Relevant bibliographies by topics / State of health (SOH)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'State of health (SOH)'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "State of health (SOH)"

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Fang, Liu, Liu Xinyi, Su Weixing, Chen Hanning, He Maowei, and Liang Xiaodan. "State-of-Health Online Estimation for Li-Ion Battery." SAE International Journal of Electrified Vehicles 9, no. 2 (December 31, 2020): 185–96. http://dx.doi.org/10.4271/14-09-02-0012.

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To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method, the algorithm proposed in this article has higher accuracy, fewer identification parameters, and lower computational complexity.
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O. Hadi, Pradita, and Goro Fujita. "Battery Charge Control by State of Health Estimation." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 3 (March 1, 2017): 508. http://dx.doi.org/10.11591/ijeecs.v5.i3.pp508-514.

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Battery lifetime is one of importance consideration in smart system with energy storage system, because it is shorter than others. Extended of battery lifetime can give benefit to entire system, especially to reduce cost. The lifetime is commonly estimated by State of Health (SOH). Decreasing of SOH indicates degradation of battery. It can be influenced by the battery operation, so that operational management is needed. This study proposes control block for charging battery by using decreasing value of SOH as reference. The control block is implemented in battery system that connected to DC bus by bidirectional chopper. Numerical simulation study is performed by using PSIM software version 10.0. The result shows that the proposed block control is successfully used. Moreover, the relative error is less than 2% for delta SOH and less than 1% for battery power.<em> </em>
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Noura, Nassim, Loïc Boulon, and Samir Jemeï. "A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges." World Electric Vehicle Journal 11, no. 4 (October 16, 2020): 66. http://dx.doi.org/10.3390/wevj11040066.

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To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, its lifetime and allows a better energy management in hybrid systems. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods meet the requirement of automotive real-time applications. The real time estimation of battery SOH is important regarding battery fault diagnosis. Moreover, being able to estimate the SOH in real time ensure an accurate State of Charge and State of Power estimation for the battery, which are critical states in hybrid applications. This study provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications. Experimental validation of an online and on-board suited SOH estimation method using model-based adaptive filtering is conducted to demonstrate its real-time feasibility and accuracy.
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Al-Gabalawy, Mostafa, Karar Mahmoud, Mohamed M. F. Darwish, James A. Dawson, Matti Lehtonen, and Nesreen S. Hosny. "Reliable and Robust Observer for Simultaneously Estimating State-of-Charge and State-of-Health of LiFePO4 Batteries." Applied Sciences 11, no. 8 (April 16, 2021): 3609. http://dx.doi.org/10.3390/app11083609.

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Batteries are everywhere, in all forms of transportation, electronics, and constitute a method to store clean energy. Among the diverse types available, the lithium-iron-phosphate (LiFePO4) battery stands out for its common usage in many applications. For the battery’s safe operation, the state of charge (SOC) and state of health (SOH) estimations are essential. Therefore, a reliable and robust observer is proposed in this paper which could estimate the SOC and SOH of LiFePO4 batteries simultaneously with high accuracy rates. For this purpose, a battery model was developed by establishing an equivalent-circuit model with the ambient temperature and the current as inputs, while the measured output was adopted to be the voltage where current and terminal voltage sensors are utilized. Another vital contribution is formulating a comprehensive model that combines three parts: a thermal model, an electrical model, and an aging model. To ensure high accuracy rates of the proposed observer, we adopt the use of the dual extend Kalman filter (DEKF) for the SOC and SOH estimation of LiFePO4 batteries. To test the effectiveness of the proposed observer, various simulations and test cases were performed where the construction of the battery system and the simulation were done using MATLAB. The findings confirm that the best observer was a voltage-temperature (VT) observer, which could observe SOC accurately with great robustness, while an open-loop observer was used to observe the SOH. Furthermore, the robustness of the designed observer was proved by simulating ill-conditions that involve wrong initial estimates and wrong model parameters. The results demonstrate the reliability and robustness of the proposed observer for simultaneously estimating the SOC and SOH of LiFePO4 batteries.
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Yao, Lei, Shiming Xu, Aihua Tang, Fang Zhou, Junjian Hou, Yanqiu Xiao, and Zhijun Fu. "A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods." World Electric Vehicle Journal 12, no. 3 (August 10, 2021): 113. http://dx.doi.org/10.3390/wevj12030113.

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Lithium-ion power batteries have been widely used in transportation due to their advantages of long life, high specific power, and energy. However, the safety problems caused by the inaccurate estimation and prediction of battery health state have attracted wide attention in academic circles. In this paper, the degradation mechanism and main definitions of state of health (SOH) were described by summarizing domestic and foreign literatures. The estimation and prediction methods of lithium-ion power battery SOH were discussed from three aspects: model-based methods, data-driven methods, and fusion technology methods. This review summarizes the advantages and disadvantages of the current mainstream SOH estimation and prediction methods. This paper believes that more innovative feature parameter extraction methods, multi-algorithm coupling, combined with cloud platform and other technologies will be the development trend of SOH estimation and prediction in the future, which provides a reference for health state estimation and prediction of lithium-ion power battery.
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Jia, Guan, and Wu. "A State of Health Estimation Framework for Lithium-Ion Batteries Using Transfer Components Analysis." Energies 12, no. 13 (June 30, 2019): 2524. http://dx.doi.org/10.3390/en12132524.

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As different types of lithium batteries are increasingly employed in various devices, it is crucial to predict the state of health (SOH) of lithium batteries. There are plenty of methods for SOH estimation of a lithium-ion battery. However, existing technologies often have computational complexity. Furthermore, it is difficult to use least the previous 30% of data of the battery degradation process to predict the SOH variation of the entire degradation process. To address this problem, in this paper, the SOH of the target battery is estimated based on the transfer of different battery data sets. Firstly, according to importance sampling (IS), valid features are extracted from cycles of charging voltage in both the source and target battery. Secondly, transfer component analysis (TCA) is used to map the source data set to the target data set. Moreover, an extreme learning machine (ELM) algorithm is employed to train a single hidden layer feed forward neural network (SLFN) for its fast training speed and facile to set up. Finally, validation experiments and the comparisons on the results are conducted. The results showed that the proposed framework has a good capability of predicting the SOH of lithium batteries.
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Yang, Yanru, Jie Wen, Yuanhao Shi, and Jianchao Zeng. "State of Health Prediction of Lithium-Ion Batteries Based on the Discharge Voltage and Temperature." Electronics 10, no. 12 (June 21, 2021): 1497. http://dx.doi.org/10.3390/electronics10121497.

