Volkan Sezer

A Novel ECMS and Combined Cost Map Approach for High-Efficiency Series Hybrid Electric Vehicles

The main aim in the control of a hybrid electric vehicle (HEV) is to decrease the fuel consumption and emissions without significant loss of driving performance. The performance of the vehicle in terms of fuel economy and emissions is very much dependent on the vehicles supervisory control strategy. In this paper, the equivalent consumption minimization strategy (ECMS) is developed with a novel approach for the charge sustaining of the batteries to provide an overall improved optimization performance for series hybrid electric vehicles (SHEVs), considering the efficiencies of the internal combustion engine (ICE), generator, and battery. Another novelty is the development of a combined map, which simultaneously facilitates the optimization of the fuel consumption and multiple emission components, unlike most past studies that have concentrated on one component at a time. After the derivation of the cost map, the algorithm is divided into two main parts. The first part optimizes the engine-generator set (GENSET), and the second part determines how much power is needed from the GENSET according to the ECMS. The algorithm is implemented using generic emission and fuel consumption maps of an actual mid-sized series hybrid bus to reduce the desired emissions. The hybrid electric vehicle in consideration is converted from a conventional bus that is driven by an ICE. The performance of the novel ECMS strategy is compared with the conventional vehicle, as well as the SHEV version that is driven by an on-off strategy. In addition to reduced fuel consumption, the results of this paper demonstrate a significant reduction of 14.58% in CO2 production with ECMS, whereas the on-off control strategy achieves only 6.47% reduction over the conventional vehicle.

A novel obstacle avoidance algorithm: Follow the Gap Method

In this paper, a novel obstacle avoidance method is designed and applied to an experimental autonomous ground vehicle system. The proposed method brings a new solution to the problem and has several advantages compared to previous methods. This novel algorithm is easy to tune and it takes into consideration the field of view and the nonholonomic constraints of the robot. Moreover the method does not have a local minimum problem and results in safer trajectories because of its inherent properties in the definition of the algorithm. The proposed algorithm is tested in simulations and after the observation of successful results, experimental tests are performed using static and dynamic obstacle scenarios. The experimental test platform is an autonomous ground vehicle with Ackermann steering geometry which brings nonholonomic constraints to the vehicle. Experimental results show that the task of obstacle avoidance can be achieved using the algorithm on the autonomous vehicle platform. The algorithm is very promising for application in mobile and industrial robotics where obstacle avoidance is a feature of the robotic system.

Conversion of a conventional electric automobile into an unmanned ground vehicle (UGV)

In this study, conversion procedure of a conventional electric automobile into an unmanned ground vehicle (UGV) is illustrated. This conversion process is divided into two main parts as, mechanical and electrical modifications. Interface circuit, interface software, additional power system, selection of the sensors and computer hardware are given in electrical modifications part. Similarly, design of braking and steering system, their computer simulations and strength analysis are given in mechanical modifications part. All these applications are illustrated on a conventional electric vehicle during this study.

Multi-sensor data fusion of DCM based orientation estimation for land vehicles

In this paper, an algorithm estimating orientation is implemented using Direction Cosine Matrix (DCM) method, chosen due to its linear process model and ease of use. Two Kalman filters were used to estimate the rotation matrix elements where the Euler Angles are easily computed. A rule based decision structure is used to choose the best measurement available in the system from GPS and digital compass. Also the dynamic motion of the vehicle is considered to overcome the slow response of the digital compass. The algorithm is tested with real time logged data set and a decision structure is developed to have the best Information provided from the multiple sensors. The algorithm is also tested under artificial GPS outages, performs successfully for both attitude and heading angles.

Propulsion system design of a hybrid electric vehicle

This paper presents the propulsion system design of an internal-combustion hybrid electric vehicle. Hybrid electric vehicles are currently the most promising solutions for reducing fuel consumption and pollutant emission levels. The choice of suitable components is the key issue in the design procedure of a hybrid electric vehicle. Different selections and different sizing choices highly influence the overall performance expected from the vehicle. Having a solid understanding of the main components present in the hybrid electric propulsion system is the best way to predict the capabilities of the vehicle performance, fuel consumption and pollutant emission levels before building a prototype. Hence, sizing and selection criteria for the major components of the hybrid electric propulsion system are given here in order to configure the vehicle correctly. Finally, a case study is presented to demonstrate the propulsion system design procedure of a parallel hybrid electric vehicle.

