Optimized energy management strategies and sizing of hybrid storage systems for transport applications

205 p. El contenido del capítulo 4, sección 4.3 está sujeto a confidencialidad.Esta tesis doctoral aborda la temática acerca del óptimo dimensionamiento y operación de sistemashíbridos de almacenamiento de energía (HESS), combinando baterías y supercapacitores, con el objetivode ser integrados en vehículos para movilidad pública en entornos urbanos. Por una parte, se propone unainnovadora estrategia energética, basada en lógica difusa, para gestionar la división de la demanda depotencia entre las fuentes de energía disponibles a bordo del vehículo. La estrategia adaptativa que sepropone evalúa la información energética actual y futura (estimada) para adaptar, de una formaoptimizada y eficiente, la operación del sistema con el objetivo de mejorar el aprovechamiento de laenergía almacenada en los recursos a bordo del vehículo.Por otro lado, se ha propuesto una metodología para la co-optimización de la estrategia de gestión ydimensionamiento del HESS. Esta metodología de optimización evalúa tanto técnica comoeconómicamente las posibles soluciones mediante un problema multi-objetivo basado en algoritmosgenéticos. Para determinar el costo de reemplazo del HESS han sido aplicados modelo de envejecimientoy estimación de vida y se ha considerado la vida útil del vehículo.Con el objetivo de validar la propuesta de esta tesis doctoral, dos casos de estudio relevantes en latransportación pública han sido seleccionados: Tranvía Eléctrico Híbrido y Autobús Eléctrico Híbrido

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.This research was funded by Basque Government and UPV/EHU projects

Optimized energy management strategies and sizing of hybrid storage systems for transport applications

205 p. El contenido del capítulo 4, sección 4.3 está sujeto a confidencialidad.Esta tesis doctoral aborda la temática acerca del óptimo dimensionamiento y operación de sistemashíbridos de almacenamiento de energía (HESS), combinando baterías y supercapacitores, con el objetivode ser integrados en vehículos para movilidad pública en entornos urbanos. Por una parte, se propone unainnovadora estrategia energética, basada en lógica difusa, para gestionar la división de la demanda depotencia entre las fuentes de energía disponibles a bordo del vehículo. La estrategia adaptativa que sepropone evalúa la información energética actual y futura (estimada) para adaptar, de una formaoptimizada y eficiente, la operación del sistema con el objetivo de mejorar el aprovechamiento de laenergía almacenada en los recursos a bordo del vehículo.Por otro lado, se ha propuesto una metodología para la co-optimización de la estrategia de gestión ydimensionamiento del HESS. Esta metodología de optimización evalúa tanto técnica comoeconómicamente las posibles soluciones mediante un problema multi-objetivo basado en algoritmosgenéticos. Para determinar el costo de reemplazo del HESS han sido aplicados modelo de envejecimientoy estimación de vida y se ha considerado la vida útil del vehículo.Con el objetivo de validar la propuesta de esta tesis doctoral, dos casos de estudio relevantes en latransportación pública han sido seleccionados: Tranvía Eléctrico Híbrido y Autobús Eléctrico Híbrido

Design and energy management of aircraft hybrid electric propulsion system.

This thesis investigates the design and development of a Hybrid Electric Propulsion System (HEPS) for aircraft. The main contributions of the study are the multi-objective system sizing and the two energy optimization algorithms. First, the system sizing method is employed to design the hybrid electric propulsion system for a prototype aircraft. The sized hybrid propulsion system can ensure that no significant performance is sacrificed and the fuel economy is improved. The novel approach in this work is a new non-dominated sorting algorithm for the Non-dominated Sorting Genetic Algorithm (NSGA). The new algorithm can improve the time complexity of non-dominated sorting process. The optimized hybrid aircraft can save up to 17% fuel, achieve higher cruising speed and rate of climb. It is concluded that the optimal results are more sensitive to the variation of battery energy density than other parameters. Next, the main components of the HEPS are modelled for example. The engine model provides an insight into the inherent relationship between the throttle command and the output torque. Regarding the d-q model of motor/generator, the estimation of torque loss at steady state is achieved using the efficiency map from experiments. The application of Shepherd model leads to the straightforward parameter identification. In this research, both non-causal and causal energy management strategies for HEPS are investigated. The main novelty when studying convex optimization is the proposal of a new lossless convexification, which simplifies the formation of the convexified problem, and the proof of equality between the original problem and convexified problem. The introduced variable—battery internal energy, is proposed to convexify the battery model. The first test case verifies that the convex relaxation does not sacrifice the optimality of the solution nor does the variable change lose the original bounds. Also, the optimal control from convex optimization is demonstrated to be robust to a disturbance in power demand. Comparison with the benchmark optimization—dynamic programming, shows that convex optimization achieves a minimal objective value with much less optimization time. Most significant is that the convexification reduces the optimization computation time to a level compatible with implementation in practical application. In causal control, the main focus is to extend the original Equivalent Consumption Minimization Strategy (ECMS) with the fuzzy control. The proposed algorithm can maintain the battery State of Charge (SoC) in a desirable range, without the requirement of off-line estimation of equivalence factor. By comparing with non-causal control—dynamic programming, the test cases validates that the fuzzy based ECMS succeeds in converting the non-causal optimization, with little sacrifice of the optimality of the solution. In other words, the prior-knowledge of flight mission is not a pre- requisite, and the fuzzy based ECMS can achieve the sub-optimal control for on-line implementation. The fuzzy based ECMS is also validated to outperform the adaptive ECMS, since it can reduce the computation time of optimization and save more fuel usage. The theoretical relationship between the equivalence factor of ECMS and the co-state variable of Hamiltonian function is also demonstrated in this thesis. The convex optimization and fuzzy based ECMS are combined to complete a flight mission with several sub-tasks. Each task has different power and SoC requirements. The test case demonstrates that only the combination of non-causal and causal optimization can satisfy the various constraints and requests of the test scenario. Compared with the engine-only powered aircraft, the hybrid powered aircraft saves 18.7% on fuel consumption. Furthermore, the hybrid propulsion system has better efficiency since it integrates the high efficient electric powertrain.PhD in Aerospac

Initialization Requirement in Developing of Mobile Learning 'Molearn' for Biology Students Using Inquiry-based learning

Inquiry-based learning is kind of learning activities that involves students’ entire capabilities in exploring and investigating particular objects or phenomenon using critical thinking skills. Recently, information technology tangibly contributes in any education aspects, including the existence of e-learning, a widely spreading learning model in the 21st century education. This study aims at initializing needs of developing mobile learning ‘Molearn’ based on inquiry-based method. By cooperating with Biology teacher community in senior high school, ‘Molearn’ provides IT-based medium in Biology learning process

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research