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Accurate state of health (SOH) prediction of lithium-ion batteries is essential for battery health management. In this paper, a novel method of predicting the SOH of lithium-ion batteries based on the voltage and temperature in the discharging process is proposed to achieve the accurate prediction. Both the equal voltage discharge time and the temperature change during the discharge process are regarded as health indicators (HIs), and then, the Pearson and Spearman relational analysis methods are applied to evaluate the relevance between HIs and SOH. On this basis, we modify the relevance vector machine (RVM) to a multiple kernel relevance vector machine (MKRVM) by combining Gaussian with sigmoid function to improve the accuracy of SOH prediction. The particle swarm optimization (PSO) is used to find the optimal weight and kernel function parameters of MKRVM. The aging data from NASA Ames Prognostics Center of Excellence are used to verify the effectiveness and accuracy of the proposed method in numerical simulations, whose results show that the MKRVM method has higher SOH prediction accuracy of lithium-ion batteries than the relevant methods.
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Qu, Shaofei, Yongzhe Kang, Pingwei Gu, Chenghui Zhang, and Bin Duan. "A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis." Energies 12, no. 17 (August 29, 2019): 3333. http://dx.doi.org/10.3390/en12173333.

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Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time.
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Che, Yunhong, Aoife Foley, Moustafa El-Gindy, Xianke Lin, Xiaosong Hu, and Michael Pecht. "Joint Estimation of Inconsistency and State of Health for Series Battery Packs." Automotive Innovation 4, no. 1 (January 8, 2021): 103–16. http://dx.doi.org/10.1007/s42154-020-00128-8.

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AbstractBattery packs are applied in various areas (e.g., electric vehicles, energy storage, space, mining, etc.), which requires the state of health (SOH) to be accurately estimated. Inconsistency, also known as cell variation, is considered a significant evaluation index that greatly affects the degradation of battery pack. This paper proposes a novel joint inconsistency and SOH estimation method under cycling, which fills the gap of joint estimation based on the fast-charging process for electric vehicles. First, fifteen features are extracted from current change points during the partial charging process. Then, a joint estimation system is designed, where fusion weights are obtained by the analytic hierarchy process and multi-scale sample entropy to evaluate inconsistency. A wrapper is used to select the optimal feature subset, and Gaussian process regression is implemented to estimate the SOH. Finally, the estimation performance is assessed by the test data. The results show that the inconsistency evaluation can reflect the aging conditions, and the inconsistency does affect the aging process. The wrapper selection method improves the accuracy of SOH estimation by about 75.8% compared to the traditional filter method when only 10% of data is used for model training. The maximum absolute error and root mean square error are 2.58% and 0.93%, respectively.
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Lee, Jong-Hyun, and In-Soo Lee. "Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result." Energies 14, no. 15 (July 26, 2021): 4506. http://dx.doi.org/10.3390/en14154506.

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Lithium batteries are the most common energy storage devices in items such as electric vehicles, portable devices, and energy storage systems. However, if lithium batteries are not continuously monitored, their performance could degrade, their lifetime become shortened, or severe damage or explosion could be induced. To prevent such accidents, we propose a lithium battery state of health monitoring method and state of charge estimation algorithm based on the state of health results. The proposed method uses four neural network models. A neural network model was used for the state of health diagnosis using a multilayer neural network model. The other three neural network models were configured as neural network model banks, and the state of charge was estimated using a multilayer neural network or long short-term memory. The three neural network model banks were defined as normal, caution, and fault neural network models. Experimental results showed that the proposed method using the long short-term memory model based on the state of health diagnosis results outperformed the counterpart methods.
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Dissertations / Theses on the topic "State of health (SOH)"

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Samolyk, Mateusz, and Jakub Sobczak. "Development of an algorithm for estimating Lead-Acid Battery State of Charge and State of Health." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2937.

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In this paper, a state of charge (SOC) and a state of health (SOH) estimation method for lead-acid batteries are presented. In the algorithm the measurements of battery’s terminal voltage, current and temperature are used in the process of SOC calculation. The thesis was written in cooperation with Micropower AB. The algorithm was designed to fulfill the specific requirements of the electric vehicles application: an error below 5% of SOC, computational simplicity and the possibility of being implemented in a basic programming languages. The current used method at Micropower, Coulomb counting, is compared with a method presented by Chiasson and Vairamohan 2005 based on modified Thevein circuit during charging and discharging of the battery. The Thevenin based method gave better result compared to Coulomb counting but seems not to fulfill Micropowers requirements. A correction method based on periods of no charging or discharging, possible to be used together with Coulomb counting as well as with the Thevenin method was developed. The evaluation method indicates that when using also the correction method the Micropowers requirements are fulfilled.
I detta papper, är ett laddningstillstånd (SOC) och hälsotillstånd (SOH) skattningsmetod för blybatterier presenteras. I algoritmen mätningarna av batteriets polspänning, ström och temperatur används i processen för SOC beräkning. Avhandlingen är skriven i samarbete med Micropower AB. Algoritmen har utformats för att uppfylla de särskilda kraven för elektriska fordon: ett fel under 5% av SOC, computational enkelhet och möjligheten att genomföras i ett grundläggande programmeringsspråk. Den nuvarande metoden vid Micropower, Coulomb räkning, jämförs med en metod som presenteras av Chiasson och Vairamohan 2005 baserad på modifierad Thevein kretsen under laddning och urladdning av batteriet.
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Fairweather, Andrew James. "State-of-Health (SoH) and State-of-Charge (SoC) determination in electrochemical batteries and cells using designed perturbation signals." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/9156/.

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Pola, Contreras Daniel. "An improved prognosis strategy with temperature dependent state-space model for the analysis of the state-of-health and state-for-charge in lithium-ion batteries." Tesis, Universidad de Chile, 2014. http://www.repositorio.uchile.cl/handle/2250/130476.

