Electrical Vehicles And Intelligent Transportation
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The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.
The aim of the conference will be to bring together the majority of leading expert scientists, thought leaders and forward looking professionals from all domains of Intelligent Transportation Systems, to share ongoing research achievements, to exchange views and knowledge and to contribute to the advances in the field. The main theme of the conference will be “ITS within connected, automated and electric multimodal mobility systems and services”.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
The scope of the 2020 IEEE/ASME AIM includes the following topics: Actuators, Automotive Systems, Bioengineering, Data Storage Systems, Electronic Packaging, Fault Diagnosis, Human-Machine Interfaces, Industry Applications, Information Technology, Intelligent Systems, Machine Vision, Manufacturing, Micro-Electro-Mechanical Systems, Micro/Nano Technology, Modeling and Design, System Identification and Adaptive Control, Motion Control, Vibration and Noise Control, Neural and Fuzzy Control, Opto-Electronic Systems, Optomechatronics, Prototyping, Real-Time and Hardware-in-the-Loop Simulation, Robotics, Sensors, System Integration, Transportation Systems, Smart Materials and Structures, Energy Harvesting and other frontier fields.
Industrial information technologies
The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...
Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...
Theory and applications of industrial electronics and control instrumentation science and engineering, including microprocessor control systems, high-power controls, process control, programmable controllers, numerical and program control systems, flow meters, and identification systems.
IEEE Transactions on Industrial Informatics focuses on knowledge-based factory automation as a means to enhance industrial fabrication and manufacturing processes. This embraces a collection of techniques that use information analysis, manipulation, and distribution to achieve higher efficiency, effectiveness, reliability, and/or security within the industrial environment. The scope of the Transaction includes reporting, defining, providing a forum for discourse, and informing ...
IEEE Intelligent Systems, a bimonthly publication of the IEEE Computer Society, provides peer-reviewed, cutting-edge articles on the theory and applications of systems that perceive, reason, learn, and act intelligently. The editorial staff collaborates with authors to produce technically accurate, timely, useful, and readable articles as part of a consistent and consistently valuable editorial product. The magazine serves software engineers, systems ...
IEEE Transactions on Intelligent Vehicles, 2017
Information obtainable from intelligent transportation systems (ITS) provides the possibility of improving safety and efficiency of vehicles at different levels. In particular, such information also has the potential to be utilized for the prediction of driving conditions and traffic flow, which allows Hybrid Electric Vehicles (HEVs) to run their powertrain components in corresponding optimum operating regions. This paper proposes to ...
IEEE Transactions on Intelligent Transportation Systems, 2014
There has been significant interest in plug-in hybrid electric vehicles (PHEVs) as a means to decrease dependence on imported oil and to reduce greenhouse gases as well as other pollutant emissions. One of the critical considerations in PHEV development is the design of its energy-management strategy, which determines how energy in a hybrid powertrain should be produced and utilized as ...
Proceedings of the 10th World Congress on Intelligent Control and Automation, 2012
The modern automobile industry is a double-edged sword, with human society to the high prosperity, at the same time it's built a peremptory energy crisis, traffic jams fronts. Electric vehicle research, let crisis do see the dawn vehicle technology development. And the electric vehicle road train research is still blank in the domestic, research and development EV road train main ...
2009 IEEE Vehicle Power and Propulsion Conference, 2009
Hybrid electric vehicles (HEV) have been developed to improve fuel efficiency and reduce emissions. The plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV) have been recently widely analyzed for their additional significant potential of improving fuel efficiency and reducing emission. For PHEV, the charge depletion model of the high voltage (HV) battery has been identified to be the appropriate ...
2018 International Conference on Robots & Intelligent System (ICRIS), 2018
In order to improve the intelligent driving ability of electric vehicles, the optimization design of the visual feature detection system for electric vehicles is carried out. An intelligent driving visual detection algorithm for electric vehicles is proposed based on edge contour feature extraction and visual information fusion. The distributed sensor technology is used to collect the intelligent driving visual information ...
