Vehicle Applications Research Articles

“Synthesis and characterization of TiO2 nanoparticles as brake fluid additives: Viscosity modulation and impact on rheological properties”

TiO2 nanoparticles were synthesized using the reduction precipitation method, verified by powder x-ray diffraction (PXRD) with an average crystallite size of 62 nm and an average particle size of 135 nm from SEM images. Rheological studies of brake fluid with nanoparticle additives revealed that while the fluid generally maintains consistent behavior, a 2 wt.% concentration showed non-Newtonian behavior at 120 °C. Viscosity increased slightly with nanoparticle addition and decreased with rising temperature, with the 0.5 wt.% sample demonstrating the most notable increase of 18.18% at 120 °C. At 30 °C, the 1 wt.% sample exhibited a 4.06% increase. The observed viscosity changes are attributed to higher internal shear stress and the need to manage dispersion solid elements. The Herschel-Bulkley model was effective in predicting sample behavior, and all samples conformed to Reynolds’ equation μ 0 = b e − aT . This research indicates that TiO2 nanoparticles can be effectively used as brake fluid additives, offering potential benefits for high-temperature applications in vehicles. Further optimization using mathematical models such as response surface methodology (RSM) is suggested for improved performance.

A reservoir computing approach in active noise control for vehicle applications

Reservoir Computing (RC) is an alternative approach to Recurrent Neural Networks (RNN) that can avoid some shortcomings of conventional gradient decent-training that are mainly related to high computational complexity and the reduced ability to "learn" long-range dependencies. In RC the input data are transformed into patterns in a high-dimensional space by an RNN called the "reservoir", which remains unchanged during training. The desired output signal is generated as a linear combination of the neuron's signals from the input-excited reservoir. The linear part of the RC architecture, which is called the "readout" can be trained with a simple learning algorithm such as linear regression. In this work an RC architecture is proposed for the attenuation of acoustic disturbances encountered in vehicle cabins such as aircrafts and yachts. At first, a dataset of acoustic pressure measurements from real-world cabins was used to generate the readout and reservoir layers. Subsequent computer simulations demonstrated that the proposed architecture can successfully attenuate such acoustic disturbances. Finally, the performance of RC in Active Noise Cancellation task was compared with both the linear FxLMS algorithm and a conventional RNN, showing that it has several advantages regarding acoustic pressure reduction combined with relatively low computational complexity.

Thermal Fault-Tolerant Asymmetric Dual-Winding Motors in Integrated Electric Braking System for Autonomous Vehicles

A conventional dual-winding (DW) motor has two internal windings consisting of a master part and a slave part, each connected to a different electronic control unit (ECU) to realize a redundant system. However, existing DW motors have a problem related to heat generation in both the healthy mode and the faulty mode of the motor operation. In the healthy mode, unexpected overloads can cause both windings to burn out simultaneously due to equal heat distribution. If the current sensor fails to measure correctly, the motor may exceed the designed current density of 4.7 [Arms/mm2] under air-cooling conditions, further increasing burnout risk. External factors such as excessive load cycles or extreme heat conditions can further exacerbate this issue. In the faulty mode, the motor requires double the current to generate maximum torque, leading to rapid temperature increases and a high risk of overheating. To address these challenges, this paper proposes the design of a thermal fault-tolerant asymmetric dual-winding (ADW) motor, which improves heat management in both healthy and faulty modes for autonomous vehicles. A lumped-parameter thermal network (LPTN) with a piecewise stator-housing model (PSMs) was employed to evaluate the coil temperature during faulty operation. An optimal design approach, incorporating kriging modeling, Design of Experiments (DOE), and a genetic algorithm (GA), was also utilized. The results confirm that the proposed ADW motor design effectively reduces the risk of simultaneous burnout in the healthy mode and overheating in the faulty mode, offering a robust solution for autonomous vehicle applications.

Corrigendum: Effect of PVdF Distribution on Properties and Performance of Dry Spray-Coated Graphite Electrodes for Lithium-Ion Batteries for Electric Vehicle Applications

Corrigendum: Effect of PVdF Distribution on Properties and Performance of Dry Spray-Coated Graphite Electrodes for Lithium-Ion Batteries for Electric Vehicle Applications

