Umit Ozguner

Cloud-Based Velocity Profile Optimization for Everyday Driving: A Dynamic-Programming-Based Solution

Driving style, road geometry, and traffic conditions have a significant impact on vehicles fuel economy. In general, drivers are not aware of the optimal velocity profile for a given route. Indeed, the global optimal velocity trajectory depends on many factors, and its calculation requires intensive computations. In this paper, we discuss the optimization of the speed trajectory to minimize fuel consumption and communicate it to the driver. With this information the driver can adjust his/her speed profile to reduce the overall fuel consumption. We propose to perform the computation-intensive calculations on a distinct computing platform called the “cloud.” In our approach, the driver sends the information of the intended travel destination to the cloud. In the cloud, the server generates a route, collects the associated traffic and geographical information, and solves the optimization problem by a spatial domain dynamic programming (DP) algorithm that utilizes accurate vehicle and fuel consumption models to determine the optimal speed trajectory along the route. Then, the server sends the speed trajectory to the vehicle where it is communicated to the driver. We tested the approach on a prototype vehicle equipped with a visual interface mounted on the dash of a test vehicle. The test results show 5%-15% improvement in fuel economy depending on the driver and route without a significant effect on the travel time. Although this paper implements the speed advisory system in a conventional vehicle, the solution is generic, and it is applicable to any kind of powertrain structure.

Lane Change and Merge Maneuvers for Connected and Automated Vehicles: A Survey

Intelligence in vehicles has developed through the years as self-driving expectations and capabilities have increased. To date, the majority of the literature has focused on longitudinal control topics (e.g. Adaptive Cruise Control (ACC), Cooperative ACC (CACC), etc.). To a lesser extent, there have been a variety of research articles specifically dealing with lateral control, e.g., maneuvers such as lane changes and merging. This paper provides a survey of this particular area of vehicle automation. The key topics addressed are control systems, positioning systems, communication systems, simulation modeling, field tests, surroundings vehicles, and human factors. Overall, there has been some successful research and field testing in lane change and merge maneuvers; however, there is a strong need for standardization and even more research to enable comprehensive field testing of these lateral maneuvers, so that commercial implementation of automated vehicles can be realized.

Decentralized control of smart grid with fixed and moving loads

Plug-in hybrid electric vehicles (PHEVs), as intermittent loads, create frequency disturbance in power system. The system needs to balance the power generation and demand. However, in regional smart grid systems fed by renewable energy sources and moveable loads, the power transfer through tie line interconnections is strongly coupled with system dynamics. This makes the frequency stability and control process very slow. In this paper, an overlapping decomposition technique of large-scale system control is used to decouple the renewable energy penetrated power system regions. A decentralized controller is then designed to maintain the frequency in a short time. Micro-hydro Simulation results demonstrate a fast frequency control process to regulate the system under input power variation from wind turbine and load from PHEVs.

Hybrid Systems Modeling and Reachability-Based Controller Design Methods for Vehicular Automation

In this study, applicability of verification and correct-by-design hybrid systems modeling and reachability-based controllers for vehicular automation are investigated. Two perspectives in hybrid systems modeling will be introduced, and then reachability analysis techniques will be developed to compute exact reachable sets from a specified unsafe set. Using level set methods, a Hamilton–Jacobi–Isaacs equation is derived whose solutions describe the boundaries of the finite time backward reachable set, which will be manipulated to design a safe controller that guarantees the safety of a given system. An automated longitudinal controller with a fully integrated collision avoidance functionality will be designed as a hybrid system and validated through simulations with a number of different scenarios in order to illustrate the potential of verification methods in automated vehicles.

Swarm-Inspired Modeling of a Highway System With Stability Analysis

Naturally occurring flocks and swarms have long commanded human attention, with much engineering inspiration drawn from the beauty, order, and capability of these highly decentralized systems. More recent simulation and modeling of swarms has given rise to interesting mathematical problems as well as useful control strategies for machine applications. Although highway systems are sometimes mentioned in the literature as a possible swarm theory application, a microscopic, decentralized model of vehicle interactions based on swarming philosophy does not exist to our knowledge. In this paper, a decentralized model made up of ordinary differential equations and smooth functions is developed. It is designed to describe the interactions of vehicles on a two-lane highway. The purpose of this new model is not primarily traffic simulation, but rather cooperative control design. The philosophy behind the modeling is borrowed from work on swarm theory, especially those simulations employing the motion control ideas known as Reynolds’ Rules. Vehicles in the swarm have different desired speeds, which can be maintained by changing lanes to avoid slower-moving lead vehicles, while also avoiding both frontal and side collisions. Stability analysis of the proposed model has been presented, as well as simulation results and possible uses.

Model-Based Estimation of Large-Scale Interconnected Power Systems with Moving PHEV Loads

Abstract Power system dynamics change because of variations in distribution circuit, performance of power generation and behavior of load. An estimation of these dynamical behaviors can be achieved using mathematical models. Interconnected systems with shared state variables require specific models to demonstrate the influence of parameter variations on all areas. Estimation is particularly difficult when the system is influenced by random load variations such as moving plug-in hybrid electric vehicle (PHEV) loads. This paper introduces a new model-based estimation technique designed for large scale interconnected systems. This high performance state estimation demonstrated an accurate effective, realtime and computationally efficient approach. State estimations and their transient behavior were successfully obtained for a power system with random loads as high as 327 level-1 charging vehicles at various storage device charge levels.