The Hopping Rotochute is a promising micro vehicle with the capability of exploring rough and complex terrains with minimum energy consumption. While it is able to fly over obstacles via thrust produced by its coaxial rotor, its physical architecture, inspired from a "Weebles Wooble," provides re-orientation wherever it hits the ground. Therefore, this aerial and ground vehicle represents a potential hybrid vehicle capable of reconnaissance and surveillance missions in complex environments. The most recent version of the Hopping Rotochute is manually controlled to follow a trajectory. The control commands, listed in a file prior to the particular mission, are executed exactly as defined, like a "batch job," regardless of the uncertain external events. This control scheme is likely to cause great deviations from the route. Consequently, the vehicle may finish the mission very far away from the desired end point. However, if a vehicle is capable of receiving the control commands during a mission, "interactive processing" can be realized and efficient path tracking would be achieved. Hence, the development of the Hopping Rotochute that follows a trajectory autonomously reveals the foundation of this thesis. Two control approaches inspired the proposed methodology for developing an autonomous trajectory-following algorithm. The first approach is rule-based control that enables decision making through conditional statements. In this thesis, rule-based control is used to select a target point for a particular hop based on the existence of an obstacle and/or wind in the environment. The second approach is model predictive control employed to predict future outputs from hop performance models. In other words, this technique approaches the problem by providing intelligence pertaining to how a particular hop will end up before being attempted. Hence, the optimum control commands are selected based on the predicted performance of a particular hop. This research demonstrates that the autonomous Hopping Rotochute can be realized by rule-based control embedded with some performance models. In the assumption of known boundaries such as wall and ceiling information, this study has two aims: (1) to avoid obstacles by creating a smaller operational volume inside the real boundaries so that the vehicle is restricted from exiting the operational volume and no violation occurs within the real boundaries; (2) to estimate the wind by previous hops to select the next hopping point with respect to the estimated wind information. Based on the developed methodology, simulations are conducted for four different scenarios in the existence of obstacles and/or wind, and the results of the simulations are analyzed. Finally, based on the statistics of simulation results, the effectiveness of the proposed methodology is discussed.

abstract: The environmental impact of the fossil fuels has increased tremendously in the last decade. This impact is one of the most contributing factors of global warming. This research aims to reduce the amount of fuel consumed by vehicles through optimizing the control scheme for the future route information. Taking advantage of more degrees of freedom available within PHEV, HEV, and FCHEV “energy management” allows more margin to maximize efficiency in the propulsion systems. The application focuses on reducing the energy consumption in vehicles by acquiring information about the road grade. Road elevations are obtained by use of Geographic Information System (GIS) maps to optimize the controller. The optimization is then reflected on the powertrain of the vehicle.The approach uses a Model Predictive Control (MPC) algorithm that allows the energy management strategy to leverage road grade to prepare the vehicle for minimizing energy consumption during an uphill and potential energy harvesting during a downhill. The control algorithm will predict future energy/power requirements of the vehicle and optimize the performance by instructing the power split between the internal combustion engine (ICE) and the electric-drive system. Allowing for more efficient operation and higher performance of the PHEV, and HEV. Implementation of different strategies, such as MPC and Dynamic Programming (DP), is considered for optimizing energy management systems. These strategies are utilized to have a low processing time. This approach allows the optimization to be integrated with ADAS applications, using current technology for implementable real time applications. The Thesis presents multiple control strategies designed, implemented, and tested using real-world road elevation data from three different routes. Initial simulation based results show significant energy savings. The savings range between 11.84% and 25.5% for both Rule Based (RB) and DP strategies on the real world tested routes. Future work will take advantage of vehicle connectivity and ADAS systems to utilize Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), traffic information, and sensor fusion to further optimize the PHEV and HEV toward more energy efficient operation. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2016

The present work reports a study related to the potential improvement of the energy performances of a heat pump based heating system for a Swedish single-family house. The analysis is focused on the design of new rule-based control strategies which employ perfect predictions of weather forecast and human behaviour information. In particular, the considered signals are the outdoor temperature, the solar radiation, the internal gain due to inhabitants’ activities and the Domestic Hot Water (DHW) consumption. The study is performed by means of the TRNSYS® simulation software in which the model of the heating system is implemented. More specifically, it is composed by a Ground Source Heat Pump (GSHP) unit, a stratified storage tank of three hundred litres and the building element. The performances of the developed control logics are evaluated using a degree-minute on/off controller as reference case. The results show that the improved control logics yield to an increase of the energy efficiency of the system as well as an enhancement of the indoor and DHW temperatures stability. / EffSys Expand P18: Smart Cotnrol Strategies for Heat Pump Systems

