nan Inayati

Comparation of the slotless brushless DC motor (BLDC) and slotted BLDC using 2D modeling

The advantages and disadvantages of slotted brushless DC motor (BLDC) and slotless BLDC make the user confused when they want to choose BLDC motors. The objective of this research was to describe the characteristic of slotted BLDC and slotless BLDC using 2D finite element method. Result of this research showed that slotted BLDC has higher torque than slotless BLDC. The power produced by slotted and slotless BLDC were almost the same, while the speed of the slotless BLDC was 10 times higher than the speed of slotted BLDC. Slotted BLDC produced more ripple torque than slotless BLDC, thus it made slotted BLDC had higher vibration than slotless BLDC.

Development of Wireless Battery Monitoring for electric vehicle

A Wireless Battery Monitoring System (WBMS) for electric vehicle has been developed for monitoring voltage, current and temperature of battery. This system consists of hardwares (sensors, a microcontroller, a bluetooth module, an Android smartphone) and software. It was designed on a low cost microcontroller ATMEGA 328 (Arduino UNO). Voltage, current and temperature data are transfered to microcontroller, then data of battery is transfered using bluetooth communication to display. In this research, data of battery monitoring are displayed on Personal Computer (PC) with LabVIEW programme and android smartphone. The monitoring system was able to show real-time data of voltage, current and temperature and display data on android smartphone and PC simultaneously.

Brushless DC motor torque improvement with mgnetic material stator core

In transportation, BLDC motors are required to have high torque. This BLDC electric motor is designed to drive an electric car. Increased torque can be done in many ways one of which is the selection of the stator material. Effect on the generated torque can be studied by replacing its stator materials. Design was begun with determining the electrical circuit and the coil for the excitation power. Electric motors which was simulated in this paper was a 3-phase BLDC motor. Voltage and current data were analyzed to study the motor performance when different core materials used. It can be concluded that applying ferrite materials stator core reduced core loss, while using ferrite core on the stator slots produces higher voltages and lower currents. Based on the magnitude of the torque produced by the electric motors, ferrite materials was more suitable to be used as slots of the stator core for better motor.

Increased efficiency BLDC motor with soft magnetic material

This paper describes the use of Design of Experiment (DOE) to find optimum design in Axial BLDC motor. Slot stator and wiring parameter was investigated. In the stator design, those four parameter was investigated. Diameter of wire, number of strand, parallel branch wire and number of conductor was study in wire parameter. Experiment was done by Maxwell software to calculate the efficiency. The parameter more affect in efficiency is number of strand and diameter of wire. The efficiency is increase.

Decision tree for state of charge (SOC) prediction of LiFePO4 battery

Battery is one of the most importance components in the field of energy system. In other hand LiFePO4 batteries have higher density of energy and more life cycle than nickel battery, but LiFePO4 need state of charge (SOC) prediction to solve the disadvantage of this battery. The objective of this study is to make method for calculate SOC that can compute it with high accuracy and fast computation time. Decision tree is one of logical computing based on supervised learning that can make precision of prediction. This study concentrated to develop decision tree for SOC prediction. Decision trees train with the real data from battery testing system. The average error from training data is 0.1789 with 0.047353 seconds time computation, and the average error from testing data is 1.9034. The conclusion from this study is decision tree can use to calculate SOC and it can be applied because of its accuracy and fast.

Series Plug in Hybrid Vehicle for Urban Driving Cycle

Urban area is the center of activities. Many people use the vehicle to cover the distance toward their activities places. In order to support the activities a large number of vehicles are moving in urban areas. Consequently, the consumption of fuel will increase from time to time and oil price has increased due to higher of demands. Thus, a plugin hybrid electric vehicle (PHEV) is proven as one of the best practical applications for transportation in order to reduce fuel consumption. One of the types of PHEV is a series PHEV (SPHEV). SPHEV is the simplest type of PHEV but still having higher efficiency of fuel than an internal combustion engine vehicle. This study was focused to discuss on the ability of SPHEV for covering distance and velocity of the urban drive cycle. Three driving cycles namely new European drive cycle (NEDC), extra urban driving cycle (EUDC), and EPA highway fuel economy cycle (HWFET) were used for the simulation using ADVISOR software to study performance of SPHEV. To achieve the best performance of SPHEV, the control strategy based on an artificial intelligence was purposed. The simulation was done by using SPHEV which consisted of15 kW battery, 41 kW engine, and 41 kW DC motor. The performance of SPHEV (fuel consumption and emission) was then compared to a gasoline engine vehicle (GEV). The results showed that SPHEV consumed less fuel and generated less emission during performing all drive cycles.