State-of-the-art Electrical Machines for Hybrid Electric Vehicles
Alberto Broggi accepts the IEEE Medal for Environmental and Safety Technologies - Honors Ceremony 2017
Transportation Electrification: Connected Vehicle Environment
From Automatic People Movers to Fully Automated Mass Transit Systems
Marin Litoiu: Beyond Network Control - End to End Performance of CPS: WF IoT 2016
A Decade of Electric/Hybrid Vehicles Design and Development at UTBM
Transportation Electrification: Green Public & Comercial Transportation
Keynote: Mario Gerla on Internet of Vehicles - WF-IoT 2015
ITEC 2014: Next Generation Combat Vehicle Electrical Power Architecture Development
ITEC 2014: Development and Innovation Activities in Germany to Accelerate Transportation Electrification
Grounding for Hybrid Vehicles
The R&D History of On-Line Electric Vehicles (OLEV)
Regenerative Energy Storage Systems for Hybrid Electric and Battery Electric Vehicles
Fuel Cell Powertrain for Hybrid Electric Vehicles for Postal Delivery
IEEE 5G Podcast with the Experts: 5G for large-scale wireless communications between autonomous vehicles
ICCE 2014: Automated Transportation Systems
Zero Emission Powertrains and Fuel Cell Engines: APEC 2019
ITEC 2014: Electrified Powertrain Vehicles: State of the Industry
The Social and Personal Impacts of AI: IEEE TechEthics Panel
Information obtainable from intelligent transportation systems (ITS) provides the possibility of improving safety and efficiency of vehicles at different levels. In particular, such information also has the potential to be utilized for the prediction of driving conditions and traffic flow, which allows Hybrid Electric Vehicles (HEVs) to run their powertrain components in corresponding optimum operating regions. This paper proposes to improve the performance of one of the most promising realtime powertrain control strategies, called adaptive equivalent consumption minimization strategy (AECMS), using predicted driving conditions. In this paper, three real-time powertrain control strategies are proposed for HEVs, each of which introduces an adjustment factor for the cost of using electrical energy (equivalent factor) in AECMS. These factors are proportional to the predicted energy requirements of the vehicle, regenerative braking energy, and the cost of battery charging and discharging in a finite time window. Simulation results using detailed vehicle powertrain models illustrate that the proposed control strategies improve the performance of AECMS in terms of fuel economy, number of engine transients (ON/OFF), and charge sustainability of the battery.
There has been significant interest in plug-in hybrid electric vehicles (PHEVs) as a means to decrease dependence on imported oil and to reduce greenhouse gases as well as other pollutant emissions. One of the critical considerations in PHEV development is the design of its energy-management strategy, which determines how energy in a hybrid powertrain should be produced and utilized as a function of various vehicle parameters. In this paper, we propose an intelligent energy-management strategy for PHEVs. At the trip level, the strategy takes into account a priori knowledge of vehicle location, roadway characteristics, and real-time traffic conditions on the travel route from intelligent transportation system technologies in generating a synthesized velocity trajectory for the trip. The synthesized velocity trajectory is then used to determine battery's charge-depleting control that is formulated as a mixed-integer linear programming problem to minimize the total trip fuel consumption. The strategy can be extended to optimize vehicle fuel consumption at the tour level if a preplanned travel itinerary for the tour and the information about available battery recharging opportunities at intermediate stops along the tour are available. The effectiveness of the proposed strategy, both for the trip- and tour-based controls, was evaluated against the existing binary-mode energy-management strategy using real-world trip/tour examples in southern California. The evaluation results show that the fuel savings of the proposed strategy over the binary-mode strategy are around 10%-15%.
The modern automobile industry is a double-edged sword, with human society to the high prosperity, at the same time it's built a peremptory energy crisis, traffic jams fronts. Electric vehicle research, let crisis do see the dawn vehicle technology development. And the electric vehicle road train research is still blank in the domestic, research and development EV road train main functional modules using CAN bus communication independently. The train operates of 3-10 vehicles connection grouping, the number of drivers can be reduced by more than 3 times, the road resources consume for nothing by up to about forty percent, transportation using efficiency of resource utilization keeps great potential.
