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On Dynamic Reconfiguration of a Large-Scale Battery System

On Dynamic Reconfiguration of a Large-Scale Battery System

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IEEE Symposium on real-time and Embedded Technology and Applications

作者： Hahnsang Kim Kang G. Shin Real-Time Computing Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

Electric vehicles powered with large-scale battery packs are gaining popularity as gasoline price soars. Large-scale battery packs usually consist of an estimated 12,000 battery cells connected in series and parallel, which are susceptible to battery-cell failures. Unfortunately, current battery-management systems are unable to handle the inevitable battery-cell failures very well. To address this problem, we propose a dynamic reconfiguration framework that monitors, reconfigures, and controls large-scale battery packs online. The framework is built upon a syntactic bypassing mechanism that provides a set of rules for changing the battery-pack configuration, and a semantic bypassing mechanism by which the battery-cell connectivity is reconfigured to recover from a battery-cell failure. In particular, the semantic bypassing mechanism is dictated by constant-voltage-keeping and dynamic-voltage-allowing policies. The former policy is effective in preventing unavoidable voltage drops during the battery discharge, while the latter policy is effective in supplying different amounts of power to meet a wide-range of application requirements. Our experimental evaluation has shown the proposed framework to enable the battery packs to be 9 times as fault-tolerant as a legacy scheme.

关键词： Large-scale systems Vehicle dynamics Centralized control Control systems Voltage real time systems Battery management systems Application software Embedded computing Power engineering computing

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Scheduling of Battery Charge, Discharge, and Rest

Scheduling of Battery Charge, Discharge, and Rest

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real-time Systems Symposium (RTSS)

作者： Hahnsang Kim Kang G. Shin Real-Time Computing Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

Electric vehicles operate inefficiently with a naive battery management system that charges or discharges battery cells in a pack based solely on application load demands. The battery pack's operation-time and lifetime can be extended significantly by effectively scheduling (the cyber part) battery charge, discharge, and rest activities, based on the battery characteristics (the physical part). We propose a set of policies for scheduling battery-cell activities, called the weighted-k round-robin (kRR) scheduling framework. This framework dynamically adapts battery-cell activities to load demands and the condition of individual cells, thereby extending the battery pack's operation-time and making them robust to anomalous voltage-imbalances. The framework comprises two key components. First, an adaptive filter estimates the upcoming load demand. Then, based on the estimated load demand, the kRR scheduler determines the number of parallel-connected cells to be discharged simultaneously. The scheduler also effectively partitions the cells in the pack, allowing the cells to be simultaneously charged and discharged in coordination with the battery reconfiguration system we developed earlier [17]. Besides the kRR scheduling framework, we characterize the discharge and recovery efficiency of a Lithium-ion battery cell. The kRR scheduling framework is shown to outperform three alternative scheduling mechanisms with respect to the operation-time by 7-56%, and improve the tolerance of voltage-imbalance by up to 50%.

关键词： real time systems Robustness Global warming Threshold voltage Processor scheduling Laboratories Battery powered vehicles Battery management systems Application software Adaptive filters

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Computational intelligence approach to load forecasting - A practical application for the desert of Saudi Arabia

Computational intelligence approach to load forecasting - A ...

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2009 12th International Conference on computer and Information Technology, ICCIT 2009

作者： Ahmmed, Suman Rahman, Dewan Md Fayzur Hasan, Md Khairul Saber, Ahmed Yousuf Rahman, Mohammad Zahidur Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology United States Department of Computer Science and Engineering Jahangimagar University Dhaka Bangladesh

ISBN: (纸本)9781424462841

This paper presents the development of an Artificial Neural Networks and Particle Swarm Optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land than other places. Thus this model is different from a typical forecasting model considering inputs and outputs. In this research paper two steps have been introduced, first load forecasting made by mapping mechanism and then optimization technique applied to improve its accuracy. This paper includes ANN and PSO models for 24-hours ahead load forecasting. ANN is an effective mathematical tool for mapping complex relationships. It is also successful for doing forecasting, categorization, classification, and so forth. On the other hand, PSO is the most promising optimization tool. It has better information sharing and conveying mechanism;it has better balance of local and global searching abilities;and can handle huge multi-dimensional optimization problems efficiently with hundreds of thousands of constraints. Thus PSO is chosen as the optimization model ofthe weight matrix of ANN. Results show that the proposed ANN-PSO performs much better than ANN for the load forecasting in a desert like Saudi Arabia. ©2009 IEEE.

