Particle-Filtering-Based State-of-Health Estimation and End-of-Life Prognosis for Lithium-Ion Batteries at Operation Temperature

We present the implementation of a particle-filtering-based framework that estimates the State-of-Health (SOH) and predicts the End-of-Life (EOL) of Lithium-Ion batteries, efficiently incorporating variations of ambient temperature in the analysis. The proposed approach uses an empirical state-space model, in which inputs are explicitly defined as the average temperature of operation and the output of an external module that detects self-recharge phenomena, on the other hand the output is a function that relates the current SOH and temperature with the Usable Capacity in that cycle. In addition, this approach allows to deal with data losses and outliers. In order to correct erroneous initial conditions in state estimates, an Outer Feedback Correction Loop is implemented. Finally, this framework is validated using degradation data from four sources: experimental degradation data from two Li-Ion 18650 cells, accelerated degradation data openly provided by NASA Ames Research Center, and artificially generated degradation data at different ambient temperatures.We present the implementation of a particle-filtering-based framework that estimates the State-of-Health (SOH) and predicts the End-of-Life (EOL) of Lithium-Ion batteries, efficiently incorporating variations of ambient temperature in the analysis. The proposed approach uses an empirical state-space model, in which inputs are explicitly defined as the average temperature of operation and the output of an external module that detects self-recharge phenomena, on the other hand the output is a function that relates the current SOH and temperature with the Usable Capacity in that cycle. In addition, this approach allows to deal with data losses and outliers. In order to correct erroneous initial conditions in state estimates, an Outer Feedback Correction Loop is implemented. Finally, this framework is validated using degradation data from four sources: experimental degradation data from two Li-Ion 18650 cells, accelerated degradation data openly provided by NASA Ames Research Center, and artificially generated degradation data at different ambient temperatures

A rest time-based prognostic framework for state of health estimation of lithium-ion batteries with regeneration phenomena

State of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framewor

State of health estimation of Li-ion batteries with regeneration phenomena: a similar rest time-based prognostic framework

State of health (SOH) prediction in Li-ion batteries plays an important role in intelligent battery management systems (BMS). However, the existence of capacity regeneration phenomena remains a great challenge for accurately predicting the battery SOH. This paper proposes a novel prognostic framework to predict the regeneration phenomena of the current battery using the data of a historical battery. The global degradation trend and regeneration phenomena (characterized by regeneration amplitude and regeneration cycle number) of the current battery are extracted from its raw SOH time series. Moreover, regeneration information of the historical battery derived from corresponding raw SOH data is utilized in this framework. The global degradation trend and regeneration phenomena of the current battery are predicted, and then the prediction results are integrated together to calculate the overall SOH prediction values. Particle swarm optimization (PSO) is employed to obtain an appropriate regeneration threshold for the historical battery. Gaussian process (GP) model is adopted to predict the global degradation trend, and linear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated using experimental data from the degradation tests of Li-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framewor

Communications concerns for reused electric vehicle batteries in smart grids

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksElectric vehicles use 10 to 25 kWh batteries. After their use in cars, these batteries are still in good condition to be used for energy storage in stationary applications and smart grid systems at all stages: generation, transmission, and distribution. However, before their reuse, there are some changes to be made on these batteries, such as those concerning communications. Electric vehicle batteries use a battery management system that controls functionality and safety, transmitting their condition and status, but also containing confidential information. This article studies two strategies to deal with communications difficulties in their second life as storage energy devices.Peer ReviewedPostprint (author's final draft

A Smart Algorithm for the Diagnosis of Short-Circuit Faults in a Photovoltaic Generator

International audienceThis paper deals with a smart algorithm allowing short-circuit faults detection and diagnosis of PV generators. The proposed algorithm is based on the hybridization of a support vector machines (SVM) technique optimized by a k-NN tool for the classification of observations on the classifier itself or located in its margin. To test the proposed algorithm performance, a PV generator database containing observations distributed over classes is used for simulation purposes

