Hasan Kurum

Investigation of End Effects in Linear Induction Motors by Using the Finite-Element Method

In rotating machines, no end effects occur because of their cylindrical geometry, which has no ends in the direction of movement. In linear motors, however, either the rotor or the stator has a finite length, and end effects occur because of the part having finite length entering or exiting the magnetic field. End effects occur as magnetic waves having attenuation, and play a major role in the performance of linear induction motors (LIMs), especially at high speeds. As the speed increases, efficiency decreases because of longitudinal end effects. This decrease in efficiency has a braking effect. In high-speed maglev devices, such as trains, the efficiency and performance of the LIMs become very important. We report on our investigation of end effects by the finite-element method.

A survey on comparison of electric motor types and drives used for electric vehicles

In this study, Switched Reluctance Motor (SRM), Induction Motor (IM), Brushless DC Motor, and Permanent Magnet Motor (PM), and their drives have been compared with the efficiency, cost, weight, cooling, maximum speed, reliability, fault tolerance, power ratings, and vehicle acceleration time. Hence, a comprehensive literature research on motor types and their drives used in EV has been made. According to these researches, some conclusions have been obtained. It has been seen that PM BLDC motors and their drives are the most efficient and have high power density, brushless DC motors and their drives have low cost, IM is appropriate for controllability and cost, the weight of SRM is low, its reliability is high, it operates fault-tolerance and according to the acceleration time, its performance is better than IM and BLDC. Hence, SRM is the most appropriate motor for EV.

Approaching car detection via clustering of vertical-horizontal line scanning optical edge flow

Here, we describe a method that detects vehicle(s) approaching from behind to a commuting car in the lane in which both are travelling. This research contributes to the development of driver assistance systems by means of informing them about the approaching traffic from behind and warn the drivers in case they are drowsy or not alert and the driving conditions are hazardous. We use the image pairs extracted from a video clip obtained from a video camera mounted on the back side of the car. This allows detection of the moving objects from the video image pairs using optical flow. Objects which are determined as not cars or vehicles have been eliminated by edge extraction. In turn, this approach leads to lessen the operation processing cost. Then, Density Histogram of Cluster Rows (DHCR) and Density Histogram of Cluster Columns (DHCC) are generated for the purpose of classification of motion vectors (MVs). Consequently, approching vehicles and cars are detected by localizing the place of the motion vector clusters using Vertical Horizontal Line Scanning (VHLS) as experimental results demonstrate.

An effective variable selection algorithm for Aggressive/Calm Driving detection via CAN bus

In this research, the aim is to come up with an algorithm determining most appropriate variables of CAN (Controller Area Network) bus data for Aggressive/Calm Driving detection problem. This study assists drivers to take attention their Aggressive/Calm Driving habits on steering wheel. System complexity increases as involving all the variables in the problem. Therefore we can get cost efficiency by eliminating variables. With this aim, the proposed algorithm is applied to find optimal variables before identifying driving mood. As an initial phase, we have realized several test-drives having employed drivers with different driving styles being aggressive and calm in order for collecting data needed. Afterwards the novel algorithm developed is applied to eliminate trivial variables. Proposed method is based on exploiting similar correlation characteristics related to variables appearing in both Aggressive and Calm driving. As applying the selection algorithm, similar relation clusters are obtained with the aim of searching for redundant variables that will be eliminated. In this manner we reach a favorable set belonging to optimal variables. This novel algorithm can be easily applied for the systems including binary data set.

Interactive risky behavior model for 3-car overtaking scenario using joint Bayesian network

In this paper, we propose a new model for 3-car interactive risky behavior of vehicles travelling in front and behind of a driver (overtaken) car. Following distance of vehicles moving in front and at rear end of the car in question plays an important role for overtaking scenario. Moreover, the distance between the car in front and the vehicle following it should be sufficiently long for preventing collision if overtaking is inevitable for the motorist behind the middle subject vehicle. Here, we consider the roles of the vehicles involved in such a scenario. We observe the behaviors of moving vehicles in front and the rear end of the subject car. To this end, front and rear car images are acquired by two cameras and subjected to vertical and horizontal optical flow edge map creation. In classification stage of the optical flow edge map clusters, a motion vector histogram thresholding method is utilized in conjunction with a decision assessment strategy based on the joint Bayesian belief network statistical model. In turn, not only the trajectories of the cars are captured but also joint behavior of three cars over-taken scenario is estimated using the proposed interactive risk model.