关键词： Neural networks

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Enabling data structure oriented performance analysis with hardware performance counter support

Enabling data structure oriented performance analysis with h...

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Workshops on Parallel Processing, Euro-Par 2008: VHPC 2008, UNICORE 2008, HPPC 2008, SGS 2008, PROPER 2008, ROIA 2008, and DPA 2008

作者： Fürlinger, Karl Terpstra, Dan You, Haihang Mucci, Phil Moore, Shirley Computer Science Division EECS Department University of California at Berkeley United States Innovative Computing Laboratory Department of Electrical Engineering and Computer Science University of Tennessee United States

ISBN: (纸本)3642009549

An interesting and as of yet under-represented aspect of program development and optimization are data structures. Instead of analyzing data with respect to code regions, the objective here is to see how performance metrics are related to data structures. With the advanced performance monitoring unit of Intel's Itanium processor series such an analysis becomes possible. This paper describes how the hardware features of the Itanium 2 processor are exploited by the perfmon and PAPI performance monitoring APIs and how PAPI's support for address range restrictions has been integrated into an existing profiling tool to achieve the goal of data structure oriented profiling in the context of OpenMP applications. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Data structures

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Load forecasting of a desert: A computational intelligence approach

Load forecasting of a desert: A computational intelligence a...

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2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09

作者： Saber, Ahmed Yousuf Al-Shareef, Abdulaziz Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO 65409-0040 United States Electrical and Computer Engineering Department King Abdulaziz University Jeddah KSA Saudi Arabia

ISBN: (纸本)9781424450985

This paper presents artificial neural networks and particle swarm optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA) of Saudi Arabia. Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land than other places. Thus this model is different from a typical forecasting model considering inputs and outputs. In this research, two models are implemented - firstly load forecasting model for prediction;however, it is not sufficient for desired level of accurate forecasting, and secondly, optimization to improve the results up to at least better than existing results. This paper includes ANN and PSO models for 24-hours ahead load forecasting. ANN is a mathematical tool for mapping complex relations;it is well proved for the successful use of prediction, function approximation with dynamics, categorization, classification, and so forth. In this research, 24- step ahead calculations are performed in the ANN model and results are moderate. On the other hand, PSO is the most promising optimization tool. It is a swarmed based optimization method;it has better information sharing and conveying mechanism;it has better balance of local and global searching abilities;it can handle huge multi-dimensional optimization problems efficiently with hundreds of thousands of constraints. Thus PSO is chosen as the optimization tool that is applied on the weight matrix of ANN to improve results. In this research, PSO reliably and accurately tracks the continuously changing weights of ANN for uncertain load demand. By analyzing the model of ANN for the load-forecasting problem of SEC-WOA with hundreds of thousands of data and changing-uncertain load demand, the PSO is applied for the ANN weight adjustment and to optimize the uncertain load demand, as the ANN is not an optimization method. Results

关键词： Neural networks

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Economic dispatch of a differential evolution based generator maintenance scheduling of a power system

Economic dispatch of a differential evolution based generato...

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IEEE General Meeting Power& Energy Society

作者： Y. Yare G. K. Venayagamoorthy A. Y. Saber Real-Time and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

This paper presents generator maintenance scheduling (GMS) and economic dispatch (ED) of the Indonesian power system based on minimization of an economic cost function for the economical and reliable operation of a power system, while satisfying crew/manpower, maintenance window, load demand, generation limits and spinning reserve constraints. Differential evolution (DE), an evolutionary computation technique and an optimization algorithm that utilizes the differential information to guide its further search, is known to effectively solve large scale optimization problems and has been widely applied in power system. In this paper, the DE approach is proposed for solving the GMS problem and the resulting optimal maintenance schedules are used by the Lambda-iteration (LI) method to calculate the ED for the 19 generating units supplying power to two major industrial parks located in Bintan and Batam in Indonesian. The simulation results show great prospect for application in control centers by solving GMS and ED problems of many utilities which seek to minimize running costs. The results obtained offer an effective alternative for solving the GMS and ED problems.