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Magíster en Ciencias de la Ingeniería, Mención Eléctrica
Ingeniero Civil Eléctrico
Actualmente existe una gran cantidad de equipos y dispositivos que utilizan baterías como su fuente primaria o secundaria de energía. Para estos sistemas es crítico contar con información del desempeño de sus baterías, dado que este conocimiento puede ayudar a tomar decisiones apropiadas y asegurar autonomía en el tiempo. Dos importantes variables que deben ser monitoreadas son el "Estado-de-Salud" (SOH, del inglés State-of-Health) y el "Estado-de-Carga" (SOC, del inglés State-of-Charge). Este trabajo se enfoca en generar esquemas de pronóstico para ambas variables, donde se tome en cuenta la temperatura de operación. Con este propósito, se diseñaron y realizaron un conjunto de pruebas de laboratorio con celdas de Ion-Litio donde se caracterizó el impacto de la temperatura en factores tales como la energía entregada en un ciclo, la impedancia interna, o tendencia de degradación. A partir de estos datos, y esquemas existentes en la literatura, se proponen modelos empíricos para la degradación y para la descarga de una batería mediante una representación de espacio-estados, definiendo directamente un estado como el SOH y el SOC respectivamente. Las estimaciones y predicciones a largo plazo se efectúan bajo un enfoque Bayesiano, basado en el filtro de partículas. Además, se propone la implementación de lazos de control externos para corregir condiciones iniciales erróneas de los estados, y un módulo de detección de outliers para trabajar con datos perdidos o inválidos. La validación de estos esquemas se realiza con datos generados en laboratorio, además de datos de degradación publicados por NASA Ames Prognostic Center of Excellence. El esquema propuesto para el SOH es capaz de incorporar explícitamente el efecto de la temperatura de operación (bajo el concepto de "Capacidad Usable"), y estimar y pronosticar el SOH a una temperatura de referencia. Por otro lado, el esquema para el SOC fue validado incluyendo una mejor representación de la fenomenología del proceso de descarga comparada a la existente, y se deja una propuesta de cómo incluir el efecto de la temperatura en el modelo. La implementación de estos esquemas de pronóstico permite la incorporación de la temperatura de operación, que a pesar de su gran influencia en el comportamiento de las baterías es considerada constante en muchos casos presentes en la literatura; además de incluir algunas mejoras prácticas en los algoritmos de estimación. Las propuestas de este trabajo dejan las bases para avanzar en la incorporación de otros fenómenos importantes como la profundidad de descarga, o la magnitud de la corriente de descarga.
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Quintero, Cedeño Vanessa Lisbeth. "Design of a medium-access-control protocol for wireless sensor networks considering the battery state of charge and state of health." Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/170130.

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Tesis para optar al grado de Doctora en Ingeniería Eléctrica
La disponibilidad de energía es una de las limitaciones que presentan las Redes de Sensores Inalámbricas (WSN, Wireless Sensor Network). Tradicionalmente, las baterías han sido utilizadas para proveer energía a los nodos de sensores y al tener una vida útil limitada afectan el tiempo de vida de la red. Soluciones como el uso de baterías de gran tamaño o el reemplazo de ellas no son viables, debido al gran número de sensores que componen la red y a que pueden ser desplegados en zonas de difícil acceso. Esta situación ha motivado que las soluciones para la conservación de la energía en las WSNs se enfoquen en el desarrollo de técnicas que actúen a nivel de las capas física y de enlace de datos, como es el caso de los protocolos de Control de Acceso al Medio (MAC, Medium Access Control). Los protocolos MAC son una de las soluciones ampliamente estudiadas y utilizadas porque permiten un equilibrio entre la conservación de energía y otros parámetros críticos de la red, como el rendimiento, latencia, reducción de colisiones y mensajes de control. También tienen la facilidad de adaptarse a las nuevas aristas de trabajo que surgen al incorporar nuevas tecnologías como lo son los Dispositivos de Recolección de Energía (EHD, Energy Harvesting Device). Otro aspecto que está siendo considerado y estudiado en el diseño de los protocolos MAC es la información que se puede extraer de la batería, ya que al estimar la capacidad disponible de la misma, el mecanismo del Duty Cycling (DuC) puede ser ajustado con el propósito de aumentar la eficiencia energética y por lo tanto, extender la vida útil de la red. Es necesario desarrollar técnicas que incorporen un mecanismo de conservación de energía que integre información de la batería a través de indicadores como el Estado de Carga (SOC, State of Charge) y Estado de Salud (SOH, State of Health) para mejorar la eficiencia energética en WSN. La idea de incorporar información de la batería se debe a que la capa MAC está a cargo de controlar los modos de operación del nodo sensor, lo que está directamente relacionado con la cantidad de corriente exigida a la batería. Conocidos los perfiles de uso de la batería es posible estimar los indicadores SOC y SOH que se han utilizado ampliamente en diversas aplicaciones para conocer la cantidad de energía disponible en la batería y la degradación que ha sufrido la misma. En este trabajo se propuso el desarrollo de un protocolo que actúa en la subcapa MAC y que considera la información de la batería para tomar decisiones con respecto al tiempo activo y de reposo del nodo de sensor, con la finalidad de promover el uso eficiente de la energía y extender la vida útil de la red. Los resultados obtenidos validan esta nueva propuesta de algoritmo y establecer pautas para guiar el diseño de protocolos MAC que se centren en minimizar el consumo de energía teniendo en cuenta los dispositivos de recolección de energía y la información de la batería.
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Lièvre, Aurélien. "Développement d'un système de gestion de batterie lithium-ion à destination de véhicules "mild hybrid" : détermination des indicateurs d'état (SoC, SoH et SoF)." Thesis, Lyon 1, 2015. http://www.theses.fr/2015LYO10065/document.

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Les véhicules hybrides se démocratisent avec une utilisation croissante des éléments de stockage à base de lithium-ion. Dans ce contexte d'exploitation, le type d'usage est atypique et dépend fortement des stratégies de répartition des énergies au sein du véhicule. Parmi les hybridations, la catégorie "mild hybrid" conserve la motorisation thermique pour l'autonomie qu'elle apporte, et lui adjoint une machine électrique associée à un élément de stockage réversible, afin de permettre une récupération de l'énergie cinétique du véhicule. L'objet de ces travaux porte sur la mise en place d'algorithmes destinés à la détermination des états de charge (SoC), de santé (SoH) et de fonction (SoF) de chacune des cellules qui compose un pack batterie lithium-ion. Ces fonctionnalités sont implantées dans un système de gestion dénommé BMS pour Battery Management System. Dans un souci de réduction des coûts de production, nos travaux s'attachent à limiter la puissance de calcul et les moyens de mesure nécessaires à la détermination de ces états. À partir de mesures effectuées lors d'une utilisation de la batterie dans une application "mild hybrid", les méthodes développées permettent la détermination des états, ainsi que d'une partie des paramètres internes aux cellules. Cette utilisation est caractérisée par de forts courants et un maintien de l'état de charge autour de 50 %, ceci afin de maximiser la disponibilité de la batterie et d'en minimiser le vieillissement. L'utilisation d'observateurs et de méthodes en boucle ouverte, à partir d'une modélisation simplifiée de cellule, nous permet d'obtenir des résultats satisfaisants avec une puissance de calcul réduite
Hybrid vehicles are developing with increasing use of energy storage elements based on lithium-ion battery. In this context, the use of battery is atypical and highly dependent on energy allocation strategies within the vehicle. Among these vehicles, the mild hybrid category retains heat engine for the autonomy that offer and adds to it an electric machine associated with a reversible storage system, to allow the kinetic energy recovery of the vehicle. The object of this work involves the development of algorithms for determining the states of charge (SoC) and health (SoH) and function (SoF) of each cell that compose a lithium-ion battery pack. These features are implemented in a Battery Management System (BMS) for industrial production. In order to reduce production costs, our work attempts to limit the computing power and the measuring sensors necessary for these states determination. From battery measurements in a "mild hybrid" use, developed methods allow the states determination, as well as some of the internal parameters of cells. This application is characterized by high currents and maintaining a SoC of around 50%, in order to maximize the availability of the battery and to minimize aging. The use of observers and estimators, using a simplified model cell, allows us to achieve satisfactory results with a reduced computing power
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Dyantyi, Noluntu. "Factors influencing fuel cell life and a method of assessment for state of health." University of the Western Cape, 2018. http://hdl.handle.net/11394/6753.

