The practically applicable endurance estimation method for multirotor unmanned aerial vehicles (UAVs) using a battery as a power source is proposed. The method considers both hovering and steady-level flights. The endurance, thrust, efficiency, and battery discharge are determined with generally available data from the manufacturer. The effects of the drag coefficient related to vehicle shape and payload weight are examined at various forward flight speeds. As the drag coefficient increases, the optimum speed at the minimum required power and the maximum endurance are reduced. However, the payload weight causes an opposite effect, and the optimal flying speed increases with an increase in the payload weight. For more practical applications for common users, the value of S × C_d is determined from a preliminary flight test. Given this value, the endurance is numerically estimated and validated with the measured flight time. The proposed method can successfully estimate the flight time with an average error of 2.3%. This method would be useful for designers who plan various missions and select UAVs.

Drive motors, which are used in the drive modules of electric cars, are interior permanent magnet motors. These motors tend to have high cogging torque and torque ripple, which leads to the generation of high vibration and noise. Several studies have attempted to determine methods of reducing the cogging torque and torque ripple in interior permanent magnet motors. The primary methods of reducing the cogging torque involve either electric control or mechanical means. Herein, the authors focused on a mechanical method to reduce the cogging torque and torque ripple. Although various methods of reducing vibration and noise mechanically exist, there is no widely-known comparative analyses on reducing the vibration and noise by designing a notched rotor shape. Therefore, this paper proposes a method of reducing vibration and noise mechanically by designing a notched rotor shape. In the comparative analysis performed herein, the motor stator and rotor were set to be the same size, and electromagnetic field analysis was performed to determine a notch shape that is suitable for the rotor and that generates reasonable vibration and noise.

The recent increase in the use of permanent magnet rotor motors underlines the importance of designing a rotor with an interior permanent magnet (IPM) structure, high power, and high efficiency. This study analyzed the rotor shapes of IPM motors for electric vehicles. Five types of motor rotors for automobiles were analyzed, including two hybrid vehicles. In order to minimize the number of variables in the analysis, the size of the motor stators was fixed and only the rotor shapes were modified to compare torque, torque ripple, efficiency and back-electromotive voltage. When the motor properties were compared as a function of rotor shape, the rotor shape with the smallest magnet volume exhibited excellent results for torque, efficiency and torque ripple.

Recent studies on autonomous vehicles focus on improving driving efficiency and ignore driving comfort. Because acceleration and jerk affect driving comfort, we propose a comfort regenerative braking system (CRBS) that uses artificial neural networks as a vehicle-control strategy for braking conditions. An autonomous vehicle driving comfort is mainly determined by the control algorithm of the vehicle. If the passenger’s comfort is initially predicted based on acceleration and deceleration limits, the control strategy algorithm can be adjusted, which would be helpful to improve ride comfort in autonomous vehicles. We implement numerical analysis of the control strategy, ensuring reduced jerk conditions. In addition, backward propagation was applied to estimate the braking force limits of the regenerative braking systems more accurately. The developed algorithm was verified through the Car Sim and MATLAB/Simulink simulations by comparing them with the conventional braking system. The proposed CRBS offers effective regenerative braking within limits and ensures increased driving comfort to passengers.

The interest in electric-powered transportation has been increased because of problems such as depletion of fossil fuel resources and environmental issues caused by internal combustion engines. The market for various kinds of small E-mobility devices (EMDs), such as electric vehicles (EVs), E-bikes, segways, and mini-boards has been growing rapidly. Of these, the distribution of the small electric vehicles called personal mobility devices (PMDs), which can address problems such as traffic issues and environmental pollution, is expected to gain momentum. However, unlike the widely used charging stations for EVs, charging systems for PMDs are insufficient. The large size of dedicated PMD charging adapters makes them inconvenient to transport. Therefore, to make PMDs more attractive, it is necessary to deploy charging stations that can charge all types of PMDs. The present study proposes a new charging system that can respond to the needs of PMDs having various voltages and implement low charging current ripple. The proposed multi-level charging system can share power by subdividing the power supply to handle the charging requirements of various PMDs. In order to verify the validity of the proposed charging system, we performed simulations and conducted experiments by building a prototype charging system.

Electric power steering (EPS) pose significant control challenges in autonomous vehicles due to their inherent complexity and non-linearity. This study explores the application of artificial neural network (ANN) to address these limitations. Two approaches are proposed: 1) an ANN-based identifier utilizing the backpropagation (BP) algorithm to learn the system’s non-linear dynamics, and 2) an ANN-based controller leveraging the Levenberg-Marquardt (LM) algorithm to improve control performance. Our findings demonstrate the efficacy of the proposed ANN-based BP algorithm in EPS system identification achieving over 99.6% accuracy in predicting EPS system dynamics compared to the traditional approach. Additionally, the LM-learned ANN-based controller aiming a faster response and precise reference tracking compared to the traditional controller method. These advancements underscore the potential of employing ANN methodologies to optimize EPS performance in autonomous vehicles.