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P. Lundsager

Bio: P. Lundsager is an academic researcher from United States Department of Energy. The author has contributed to research in topics: Wind power & Electric power system. The author has an hindex of 11, co-authored 25 publications receiving 897 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, a lifetime model for lead-acid batteries is presented that allows comparison of the impact of different operating conditions, different system sizing and different battery technologies on battery lifetime.

265 citations

01 Apr 2005
TL;DR: In this paper, the authors investigated and developed two different battery life prediction methodologies with specific reference to their use in hybrid renewable energy systems and the results from battery tests designed to exercise batteries in similar modes to those that they experience in hybrid systems have also been analysed.
Abstract: The performance and lifetime of energy storage in batteries are an important part of many renewable based energy systems. Not only do batteries impact on the system performance but they are also a significant expenditure when considering the whole life cycle costs. Poor prediction of lifetime can, therefore, lead to uncertainty in the viability of the system in the long term. This report details the work undertaken to investigate and develop two different battery life prediction methodologies with specific reference to their use in hybrid renewable energy systems. Alongside this, results from battery tests designed to exercise batteries in similar modes to those that they experience in hybrid systems have also been analysed. These have yielded battery specific parameters for use in the prediction software and the first results in the validation process of the software are also given. This work has been part of the European Union Benchmarking research project (ENK6-CT-2001-80576), funded by the European Union, the United States and Australian governments together with other European states and other public and private financing bodies. The project has concentrated on lead acid batteries as this technology is the most commonly used. Through this work the project partner institutions have intended to provide useful tools to improve the design capabilities of organizations, private and public, in remote power systems. (au)

210 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a procedure that can be used for lifetime prediction, outlines some of the requirements for a prediction, and discusses the principles of battery models and their potential use in lifetime prediction.

180 citations

Journal ArticleDOI
TL;DR: In this paper, the authors describe the process and the results of creating RES categories of similar operating conditions for batteries, which can also be used for lifetime considerations of RES components, for making recommendations and for analysing the properties and performance of a RES and its components.

91 citations

Proceedings ArticleDOI
05 Nov 2002
TL;DR: The integration of wind power in the power system is therefore an issue in order to optimise the utilisation of the resource and to continue the high rate of installation of generating capacity, which is necessary to achieve the goals of sustainability and security of supply.
Abstract: Wind power is going through a very rapid development. It is the fastest growing power source in the world, the technology is being developed rapidly and wind power is supplying significant shares of the energy in large regions. The integration of wind power in the power system is therefore now an issue in order to optimise the utilisation of the resource and in order to continue the high rate of installation of generating capacity, which is necessary in order to achieve the goals of sustainability and security of supply. This development of the wind power from just being a negative load to have power plant like features of course also bring with it large challenges. These challenges include connection of wind farms to weak grids, securing power quality, reduction or elimination of power fluctuations, prediction of power output and changes in operating strategies of conventional power plants. Many of these challenges can be faced by the application of power electronics in wind turbines or in wind farms, but many of them will also require other technologies to be developed such as fuel cells and energy storage. The key issues for success is the utilisation of power electronics and information technology. The paper highlights, through examples (from Denmark and from finalised and current research work), the challenges and present the current research direction.

49 citations


Cited by
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Journal ArticleDOI
Languang Lu1, Xuebing Han1, Jianqiu Li1, Jianfeng Hua, Minggao Ouyang1 
TL;DR: In this article, a brief introduction to the composition of the battery management system (BMS) and its key issues such as battery cell voltage measurement, battery states estimation, battery uniformity and equalization, battery fault diagnosis and so on, is given.

3,650 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a summary of techniques, models, and algorithms used for battery ageing estimation, going from a detailed electrochemical approach to statistical methods based on data, and their respective characteristics are discussed.

1,224 citations

Journal ArticleDOI
TL;DR: In this article, a comprehensive review of the battery state of charge estimation and its management system for the sustainable future electric vehicles (EVs) applications is presented, which can guarantee a reliable and safe operation and assess the battery SOC.
Abstract: Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

1,150 citations

Journal ArticleDOI
TL;DR: In this paper, the authors analyze the stability problems of grid connected inverters used in distributed generation and demonstrate that the use of active damping helps to stabilise the system in respect to many different kinds of resonances.
Abstract: The aim of this paper is to analyze the stability problems of grid connected inverters used in distributed generation. Complex controllers (e.g., multiple rotating dq-frames or resonant-based) are often required to compensate low frequency grid voltage background distortion and an LCL-filter is usually adopted for the high frequency one. The possible wide range of grid impedance values (distributed generation is suited for remote areas with radial distribution plants) challenge the stability and the effectiveness of the LCL-filter-based current controlled system. It has been found out and it will be demonstrated in this paper that the use of active damping helps to stabilise the system in respect to many different kinds of resonances. The use of active damping results in an easy plug-in feature of the generation system in a vast range of grid conditions and in a more flexible operation of the overall system able to manage sudden grid changes. In the paper, a vast measurement campaign made on a single-phase system and on a three-phase system used as scale prototypes for photovoltaic and wind turbines, respectively, validate the analysis.

997 citations

Journal ArticleDOI
TL;DR: This review categorises data-driven battery health estimation methods according to their underlying models/algorithms and discusses their advantages and limitations, then focuses on challenges of real-time battery health management and discuss potential next-generation techniques.
Abstract: Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

538 citations