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Philosophiae Doctor - PhD
Proton exchange membrane fuel cells (PEMFC) converts chemical energy from the electrochemical reaction of oxygen and hydrogen into electrical while emitting heat, oxygen depleted air (ODA) and water as by-products. The by-products have useful functions in aircrafts, such as heat that can be used for ice prevention, deoxygenated air for fire retardation and drinkable water for use on board. Consequently, the PEMFC is also studied to optimize recovery of the useful products. Despite the progress made, durability and reliability remain key challenges to the fuel cell technology. One of the reasons for this is the limited understanding of PEMFC behaviour in the aeronautic environment. The aim of this thesis was to define a comprehensive non-intrusive diagnostic technique that provides real time diagnostics on the PEMFC State of Health (SoH). The framework of the study involved determining factors that have direct influence on fuel cell life in aeronautic environment through a literature survey, examining the effects of the factors by subjecting the PEMFC to simulated conditions, establishing measurable parameters reflective of the factors and defining the diagnostic tool based on literature review and this thesis finding.
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Urbain, Matthieu. "Modélisation électrique et énergétique des accumulateurs Li-Ion. Estimation en ligne de la SOC et de la SOH." Thesis, Vandoeuvre-les-Nancy, INPL, 2009. http://www.theses.fr/2009INPL028N/document.

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Ce mémoire traite de la modélisation électrique des accumulateurs lithium-ion, de l’estimation de leur état de charge (SOC) et de leur état de santé (SOH). Le premier chapitre revient sur les généralités concernant la technologie lithium-ion : caractéristiques, performances, constitution de l’élément de stockage, choix et nature des électrodes, conséquences qui en découlent d’un point de vue énergétique. Le principe de fonctionnement et les équations générales des phénomènes électrochimiques sont aussi développés. Des exemples d’application dans différents secteurs industriels sont ensuite proposés pour plusieurs gammes de puissance et d’énergie. Le second volet aborde la modélisation électrique des accumulateurs lithium-ion. Pour une meilleure compréhension des phénomènes complexes mis en jeu au sein des batteries, des éléments de modélisation physique sont exposés. Puis nous envisageons une synthèse des différents modèles de nature électrique rencontrés dans la littérature. Sur la base de campagnes de mesures menées sur un élément lithium-ion de 6,8 Ah, nous proposons, dans un troisième chapitre, notre propre modèle électrique équivalent valable pour les phases de décharge et de relaxation. En particulier nous déclinons plusieurs solutions pour distribuer l’énergie et rendre compte des différents effets de ligne. Les outils de caractérisation et les procédures d’extractions des paramètres sont traités en détail. Dans un dernier chapitre nous étudions les possibilités d’estimer en ligne l’état de charge (SOC) et l’état de santé (SOH) d’un élément lithium-ion en cours d’exploitation. Après un bref rappel des méthodes académiques et industrielles actuelles, nous nous orientons vers l’emploi d’un filtre de Kalman. Afin d’estimer ses performances par rapport au coulombmètre, nous proposons un modèle et un algorithme que nous évaluons par simulation et testons sur élément réel
This dissertation of thesis deals with the electrical modelling of lithium-ion accumulators and the determination of both state-of-charge (SOC) and state-of-health (SOH). The first chapter is focused on generalities about lithium-ion technology: characteristics, qualities, constitution of the storage device, choice and nature of the electrodes and their consequences on energetical features. The principle and the general equations of the electrochemical phenomena are developed as well. Application examples from different industrial areas are displayed for several power and energy ranges. The second section is about the electrical modelling of lithium-ion accumulators. With a view to better understand the complex electrochemical phenomena, elements of physical modelling are proposed. Then, the synthesis of different electrical models released in the press is considered. On the basis of experimental campaigns lead on a 6.8 Ah lithium-element, we proposed, in a third chapter, our own equivalent electrical model suitable for both discharge phases and relaxation period. In particular, we depict several alternatives to distribute the energy and describe the different line effects. Both characterization tools and parameters extraction procedure are clearly detailed. In the last section, we tackle both SOC and SOH on-line determination. After a short review of academicals and industrial solutions, we rapidly head towards the use of a Kalman filter. In order to compare its features versus the coulombmeter, we propose a model and an algorithm, numerical simulations and experimental tests are performed
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Ovejas, Benedicto Victòria Júlia. "Determination of the state of health of Li-ion batteries : the irreversible entropy production approach." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/461681.

