Real-Time Optimal Control of a Plug-in Hybrid Electric Vehicle Using Trip Information

The plug-in hybrid electric vehicle (PHEV) is a promising option for future sustainable transportation. It offers better fuel economy and lower emissions than conventional vehicles. This thesis has developed a novel energy-optimal powertrain controller for PHEVs. The controller will be broadly applicable to all PHEV models; however, it will be fine-tuned to the Toyota Prius Plug-in Hybrid for testing and validation. The controller will take advantage of advancements in vehicle intelligent and communications technologies, such as Global Positioning System (GPS), Intelligent Transportation System (ITS), Geographic Information System (GIS), radar, and other on-board sensors, to provide look-ahead trip data. These data are critical to increasing fuel economy as well as driving safety. This PhD research has developed three energy-optimal systems for PHEVs: Trip Planning module, Route-based Energy Management System (Route-based EMS), and Ecological Cruise (Eco-Cruise) Controller. The main objective of these energy-optimal systems is to minimize the total energy cost, including both electricity derived from the grid and fuel. The upper-level system is Trip Planning, using an algorithm designed to take advantage of previewed trip information to optimize State of Charge (SOC) profiles. The Route-based EMS optimally distributes propulsion power between the batteries and engine. Finally, the Eco-Cruise controller adjusts the speed considering upcoming trip data. Real-time implementation has remained a major challenge in the design of complex control systems. To address this hurdle, simple and efficient models and fast optimization algorithms are developed for each energy-optimal strategy. A Real-time Cluster-based Optimization is developed to solve the Trip Planning problem in real-time. The Route-based EMS is developed based on Equivalent Consumption Minimization Strategy (ECMS) to optimally distribute propulsion power between two energy sources. And, a Nonlinear Model Predictive Control (NMPC) is utilized to obtain optimum traction or regenerative torques in Eco-Cruise controller. Model-in-the-Loop (MIL) and Hardware-in-the-Loop (HIL) testing are critical steps in control validation and in ensuring real-time implementation capability. The MIL results show that the novel energy-optimal powertrain controller can improve the total energy cost by up to %20 compare to benchmark rule-based controller. The HIL test results demonstrate that the computational time for energy-optimal strategies are less than the target sampling-time, and they can be implemented in real-time

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

A decentralized control and optimization framework for autonomic performance management of web-server systems

Web-based services such as online banking and e-commerce are often hosted on distributed computing systems comprising heterogeneous and networked servers in a data-center setting. To operate such systems efficiently while satisfying stringent quality-of-service (QoS) requirements, multiple performance-related parameters must be dynamically tuned to track changing operating conditions. For example, the workload to be processed may be time varying and hardware/software resources may fail during system operation. To cope with their growing scale and complexity, such computing systems must become largely autonomic, capable of being managed with minimal human intervention.This study develops a distributed cooperative-control framework using concepts from optimal control theory and hybrid dynamical systems to adaptively manage the performance of computer clusters operating in dynamic and uncertain environments. As case studies, we focus on power management and dynamic resource provisioning problems in such clusters.First, we apply the control framework to minimize the power consumed by a server cluster under a time-varying workload. The overall power-management problem is decomposed into smaller sub-problems and solved in cooperative fashion by individual controllers on each server. This approach allows for the scalable control of large computing systems. The control framework also adapts to controller failures and allows for the dynamic addition and removal of controllers during system operation. We validate the proposed approach using a discrete-event simulator with real-world workload traces, and our results indicate that the controllers achieve a 55% reduction in power consumption when compared to an uncontrolled system in which each server operates at its maximum frequency at all times.We then develop a distributed resource provisioning framework to achieve di®erentiated QoS among multiple online services using concepts from hybrid control. We use a discrete hybrid automaton to model the operation of the computing cluster. The resource provisioning problem combining both QoS control and power management is then solved using a decentralized model predictive controller to maximize the operating profits generated by the cluster according to a specified service level agreement. Simulation results indicate that the controller generates 27% additional profit when compared to an uncontrolled system.Ph.D., Electrical Engineering -- Drexel University, 200

Mixed Logic Dynamical Model of a Hydroelectric Power Plant

In this work we present the model of a hydroelectric power plant in the framework of Mixed Logic Dynamical (MLD) systems. Each outflow unit exhibits a hybrid behavior since flaps, gates and turbines are controlled with logical inputs and the outflow dynamics depend on the logical state of the unit. We show how to derive detailed models of each unit considering not only standard operating conditions, but also emergencies and startup and shut-down procedures

Optimal Control for Precision Irrigation of a Large‐Scale Plantation

Distributing water optimally is a complex problem that many farmers face yearly, especially in times of drought. In this work, we propose optimization‐based feedback control to improve crop yield and water productivity in agriculture irrigation for a plantation consisting of multiple fields. The interaction between soil, water, crop (sugarcane in this work), and the atmosphere is characterized by an agro‐hydrological model using the crop water productivity modeling software AquaCrop‐OS. To optimally distribute water over the fields, we propose a two‐level optimal control approach. In this approach, the seasonal irrigation planner determines the optimal allocation of water over the fields for the entire growth season to maximize the crop yield, by considering an approximation of the crop productivity function. In addition, the model predictive controller takes care of the daily regulation of the soil moisture, respecting the water distribution decided on by the seasonal planner. To reduce the computational complexity of the daily controller, a mixed‐logic dynamical model is identified based on the AquaCrop‐OS model. This dynamical model incorporates saturation dynamics explicitly to improve model quality. To further improve performance, we create an evapotranspiration model by considering the expected development of the crop over the season using remote‐sensing‐based measurements of the canopy cover. The performance of the two‐level approach is evaluated through a closed‐loop simulation in AquaCrop‐OS of a real sugarcane plantation in Mozambique. Our optimal control approach boosts water productivity by up to 30% compared to local heuristics and can respect water use constraints that arise in times of drought.Water ResourcesDelft Center for Systems and Contro