AI/ML-based services and applications for 6G-connected and autonomous vehicles

AI and ML emerge as pivotal in overcoming the limitations of traditional network optimization techniques and conventional control loop designs, particularly in addressing the challenges of high mobility and dynamic vehicular communications inherent in the domain of connected and autonomous vehicles (CAVs). The survey explores the contributions of novel AI/ML techniques in the field of CAVs, also in the context of innovative deployment of multilevel cloud systems and edge computing as strategic solutions to meet the requirements of high traffic density and mobility in CAV networks. These technologies are instrumental in curbing latency and alleviating network congestion by facilitating proximal computing resources to CAVs, thereby enhancing operational efficiency also when AI-based applications require computationally-heavy tasks. A significant focus of this survey is the anticipated impact of 6G technology, which promises to revolutionize the mobility industry. 6G is envisaged to foster intelligent, cooperative, and sustainable mobility environments, heralding a new era in vehicular communication and network management. This survey comprehensively reviews the latest advancements and potential applications of AI/ML for CAVs, including sensory perception enhancement, real-time traffic management, and personalised navigation.

Silicon heterojunction back-contact solar cells by laser patterning.

Back-contact silicon solar cells, valued for their aesthetic appeal because they have no grid lines on the sunny side, find applications in buildings, vehicles and aircraft and enable self-power generation without compromising appearance1-3. Patterning techniques arrange contacts on the shaded side of the silicon wafer, which offers benefits for light incidence as well. However, the patterning process complicates production and results in power loss. We employed lasers to streamline the fabrication of back-contact solar cells and enhance the power-conversion efficiency. Using this approach, we produced a silicon solar cell that exceeded 27% efficiency. Hydrogenated amorphous silicon layers were deposited onto the wafer for surface passivation and to collect light-generated carriers. A dense passivating contact, which differs from conventional technology practice, was developed. Pulsed picosecond lasers operating at different wavelengths were used to create the back-contact patterns. The approach developed is a streamlined process for producing high-performance back-contact silicon solar cells, with a total effective processing time of about one-third that of the emerging mainstream technology. To meet the terawatt demand, we developed indium-less cells at 26.5% efficiency and precious silver-free cells at 26.2% efficiency. Thus, the integration of solar solutions into buildings and transportation is poised to expand with these technological advances.

Integrated RES With Intelligent MPPT For Efficient Power Generation & Wireless Transmission for PV Applications

The increasing global energy demand and the urgent need to shift to sustainable practices have made the integration of renewable energy sources (RESs) into distribution power systems imperative. This project's main objective is to implement rooftop photovoltaic (PV) systems as an essential part of low-voltage (LV) DC networks' building-integrated centralized generation Maximum Power Point Tracking (MPPT) using Artificial Neural Networks (ANNs) is used to maximize power extraction from photovoltaic (PV) panels and boost power generation efficiency, particularly in partially shaded circumstances. After that, the PV system's generated power is sent to a Luo converter, which effectively tracks and optimizes the power production. For wireless power transmission, the Luo converter's output is then fed into a high-frequency converter. This wireless power transmission improves the system's adaptability and scalability by facilitating smooth energy transfer without the requirement for physical connections. An isolation transformer is attached to the high-frequency converter's output in order to guarantee the system's dependability and safety. The high-frequency converter is isolated from the downstream components by the isolation transformer, which also offers protection against electrical risks. Finally, a battery made especially for use in electric vehicle (EV) applications receives the transformer's isolated output, which is also supplied to DC loads. Finally, we will use the MATLAB 2021a / Simulink program to do a number of numerical simulations in order to validate the suggested controls. The output voltage of 130v is stored in the battery if a voltage of 65 v is taken as output from the photovoltaic system.

An improved finite set model predictive control of SRM drive based on a voltage vectors strategy for low torque ripple

The robust rotor structure and fault-tolerance characteristics of the Switched Reluctance Motors (SRMs) are the best choice for next-generation Electric Vehicle (EV) applications. This machine has few restraints like high torque and flux ripples. However, the existing Model Predictive Control (MPC) using multiple control objectives and maximum sectors in the switching table results in high torque ripples due to the improper sector partition, Voltage Vectors (VVs) and weight factor (K1) selection. This paper proposes a Finite Set-Model Predictive Control (FS-MPC) for an analytical model of a non-linearity SRM machine to analyze the torque ripple performance. The proposed VVs are derived using sector partition based on the rotor position. The control is designed as a single cost function with the weighting factor contributing to smooth torque by selecting optimal control signals. Simulation studies and experiments with a four-phase 8/6 SRM drive verifies the enhanced FS-MPC for real-time implementation. The dynamic speed and ripple values of SRM Drives are measured using a mixed signal oscilloscope and the sensor probes. The laboratory outcomes calculate the analytical equations to validate the findings. The calculated value of torque ripple is 9 % through this FS-MPC. The study reveals that the proposed method is well suited for torque ripple reduction than flux ripples.