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In recent years, portable applications have experienced an exponential growth and consequently, the demand of batteries has increased accordingly. It is widely known, though, that the performance of batteries decreases with time and use. This loss of performance is easured by the State-of-Health (SoH) of the cells. However, there is no consensus in defining this parameter. Experimental, theoretical or even heuristic approaches can be found in literature and commercial systems, but usually, they only work for particular conditions and they are not linked to the degradation suffered by the cells themselves. The aim of this study is to find a parameter directly related to this degradation. For this purpose, we investigate the irreversible entropy production in Li-ion cells because irreversible entropy is related to energy dissipation and thus, to irrversibilities due to system or energy degradation. In order to evaluate the degradation of the cells and its correspondence to irreversible entropy generation, we studied different Li-ion chemistries (NMC, LFP and LCO). Batteries were cycled at different discharge rates (close to and far from equilibrium) and evaluated at different SoHs. Therefore, capacity fade and impedance rise (the most commonly used techniques in SoH determination) were characterized and related to irreversible entropy generation. In addition, post-mortem analysis was carried out to achieve a deeper knowledge of the causes and effects of degradation. As a result of this study, we introduced a new parameter for system degradation characterization, the Relative-Entropy-Production (REP), defined as the irreversible entropy generation ratio at actual state and the initial state. In particular, we found irreversible entropy production evaluated at low discharge rates was higher as more degraded were the NMC cells. In the case of LFP cells, irreversible entropy production decreased during initial cycles but then increased towards the EoL. This behavior coincided with a capacity increase during initial cycles. In addition, we found a relationship between irreversible entropy generation and the phase transformations taking place during the discharge processes in all the evaluated cells because the materials undergoing phase transformations expand and contract yielding to cracks and other structural. Irreversible entropy production is found to be a promising magnitude to characterize battery aging. Even though much research has still to be carried out, the idea is to define, in the future, a threshold in irreversible entropy production that the cells can stand before considering their EoL is reached.
En els darrers anys, la demanda de bateries ha augmentat considerablement gràcies a la creixent proliferació de dispositius portàtils. Tot i això, és ben sabut que el funcionament de les bateries empitjora amb el temps i l'ús. Aquesta pèrdua de rendiment es mesura amb un paràmetre anomenat State-oh-Health (SoH) encara que, avui dia, no s'ha arribat a un consens per a definir-lo. A la literatura o als mateixos sistemes comercials s'hi poden trobar aproximacions experimentals, teòriques o heurístiques, que generalment funcionen en situacions particulars i que, moltes sovint, no estan directament relacionades amb la degradació que pateixen les cel·les. L'objectiu d'aquest estudi és trobar un paràmetre que estigui directament relacionat amb la degradació patida per les cel·les. Per aquest motiu, ens hem centrat en la producció d'entropia irreversible perquè aquesta està relacionada amb la dissipació d'energia i, per tant, amb les irreversibilitats degudes a la degradació del sistema o de l'energia. Es va treballar amb vàries químiques de bateries d'ions de liti (NMC, LFP i LCO) per tal d’avaluar la degradació patida per aquestes i la correspondència amb la generació d'entropia irreversible. Aquestes cel·les van ser avaluades a taxes baixes i elevades a diferents nivells de SoH. En particular, la disminució de capacitat i l’augment d’impedància, que són les tècniques més utilitzades per a determinar el SoH, van ser determinades i posteriorment relacionades amb la generació d’entropia irreversible. A més a més, l’anàlisi post-mortem de les cel·les ens va permetre obtenir un coneixement major de les causes i els efectes de la degradació. Com a resultat d’aquest estudi, hem introduït un nou paràmetre per a la caracterització de la degradació d’un sistema. Aquest paràmetre l’hem anomenat Relative-Entropy-Production (REP) i l’hem definit com la relació entre la generació d’entropia irreversible en el moment actual i l’estat inicial. En particular, hem trobat que la producció d’entropia irreversible a taxes baixes de descàrrega és més gran com més degradades estan les cel·les de NMC. En canvi, en el cas de les cel·les de LFP, hem trobat que la generació d’entropia irreversible disminueix durant els primers cicles per després augmentar fins al final de la seva vida útil. S’ha vist que aquesta disminució coincideix amb un increment de la capacitat. A més a més, a totes les cel·les amb les que hem treballat, hem trobat una relació entre la producció d’entropia irreversible i les transformacions de fase que tenen lloc als elèctrodes durant la descàrrega. Aquesta relació ha sigut associada al fet de que els materials que pateixen una canvi de fase s’expandeixen i es contrauen el que fa que es produeixin fractures o esquerdes o altres modificacions estructurals. Totes elles produeixen degradació i, per tant, generen entropia irreversible. S’ha trobat que REP i la generació d’entropia irreversible són magnituds prometedores per a caracteritzar l’envelliment de bateries. Encara que queda molta feina per fer, la idea és, en un futur, poder definir un llindar de REP o de generació d’entropia irreversible que les cel·les siguin capaces de suportar abans no es consideri que han assolit el final de les seves vides útils.
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Nazer, Rouba Al. "Système de mesure d'impédance électrique embarqué, application aux batteries Li-ion." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT007/document.

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La mesure d'impédance électrique en embarqué sur véhicule est un sujet clé pour améliorer les fonctions de diagnostic d'un pack batterie. On cherche en particulier à fournir ainsi des mesures supplémentaires à celles du courant pack et des tensions cellules, afin d'enrichir les indicateurs de vieillissement dans un premier temps, et d'état de santé et de charge dans un second temps. Une méthode classique de laboratoire pour obtenir des mesures d'impédance d'une batterie est la spectroscopie d'impédance électrochimique (ou EIS). Elle consiste à envoyer un signal sinusoïdal en courant (ou tension) de fréquence variable balayant une gamme de fréquences d'intérêt et mesurer ensuite la réponse en tension (ou courant) pour chaque fréquence. Une technique d'identification active basée sur l'utilisation des signaux large bande à motifs carrés est proposée. En particulier, des simulations ont permis de comparer les performances d'identification de différents signaux d'excitation fréquemment utilisés dans le domaine de l'identification et de vérifier les conditions correspondant à un comportement linéaire et invariant dans le temps de l'élément électrochimique. L'évaluation de la qualité d'estimation est effectuée en utilisant une grandeur spécifique : la cohérence. Cette grandeur statistique permet de déterminer un intervalle de confiance sur le module et la phase de l'impédance estimée. Elle permet de sélectionner la gamme de fréquence où la batterie respecte les hypothèses imposées par la méthode d'identification large bande. Afin de valider les résultats, une électronique de test a été conçue. Les résultats expérimentaux permettent de mettre en valeur l'intérêt de cette approche par motifs carrés. Un circuit de référence est utilisé afin d'évaluer les performances en métrologie des méthodes. L'étude expérimentale est ensuite poursuivie sur une batterie Li-ion soumise à un courant de polarisation et à différents états de charge. Des essais comparatifs avec l'EIS sont réalisés. Le cahier de charge établi à l'aide d'un simulateur de batterie Li-ion a permis d'évaluer les performances de la technique large bande proposée et de structurer son utilité pour l'estimation des états de vieillissement et de charge
Embedded electrical impedance measurement is a key issue to enhance battery monitoring and diagnostic in a vehicle. It provides additional measures to those of the pack's current and cell's voltage to enrich the aging's indicators in a first time, and the battery states in a second time. A classical method for battery impedance measurements is the electrochemical impedance spectroscopy (EIS). At each frequency, a sinusoidal signal current (or voltage) of a variable frequency sweeping a range of frequencies of interest is at the input of the battery and the output is the measured voltage response (or current). An active identification technique based on the use of wideband signals composed of square patterns is proposed. Particularly, simulations were used to compare the performance of different excitation signals commonly used for system identification in several domains and to verify the linear and time invariant behavior for the electrochemical element. The evaluation of the estimation performance is performed using a specific quantity: the spectral coherence. This statistical value is used to give a confidence interval for the module and the phase of the estimated impedance. It allows the selection of the frequency range where the battery respects the assumptions imposed by the non-parametric identification method. To experimentally validate the previous results, an electronic test bench was designed. Experimental results are used to evaluate the wideband frequency impedance identification. A reference circuit is first used to evaluate the performance of the used methodology. Experimentations are then done on a Li–ion battery. Comparative tests with EIS are realized. The specifications are established using a simulator of Li-ion battery. They are used to evaluate the performance of the proposed wide band identification method and fix its usefulness for the battery states estimation: the state of charge and the state of health
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Abdennadher, Mohamed Karim. "Étude et élaboration d’un système de surveillance et de maintenance prédictive pour les condensateurs et les batteries utilisés dans les Alimentations Sans Interruptions (ASI)." Thesis, Lyon 1, 2010. http://www.theses.fr/2010LYO10101/document.

