Electric Waterborne Public Transportation in Venice: a Case Study

The paper reports the results of a study for moving the present diesel-based watercraft propulsion technology used for public transportation in Venice city and lagoon to a more efficient and smart electric propulsion technology, in view of its adopted in a near future. Energy generation and storage systems, electrical machines and drives, as well as economic, environmental and social issues are presented and discussed. Some alternative solutions based on hybrid diesel engine and electric and full electric powertrains are compared in terms of weights, costs and payback times. Previews researches on ship propulsion and electric energy storage developed by the University of Padua and preliminary experiences on electric boats carried out in Venice lagoon by the municipal transportation company ACTV and other stakeholders are the starting point for this study. Results can be transferred to other waterborne mobility systems

Fuzzy logic control for energy saving in autonomous electric vehicles

Limited battery capacity and excessive battery dimensions have been two major limiting factors in the rapid advancement of electric vehicles. An alternative to increasing battery capacities is to use better: intelligent control techniques which save energy on-board while preserving the performance that will extend the range with the same or even smaller battery capacity and dimensions. In this paper, we present a Type-2 Fuzzy Logic Controller (Type-2 FLC) as the speed controller, acting as the Driver Model Controller (DMC) in Autonomous Electric Vehicles (AEV). The DMC is implemented using realtime control hardware and tested on a scaled down version of a back to back connected brushless DC motor setup where the actual vehicle dynamics are modelled with a Hardware-In-the-Loop (HIL) system. Using the minimization of the Integral Absolute Error (IAE) has been the control design criteria and the performance is compared against Type-1 Fuzzy Logic and Proportional Integral Derivative DMCs. Particle swarm optimization is used in the control design. Comparisons on energy consumption and maximum power demand have been carried out using HIL system for NEDC and ARTEMIS drive cycles. Experimental results show that Type-2 FLC saves energy by a substantial amount while simultaneously achieving the best IAE of the control strategies tested

On Advanced Mobility Concepts for Intelligent Planetary Surface Exploration

Surface exploration by wheeled rovers on Earth's Moon (the two Lunokhods) and Mars (Nasa's Sojourner and the two MERs) have been followed since many years already very suc-cessfully, specifically concerning operations over long time. However, despite of this success, the explored surface area was very small, having in mind a total driving distance of about 8 km (Spirit) and 21 km (Opportunity) over 6 years of operation. Moreover, ESA will send its ExoMars rover in 2018 to Mars, and NASA its MSL rover probably this year. However, all these rovers are lacking sufficient on-board intelligence in order to overcome longer dis-tances, driving much faster and deciding autonomously on path planning for the best trajec-tory to follow. In order to increase the scientific output of a rover mission it seems very nec-essary to explore much larger surface areas reliably in much less time. This is the main driver for a robotics institute to combine mechatronics functionalities to develop an intelligent mo-bile wheeled rover with four or six wheels, and having specific kinematics and locomotion suspension depending on the operational terrain of the rover to operate. DLR's Robotics and Mechatronics Center has a long tradition in developing advanced components in the field of light-weight motion actuation, intelligent and soft manipulation and skilled hands and tools, perception and cognition, and in increasing the autonomy of any kind of mechatronic systems. The whole design is supported and is based upon detailed modeling, optimization, and simula-tion tasks. We have developed efficient software tools to simulate the rover driveability per-formance on various terrain characteristics such as soft sandy and hard rocky terrains as well as on inclined planes, where wheel and grouser geometry plays a dominant role. Moreover, rover optimization is performed to support the best engineering intuitions, that will optimize structural and geometric parameters, compare various kinematics suspension concepts, and make use of realistic cost functions like mass and consumed energy minimization, static sta-bility, and more. For self-localization and safe navigation through unknown terrain we make use of fast 3D stereo algorithms that were successfully used e.g. in unmanned air vehicle ap-plications and on terrestrial mobile systems. The advanced rover design approach is applica-ble for lunar as well as Martian surface exploration purposes. A first mobility concept ap-proach for a lunar vehicle will be presented

Carbon Emission Policies in Key Economies

The Australian Government asked the Productivity Commission to undertake a study on the ‘effective’ carbon prices that result from emissions and energy reduction policies in Australia and other key economies (the UK, USA, Germany, New Zealand, China, India, Japan and South Korea). The Commissions research report, released 9 June 2011, provides a stocktake of the large number of policy measures in the electricity generation and road transport sectors of the countries studied. And it provides estimates of the burdens associated with these policies in each country and the abatement achieved. While the results are based on a robust methodology, data limitations have meant that some estimates could only be indicative. More than 1000 carbon policy measures were identified in the nine countries studied, ranging from (limited) emissions trading schemes to policies that support particular types of abatement technology. While these disparate measures cannot be expressed as an equivalent single price on greenhouse gas emissions, all policies impose costs that someone must pay. The Commission has interpreted ‘effective’ carbon prices broadly to mean the cost of reducing greenhouse gas emissions — the ‘price’ of abatement achieved by particular policies. The estimated cost per unit of abatement achieved varied widely, both across programs within each country and in aggregate across countries. The relative cost effectiveness of price-based approaches is illustrated for Australia by stylised modelling that suggests that the abatement from existing policies for electricity could have been achieved at a fraction of the cost. The estimated price effects of supply-side policies have generally been modest, other than for electricity in Germany and the UK. Such price uplifts are of some relevance to assessing carbon leakage and competitiveness impacts, but are very preliminary and substantially more information would be required.carbon pricing; cost abatement; greenhouse gas emissions; abatement technology; carbon policy; energy reduction policy; emissions trading scheme; carbon leakage