A Regression Algorithm for the Smart Prognosis of a Reversed Polarity Fault in a Photovoltaic Generator

International audienceThis paper deals with a smart algorithm allowing reversed polarity fault diagnosis and prognosis in PV generators. The proposed prognosis (prediction) approach is based on the hybridization of a support vector regression (SVR) technique optimized by a k-NN regression tool (K-NNR) for undetermined outputs. To test the proposed algorithm performance, a PV generator database containing sample data is used for simulation purposes

Particle Filter-Based Prognostics for an Electrolytic Capacitor Working in Variable Operating Conditions

International audiencePrognostic models should properly take into account the effects of operating conditions on the degradation process and on the signal measurements used for monitoring. In this work, we develop a Particle Filter-based (PF) prognostic model for the estimation of the Remaining Useful Life (RUL) of aluminum electrolytic capacitors used in electrical automotive drives, whose operation is characterized by continuously varying conditions. The capacitor degradation process, which remarkably depends from the temperature experienced by the component, is typically monitored by observing the capacitor Equivalent Series Resistance (ESR). However, the ESR measurement is influenced by the temperature at which the measurement is performed, which changes depending on the operating conditions. To address this problem, we introduce a novel degradation indicator independent from the measurement temperature. Such indicator can, then, be used for the prediction of the capacitor degradation and its RUL. For this, we develop a Particle Filter prognostic model, whose performance is verified on data collected in simulated and experimental degradation tests

Baterías de Ion Litio: características y aplicaciones

Batteries have been part of our life for over 100 years. They have been used in different applications from a simple scientific calculator to electric vehicles, robots, and satellites. Over the years, various types of batteries have all been manufactured for better performance. At present, lithium-ion batteries have been used more frequently thanks to their high energy density, high energy efficiency, and long life. This paper presents a summary of the relevant aspects of lithium-ion batteries. The article developed introduces the terminology related to the world of batteries. Besides, it studies the characteristics and tools that make lithium-ion batteries one of the most commonly used batteries. On the other hand, this study mentions relevant aspects of a battery management system, a necessary tool to safeguard the operation and life of the battery.Las baterías han sido parte de nuestra vida por más de 100 años. Ellas han sido utilizadas en diferentes aplicaciones desde una simple calculadora científica hasta en vehículos eléctricos, robots y satélites. A través de los años diversos tipos de baterías han sido fabricados todos con la finalidad de mejorar su rendimiento. En la actualidad las baterías de iones de litio han sido usadas con mayor frecuencia debido a su alta densidad de energía, su alta eficiencia energética y a su prolongado tiempo de vida. Este trabajo presenta un resumen de aspectos relevantes sobre las baterías de iones de litio. El artículo desarrollado introduce la terminología relacionada al mundo de las baterías. Además, estudia las características y herramientas que hacen a las baterías de iones de litio una de las baterías más utilizadas actualmente. Por otro lado, este estudio menciona aspectos relevantes de un sistema de gestión de baterías, herramienta necesaria para salvaguardar el funcionamiento y vida de la batería. &nbsp

Advanced Battery Technologies: New Applications and Management Systems

In recent years, lithium-ion batteries (LIBs) have been increasingly contributing to the development of novel engineering systems with energy storage requirements. LIBs are playing an essential role in our society, as they are being used in a wide variety of applications, ranging from consumer electronics, electric mobility, renewable energy storage, biomedical applications, or aerospace systems. Despite the remarkable achievements and applicability of LIBs, there are several features within this technology that require further research and improvements. In this book, a collection of 10 original research papers addresses some of those key features, including: battery testing methodologies, state of charge and state of health monitoring, and system-level power electronics applications. One key aspect to emphasize when it comes to this book is the multidisciplinary nature of the selected papers. The presented research was developed at university departments, institutes and organizations of different disciplines, including Electrical Engineering, Control Engineering, Computer Science or Material Science, to name a few examples. The overall result is a book that represents a coherent collection of multidisciplinary works within the prominent field of LIBs