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Pour assurer une énergie électrique de qualité et de façon permanente, il existe des systèmes électroniques d’alimentation spécifiques. Il s’agit des Alimentations Sans Interruptions (ASI). Une ASI comme tout autre système peut tomber en panne ce qui peut entrainer une perte de redondance. Cette perte induit une maintenance corrective donc une forme d’indisponibilité ce qui représente un coût. Nous proposons dans cette thèse de travailler sur deux composants parmi les plus sensibles dans les ASI à savoir les condensateurs électrolytiques et les batteries au plomb. Dans une première phase, nous présentons, les systèmes de surveillance existants pour ces deux composants en soulignant leurs principaux inconvénients. Ceci nous permet de proposer le cahier des charges à mettre en œuvre. Pour les condensateurs électrolytiques, nous détaillons les différentes étapes de caractérisation et de vieillissement ainsi que la procédure expérimentale de vieillissement standard accéléré et les résultats associés. D’autre part, nous présentons les résultats de simulation du système de surveillance et de prédiction de pannes retenu. Nous abordons la validation expérimentale en décrivant le système développé. Nous détaillons les cartes électroniques conçues, les algorithmes mis en œuvre et leurs contraintes d’implémentation respectifs pour une réalisation temps réel. Enfin, pour les batteries au plomb étanches, nous présentons les résultats de simulation du système de surveillance retenu permettant d’obtenir le SOC et le SOH. Nous détaillons la procédure expérimentale de vieillissement en cycles de charge et décharge de la batterie nécessaire pour avoir un modèle électrique simple et précis. Nous expliquons les résultats expérimentaux de vieillissement pour finir avec des propositions d’amélioration de notre système afin d’obtenir un SOH plus précis
To ensure power quality and permanently, some electronic system supplies exist. These supplies are the Uninterrupted Power Supplies (UPS). An UPS like any other system may have some failures. This can be a cause of redundancy loss. This load loss causes a maintenance downtime which may represent a high cost. We propose in this thesis to work on two of the most sensitive components in the UPS namely electrolytic capacitors and lead acid batteries. In a first phase, we present the existing surveillance systems for these two components, highlighting their main drawbacks. This allows us to propose the specifications which have to be implemented for this system. For electrolytic capacitors, we detail different stages of characterization ; the aging accelerated standard experimental procedure and their associated results. On the other hand, we present the simulation results of monitoring and failure prediction system retained. We discuss the experimental validation, describing the developed system. We detail the electronic boards designed, implemented algorithms and their respective constraints for a real time implementation. Finally, for lead acid batteries, we present the simulation results of the monitoring system adopted to obtain the SOC and SOH. We describe the aging experimental procedure of charging and discharging cycles of the batteries needed to find a simple and accurate electric models. We explain the aging experimental results and in the end we give suggestions for improving our system to get a more accurate SOH
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Books on the topic "State of health (SOH)"

1

Council, North Dakota State Health. State health plan. Bismarck, ND: State Dept. of Health and Consolidated Laboratories, 1990.

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Council, New York Statewide Health Coordinating. State health plan. [Albany: New York Statewide Health Coordinating Council, 1986.

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Hawaii. Department of Health. Report to the twentieth Legislature, State of Hawaii, 2000 on act 192, SLH 1999 requesting the Department of Health to conduct soil sample surveys as part of an epidemiologic investigation. Hawaii: The Department, 1999.

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Maine. Governor. Office of Health Policy & Finance. Maine's state health plan. [Augusta, Me.]: Governor's Office of Health Policy & Finance, 2004.

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New York (State). Dept. of Health. Division of Planning, Policy, and Resource Development., ed. State health plan, 1986. [Albany]: The Council, 1987.

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Eastern Health and Social Services Board, Northern Ireland. Area Department of Community Medicine. State of children's health. [Belfast]: [EHSSB], 1989.

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Services, Iowa Maternal and Child Health. State plan. Iowa City, Iowa: University Hospital School, University of Iowa, 1992.

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Hawaii. Maternal and Child Health Branch. Report to the Twenty-First Legislature, State of Hawaii, 2002: In compliance with Act 216, SLH 1997, relating to fees collected by the Department of Health for the Domestic Violence Prevention Special Fund. Honolulu, Hawaii: Dept. of Health, 2001.

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Ohio. Statewide Health Coordinating Council. The Ohio state health plan: Family health component. [Columbus, Ohio]: The Council, 1985.

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Phillips, Luci. Washington State rural health databook. Olympia, Wash: Washington State Dept. of Health, 1997.

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Book chapters on the topic "State of health (SOH)"

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Haugan, Gørill, and Monica Eriksson. "Future Perspectives of Health Care: Closing Remarks." In Health Promotion in Health Care – Vital Theories and Research, 375–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63135-2_26.

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AbstractThe Covid-19 pandemic has demonstrated the vulnerability of our health care systems as well as our societies. During the year of 2020, we have witnessed how whole societies globally have been in a turbulent state of transformation finding strategies to manage the difficulties caused by the pandemic. At first glance, the health promotion perspective might seem far away from handling the serious impacts caused by the Covid-19 pandemic. However, as health promotion is about enabling people to increase control over their health and its determinants, paradoxically health promotion seems to be ever more important in times of crisis and pandemics. Probably, in the future, pandemics will be a part of the global picture along with the non-communicable diseases. These facts strongly demand the health care services to reorient in a health promoting direction.The IUHPE Global Working Group on Salutogenesis suggests that health promotion competencies along with a reorientation of professional leadership towards salutogenesis, empowerment and participation are required. More specifically, the IUHPE Group recommends that the overall salutogenic model of health and the concept of SOC should be further advanced and applied beyond the health sector, followed by the design of salutogenic interventions and change processes in complex systems.
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Hu, Yi. "A Nation-State? A Democratic State?" In Rural Health Care Delivery, 237–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39982-4_23.

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Aspevig, James. "State Public Health Informatics: Perspective from a Low Population State." In Health Informatics, 555–72. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4237-9_28.

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Liebow, Mark, and Tina Liebling. "State-Level Advocacy." In Health Care Advocacy, 93–99. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6914-9_9.