The present work deals with a study related to the analysis of weather data for heat pump system control improvement based on both instantaneous and forecasted measurements. In particular, the analysis is firstly focused on the comparison of multiple weather sources for the assessment of weather forecast uncertainties, based on the evaluation of errors in prediction with respect to measured values. Afterwards, the results are compared with the ones related to persistent predictions methods that assumes the state of the atmosphere to be stationary over the considered time interval. The development and testing of a new preliminary “predictive” control logic is also performed, thanks to TRNSYS numerical simulations, considering a typical Swedish single-family house located in Stockholm, with the aim of optimizing the operation of a heat pump heating system based on solar radiation prediction to yield energy and cost savings. With the crucial points of accuracy and precision by which the local weather processes can be predicted, the same TRNSYS model is run accounting for perfect predictions and solar radiation forecasted values. From this perspective, given the fact that forecast of solar radiation are usually absent within most of the weather forecast datasets, a deep analysis is also performed on hourly measurements of solar radiation to define a simple and effective methods to calculate hourly solar radiation predictions. The results show that, when a short-time horizon is considered, persistent predictions allow to provide forecasts with a sufficient accuracy, whereas, when longer horizon time are considered, significantly higher errors are calculated when persistent prediction techniques are adopted. Independently of the uncertainties considered for weather forecasts, the improved control logics demonstrated a potential for energy savings and improvements in indoor temperature stability when compared with a reference case of variable speed compressor with PID controller. / EffSys Expand P18: Smart Cotnrol Strategies for Heat Pump Systems

Energy storage systems with many Lithium Ion battery cells per string require sophisticated balancing hardware due to individual cells having manufacturing inconsistencies, different self discharge rates, internal resistances and temperature variations. For capacity maximization, safe operation, and extended lifetime, battery balancing is required. Redistributive Non-Dissipative balancing further improves the pack capacity and efficiency over a Dissipative approach where energy is wasted as heat across shunt resistors. Redistribution techniques dynamically shuttle charge to and from weak cells during operation such that all of the stored energy in the stack is utilized. This thesis identifies and develops different balancing control methods. These methods include a unconstrained optimization problem using a Linear Quadratic Regulator (LQR) and a constrained optimization problem using Model Predictive Control (MPC). These methods are benchmarked against traditional rule based (RB) balancing. The control systems are developed using MATLAB/Simulink and validated experimentally on a multiple transformer individual cell to stack topology. The implementation uses a DC2100A Demo-board from Linear Technology with bi-directional flyback converters to transfer the energy between the cells. The results of this thesis show that the MPC control method has the highest balancing efficiency and minimum balancing time. / Thesis / Master of Applied Science (MASc)

<p>An emerging technology in the field of transient thermal management is thermal energy storage, or TES, which enables temporary, on-demand heat rejection via storage as latent heat in a phase-change material.  Latent TES devices have enabled advances in many thermal management applications, including peak load shifting for reducing energy demand and cost of HVAC systems and providing supplemental heat rejection in transient thermal management systems.  However, the design of a transient thermal management system with integrated storage comprises many challenges which are yet to be solved.  For example, design approaches and performance metrics for determining the optimal dimensions of the TES device have only recently been studied.  Another area of active research is estimation of the internal temperature state of the device, which can be difficult to directly measure given the transient nature of the thermal storage process.  Furthermore, in contrast to the three main functions of a thermal-fluid system--heat addition, thermal transport, and heat rejection--thermal storage introduces the need for active, real-time control and automated decision making for managing the operation of the thermal storage device. </p> <p>In this thesis, I present the design process for integrating thermal energy storage into a single-phase thermal management system for rejecting transient heat loads, including design of the TES device, state estimation and control algorithm design, and validation in both simulation and experimental environments. Leveraging a reduced-order finite volume simulation model of a plate-fin TES device, I develop a design approach which involves a transient simulation-based design optimization to determine the required geometric dimensions of the device to meet transient performance objectives while maximizing power density.  The optimized TES device is integrated into a single-phase thermal-fluid testbed for experimental testing.  Using the finite volume model and feedback from thermocouples embedded in the device, I design and experimentally validate a state estimator based on the state-dependent Riccati equation approach for determining the internal temperature distribution to a high degree of accuracy.  Real-time knowledge of the internal temperature state is critical for making control decisions; to manage the operation of the TES device in the context of a transient thermal management system, I design and test, both in simulation and experimentally, a logic-based control strategy that uses fluid temperature measurements and estimates of the TES state to make real-time control decisions to meet critical thermal management objectives. Together, these advances demonstrate the potential of thermal energy storage technology as a component of thermal management systems and the feasibility of logic-based control strategies for real-time control of thermal management objectives.</p>