Designing of Inverted F Antenna and Utilizing of Blockchain in Vehicle Systems

Raed Daraghma, Eman Daraghmi, Yousef Daraghmi, Hacene FoThis paper is the first to integrate a blockchain system to improve the manufacturing efficiency and reliability of 5G F Inverted antennas. The recommended antenna is constructed from a single side of a premium aluminum conductor, the width of the radiator is 0.564 mm, the thickness of the conductor is 0.77 mm ground plane is 29.3 x 29.3 mm2 dimension. The antenna is designed to work at a frequency of 5.9 GHz, therefore it can be used for vehicle applications. It is designed to be inserted as an integrated antenna into an IOT device and is composed of Microstrip F shapes. Therefore, a rectangular Microstrip patch F antenna was built and its performance was examined in this work. 5.9 GHz is the antenna's resonance frequency range, which is suitable for vehicle applications. The simulation software for this work was Computer Simulation Technology (CST) software. Antenna performance was compared concerning gain, bandwidth, and return on loss. By enhancing the production efficiency and reliability of 5G Inverted F antennas, our study sets a new benchmark for the integration of blockchain and smart contract technologies, paving the way for ground-breaking advancements in manufacturing techniques. The relevance of creating an inverted F antenna for the Vehicle Systems environment is increased by this research. Typically, rod antennas are found in automobiles. However, the Vehicle Systems industry's requirements for meeting the demands of human resources with a variety of applications at different frequencies are not met by the rod antenna now in use. Because of their important characteristics, which include wideband matching, omnidirectional pattern, high efficiency, and compact size, microstrip patch Inverted F antennas are highly favored in the Vehicle Systems industry. uchal

A review of the application development and key technologies of rotary engines under the background of carbon neutrality

Rotary engines possess advantages such as light weight, high power density, and strong fuel adaptability, which are essential for powertrain systems. They hold great potential in the field of power machinery. However, the development of rotary engines has been constrained by increasingly stringent emission regulations. Despite this, numerous scholars and institutions continue to optimize rotary engines, leading to a proliferation of related research. It is necessary to synthesize these findings to guide future promotion and application efforts. This review examines the current applications and development status of rotary engines globally. It summarizes the development of key technologies such as sealing techniques, rotor configurations, and ignition devices, which have achieved lower energy consumption and emissions. The suitability of rotary engines for automotive and unmanned aerial vehicle applications has also been reported. To further reduce carbon emissions, researchers have explored zero-carbon fuel rotary engines. Hydrogen-fueled rotary engines have demonstrated higher combustion efficiency. By integrating EGR, load control strategies, and ignition system layouts, they have addressed issues such as NOx emissions, knock, and leakage. The development of ammonia-hydrogen hybrid fuels is also a potential effective solution. Continuous innovative research indicates that, with their inherent advantages and the integration of alternative fuels and advanced ignition technologies, rotary engines have the potential to meet future energy and environmental requirements. This positions them as strong contenders in future diversified power systems.

Technological Development of Hybrid Aquatic-Aerial Vehicle based on Bionics: Research Progress and Trends

In both military and civilian domains, the spacecraft is commonly employed to accomplish various tasks. With the ongoing advancements in mil-itary strategy and civilian technologies, task environments have become increasingly complex. Consequently, there’s a growing trend across all sectors towards integrating water and air operations to enhance task completion. With the continuous progress of biology and mechanics, the development direction of hybrid aquatic-aerial vehicle has emerged in the direction of bionic vehicle. The purpose of this paper is to analyze the current research status and development trend of different hybrid aquatic-aerial vehicles based on bionics. Firstly, the early research and application of hybrid aquatic-aerial vehicles are reviewed by time tracing method. Then, the implications of different biological models for the development of hybrid aquatic-aerial vehicles are analyzed and the research status of different vehicles is discussed. Additionally, the paper analyzes the advantages and disadvantages of hybrid aquatic-aerial vehicles and other vehicle types. It also proposes solutions, primarily focusing on methods for integrating different vehicle types. The current research status, advantages, and disadvantages of various hybrid aquatic-aerial vehicles are analyzed in this paper. It also suggests feasible schemes for hybrid aquatic-aerial vehicles, serving as a valuable research reference in certain areas.