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Reisman, David. "The State Sector." In Market and Health, 109–25. London: Palgrave Macmillan UK, 1993. http://dx.doi.org/10.1007/978-1-349-22958-1_6.

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Abbott, Malcolm. "Health care." In Markets and the State, 225–38. First Edition. | New York : Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781351215626-16.

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Wang, Jianmin, Zhe Li, Xiao Li, and Youyi Zhao. "A Novel SOH Prediction Framework for the Lithium-ion Battery Using Echo State Network." In Neural Information Processing, 438–45. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12637-1_55.

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Greve, Bent. "Health care." In Welfare and the Welfare State, 191–202. Second edition. | Abingdon, Oxon ; New York, NY : Routledge, 2020.: Routledge, 2019. http://dx.doi.org/10.4324/9780429341199-11.

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Reisman, David. "State Regulation." In The Political Economy of Health Care, 161–84. London: Palgrave Macmillan UK, 1993. http://dx.doi.org/10.1057/9780230378308_9.

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Brunton, Deborah. "Health and the state." In Medicine in Modern Britain 1780-1950, 99–114. Milton Park, Abingdon, Oxon; New York, NY:: Routledge, 2018. http://dx.doi.org/10.4324/9780429488504-7.

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Conference papers on the topic "State of health (SOH)"

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Zhao, Xiaowei, Guoyu Zhang, and Lin Yang. "A Strategy for Estimating State-of-Charge and State-of-Health of Li-Ion Batteries in Electric and Hybrid Electric Vehicles." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-87324.

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A task that has to be solved for the application of batteries in vehicles with an electric drive train is the determination of the actual state-of-health (SOH) and state-of-charge (SOC) of the battery cells. In this paper, an on board strategy for estimating SOC and SOH of Li-ion batteries is proposed. The equivalent circuit model is used for both SOC and SOH estimations. In SOH algorithm, the estimated value of battery capacity not only reflects the aging degree of battery pack, but also provides information for SOC estimation. Meanwhile, the extended Kaiman filtering is used in SOC estimation. Because the performance of the equivalent circuit model will be better at small currents than at high currents, extended Kaiman filtering is substituted by Ampere-Hour counting when the absolute value of current is greater than a calibration value. The Digatron battery tester was used to evaluate the proposed estimation method, and results show that the estimation method has high accuracy and efficiency at ordinary temperatures.
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Lindsay, Steve, and Diane M. Woodbridge. "Spacecraft State-of-health (SOH) Analysis via Data Mining." In SpaceOps 2014 Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2014. http://dx.doi.org/10.2514/6.2014-1733.

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Zhou, Xin, Jeffrey L. Stein, and Tulga Ersal. "Battery State of Health Monitoring by Estimation of the Number of Cyclable Li-Ions." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9730.

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This paper introduces a method to monitor battery state of health (SOH) by estimating the number of cyclable Li-ions, a health-relevant electrochemical variable. SOH monitoring is critical to battery management in particular for balancing the trade-off between maximizing system performance and minimizing battery degradation. However, SOH-related electrochemical variables cannot be directly measured non-invasively. Hence, estimation algorithms are needed to track those variables non-destructively while the battery is in use. In this paper, the extended Kalman filter (EKF) is used to estimate the number of cyclable Li-ions as an unknown battery parameter. Simulations are performed using an example parameter set for a hybrid-electric-vehicle battery whose cathode material is LiMn2O4 mixed with other Li-compounds to obtain estimation results under a typical electric vehicle current profile that consists of a 1 C constant current charge mode and a discharge current profile for an electric vehicle subject to the Urban Dynamometer Driving Schedule cycle. The simulations show promising results in estimation of the number of cyclable Li-ions using the EKF under the ideal conditions. Next, robustness of the algorithm under non-ideal conditions (i.e., with SOC estimation error, modeling error, and measurement noise) is analyzed, and it is shown that estimation of the number of cyclable Li-ions using the EKF preserves high accuracy even under these non-ideal conditions. The proposed estimation technique for the number of cyclable Li-ions can also be applied to other parameter sets and batteries with other cathode materials to monitor the SOH change resulting from any degradation mechanism that consumes cyclable Li-ions.
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Lee, Suhak, Jason B. Siegel, Anna G. Stefanopoulou, Jang-Woo Lee, and Tae-Kyung Lee. "Comparison of Individual-Electrode State of Health Estimation Methods for Lithium Ion Battery." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9014.

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It is essential to understand the state-of-health (SOH) of the individual electrode to avoid accelerating degradation of Li-ion battery. Electrode SOH can be quantified based on estimating the capacity and the utilization range of each electrode. Here, we introduce two methods: i) voltage fitting (VF) and ii) peak alignment (PA), and compare their ability to estimate the electrode SOH parameters. Both methods assume the half-cell open-circuit potentials (OCPs) are invariant functions of the stoichio-metric states with the cell aging, which can make the accuracy of the electrode parameter estimation vulnerable to degradation that would cause changes in the half-cell OCP curves. This hypothesis is verified experimentally by applying the two methods to aged cells cycled at high temperature. A discernible misalignment of the peaks is observed in the differential voltage curve from the VF method indicating the estimation result is incorrect, even though it reconstructs the OCV with the small error and estimates the cell capacity accurately. Therefore, it is seen that the lower voltage error and the accurate cell capacity estimate do not necessarily promise a better estimation accuracy for the electrode SOH parameters.
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Bai, Guangxing, and Pingfeng Wang. "A Self-Cognizant Dynamic System Approach for Health Management: Lithium-Ion Battery Case Study." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34560.

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Safe and reliable operation of lithium-ion batteries as major energy storage devices is of vital importance, as unexpected battery failures could result in enormous economic and societal losses. Accurate estimation of the state-of-charge (SoC) and state-of-health (SoH) for an operating battery system, as a critical task for battery health management, greatly depends on the validity and generalizability of battery models. Due to the variability and uncertainties involved in battery design, manufacturing, and operation, developing a generally applicable battery physical model is a big challenge. To eliminate the dependency of SoC and SoH estimation on battery physical models, this paper presents a generic self-cognizant dynamic system approach for lithium-ion battery health management, which integrates an artificial neural network (ANN) with a dual extended Kalman filter (DEKF) algorithm. The ANN is trained offline to model the battery terminal voltages to be used by the DEKF. With the trained ANN, the DEKF algorithm is then employed online for SoC and SoH estimation, where voltage outputs from the trained ANN model are used in DEKF state-space equations to replace the battery physical model. Experimental results are used to demonstrate the effectiveness of the developed self-cognizant dynamic system approach for battery health management.
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Topan, Paris Ali, M. Nisvo Ramadan, Ghufron Fathoni, Adha Imam Cahyadi, and Oyas Wahyunggoro. "State of Charge (SOC) and State of Health (SOH) estimation on lithium polymer battery via Kalman filter." In 2016 2nd International Conference on Science and Technology-Computer (ICST). IEEE, 2016. http://dx.doi.org/10.1109/icstc.2016.7877354.

