Fotis Kopsaftopoulos

Bio: Fotis Kopsaftopoulos is an academic researcher from Rensselaer Polytechnic Institute. The author has contributed to research in topics: Structural health monitoring & Computer science. The author has an hindex of 15, co-authored 79 publications receiving 935 citations. Previous affiliations of Fotis Kopsaftopoulos include University of Patras & Technical University of Madrid.

Damage detection sensitivity characterization of acousto-ultrasound-based structural health monitoring techniques:

Abstract: Reliability quantification is a critical and necessary process for the evaluation and assessment of any inspection technology that may be classified either as a nondestructive evaluation or structural health monitoring technique. Based on the sensitivity characterization of nondestructive evaluation techniques, appropriate processes have been developed and established for the reliability quantification of their performance with respect to damage/flaw detection in materials or structures. However, in the case of structural health monitoring methods, no such well-defined and general applicable approaches have been established for neither active nor passive sensing techniques that allow for their accurate reliability quantification. The objective of this study is to characterize the sensitivity of active-sensing acousto-ultrasound-based structural health monitoring techniques with respect to damage detection, as well as to identify the parameters that influence their sensitivity. With such an understanding, ...

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

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.

A regularity statistic for medical data analysis

Abstract: A new statistic has been developed to quantify the amount of regularity in data. This statistic, ApEn (approximate entropy), appears to have potential application throughout medicine, notably in electrocardiogram and related heart rate data analyses and in the analysis of endocrine hormone release pulsatility. The focus of this article is ApEn. We commence with a simple example of what we are trying to discern. We then discuss exact regularity statistics and practical difficulties of using them in data analysis. The mathematic formula development for ApEn concludes the Solution section. We next discuss the two key input requirements, followed by an account of a pilot study successfully applying ApEn to neonatal heart rate analysis. We conclude with the important topic of ApEn as a relative (not absolute) measure, potential applications, and some caveats about appropriate usage of ApEn. Appendix A provides example ApEn and entropy computations to develop intuition about these measures. Appendix B contains a Fortran program for computing ApEn. This article can be read from at least three viewpoints. The practitioner who wishes to use a "black box" to measure regularity should concentrate on the exact formula, choices for the two input variables, potential applications, and caveats about appropriate usage. The physician who wishes to apply ApEn to heart rate analysis should particularly note the pilot study discussion. The more mathematically inclined reader will benefit from discussions of the relative (comparative) property of ApEn and from Appendix A.

A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems

Abstract: With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state-of-charge (SOC), state-of-energy (SOE), state-of-power (SOP), state-of-function (SOF), state-of-health (SOH), remaining useful life (RUL), remaining discharge time (RDT), state-of-balance (SOB), and state-of-temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next-generation BMSs.