Publications

Several studies exist on topics of semi-active suspension and vehicle cruise control systems in the literature, while many of them just consider actual road distortions and terrain characteristics, these systems are not adaptive and their subsystems designed separately. This study introduces a new method where the integration of look-ahead road data in the control of the adaptive semi-active suspension, where it is possible to the trade-off between comfort and stability orientation. This trade-off is designed by the decision layer, where the controller is modified based on prehistorical passive suspension simulations, vehicle velocity and road data, while the behavior of the controller can be modified by the use of a dedicated scheduling variable. The adaptive semi-active suspension control is designed by using Linear Parameter Varying (LPV) framework. In addition to this, it proposes designing the vehicle velocity for the cruise controller by considering energy efficiency and comfort together. TruckSim environment is used to validate the operation of the proposed integrated cruise and semi-active suspension control system.

Several semi-active suspension control systems have been studied and adapted to vehicles in the past. Many of these systems work with actual road conditions, while oncoming road conditions are not considered. This paper presents a method to integrate look-ahead road information in the adaptive semi-active suspension control. Oncoming road conditions and categories are known by using a global positioning system and historic road information. ISO 2631-1 standard was considered to categorize the road with different distortion types based on vibrations acting on the passengers of the vehicle. The adaptive semi-active suspension control is designed using Linear Parameter Varying (LPV) framework. The behavior of the controller can be modified by the use of a dedicated scheduling variable. This corresponding scheduling variable for the adaptive semi-active suspension system is defined by considering the road category. The selection of the scheduling variable considers a look-ahead estimation algorithm based on prehistoric simulations of passive suspension. The operation of the integrated adaptive semi-active suspension system is demonstrated through real-time simulation in TruckSim environment with real geographical data. In order to prove the effectiveness of the introduced method two different simulations have been evaluated and compared: one with conventional semi-active suspension and another with an adaptive semi-active suspension. The simulation results show that the overall performance in road holding, suspension deflection and ride comfort has been improved, which effectively demonstrates the advantage of presented adaptive semi-active suspension control based on look-ahead information.

Semi-active suspension control and vehicle cruise control systems have already been developed by researchers and adapted by automotive companies. Most of these systems react on actual road irregularities and terrain characteristics, and the control for each subsystem is designed separately. However, since oncoming road conditions can be known by using historic road information and GPS navigation system, the paper introduces a method to build in look-ahead road data in the control of the adaptive semi-active suspension, moreover, design the vehicle velocity for the cruise controller considering comfort and energy efficiency at the same time. The operation of the presented integrated suspension and velocity control system is validated by a real data simulation in TruckSim environment.

In this chapter a reconfigurable trajectory -tracking control design method has been presented for autonomous in-wheel electric vehicles with independently controlled hub motors and the steer-by-wire steering system. The high-level control reconfiguration has been implemented through the design of a scheduling variable using the LPV framework in order to deal with fault events, while in normal operating conditions the objective of the reconfiguration is to maximize battery SOC, thus enhancing the range of the in-wheel electric vehicle. The energy optimal control reconfiguration has been designed based on the results of preliminary simulations with a high-fidelity vehicle and electrical models on different road conditions. Finally, the efficiency of the proposed method has been demonstrated in a real-data CarSim simulation, showing significant energy saving by the proposed method

Intelligent Transportation System has been the driving forces to enable the paradigm of autonomous vehicles, smart roads and Internet of Things (IoT). For the safety and security of the traffic and transportation, stabilization of the technology and system is necessary. In addition to this, security of intelligent transportation system also influences the smart security and safety of vehicles, pedestrians and drivers. Thus, it is one of the most important application for the daily technology. There has been significant study related to security in vehicular network systems for intelligent transportation system usages. In this study, smart road and intelligent transportation system terms were explained. Attacks and threats of intelligent transportation system were evaluated with their security solutions while security objectives and architecture of intelligent transportation system and smart road were examined. During the evaluation, The European Telecommunications Standards Institute security standards were considered. It is possible to deduce that with developed technology, attack and threats level will be much bore pre-cariousness. New threats and attacks have to be investigate and simulate to find the solution for them.

Due to development in technology; technological revolution has been occurred in many sectors. The automotive sector is at the head of these technological revolutions. Autonomous vehicle technology and the development of sensors, cameras, radar and decision-making mechanisms under this technology have made the design and development of autonomous vehicles possible for every company. The aim of this study is to analyze the public’s confidence in autonomous vehicles. In this study, driver and pedestrian/passenger trust was analyzed with online survey which was performed with 107 participants. Furthermore, briefly autonomous vehicle market analysis was performed with same survey and same participants. While 60,7% of participants have basics knowledge about autonomous vehicles and their systems, 10,3% of attenders didn’t have any knowledge before. The presence of autonomous vehicles in the traffic is not disturb 73,8% of participants, conversely it can be problem for 6.5% of attenders. 63,5% of participants can drive on same line with autonomous vehicle while 9,6% of attenders do not prefer that. 60,7% of respondents have trust to autonomous vehicle as pedestrian who crosses over. 56,1% of participants prefer domestic produced autonomous vehicle instead of other brand who produces autonomous vehicle and 66,3% of attenders prefer autonomous vehicle in lieu classical vehicle in case of availability. According to this analysis, majority of community in Turkey have positive perspective on autonomous vehicle.

The paper deals with energy optimal control allocation of an in-wheel electric vehicle with autonomous trajectory tracking. The proposed method is based on both high-level control allocation between steering intervention and torque vectoring minimizing cornering resistance of the vehicle, and a low-level multi-criteria torque distribution method considering power consumption of the electric in-wheel motors. The aim of the design is to enhance battery state-of-charge (SOC), extending the range of the electric vehicle. The reconfiguration control design is founded on Linear Parameter Varying (LPV) framework, while the wheel torque distribution is calculated using constrained optimization techniques. The operation of the energy optimal reconfiguration control is demonstrated in CarSim simulation environment with a detailed battery and electric motor model.