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Efficient renewable propulsions: energy performance in traffic

Content: The sustainable vehicle traffic of the future will be based on renewable solar and wind (both electric), biological and other energies. Non-fossil vehicles can run on batteries, fuel cells or combustion engines. Fuels made from electricity can be liquid, gas, hydrogen, liquid biological or gaseous. Every propulsion and fuel have their own advantages and disadvantages. Above all, gas and liquids are light and easily stored, whereas storing electricity in batteries is heavy and expensive. However, no form of vehicle propulsion reaches the energy efficiency of the battery electric vehicle (BEV). In this workshop, we will calculate and compare the performances of the different conversion chains (e.g. electricity – hydrogen – gas – combustion) in order to know, once the wind or solar electric power is generated, how to use it in the most efficient way; or given a defined agricultural area, how to use it to drive a maximum distance. In the sustainable traffic of the future, we must use the natural supply of renewable energy in an intelligent, appropriate, and economical way.

Required skills/abilities: Basic knowledge of energy engineering.

Tools: Calculator or smartphone.

Audience: Engineers, technicians, researchers and students of energy, automotive, electrical, and related engineering.

Duration:  3 hours

Date/Time: May 26, 2021 (9:30-12:30 GMT-5).

Registration link: Available from May 3, 2021.

Instructor:  Klaus Kuhnke, Dr. rer. nat. Hochschule Osnabrück (Osnabrück University of Applied Sciences), Germany

Klaus Kuhnke is a physicist from the University of Göttingen, Germany. He has a PhD in Physics in which he conducted research on laser systems as light sources for high-speed holography. He has been an assistant professor at the National Engineering School of Tunisia, and a scientist at the Institute for Solar Energy Research (ISFH) in Hannover, Germany. He is president of the Osnabrück Solar Energy Association, with teaching and training collaborations at the Universities of Minsk, San Salvador (UCA), Public University of Navarra, Le Havre, Tarbes and Fez (Morocco). He is currently a Professor of Renewable Energies and Physics at the University of Applied Sciences in Osnabrück, Germany. His research focuses on the development of renewable energies and their use for sustainable mobility, as well as on specific applications of wind and photovoltaic energy to support the change of energy model in Germany, a country that plans to close its nuclear power plants by 2022.


Modeling and simulation of solar photovoltaic systems

Content: This workshop aims to show how to model a solar photovoltaic plant (including solar panels, converters and control system), using Simulink (MATLAB’s visual programming environment) and the Simscape Electrical toolbox (library of components for modeling and simulating electrical, electronic and mechatronic systems, including renewable energy systems), in order to simulate the behavior of the installation under different operating conditions, such as incident radiation and temperature. The tools that will be shown in this workshop can be used in teaching and research of solar photovoltaic plants, by allowing to model any installation (with different panels, power levels, applications, etc.) and to evaluate its behavior and that of each of the components under different operating conditions, as a preliminary step for the validation of the design of prototypes of equipment or control systems, without using expensive real equipment.

Required skills/abilities: Knowledge of electricity, solar photovoltaic energy and MATLAB is required.

Tools: Matlab, Simulink, Simscape Electrical.

Audience: Students, teachers, and researchers in electrical engineering, electronics and related fields.

Duration:  2.5 hours

Date/Time: May 26, 2021 (9:30-12:00 GMT-5).

Registration link: Available from May 3, 2021.

Instructor:  Prof. Dr. Luis M. Fernández Ramírez. Head of the Sustainable and Renewable Electrical Technologies Research Group. Department of Electrical Engineering, Universidad de Cádiz, Spain.

Industrial Engineer (specializing in Electricity) from the University of Seville, Dr. Industrial Engineer from the University of Cadiz and Professor of the Electrical Engineering Department at the University of Cadiz, Spain. He is currently the Coordinator of the PhD Program in Energy and Sustainable Engineering and Head of the Sustainable and Renewable Electrical Technologies Research Group (PAIDI-TEP023). He has worked as a design engineer and M&O manager of a company dedicated to the design of wind turbines, construction and operation of wind farms, wind power developments, etc. He has extensive research experience, supported by publications (140), doctoral theses (8) and research projects/contracts (15) in the development of electrical, electronic and control technologies in the field of smart grids, renewable energies (wind energy and photovoltaic solar energy), energy storage systems, hydrogen technologies, electric vehicles, power electronic converters, intelligent control systems and energy management. He is an evaluator of research projects, member of the Editorial Board of 7 international journals, Guest Editor of 6 Special Issues in international journals, and member of the scientific committee of 8 international conferences. He is also an IEEE Senior Member and member of 2 IEEE Smart Cities Committees.


Use of Neural Networks and Deep Learning for classification and prediction

Content: Artificial Intelligence (AI), understood as the ability to provide computers with elements of human intelligence such as: learning, decision making, classification, memory, and autonomy, among others, is one of the most important disciplines worldwide. The governments of the major powers, the most important companies and the main universities of the world dedicate a great part of their research to the applications of intelligent systems in the diverse areas of daily life. One of the most ambitious AI techniques are neural networks, since they allow learning, through data and observations, behavioral patterns that could be imperceptible by other systems. Additionally, in recent years it has been enhanced with the creation of new techniques based on Deep Learning, which has allowed it to be massively used in many industrial, business, medical, educational, and artistic applications. In this theoretical-practical workshop, the principles of neural networks operation will be known, and practical activities will be carried out using the Python programming language, which is the most widely used at the moment, and through a simple environment based on the use of a web browser (Colaboratory).

Required skills/abilities: Use of internet browsers, basic knowledge of algebra and basic notions of statistics.

Tools: Python through Google Colaboratory, which are free tools that require no installation or configuration ( The programs and data to be used will be provided through a link to a Colaboratory Notebook.

Audience: Academic community in general.

Duration:  4 hours

Date/Time: May 26, 2021 (9:00-13:00 GMT-5).

Registration link: Available from May 3, 2021.

Instructor:  PhD. Francklin Rivas Echeverría, Universidad Técnica Federico Santa María, Chile.

Professor Francklin Rivas Echeverría is a Systems Engineer (1993), Master in Control Engineering (1996) and Doctor in Applied Sciences (2000). He is a lawyer with Cum Laude distinction (2017). He has a Diploma in Administrative Law, one in Strategic Management and another in Research Methodology. He is Academic and Assistant Director of Outreach at Universidad Técnica Federico Santa María, Chile; Retired Professor of the School of Systems Engineering at Universidad de Los Andes; Professor at Pontificia Universidad Católica del Ecuador headquarter Ibarra; Visiting Professor at Yachay Tech, Ecuador. He has published more than 250 scientific articles in international journals, books and conference proceedings. He is co-author or editor of 15 books. He has been invited to give lectures and tutorials in several parts of the world. He has been a consultant for the United Nations Organization (UNO), Kuwait Oil Company (KOC), Halliburton, Venezuelan oil industry, steel companies, Aluminum companies, Mass Transportation Systems, Health Systems, different public organizations, and universities. He has directed more than 70 projects or thesis at bachelor, master, and doctorate levels. His main line of research is related to Artificial Intelligence and its applications. He was recognized by the International Magazine “Gerente” for being among the 100 most successful Venezuelan Managers. He was recognized by the International Magazine “Gerente” for being among the 100 most successful Venezuelan Managers.


Stochastic modeling of heterogeneous materials for bioengineering applications

Content: Unlike classical homogeneous materials, the numerical simulation of heterogeneous materials represents a challenge from the computational point of view. The study of the mechanical behavior of heterogeneous materials usually considers the separation of the domain scale into two; the micro- and macro-scales. The development of these models couples the micro-scale characterization with the homogenized response at the macro-scale level. This approach is computationally expensive as it requires not only a conformal description of the material morphology and constituent behavior at the microstructure level but also the solution of problems with auxiliary boundary conditions on volume representative elements. To overcome these difficulties, different strategies have been recently proposed that consider the description of an intermediate scale, or meso-scale, where the level of detail of the material is smoothed and consequently the computational cost of the homogenization process for the response to external mechanical stresses is alleviated.
In this context, this workshop aims to introduce participants to the concepts and techniques used in the development of heterogeneous material models. Specifically, we will focus on the construction of stochastic models that describe the spatial variability of the mechanical properties of healthy or diseased bone tissues under the hypothesis of isotropy or orthotropy, in order to explore the influence of the parameters that define the meso-structure of the material on the uncertainties of the effective response to external load stresses.

Required skills/abilities: Basic programming, preferably using Matlab.

Tools: Matlab

Audience: Undergraduate or graduate students and research professors with interest in materials modeling with applications in bioengineering.

Duration:  3 hours

Date/Time: May 26, 2021 (14:30-17:30 GMT-5).

Registration link: Available from May 3, 2021.

Instructor:  PhD. José A. Alvarado Contreras. School of Mechanical Engineering, Universidad de Los Andes, Venezuela.

He graduated in Mechanical Engineering and had a master’s degree in Applied Mathematics in Engineering from Universidad de Los Andes in Venezuela. He obtained a PhD degree in Civil Engineering from the University of Waterloo in Canada. He spent 3 years as a postdoctoral researcher at San Diego State University in the United States in mathematical modeling of sintering processes affected by gravity acceleration funded by the National Aeronautics and Space Administration (NASA). He is currently a Professor at the School of Mechanical Engineering of Universidad de Los Andes and Coordinator of the master’s program in Mechanical Engineering at the same institution. He is a visiting professor at Moscow Engineering and Physics University (Russia) and at Universidad Politécnica Salesiana (Ecuador). He has taught undergraduate courses in Strength of Materials, Mechanics of Materials, Mechanical Design and Materials Science. He has also taught graduate courses in Numerical Methods, Mechanics of Continuous Media, Solid Mechanics and Finite Elements. He has dedicated his research work to the modeling of the mechanical behavior of materials with emphasis on multiscale modeling, continuum damage mechanics and consolidation of porous materials.


Simulation tools in electric traction

Content: In this workshop, we will study different simulation tools with application to electric traction systems. Their use allows the analysis of different aspects such as power losses, harmonic distortion and efficiency that are present in the topologies of three-phase converters used for controlling motors in an electric vehicle system. The first part of the workshop contemplates an analysis of the different converter topologies with VSI voltage source, CSI current source and impedance networks (Z, Qzsi) and their different modulation techniques used in electric traction, by using the PSIM simulation tool. The second part of the workshop deals with the use of silicon carbide (SiC) devices in converters, and the use of different tools to develop specific models with the operating characteristics of these devices, which allow working at a higher switching frequency, higher temperature range and reduce power losses, which would mean an increase in efficiency in the power system. In addition, different methodologies are analyzed for developing simulations of the different elements that make up the electric traction by using different tools to obtain results and efficiency regarding energy consumption.

Required skills/abilities: Basic knowledge of electronics, and hybrid and electric vehicle configurations.


Audience: Electronic engineering, electrical engineering and automotive engineering students.

Duration:  3 hours

Date/time: May 26, 2021 (14:30-17:30 GMT-5).

Registration link: Available from May 3, 2021.

Instructor:  PhD. Efrén Fernández Palomeque – Universidad del Azuay, Ecuador

He graduated Electronic Engineer from Universidad del Azuay. He has a Master’s Degree in Industrial Control and Automation from ESPOL, and is also a PhD in Electronic Engineering in the area of electric traction system from the Polytechnic University of Catalonia. He has worked as a professor at Universidad Espíritu Santo, Universidad Internacional del Ecuador and Universidad Politécnica Salesiana in Cuenca. He is currently a full professor at Universidad del Azuay and a member of the ERGON Research Group attached to the Automotive Mechanics Engineering program. He is the director of the Master in Electric Propulsion Systems attached to the graduate department of Universidad del Azuay. He has served as Technical Trainer of the company Cise Electronics (United States), Technical Advisor of the company EAATA (Barcelona, Spain) and founder of the Ecuadorian brand Dr. ECUS Solutions. He has represented Ecuador in different international congresses and has several publications in magazines related to the area of electric mobility and optimization of converter topologies in electric traction. He has 10 years of experience in the development and implementation of projects focused on electronic systems in automobiles and optimization in the conversion of conventional automotive systems to electric.

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