Les Energies Marines Renouvelables (EMR) se développent aujourd’hui très vite tant au niveau de la recherche amont que de la R&D, et même des premiers démonstrateurs à la mer. Parmi ces EMR, l'énergie des vagues présente un potentiel particulièrement intéressant. Avec une ressource annuelle brute moyenne estimée à 40 kW/m au large de la côte atlantique, le littoral français est plutôt bien exposé. Mais l’exploitation à grande échelle de cette énergie renouvelable ne sera réalisable et pertinente qu'à condition d'une bonne intégration au réseau électrique (qualité) ainsi que d'une gestion et d'un dimensionnement optimisé au sens du coût sur cycle de vie. Une première solution de génération tout électrique pour un houlogénérateur a d’abord été évaluée dans le cadre de la thèse de Marie RUELLAN menée sur le site de Bretagne du laboratoire SATIE (ENS de Cachan). Ces travaux ont mis en évidence le potentiel de viabilité économique de cette chaîne de conversion et ont permis de poser la question du dimensionnement de l’ensemble convertisseur-machine et de soulever les problèmes associés à la qualité de l’énergie produite. Puis une seconde thèse a été menée par Judicaël AUBRY dans la même équipe de recherche. Elle a consisté, entre autres, en l’étude d’une première solution de traitement des fluctuations de la puissance basée sur un système de stockage par supercondensateurs. Une méthodologie de dimensionnement de l’ensemble convertisseur-machine et de gestion de l’énergie stockée fut également élaborée, mais en découplant le dimensionnement et la gestion de la production d’énergie et de ceux de son système de stockage. Le doctorant devra donc : 1. S’approprier les travaux antérieurs réalisés dans le domaine de la récupération de l’énergie des vagues ainsi que les modèles hydrodynamiques et mécaniques réalisés par notre partenaire : le LHEEA de l’Ecole Centrale de Nantes - 2. Résoudre le problème du couplage entre dimensionnement/gestion de la chaîne de conversion et dimensionnement/gestion du système de stockage. 3. Participer à la réalisation d’un banc test à échelle réduite de la chaine électrique et valider expérimentalement les modèles énergétiques du stockage et des convertisseurs statiques associés - 4. Proposer une méthodologie de dimensionnement de la chaine électrique intégrant le stockage et les lois de contrôle préalablement élaborées 5. Déterminer les gains en termes de capacités de stockage obtenus grâce à la mutualisation de la production (parc de machines) et évaluer l’intérêt d’un stockage centralisé - 6. Analyser l’impact sur le réseau d’une production houlogénérée selon divers scenarii, modèles et outils développés par tous les partenaires dans le cadre du projet QUALIPHE. L’exemple traité sera celui de l’Ile d’Yeu (en collaboration avec le SyDEV. / The work of this PhD thesis deals with the minimization of the per-kWh cost of direct-drive wave energy converter, crucial to the economic feasibility of this technology. Despite the simplicity of such a chain (that should provide a better reliability compared to indirect chain), the conversion principle uses an oscillating system (a heaving buoy for example) that induces significant power fluctuations on the production. Without precautions, such fluctuations can lead to: a low global efficiency, an accelerated aging of the fragile electrical components and a failure to respect power quality constraints. To solve these issues, we firstly study the optimization of the direct drive wave energy converter control in order to increase the global energy efficiency (from wave to grid), considering conversion losses and the limit s from the sizing of an electrical chain (maximum force and power). The results point out the effect of the prediction horizon or the mechanical energy into the objective function. Production profiles allow the study of the flicker constraint (due to grid voltage fluctuations) linked notably to the grid characteristics at the connection point. Other models have also been developed to quantify the aging of the most fragile and highly stressed components, namely the energy storage system used for power smoothing (with super capacitors or electrochemical batteries Li-ion) and power semiconductors.Finally, these aging models are used to optimize key design parameters using life-cycle analysis. Moreover, the sizing of the storage system is co-optimized with the smoothing management.