Designing in-wheel switched reluctance motor for electric vehicles

Estimation of dimension parameters for an electrical machine has great importance before manufacturing. For this reason, analytical design should be performed in an optimum form. While motor analysis is accomplished by package programs, initial size parameters are intutivily provided and then various trials are examined to get optimum results. In this study, we are trying to find dimensional and electrical parameters generating mathematical equations in analytic approaches for In-Wheel Switched Reluctance Motor (IW-SRM), which will be employed by Electric Vehicle (EV). Therefore, optimum motor parameters for required speed and torque have been estimated by solving generated equations for in-wheel SRM with 18/12 poles via MATLAB. Using the parameters, analysis of in-wheel SRM has been carried out 3D Finite Element Method (FEM) by Ansoft Maxwell 15.0 Package Software. Consequently, the accuracy of the estimated parameters has been validated by the results of Maxwell 3D FEM.

Similar association set based attribute selection algorithm for road profile detection

In this study, our goal is to develop an algorithm for selecting most appropriate attributes of CAN (Controller Area Network) bus data in association with Uphill/Downhill road detection problem. In turn, drivers are made aware of Uphill/Downhill situations on steering wheel. In a sense, drivers are enabled to have optimal driving performance for roads at different slopes. Initially, the system collects vehicle dynamic data. Then the proposed algorithm is applied to find optimal attributes before estimating road profile on which vehicle travels. In order to estimate Uphill/Downhill case, we initially consider all the vehicle parameters of the system. Therefore, we drive a car at different road slopes. In these experiments, we collect the relevant data to evaluate them in following stages. A new algorithm is developed and applied to eliminate attributes with least significance. Hence, at the first stage correlated attributes are sought as key features. That is, we search for attributes with similar correlation characteristics for both Uphill and Downhill roads. Similar association sets are established to search for redundant attributes that will be eliminated by our feature selection algorithm. In turn, an optimal set of attributes is determined for the goal driven system development. Proposed algorithm can be employed by any system that includes redundant attribute set.

Dynamic risk modeling for safe car parking in climbing over urban curbs

In urban areas, safe and secure vehicle parking presents various problems as vehicles are driven at low speeds toward available parking spots. If there is an obstacle in front or back of the car, drivers have to accelerates their cars from zero to higher speed to pass over the obstruction. Obstacle could be curbs, bumper or any rim over the parking area. In this case, we assume the obstacle to be a curb. Therefore, driver has to get over the obstacle by stepping on the gas of his car. This action can result in hazardous situation to the car, pedestrians or obstacles around the car. In the proposed system, we estimate jumping distance of the car considering major components attributing to scenario. As outcomes, we have obtained the balance between the car performance and steep level of the curb. This would be a guidance not only designing urban areas but also estimating dynamic behavior of the car after detecting the obstacle profile.

Lagrange multipliers for the intermediate metallic regions in the computation of the potential distribution of insulator chains by FEM

It is very important to predict the behaviour of the high voltage insulators at the operating conditions from the point of improving of the design and practical selection of the insulator type. Therefore, it is necessary to compute the potential and electric field distributions and to determine the critical regions forced much more by the electric field. In case the insulator surface is clean or the pollution film at the surface is dry, a little capacitive current flows and the solution of the problem results in a capacitive potential distribution. In the numerical computational methods of potential distribution of insulator chains, it requires to insert into the resulting equation system as boundary conditions of the intermediate metallic zones that connect two units that have the same potential at every point. In this work, the potential distribution of clean insulator chains have been computed using the Finite Element Method (FEM). The metallic zones between the units have been taken into account as equipotential surfaces by Lagrange Multipliers. The computed results were compared with experimental results obtained by using the Conducting Paper Method and good agreement was observed.

Road-Geometry-Based Risk Estimation Model for Horizontal Curves

Rural roads present potential risks for drivers. One of them is horizontal curve, which poses higher risk than freeway. This is the major theme for the presented work here aiming to develop a model that predicts risk of curved roads. Major road geometry components associated with curve structure are road slope type being uphill or downhill, road curvature, and curve direction along with vehicle speed as being a critical factor. In this study, cameras mounted in rear and front ends of a vehicle that capture road images are utilized to detect the components emerging risk. This two-view approach is exploited to obtain vehicle speed and bend slope type, whereas curve direction and road curvature are determined by single-view front camera. The proposed approach is leveraged by geometrical derivations using salient visual clues such as vanishing points and road boundary. Additionally, velocity is estimated by reverse-view technique, that is, plane of front view at instance t and the plane of rear view in t+1. Subsequently, overall potential hazard is predicted by assigning weights for each risk components via developed risk estimation model. The proposed model would be an integral part of an advanced driver assistant system by alerting driver about the prominent risk of horizontal curve ahead of time.