Rami Abousleiman

Smart Charging: System Design and Implementation for Interaction Between Plug-in Electric Vehicles and the Power Grid

Recent years have witnessed steady sales increase of electric and plug-in electric vehicles (PEVs). Stringent government regulations alongside persistent pressure from environmental groups will further increase the demand and sales of such vehicles. This trend might put pressure on the grid and local power distribution circuits and transformers. A sudden increase in power consumption, mainly caused by people charging their vehicles at the same time, and during the already peak load on the utility, may cause problems. These problems are mainly caused by the heavy demand on the local voltage transformers and the power sources. Power generation and distribution utilities may need to incur significant cost increases if this problem is not handled appropriately. In this paper, we present a novel design and implementation logic for a smart grid system that allows for dynamic interaction between electric/PEVs and the power grid. This smart and dynamic interaction will allow for safe and semi-stable load on the grid and thus minimizing the cost and preventing damage caused by the excessive loads. The complete system design is presented in this paper with emphasis on the developed algorithms. It has been implemented and tested on the Chryslers PEV RAM 1500 U.S. Department of Energy pickup trucks.

MICRORAPTOR: A LOW-COST AUTONOMOUS QUADROTOR SYSTEM

This paper describes Microraptor, a complete low-cost autonomous quadrotor system designed for surveillance and reconnaissance applications. The Microraptor ground station is custom-made and features a graphical user interface that presents and allows the manipulation of various flight parameters. The aerial vehicle is a 4-rotor vertical takeoff and landing (VTOL) vehicle that features the advantages of traditional helicopters with significant reduction in mechanical complexity. The vehicle frame is a handmade magnesium and carbon fiber structure. The onboard avionics system is a custom dual processor design capable of autonomous path navigation and data exchange with the ground station. The vehicle is outfitted with a video and still-photo system that provides real-time images to the system operator through the GUI. The system is being developed at Oakland University by a team of multidisciplinary undergraduate and graduate engineering students. Microraptor placed 5th at the 2008 Association for Unmanned Vehicle Systems International (AUVSI) Unmanned Aerial Systems (UAS) Competition and is set to compete again in June of 2009.Copyright

Energy-efficient routing for electric vehicles using metaheuristic optimization frameworks

Electric vehicles are gaining an increased market share. People are becoming more acceptable of this new technology as it continues to gain momentum especially in the North American and European markets. The main reasons behind this trend are the growing concerns about the environment, energy dependency, and the unstable fuel prices. Traditional source-to-destination routing problems are designed for conventional fossil-fuel vehicles. These routing methods are based on Dijkstra or Dijkstra-like algorithms and they either optimize the traveled time or the traveled distance. These optimizers will most likely not yield an energy efficient route selection for an electric vehicle. Electric vehicles might regenerate energy causing negative edge costs that deem Dijkstra or Dijkstra-like algorithms not useful for this application (at least without some modifications). In this paper, we present examples of why traditional routing algorithms would not work for electric vehicles. A metaheuristic study of the energy-efficient routing problem is presented. Ant Colony Optimization and Particle Swarm Optimization are then used to solve the energy efficient routing problem for electric vehicles. The 2 metaheuristic methods are analyzed and studied; the results and performance of each are then compared and contrasted.

A programmable charger for monitoring and control of multi-cell lithium-ion batteries

Lithium-Ion batteries are becoming the favorable selection for most portable electronics and more recently electric and hybrid-electric vehicles. This is mainly due to their high energy density and low self-discharge rate. On the other hand, lithium-ion batteries require supervision while charging and discharging to maintain the voltage and current within safe limits. This paper presents a real-time programmable approach to charge lithium-ion batteries with multiple cells. Mathworks Simulink environment is used in real-time to control a current source to charge the battery. The control logic monitors the battery cells voltages that are read through an A/D converter and then calculates the desired charging current levels. The proposed system allows engineers and researchers to easily implement and test different lithium ion battery charging methods. Furthermore, the Simulink environment allows real-time monitoring and permits charging profiles to be implemented at ease. The system is demonstrated through two different charging methods. The variations of the charging current and cell voltages are presented and compared, demonstrating the flexibility of the system.

A Bellman-Ford approach to energy efficient routing of electric vehicles

Most experts foresee more demand for electric and plug-in electric vehicles. This demand is triggered by environmental concerns, energy dependency, and unstable fuel prices. Available vehicle routing algorithms are designed for fossil-fuelled vehicles. These algorithms optimize for the shortest distance or the shortest travel time between 2 points. Dijkstra or Dijkstra-like algorithms are mostly used for solving such optimization problems. Energy-efficient routing for electric vehicles, on the other hand, requires different approaches as it cannot be solved using Dijkstra or Dijkstra-like algorithms. Negative path costs generated by regenerative braking, battery power and energy limits, and vehicle parameters that are only available at query time, make the task of electric vehicle energy-efficient routing a challenging problem. In this paper, we present a solution approach to the electric vehicle energy efficient routing problem using Bellman-Ford. Bellman-Ford is a deterministic optimization method that is capable of solving routes with negative paths. A model representing electric vehicles is presented. Bellman-Ford search is then applied on the model and is used to find the most energy efficient route. The generated solution is then used to guide the electric vehicle through the desired path. The performance of the Bellman - Ford algorithm is then studied by applying the implemented algorithm on different map sizes.

Statistical Algorithm for Attitude Estimation from Real-Time Aerial Video

Almost all autonomous unmanned aerial vehicles are used for reconnaissance and intelligence gathering roles. This means that cameras, video transmitters, and/or video recorders are already integrated in the system and are part of the unmanned aerial vehicle payload. Current attitude estimation sensors are expensive, heavy, and consume more power than most micro aerial vehicles can tolerate. Vision-based attitude estimation can be used to augment inertial sensors for increased accuracy, or as primary pitch and roll sensing resulting in reduced vehicle cost, size, and weight. This paper presents a fast, real-time algorithm to estimate pitch and roll angles for an aerial vehicle from video frames captured using a downward-pointingcamerawithamountedfisheyelens.Thefisheyelensisinstalledtoensure the visibility of most of the earth’s horizon at sufficient altitudes. Attitude angles are estimated by the horizontal and vertical movement of the horizon circle, which moves in relation to the center of the video frame image. The system was tested and implemented on a radio controlled plane and the results proved to be successful with over 85% of the results within ±3 ◦ when compared to a traditional inertial measurement unit.

Real-time Estimation of UAV Attitude from Aerial Fisheye Video

Many autonomous unmanned aerial vehicles carry video cameras as part of their payload. Outdoor video captured by such cameras can be used to estimate vehicle attitude by detecting the horizon location and curvature in each video frame. These estimates can serve as redundant data used for fault-tolerance purposes, to augment inertial sensors for increased accuracy, or as primary pitch and roll sensors resulting in reduced vehicle cost, size, and weight. This paper presents a fast algorithm to estimate pitch and roll angles from real-time video frames that are captured using a downward pointing camera equipped with a fisheye lens. Preliminary results are presented and compared to estimates from a traditional inertial measurement unit.