendah emisi. Pada bab ke dua akan mendiskusikan tentang teknologi kontrol injeksi, peningkatan efisiensi, dan pengurangan emisi pada mesin otomotif baik perkembangan saat ini dan tantangan masa depan. Bab ke tiga membahas tentang inti dari kegiatan riset berupa implementasi kontrol injeksi pada penggunaan bahan bakar alternatif di bidang otomotif berupa kontrol injeksi mode ganda dan tunggal untuk biodiesel, kontrol injeksi untuk dimethyl ether (DME), kontrol injeksi untuk natural gas, biogas dan hidrogen. Bab keempat membahas tentang peluang dan tantangan kontrol injeksi bahan bakar alternatif untuk mendukung program net zero emission di Indonesia. Bab ke lima Kesimpulan dan Bab ke enam sebagai penutup. Pendekatan yang lebih baik dalam hal ini penerapan teknologi kontrol injeksi untuk bahan bakar alternatif dimana penelitian dan ujicoba tersebut telah menunjukkan peningkatan efisiensi dan pengurangan emisi dari mesin, maka hal tersebut perlu dilaksanakan sesegera mungkin di Indonesia. Kemajuan yang berkelanjutan mengharuskan kita merekrut generasi muda paling cerdas untuk terlibat dalam upaya mewujudkan masa depan mesin pembakaran dalam yang cerah, berkelanjutan dan ramah lingkungan.

This study presents the development and implementation of a PID steering control system for lane-keeping assistance using an IMU sensor and ROS (Robot Operating System). The system leverages the capabilities of the Teensy 4.1 microcontroller and the HWT905-TTL IMU sensor to maintain precise steering adjustments, crucial for autonomous vehicle applications. The PID controller, designed to respond dynamically to real-time feedback, ensures that the motor reaches designated target angles, even under varying test conditions. The control system was tuned experimentally to achieve optimal response times, resulting in a rise time of approximately 2 seconds, a steady-state error within ±1-2 degrees, and a maximum overshoot of 4.09%. Performance metrics such as the Integral of Squared Error (ISE) and the Integral of Time-weighted Squared Error (ITSE) further confirmed system accuracy and stability over time. Results indicate that the proposed control system effectively maintains lane-keeping with minimal error, demonstrating potential for future deployment in real-world autonomous driving scenarios. Future work will focus on enhancing system adaptability through advanced tuning techniques and sensor fusion to improve robustness in dynamic environments.

The effect was investigated of ethanol together with biodiesel in tri-blend fuels on the combustion characteristics of a compression ignition engine. In addition, the sole influences of ethanol and biodiesel were clarified and the operating conditions (engine speed and load) were evaluated for their contributions to the effects of ethanol and biodiesel. Because this research aimed to bridge the gap between research and implementation, the biodiesel concentration in commercial fuel currently available was the first criterion for the blend ratio. Therefore, amounts of 3, 7, or 10 % biodiesel in the biodiesel-diesel blends (B3, B7, and B10) were mixed with ethanol. Phase stability was the second factor used to determine the suitable ethanol concentration in the tri-blend. Additionally, the ratios of each blend were compared regarding the effect of ethanol and biodiesel alone, as well as their combination. Finally, four different ratios of ethanol and biodiesel—B7, 5 % ethanol in B3 (B3E5) and in B7 (B7E5), and 10 % ethanol in B10 (B10E10)—were investigated in a four-cylinder commercial diesel engine with varying engine speeds and loads. The results showed that ethanol significantly retarded the start of combustion, while the ignition was noticeably advanced by biodiesel. The high cetane value of biodiesel was the primary factor to accelerate the chemical reaction, while the high heat of vaporization of ethanol was the main contributor to decelerating the physical phenomena during the auto-ignition process. Therefore, adding biodiesel as the emulsifier in an ethanol-diesel emulsion could compensate for the delayed ignition due to the properties of ethanol. As a result, the combustion levels of B7E5 and B7 were similar at low engine speeds. The ignition delay of B10E10 was the same as for B3E5 but later than for B7. The effects of ethanol and biodiesel were promoted by the operating conditions. An increase in the engine speed intensified the effect of ethanol on the ignition delay. Even a small amount of ethanol in the blend delayed combustion substantially. Furthermore, the engine speed strengthened the influence of the engine load. For the high load condition, puffing (the micro-explosion resulting from the emulsion blend) seemed to occur and to accelerate the combustion of the ethanol blend. Due to slight changes in the combustion behavior for all operating conditions, B7E5 was considered a highly promising fuel based on this study.

The rapid evolution of electric vehicle (EV) technology emphasizes the need for eco-friendly solutions to fight climate change and promote zero-emission. And suitable for the transportation on the large cities with high levels of mobility. This study explores the features and effectiveness of the Micro Electric Vehicle (MEVi) prototype based on motor usage of the accessible power source. The vehicle is powered by a 750 W/1HP brushless DC (BLDC) motor and a 48V 41.6Ah LI-ion battery source. A programmable controller is utilized in regulating the operation of the electric motor. The tests were carried out at two varying speed modes, with and without load operation on the track. Power consumption at 72 Watts and 119 Watts was achieved during the no-load experiment, while the loaded experiments yielded 182 and 414 watts respectively. Programmable settings are utilized with a controller to regulate the electric motor's operations. Throughout the test, the power efficiency achieved an average percentage of 70% when the motor was without a load and 15% under load testing.

In the internal combustion engine types, the gasoline compression ignition engine (GCI engine) presents the potential and effective method to improve engine thermal efficiency and lower pollution emissions when compared to the spark-ignition engine (SI) and the compression ignition engine (CI engine), respectively. To improve those advantages of the GCI engine, new engine technologies are being developed to help the engine efficiently work with higher compression ratios or lower octane gasoline fuel at part load conditions. However, high smoke, soot, HC and CO formations, the part-load stability of the combustion phase, or autoignition at high load conditions are still challenging with the GCI engine. This chapter will introduce some technologies that help solve the GCI engine's challenges, these technologies are such as: injection strategy, exhaust residual gas strategy, biodiesel addition, and oxygen content. After the aforementioned technological implementations, a detailed investigation will be carried out to lay the scope on the GCI engine performance and its emission characteristics. Multiple injections may help improve combustion stability and engine efficiency when compared to a single injection strategy. The HC and CO emissions can be decreased when the engine applies a multiple injection strategy and GB05 as fuel. The increase in EGR helps to reduce autoignition for both single and numerous ignition strategies. The oxygen concentration has a sensitive effect on the delay of the ignition process. The reduced amount of oxygen concentration induces an increase in the ignition delay, which helps to reduce the auto-ignition.

This paper presents a study about design and simulation test on performance of range extender-based spark ignition engine fuelled with biogas applied for small electric vehicles which appropriate for developing countries, where the charging station infrastructures are limited. The study was conducted firstly by designing a two cylinders range extender-based spark ignition engine for 35 kW of electric vehicle. To analyse the performance and emission behaviours of the engine, a simulation study using AVL BOOST was conducted with variation of gasoline-biogas fuels blend from 0 to 100 % by volume. The results showed that engine indicated mean effective pressure, power, and indicated efficiency decrease correspond to the biogas content in fuel. The nitrogen oxide emission of range extender spark ignition engine was decreased when using higher content of biogas, but the emissions of hydrocarbon and carbon monoxide were increased. Anyhow, the range extender SI engine fulfils small electric vehicle needs which the charging power is at least 20 kW. Thus, the designed two cylinders engine can be operated using biogas fuel and the performance fulfil the requirement.