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Capitaine, Jules-Adrien, and Qing Wang. "Design of a Test Platform for the Determination of Lithium-Ion Batteries State-of-Health." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85829.

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This paper presents a novel design for a test platform to determine the State of Health (SOH) of lithium-ion batteries. The SOH is a key parameter of a battery energy storage system and its estimation remains a challenging issue. The batteries that have been tested are 18650 li-ion cells as they are the most commonly used batteries on the market. The test platform design is detailed from the building of the charging and discharging circuitry to the software. Data acquired from the testing circuitry is stored and displayed in LabView to obtain charging and discharging curves. The resulting graphs are compared to the outcome predicted by the battery datasheets, to verify the platform delivers coherent values. The SOH of the battery is then calculated using a Coulomb Counting method in LabView. The batteries will be discharged through various types of resistive circuits, and the differences in the resulting curves will be discussed. A single battery cell will also be tested over 30 cycles and the decrease in the SOH will be clearly pointed out.
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Pattel, Bibin, Hoseinali Borhan, and Sohel Anwar. "An Evaluation of the Moving Horizon Estimation Algorithm for Online Estimation of Battery State of Charge and State of Health." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-37140.

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Moving Horizon Estimation (MHE) has emerged as a powerful technique for tackling the estimation problems of the state of dynamic systems in the presence of constraints, nonlinearities and disturbances. In this paper, the Moving Horizon Estimation approach is applied in estimating the State of Charge (SoC) and State of Health (SoH) of a battery and the results are compared against those for the traditional estimation method of Extended Kalman Filter (EKF). The comparison of the results show that MHE provides improvement in performance over EKF in terms of different state initial conditions, convergence time, and process and sensor noise variations.
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Lui, Yu Hui, Meng Li, Mohammadkazem Sadoughi, Chao Hu, and Shan Hu. "Physics-Based State of Health Estimation of Lithium-Ion Battery Using Sequential Experimental Design." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-86358.

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State of health (SOH) estimation is a critical yet challenging task due to the complex degradation process of lithium-ion (Li-ion) battery. This paper proposes to combine physics-based modeling of Li-ion battery and sequential design of simulation experiments to build an accurate SOH estimator in a computationally efficient manner. A novel sequential backward optimization process is adopted to build a multivariate Gaussian process model that quantifies three degradation modes in a Li-ion battery cell: loss of lithium inventory and losses of active materials in the positive and negative electrodes. The sequential process for the design of simulation experiments is realized via the use of an acquisition function, the maximization of which gives rise to a new sample point in the design space for the next experiment. The acquisition function achieves an optimal balance between exploration of new regions in the design space with high prediction uncertainty and exploitation of challenging regions with high response nonlinearity. The preliminary results from COMSOL Multiphysics degradation scenario simulations show that the SOH estimator designed with the sequential sampling process can provide faster error decay in degradation estimation when compared to that without the sequential sampling process.
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Lee, Seungchul, Harry Cui, Mohammad Rezvanizaniani, and Jun Ni. "Battery Prognostics: SoC and SoH Prediction." In ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/msec2012-7345.

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Battery applications (computer, cell phones or even in cars) have been extensively used in our daily life. The reasons for their success and extensive usage in the real world applications are their light weight, smaller sizes and greater energy densities. These unique characteristics render this class of battery an ideal candidate for powering electrical vehicles. However, due to lack of battery information, often time we will observe machine down time, operation malfunctioning, and even some catastrophic failure due to fast battery degradation and depletion. Thus, much of the attention has been focused on prognostics and health management of battery technologies for the stated purpose. In this paper, we will present two main algorithms that cannot only estimate a one-step-ahead prediction of the battery state but also can estimate the battery remaining useful life. The first method is the linear prediction error method. The second approach is the neural network algorithms. Both methods can predict the battery information accurately. However, particular algorithm specializes in different area of interest.
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Reports on the topic "State of health (SOH)"

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Abel, Keith H., Ted W. Bowyer, James C. Hayes, Tom R. Heimbigner, Mark E. Panisko, Justin I. McIntyre, and Robert C. Thompson. Ideas and Concepts for Diagnosis of Performance and Evaluation of Data Reliability Based Upon ARSA State-of-Health (SOH) Data. Office of Scientific and Technical Information (OSTI), April 2000. http://dx.doi.org/10.2172/15001063.

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KH Abel, TW Bowyer, JC Hayes, TR Heimbigner, ME Panisko, JI McIntyre, and RC Thompson. Ideas and concepts for diagnosis of performance and evaluation of data reliability based upon ARSA state-of-health (SOH) data. Office of Scientific and Technical Information (OSTI), April 2000. http://dx.doi.org/10.2172/754183.

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Keenan, Teresa A. The State of Hearing Health. AARP Research, February 2019. http://dx.doi.org/10.26419/res.00279.001.

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Keenan, Teresa A. The State of Hearing Health: Annotated Questionnaire. AARP Research, February 2019. http://dx.doi.org/10.26419/res.00279.002.

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McCarthy, Douglas McCarthy, David C. Radley Radley, and Susan L. Hayes Hayes. 2018 Scorecard on State Health System Performance. New York, NY United States: Commonwealth Fund, May 2018. http://dx.doi.org/10.15868/socialsector.30564.

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Horwitz, Jill, and Daniel Polsky. Cross Border Effects of State Health Technology Regulation. Cambridge, MA: National Bureau of Economic Research, January 2014. http://dx.doi.org/10.3386/w19801.

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Gruber, Jonathan. State Mandated Benefits and Employer Provided Health Insurance. Cambridge, MA: National Bureau of Economic Research, December 1992. http://dx.doi.org/10.3386/w4239.

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Pakes, Ariel, Jack Porter, Mark Shepard, and Sophie Calder-Wang. Unobserved Heterogeneity, State Dependence, and Health Plan Choices. Cambridge, MA: National Bureau of Economic Research, July 2021. http://dx.doi.org/10.3386/w29025.

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LoSasso, Anthony, and Thomas Buchmueller. The Effect of the State Children's Health Insurance Program on Health Insurance Coverage. Cambridge, MA: National Bureau of Economic Research, December 2002. http://dx.doi.org/10.3386/w9405.

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Abbadi, Mohammed, Carolyn Alsup, Carolyn Benyshek, Celestine Booth, Fernando Cossich, Juan Cuadrado, Kenneth Dyer, et al. Health Care. The State of the Industry. Spring 2008. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada519423.

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