M. Kaya

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.

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.

A smart trip advice model by inter-car voting data over Internet towards pleasant driving

Almost every day we can observe that most of people spend quite a long time in traffic environment. In the course of driving, motorists occasionally desire to get traditional travel information, such as traffic, road, car and safety related driving data. In the near future, we shall quite likely mention about new generation drivers who wish to get social based data and its manipulation. Hence, we should develop new driver necessity strategies satisfying Internet based requirements of new generation drivers. Here, we propose a system offering sub-trip options without ruining drivers primary trip plan. In fact, we consider driving efficiency taking pleasure in driving by means of social computing rather exploiting traditional data manipulations such as safe driving, fuel-efficient driving, and obtaining traffic data. For the sake of new generation driver wishes, we develop a model using trip data and feedback information provided by drivers earlier visiting intended attraction place. Then, we utilize a social platform associated with central server holding social data such as road safety, weather conditions, and satisfaction rate of attraction place. In this study, we earlier define driver profiles as gourmand, shopaholic, adventurer, art lover, and technophile. Therefore, we propose a system satisfying possible sub-trip expectations of drivers having different profiles. Eventually, we have modeled a new generation inter car sub-route advice platform for pleasant and efficient trip.

Estimation of confidence regions and severity of undefined faults in driving using synthetic disturbance signals

In this study, we aim to determine fault severity in an electric car that may be caused by yawing due to such disturbances as non-uniform road pavements, in-wheel bearing clearance, suspension system, driver under Influence of alcohol (DUI), and tire deformation. The major research contribution herein is to alert drivers about an unforeseen situation on steering wheel whether it refers to a severe fault or contemporary states. In a sense, the undertaken study serves as a state of the art driving assistant system operating under unsteady conditions. We determine the fault severity of the system via classifying it into specified confidence regions by estimating the deviation from a monotonous straight route for any unstable situation. In this way, the proposed system informs driver to gain an insight about the severity level of arising problematic scenario. In order to realize the classification of confidence regions, we initially obtain the overall dynamic model of the system. Then, disturbance functions with different amplitudes and frequencies are characterized and included in the dynamic system specification. Here, the confidence regions have been constructed as to respective fault severity level of the car through the system response. Trajectory of vehicle in dynamic driving conditions considering these perturbations and noises are interrelated through the Kalman filtering to predict deviations from the desired trajectory and the prediction error. In simulation scenarios, Dynamic Time Warping (DTW) is employed to obtain deviation from ground truth under different noise functions, and results are sketched graphically assigning rate of fault severity into specified confidence regions. Initially, we have modeled the proposed system considering an electric car although the idea can readily be generalized for all cars with four tires. Presently, fault severity with classified confidence regions has been investigated under a simple car model.

Dynamic risk assesment for driver response in passing over obstacles

In the urban areas, drivers may need to step over obstacles for safety caution. At this stage, drivers have to slow down and move at low speed while passing over curbs. If there is an obstacle in front or back of the car, driver should step on the gas to speed up the car from zero to cautionary speed to pass over it. The obstacle in question could be curbs, bumper or rim over urban areas. In this scenario, we consider a particular obstacle, namely, a curb. Hence, the driver has to climb over the curb by stepping on the gas, which, in turn, creates a critical driving situation emerging risky move contemplating the car driven or pedestrians as well as obstacles around the car. In this work, depending on response time of driver, while car crosses over obstacle, lateral distance of car taken unintentionally have been estimated. Considering this distance, emerging risk by the situation has been investigated along with influence of height on risk severity experimentation. Relationship among response time of driver, obstacle height and unintentional moving backward distance is determined and presented as a parametric response or outcome function. The study is extended further for modeling unintentional driving behavior in passing over obstacle risk assessment. Subsequently, the undertaken research has enabled us to deduce compensation between the driver response, steep level and geometry of the curb. Furthermore, we give an assessment for estimating the risk causing of the driver response in passing over curb.