Self-Positioning Smart Buoys, The \u27Un-Buoy\u27 Solution: Logistic Considerations Using Autonomous Surface Craft Technology and Improved Communications Infrastructure

Moored buoys have long served national interests, but incur high development, construction, installation, and maintenance costs. Buoys which drift off-location can pose hazards to mariners, and in coastal waters may cause environmental damage. Moreover, retrieval, repair and replacement of drifting buoys may be delayed when data would be most useful. Such gaps in coastal buoy data can pose a threat to national security by reducing maritime domain awareness. The concept of self-positioning buoys has been advanced to reduce installation cost by eliminating mooring hardware. We here describe technology for operation of reduced cost self-positioning buoys which can be used in coastal or oceanic waters. The ASC SCOUT model is based on a self-propelled, GPS-positioned, autonomous surface craft that can be pre-programmed, autonomous, or directed in real time. Each vessel can communicate wirelessly with deployment vessels and other similar buoys directly or via satellite. Engineering options for short or longer term power requirements are considered, in addition to future options for improved energy delivery systems. Methods of reducing buoy drift and position-maintaining energy requirements for self-locating buoys are also discussed, based on the potential of incorporating traditional maritime solutions to these problems. We here include discussion of the advanced Delay Tolerant Networking (DTN) communications draft protocol which offers improved wireless communication capabilities underwater, to adjacent vessels, and to satellites. DTN is particularly adapted for noisy or loss-prone environments, thus it improves reliability. In addition to existing buoy communication via commercial satellites, a growing network of small satellites known as PICOSATs can be readily adapted to provide low-cost communications nodes for buoys. Coordination with planned vessel Automated Identification Systems (AIS) and International Maritime Organization standards for buoy and vessel notificat- - ion systems are reviewed and the legal framework for deployment of autonomous surface vessels is considered

Autonomous Recharging and Flight Mission Planning for Battery-operated Autonomous Drones

Autonomous drones (also known as unmanned aerial vehicles) are increasingly popular for diverse applications of light-weight delivery and as substitutions of manned operations in remote locations. The computing systems for drones are becoming a new venue for research in cyber-physical systems. Autonomous drones require integrated intelligent decision systems to control and manage their flight missions in the absence of human operators. One of the most crucial aspects of drone mission control and management is related to the optimization of battery lifetime. Typical drones are powered by on-board batteries, with limited capacity. But drones are expected to carry out long missions. Thus, a fully automated management system that can optimize the operations of battery-operated autonomous drones to extend their operation time is highly desirable. This paper presents several contributions to automated management systems for battery-operated drones: (1) We conduct empirical studies to model the battery performance of drones, considering various flight scenarios. (2) We study a joint problem of flight mission planning and recharging optimization for drones with an objective to complete a tour mission for a set of sites of interest in the shortest time. This problem captures diverse applications of delivery and remote operations by drones. (3) We present algorithms for solving the problem of flight mission planning and recharging optimization. We implemented our algorithms in a drone management system, which supports real-time flight path tracking and re-computation in dynamic environments. We evaluated the results of our algorithms using data from empirical studies. (4) To allow fully autonomous recharging of drones, we also develop a robotic charging system prototype that can recharge drones autonomously by our drone management system

Control algorithms for e-car

Cílem práce byl návrh a implementace řídicích algoritmů pro optimalizaci spotřeby energie elektrického vozidla. Hlavním úkolem byla optimalizace rozložení energie mezi hlavním zdrojem energie (bateriemi) a super-kapacitory v průběhu jízdního cyklu. Jízdní výkonový profil je odhadován a předpovězen na základě 3D geografických souřadnic a matematického modelu vozidla. V první části jsou uvedeny komponenty vozidla a jejich modely. Poté jsou představeny algoritmy na základě klouzavého průměru a dynamického programování. Byly provedeny simulace a analýzy pro demostraci přínosů algoritmů. V poslední části je popsána Java implementace algoritmů a také aplikace pro operační systém Android.The aim of this work is to design and implement energy consumption optimization control algorithms for electric vehicle. The main objective is to optimize the power-split-ratio between the main power source (batteries) and the super-capacitors during the driving cycle. The driving power profile is estimated and predicted using 3D geographic data and vehicle model. In the first part, vehicle components modelling is introduced. Then, moving average based algorithm and dynamic programming algorithm are presented. Simulations and analysis are provided to show algorithms' benefits. In the last part, Java implementation and also Android operating system application are described.