OPTIMIZATION OF AN INTELLIGENT CONTROLLED BRIDGELESS POSITIVE LUO CONVERTER FOR LOW-CAPACITY ELECTRIC VEHICLES

This paper presents a novel approach to power conversion in electric vehicle (EV) applications. The proposed converter, a Bridgeless Single Stage Positive Luo Converter (BSPLC), is optimized to enhance efficiency and reduce losses in low-capacity EVs. Traditional converters experience higher losses due to their passive components and bridge circuits. By eliminating the bridge components, the converter achieves higher efficiency. An intelligent fuzzy logic controller is employed to provide adaptive control and stabilize output under varying input conditions, improving performance and response time. The converter design is further optimized through parameter tuning and simulation to achieve minimal ripple and maximum power efficiency at 92%. The proposed solution is ideal for low-capacity EVs, as it ensures enhanced power conversion, reduced thermal stress, and improved battery life. The study demonstrates the converter’s capability to meet the growing demands for energy-efficient solutions in modern electric mobility.

Analysis of Cascaded H Bridge Multilevel Inverter for Three-Phase Induction Motor Drive

The last ten years have seen a rise in the usage of multilayer inverters. Because they can produce waveforms with a greater harmonic spectrum and reliable output. Several high voltages and high-power applications are suitable for these new inverters. A multilayer converter not only produce high power ratings, but it also saves time and money and enhances the system performance in terms of harmonics, dv/dt strains, and pressures on the motor bearings. Owing to its flexibility and modularity, the possible topology for a power application is the cascaded H-bridge multilevel inverter (CMI) with separated DC sources. This study investigates a cascade H bridge multilevel inverter, such as a Three, Five, Seven, Nine and Eleven level inverter in order to drive a three phase AC induction motor. A Level shifted Pulse Width Modulation (LSPWM) technique was applied for the CHBMLI. Specific emphasis is given to aspects like modulation and total harmonic distortion that are preferable or necessary for multi-level converters. For both intellectual and scientific considerations, investigating sine triangle carrier pulse width modulation is deemed to be the most auspicious approach. Analysis of the many performance parameters, including load torque, motor speed, and efficiency, an extensive survey is conducted. MATLAB / Simulink is built into the entire system, and simulation results are developed. The purpose of this research is to demonstrate the performance of 9-level and 11-level fed induction motor driving results for Electric Vehicle applications. CHBMLI fed motor drive is better suitable for Induction Motor Drives.

Adaptive Current Angle Compensation Control Based on the Difference in Inductance for the Interior PMSM of Vehicles

Achieving good performance in terms of a fast and accurate maximum torque per ampere (MTPA) control method depends on both accurate parameter estimation and a sophisticated control strategy. However, it often requires a complex and long computational process. This paper proposes an efficient control method using the relationship of torque and current angle for maximum torque per ampere control of the interior permanent magnet synchronous motor (IPMSM) for vehicle application. It was found that it is not necessary for the control method to determine the inductance in the d-q axes; the identification of the difference between each axis is enough. Furthermore, this paper presents a simple and effective procedure to estimate the difference in inductance online, where the linear iron loss calculation method is also designed to support the above process. The proposed control method was experimentally validated on a 5 kW prototype by a TMS320F28335 microcontroller and DSPACE synchronized with a personal computer. The results show that the control process has faster and more accurate performance than the conventional method.

Quasi‐Z‐source interleaved DC‐DC converter for fuel cell vehicle application

AbstractIn this study, a step‐up DC‐DC converter with a combination of a two‐phase interleaved structure and a quasi‐Z impedance network is proposed for fuel cell vehicle application. The operation of the converter in a small duty cycle (0 < D < 0.5) reduces the conductive losses of the switches and as a result increases the efficiency of this converter compared to conventional boost converters. Also, due to the common ground between the input and output, unlike the floating interleaved boost converter (FIBC) and the parallel input‐series output converter (PISO), it does not face the problem of imposing additional EMI on the converter circuit. This converter has a low input current ripple, suitable voltage gain for fuel cell vehicles system, as well as high reliability due to the ability to operate with one phase in case of failure in the other phase. It can be a suitable candidate for practical application in fuel cell vehicles. The principles of operation and the main characteristics of the converter such as voltage gain, input current ripple, and voltage and current stress of the elements are explained and also a 120‐W experimental prototype with 12‐V input voltage and 60‐V output voltage is made to validate the theoretical analysis results.