Towards autonomous navigation of unsignalized intersections under uncertainty of human driver intent

In a mixed environment of autonomous driverless vehicles and human driven vehicles operating on the same road, identifying intentions of human drivers and interacting with them in a compliant and responsible manner becomes a challenging problem for the driverless vehicles. In this paper, the problem of vehicle interaction at an intersection merging scenario is formulated as an Intention-Aware motion planning problem using the tools from Mixed Observability Markov Decision Process (MOMDP). We utilize the tools from recent intention aware planning framework to demonstrate a merging behavior in the presence of human drivers by trying to infer and act according to the intentions of the human drivers. A driver behavior model for T-junction intersections is developed in order to calculate the probabilistic state transition functions of the MOMDP model. With proposed solution, it is demonstrated that using intention aware planning improves performance in comparison to present time to merge approach by lowering accident probability and intersection navigation duration. The proposed method is tested on a real autonomous vehicle (AV) in the presence of human driven vehicles to validate our approach.

Framework for development of driver adaptive warning and assistance systems that will be triggered by a driver inattention monitor

This paper reports preliminary work on an experimental vehicle and simulators and their initial use towards the development of driver adaptive warning and assistance systems that will be triggered by driver inattention monitor in the Drive Safe national consortium project in Turkey. Determination of driver inattention based on visual, sound and driving input data collected from the driver while driving on a test route or its replication in a simulator environment is conducted by the other research groups in the consortium and is not presented here. The paper first gives information about the Drive Safe project and its goals and concentrates on the construction of a data acquisition vehicle used for driver modelling, preliminary work on simulators used for development and testing in the lab, and initial work on development of warning and assistance systems. The warning systems considered are collision risk warning and rollover risk warning. The assistance systems considered are collision avoidance assistance and dynamic stability enhancement assistance, which includes yaw and roll dynamics stability augmentation. Preliminary results are given in the paper on the experimental vehicle and simulators and driver warning and assistance systems.

Real-World Data Collection with UYANIK

In this chapter, we present data collection activities and preliminary research findings from the real-world database collected with “UYANIK,” a passenger car instrumented with several sensors, CAN-Bus data logger, cameras, microphones, data acquisitions systems, computers, and support systems. Within the shared frameworks of Drive-Safe Consortium (Turkey) and the NEDO (Japan) International Collaborative Research on Driving Behavior Signal Processing, close to 16 TB of driver behavior, vehicular, and road data have been collected from more than 100 drivers on a 25 km route consisting of both city roads and The Trans-European Motorway (TEM) in Istanbul, Turkey. Challenge of collecting data in a metropolis with around 12 million people and famous with extremely limited infrastructure yet driving behavior defying all rules and regulations bordering madness could not be “painless.” Both the experience gained and the preliminary results from still on-going studies using the database are very encouraging and give comfort.

A new fuzzy speed control strategy considering lateral vehicle dynamics

This paper introduces a new speed control strategy for autonomous/semi-autonomous navigation of ground vehicles. Different from the previous studies, steering angle is considered in addition to speed error and integral of the speed error. Because of the highly nonlinear nature of vehicle model, fuzzy logic strategy is used for controller design. Vehicle modeling equations and used parameters are also illustrated in the paper. Simulations are carried out to verify and demonstrate the effectiveness of the new method over the classical one which does not consider the steering angle. Both of these two methods have similar performances when steering angle is relatively low. However, in a more aggressive steering scenario, classical approach fails and vehicle loses its yaw stability while our method still continues to track the speed with a stable yaw dynamics.

Intelligent Decision Making for Overtaking Maneuver Using Mixed Observable Markov Decision Process

ABSTRACT Overtaking maneuver is one of the most dangerous scenarios for road vehicles especially in two-way roads. In this article, we propose a new formulation for the problem of overtaking in two-way roads using the tools from the Mixed Observable Markov Decision Process (MOMDP). This new formulation helps us to find the optimum strategy considering the uncertainties in the problem. Due to its computational complexity, solutions of Markov-based decision processes are very complicated, especially for the problems with measurement uncertainties. With the help of the efficient solvers and development and evolutions in computational technology, we show the applicability of Markov-based decision processes for the overtaking problem. The proposed method is tested in simulations and compared with other stochastic-variant Markov Decision Process (MDP) and classical time to collision (TTC) approaches. The proposed MOMDP solution improves the performance in comparison to both MDP and classical TTC approaches by lowering collision probability and overtaking duration.