Hybrid electric vehicles (HEV) have been developed to improve fuel efficiency and reduce emissions. The plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV) have been recently widely analyzed for their additional significant potential of improving fuel efficiency and reducing emission. For PHEV, the charge depletion model of the high voltage (HV) battery has been identified to be the appropriate model to ensure that the vehicle reaches its destination while vehicle HV battery is at its minimum threshold, thus allowing minimal use of internal combustion engine (ICE). For EV, the charge depletion model is the natural phenomenon due to the vehicle architecture. However, the charge depletion model would not ensure optimization in fuel efficiency and reduced emissions due to its lack of knowledge of vehicle's relative traffic information. This drawback in the charge depletion model may be eliminated with the development of intelligent transportation system (ITS) which allows vehicles and road infrastructures to share historical, real time and predictive future traffic information. In this paper, the charge depletion model and a proposed intelligent predictive charge depletion model are compared. The overall performance, including trip time and fuel consumption of the models under a selection of drive cycles, will be demonstrated via simulation results.
In order to improve the intelligent driving ability of electric vehicles, the optimization design of the visual feature detection system for electric vehicles is carried out. An intelligent driving visual detection algorithm for electric vehicles is proposed based on edge contour feature extraction and visual information fusion. The distributed sensor technology is used to collect the intelligent driving visual information of electric vehicle, and the principal component analysis and image filtering are carried out according to the collected image information. The edge contour of the filtered driving visual image is detected, the high resolution visual image feature is extracted, and the quantized fusion tracking recognition is carried out according to the extracted feature quantity. The hardware design of the detection system is obtained under the embedded environment. Simulation result shows that the system designed in this paper has good accuracy in detecting the visual features of intelligent driving of electric vehicles. The system is robustness and has good human-computer interaction.
This paper focuses on the combination of intelligent transportation technology and hybrid electric vehicle energy management strategy. An energy management strategy based on road grade preview is proposed. This control strategy utilizes the Global Positioning System (GPS) and geographic information systems (GIS) to accomplish the road slope grade prediction. Furthermore, the knowledge of road event (trip distance, road grade, altitude, velocity, etc.) are used to predict the electric energy consumption. With exact calculation of the charge timing, SOC planned before is charged to the target value in order to avoid battery over discharge during uphill. The simulation model for plug- in hybrid electric system is also established on MATLAB/Simulink platform. Finally, the proposed energy management strategy is verified, which can predict the energy consumption accurately and avoid SOC over discharge.
This paper presents the computer and sensor architecture of a new vehicle and the management system at the core of a new urban transport system now under development at INRIA. The system is operational since june 2001 with 5 electric vehicles developed in cooperation with Yamaha.
Although Electric vehicle uses green energy and has green impacts on the environment, it is still not getting popularity comparing to the gasoline engine vehicles. Latest models of Electric Vehicles are being coming up with exclusive driving assist modes but they have been very high priced. This thesis paper represents some modification and low cost efficient autonomous control system algorithm which will make an electric vehicle smart and flexible to use. In our concept car we have used four-wheel drive system and double axle rotation which ensures a quick U-turn and short space rotation. We have used smart autonomous control system which includes auto driving (limited to short distance), auto parking, several driving assist modes such as traction control through hill track detection, adaptive cruise control. This system also detects unwanted axle rotation and can stop the car if finds obstacles in front of it.
Electric vehicles (EVs) are regarded as one of the most effective tools to reduce the oil demands and gas emissions. And they are welcome in the near future for general road transportation. When EVs are connected to the power grid for charging and/or discharging, they become gridable EVs (GEVs). These GEVs will bring a great impact to our society and thus human life. This paper investigates and discusses the opportunities and challenges of GEVs connecting with the grid, namely, the vehicle-to-home (V2H), vehicle-to-vehicle (V2V), and vehicle-to-grid (V2G) technologies. The key is to provide the methodologies, approaches, and foresights for the emerging technologies of V2H, V2V, and V2G.
In the last few years, significant efforts have been devoted to developing intelligent and sustainable transportation to address pollution problems and fuel shortages. Transportation agencies in various countries, along with several standardization organizations, have proposed different types of energy sources (such as hydrogen, biodiesel, electric, and hybrid technologies) as alternatives to fossil fuel to achieve a more ecofriendly and sustainable environment. However, to achieve this goal, there are significant challenges that still need to be addressed. We present a survey on sustainable transportation systems that aim to reduce pollution and greenhouse gas emissions. We describe the architectural components of a future sustainable means of transportation, and we review current solutions, projects, and standardization efforts related to green transportation with particular focus on electric vehicles. We also highlight the main issues that still need to be addressed to achieve a green transportation management system. To address these issues, we present an integrated architecture for sustainable transportation management systems.
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