关键词： Power system economics Power generation economics Power generation Power systems Power system reliability Maintenance Job shop scheduling Processor scheduling Cost function Spinning

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Getting an NSF CAREER award and beyond

Getting an NSF CAREER award and beyond

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IEEE PES Power Systems Conference and Exposition, PSCE

作者： Ganesh K. Venayagamoorthy Real-Time and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

The National science Foundation (NSF), USA, CAREER program is a premier program that emphasizes the importance the Foundation places on the early development of academic careers solely dedicated to stimulating the discovery process in which the excitement of research enriched by inspired teaching and enthusiastic learning. This paper presents the research and education experiences gained by the principal investigator - Venayagamoorthy and his research collaborators and students as a result of a NSF CAREER proposal been awarded by the Power, Control and Adaptive Networks (PCAN) program of the electrical, Communications and Cyber Systems Division, effective June 1, 2004. The main emphasis of this paper is how and what a CAREER proposal awardee do to be a double winner in all aspects - research, education and outreach. In addition, suggestions on writing a winning NSF CAREER proposal are presented.

关键词： Awards

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Scheduling parallel applications on Utility Grids: time and cost trade-off management

Scheduling parallel applications on Utility Grids: Time and ...

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32nd Australasian computer science Conference, ACSC 2009

作者： Garg, Saurabh Kumar Buyya, Rajkumar Siegel, H.J. Grid Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne VIC 3010 Australia Electrical and Computer Engineering Department Colorado State University Fort Collins United States Computer Science Department Colorado State University Fort Collins United States

ISBN: (纸本)9781920682729

With the growth of Utility Grids and various Grid market infrastructures, the need for efficient and cost effective scheduling algorithms is also increasing rapidly, particularly in the area of meta-scheduling. In these environments,users not only may have conflicting requirements with other users, but also they have to manage the trade-off between time and cost such that their applications can be executed most economically in the minimum time. Thus, choosing of the best Grid resources becomes a challenge in such a competitive market. This paper presents two novel heuristics for scheduling parallel applications on Utility Grids that manage and optimize the trade-off between time and cost constraints. The performance of the heuristics is evaluated through extensive simulations of a real-world environment with real parallel workload models to demonstrate the practicality of our algorithms. We compare our scheduling algorithms against other common algorithms used by current meta-schedulers. The results shows that our algorithms outperform other algorithms by minimizing the time and cost of application execution on Utility Grids. Copyright © 2009, Australian computer Society, Inc.

关键词： Scheduling algorithms

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real-time implementation of an intelligent algorithm for electric ship power system reconfiguration

Real-time implementation of an intelligent algorithm for ele...

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IEEE Electric Ship Technologies Symposium, ESTS

作者： Pinaki Mitra Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla Missouri USA

The naval electric ship is often subject to severe damages under battle conditions. The damages or faults might even affect the generators and as a result, critical loads might suffer from power deficiency which may lead to an eventual collapse of rest of the system. In order to serve the critical loads and maintain a proper power balance without excessive generation, the ship power system requires a fast reconfiguration of the remaining system under fault conditions. A fast intelligent algorithm using the small population based particle swarm optimization (SPPSO) for dynamic reconfiguration of the available generators and loads when a fault in the ship power system is detected is presented in this paper. SPPSO is a variant of PSO which operates with fewer particles and a regeneration concept, where new potential solutions are generated every few iterations. This concept of regeneration makes the algorithm fast and enhances its exploration capability to a large extent. The strength of the proposed reconfiguration strategy is first illustrated with Matlab results and then with a real-time implementation on a real time digital simulator and a digital signal processor.

关键词： real time systems Marine vehicles Power systems Signal processing algorithms Power system faults Power generation Power system dynamics Particle swarm optimization electrical fault detection Fault detection

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Comparison of feedforward and feedback neural network architectures for short term wind speed prediction

Comparison of feedforward and feedback neural network archit...

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International Joint Conference on Neural Networks (IJCNN)

作者： Richard L. Welch Stephen M. Ruffing Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri S & T MO USA

This paper compares three types of neural networks trained using particle swarm optimization (PSO) for use in the short term prediction of wind speed. The three types of neural networks compared are the multi-layer perceptron (MLP) neural network, Elman recurrent neural network, and simultaneous recurrent neural network (SRN). Each network is trained and tested using meteorological data of one week measured at the National Renewable Energy laboratory National Wind Technology Center near Boulder, CO. Results show that while the recurrent neural networks outperform the MLP in the best and average case with a lower overall mean squared error, the MLP performance is comparable. The better performance of the feedback architectures is also shown using the mean absolute relative error. While the SRN performance is superior, the increase in required training time for the SRN over the other networks may be a constraint, depending on the application.

关键词： Neurofeedback Neural networks Feedforward neural networks Wind speed Recurrent neural networks Multi-layer neural network Particle swarm optimization Multilayer perceptrons Testing Meteorology

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