Real-Time Simulation and Hardware-in-the-Loop Testing Based on OPAL-RT ePHASORSIM: A Review of Recent Advances and a Simple Validation in EV Charging Management Systems

Phasor-domain (RMS) simulations have become increasingly vital in modern power system analysis, particularly as the complexity and scale of these systems have expanded with the integration of renewable energy sources. ePHASORSIM, an advanced phasor-based simulation tool developed by OPAL-RT, plays a crucial role in this context by enabling real-time phasor-domain simulation and hardware-in-the-loop testing. To keep pace with these evolving needs, continuous efforts are being made to further improve the accuracy, efficiency, and reliability of ePHASORSIM-based simulations. These efforts include automating model conversion processes for enhanced integration with ePHASORSIM, extending ePHASORSIM’s simulation range with custom models, developing hybrid co-simulation techniques involving ePHASORSIM and an EMT simulator, enhancing simulation scalability, and refining HIL testing to achieve more precise validation of control and protection systems. This paper provides a comprehensive review of these recent advances. Additionally, the paper discusses the conversion of models from PowerFactory—a widely used and comprehensive modeling environment—to ePHASORSIM through both automated tools and manual methods using Excel workbooks, which has been discussed little in the literature. Furthermore, as ePHASORSIM is a relatively new tool with limited cross-validation studies, the paper aims to contribute to this area by presenting a comparative validation against DIgSILENT PowerFactory, with a specific emphasis on its application in electric vehicle charging management systems.

Motion planning of multiple unmanned vehicles using sequential convex programming‐based distributed model predictive control

AbstractThis article tackles the complex challenge of multi‐vehicle motion planning by presenting the novel Sequential Convex Programming‐based Distributed Model Predictive Control (SCP‐DMPC) algorithm. Existing methods often struggle with the nonconvex nature of optimization in multi‐vehicle motion planning, frequently compromising on computational efficiency. SCP‐DMPC innovatively combines the predictive control of DMPC with the optimization prowess of SCP to address these issues efficiently, while adhering to both physical and operational constraints. To ensure precise attainment of target poses, a three‐stage control strategy is introduced, with theoretical analysis on its recursive feasibility and asymptotic stability, enhancing the reliability of the algorithm. Moreover, a heuristic‐based deadlock resolution scheme is devised to prevent vehicle stalling, a common issue in cooperative motion. Validated through simulations in challenging scenarios, including symmetric position swapping and obstacle‐laden formation transition, SCP‐DMPC demonstrates superior adaptability, precision, and efficiency. These results underscore its potential for robust, real‐time unmanned vehicle applications, suggesting new directions for future research and development in the field.

The efficiency of unmanned aerial vehicles application for rapeseed productivity in Ukraine

The efficiency of unmanned aerial vehicles application for rapeseed productivity in Ukraine

Survey on Unmanned Aerial Vehicle Communications for 6G

Although the application of the fifth-Generation (5G) mobile communication has brought tremendous innovations to the daily life of human beings, e.g., autonomous vehicles and internet of everything, the upcoming huger data requirement leads to the emergence of the sixth-Generation (6G) mobile communication. Compared to 5G, the transmission rate, time delay, and wireless coverage need to be improved significantly. Thus, in this paper the applications of Unmanned Aerial Vehicles (UAVs) to the ubiquitous, intelligent and coupling 6G network are surveyed. First, the utilization of UAVs in the framework of space-air-ground-sea integrated network is demonstrated, and the roles and functions of UAVs in different scenarios are emphasized, e.g., the swarm base stations, the deployment for holographic projection, the long-distance relaying and the data collection. Then, the potential 6G key techniques of terahertz, ultra-massive multiple-input and multiple-output, endogenous artificial intelligence, Intelligent Reflecting Surface (IRS), intelligent edge computing, blockchain and integrated sensing and communication for UAV communications are investigated. Finally, the future challenges of UAV communications for 6G, including the limited duration, integration of networks, compatibility of IRS, development of THz communications, and user security are discussed.

Modeling of genetic algorithm tuned adaptive fuzzy fractional order PID speed control of permanent magnet synchronous motor for electric vehicle

This study presents a novel Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFFOPID) controller for enhancing the speed control performance of permanent magnet synchronous motor (PMSM) drives in Electric Vehicles. The proposed GA-AFFFOPID controller, which combines the advantages of genetic algorithm optimization and adaptive fuzzy fractional-order PID control, represents a unique and innovative approach to address the control challenges associated with PMSM drives. Permanent magnet synchronous motor technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in electric vehicle drive systems. However, the inherent non-linearity, dynamics, and uncertainties of permanent magnet synchronous motors pose significant control challenges. The exceptional performance of the GA-AFFFOPID controller, demonstrated through its superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances, underscores the significant advancements enabled by the genetic algorithm optimization technique in improving the control performance of PMSM drives for electric vehicle applications. Comparative analysis with traditional control methods demonstrates the exceptional performance of the Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative controller. These findings highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications.