Prof. Hartmut Hinz
Frankfurt University of Applied Sciences, Computer Science and Engineering, Germany
Biography: Hartmut Hinz received the diploma degree in electrical engineering from the University of Applied Sciences, Aachen and the Ruhr University, Bochum in Germany in the years 1990 and 1994 respectively. He received the Ph.D. degree from the Technical University, Darmstadt in Germany in 2000. Between 1999 and 2009, he was with General Motors Fuel Cell Activities, most recently as project leader for the development of high voltage systems for fuel cell vehicles. In 2009, he was appointed as a Professor for power electronics at the University of Applied Sciences, Frankfurt in Germany. Since 2011, he has been the head of the electrical power engineering study program. From 2010 to 2016, he was a visiting professor at the Vietnamese-German University in Ho Chi Minh City, Vietnam. His research interests are in the areas power electronics for electric vehicles and renewable energy; electrical energy storage and distributed power generation.
Power electronics for electric vehicles and renewable energy
Electrical energy storage and distributed power generation
Title: Lithium-Ion Battery Simulation Models for Electrical Energy Storage Systems in Decentralized Power Generation
Abstract: In contrast to traditional power generation, in which centralized power plants such as coal or nuclear power plants with large installed capacities transmit energy over high and medium voltage grids to consumers, decentralized power generation locates the generation plants close to the consumers. In these systems, the installed capacity is significantly lower, indeed, it enables customized integration of renewable energy as well as an utilization of cogeneration (combined heat and power). Thermal and electrical storage systems are required for ensuring a reliable supply to consumers at all times. While peak load boilers are standard in the thermal system, electrochemical storage units are used in the electrical system, which offer scope for optimization in terms of dimensioning and operational management. This contribution first introduces a decentralized power generation consisting of a cogeneration, a photovoltaic and a lithium-ion battery system for supplying townhouses. In view of an almost grid-independent operation of the plant, an optimization of the battery management is required. This task can be supported by system simulations, which allows a preliminary investigation of optimized operational management methods. For this purpose, it is necessary to use models of lithium-ion batteries which meet the requirements in terms of accuracy and simulation time. This keynote presentation will provide an overview of battery models known from the literature that can be applied for simulating the electrochemical behavior appropriately. Finally, it is demonstrated how parameterized models of the entire battery system of a decentralized power generation can be validated by means of a case study.
Prof. Xiaofang Yuan
Hunan University, China
Biography: Yuan Xiaofang, Ph.D., professor of Hunan University, doctoral supervisor, researcher of National Engineering Laboratory of Robot Vision Perception and Control Technology. Mainly engaged in research work in the fields of intelligent automation engineering and application, key technologies of new energy vehicles, robot motion control, etc. He presided over more than 20 scientific research projects, including 2 national key R&D programs, 2 general programs of the National Natural Science Foundation of China/1 youth program, Doctor Program Fund of the Ministry of Education, China Post-doctoral Fund, Hunan Science and Technology Program, Hunan Natural Science Foundation, etc. He has published more than 50 papers in international authoritative journals such as IEEE Transactions in the field of intelligent control theory and application, including 40 papers indexed by SCI.
Intelligent engineering and application
Key technologies of new energy vehicles
Robot motion control
Title: 3D Map-based Path Planning for Autonomous Vehicles
Abstract: In the traditional vehicle area, energy-based motion control technology and battery technology are usually employed to solve the energy-saving problem for vehicles. Our research proposes a new solution from the perspective of path planning. For vehicles traveling on the complex 3D terrains, the energy consumption of up-slope is far greater than that of the flat road and down-slope. To realize this, a path with a good trade-off between the energy consumption and distance would be the expected route for electric vehicle and mining transportation. A novel multi-objective path planning method is investigated to solve this problem for EV and mining truck. The simulation experiments prove that the proposed method can generate an optimal path which saves much energy in comparison with the path provided by the distance-based method.
Assoc. Prof. Wei Min Huang
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
Biography: Prof. Wei Min Huang is currently an Associate Professor at the School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore. He has over 18 years of experience on shape memory materials and technologies. He has published two books (Thin Film Shape Memory Alloys [Cambridge University Press, 2009]; Polyurethane Shape Memory Polymers [CRC, 2011]) and over 170 papers in journals, such as Drug Delivery Review and Materials Today, etc, and has been invited to review manuscripts from over 180 international journals and proposals from American Chemical Society etc. He is currently on the editorial board of over a dozen of journals.
Shape memory materials
Technique and application
Title: Rapid manufacturing anywhere, anytime
Abstract: Rapid manufacturing on space/air/sea/land missions, where either gravitational force is missing or severe random disturbance may present continuously, is highly in demand. However, till today, there is no reliable technique for such working environments. The purpose of this study is to develop a technology for rapid 3D printing in solid state of polymeric materials to get rid of the problems in harsh working environment.The basic concept is to cross-link by either UV-light or photo-induced-heat of polymeric materials in the solid state for rapid volumetric additive manufacturing. The uncross-linked parts can be removed by heating or cooling for melting, or washing away by solvent. Finally, the shape memory effect (SME) of the cross-linked polymers is applied to ensure high accuracy of the printed items.We have successfully demonstrated this concept using thermal gel, UV cross-linkable vitrimer and other conventional materials.
Assoc. Prof. Guanglei Wu
Dalian University of Technology
Biography: Dr. Guanglei Wu received his PhD in robotics from Aalborg University, Denmark, 2013, and and worked as an industrial Postdoc fellow in Aalborg University from 2014 to 2016. He was a visiting scholar in the Research Institute in Communications and Cybernetics of Nantes (IRCCyN, currently reorganized as Laboratory of Digital Sciences of Nantes-LS2N) in June-July 2012, in McGill University in Aug. 2015, and in Aarhus University in 2020. Currently, he is an associate professor in School of Mechanical Engineering, Dalian University of Technology (DUT). His research interests include robotic technology, conceptual design and performance evaluation of robots, robot dynamics and control, industrial robots and their applications. He has published one monography by Springer, 9 patents, and over 80 peer-reviewed articles in international journals and conferences. He was the awardee of DUT Xinhai 1000 Youth Talent program, IFToMM Asian MMS & CCMMS 2016, Dept. Sci. Tech. Liaoning province, Longcheng Talent program by Changzhou Municipality. He is the referee for over 50 international journals and conferences in the fields of mechanisms and robots.
theory of mechanism
Title: Path planning of industrial robots for obstacle avoidance
Abstract: With the rapid development of robotic technologies, industrial robots have extensively found their applications such as welding, assembly, handling and spraying applications, etc. In general, there will be obstacle spaces composed of different objects in the working area of the robotic manipulators, which affects the efficiency of the manipulator in performing tasks. Therefore, path planning algorithm with the inclusion of obstacle avoidance is beneficial to save operation time to increase the working efficiency, which can significantly improve productivity and manufacturing quality. In this talk, the algorithm for path planning of robotic manipulators with the consideration of obstacle avoidance is discussed and a new algorithm is proposed. The algorithm adopts the combination of the artificial potential field method and bi-directional growth of rapidly exploring random tree algorithm, named as BiRRTAPF, which can significantly reduce the nodes in the path growth to speed up the path search. Path planning of a 5-dof robotic arm in multi-obstacle environment is carried out with the proposed algorithm to accomplish the prescribed pick-and-place operation with the CoppeliaSim software. The results and comparative study among different algorithms show that the proposed BiRRT-APF algorithm can speed up the convergence of the optimal path, with reduced computational burden and better the obstacle avoidance performances in complex environments of robot, through which the efficiency and effectiveness of the proposed algorithm of path planning for robotic manipulators are verified.