Author
A. Ruddell
Bio: A. Ruddell is an academic researcher from Council for the Central Laboratory of the Research Councils. The author has contributed to research in topics: Renewable energy & European union. The author has an hindex of 4, co-authored 7 publications receiving 280 citations.
Papers
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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
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
TL;DR: The thermal characteristics of each technology are considered in the context of the storage requirements in various renewable energy applications, with particular focus on the thermal performance according to environmental and other operational conditions as discussed by the authors.
15 citations
18 May 2003
TL;DR: In this paper, the authors evaluated nine different technologies for the storage of renewable energy, either in stand-alone or in grid-connected applications, and defined the strains put by a PV application on a storage function with reference to existing systems.
Abstract: In order to increase the use of renewable energies, the topic of RE storage is a critical issue. Within the European thematic network INVESTIRE, nine different technologies are evaluated for the storage of renewable energy, either in stand-alone or in grid-connected applications. In this paper, the strains put by a PV application on a storage function are defined with reference to existing systems. Furthermore, the way different storage technologies can answer these needs is evaluated. The technologies assessed are lead/acid, lithium systems, double-layer capacitors, nickel systems, electrolyser/hydrogen storage/fuel-cell associations, flywheels, redox flow batteries, compressed air and metal-air systems. The analysis will allow the assessment of hybridising possibilities and of future RTD goals.
5 citations
23 Jun 2003
TL;DR: First results in the development of a similarity index and in identifying and describing categories of similar use are presented, which will make it possible to match any installation to a category which resembles its operating conditions most closely.
Abstract: This paper describes a general method on how to monitor renewable energy systems (RES) and evaluate the data in such a way that the operating conditions and performance of individual components and the system as a whole can be analysed and compared. The enormous variation between RES, which is the result of user requirements and local climatic conditions, makes it necessary to classify different installations into categories of similar use. Only installations belonging to the same category of use can be compared in a meaningful manner. First results in the development of a similarity index and in identifying and describing categories of similar use are presented. The project results will make it possible to match any installation to a category which resembles its operating conditions most closely.
2 citations
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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
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
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
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
TL;DR: A review of battery prognostics and health management techniques, with a focus on major unmet needs in this area for battery manufacturers, car designers, and electric vehicle drivers, is provided in this article.
499 citations