ELECTRIMACS 2022
Selected Papers – Volume 1
- 2023
- Book
- Editors
- Serge Pierfederici
- Jean-Philippe Martin
- Book Series
- Lecture Notes in Electrical Engineering
- Publisher
- Springer International Publishing
About this book
This book collects a selection of papers presented at ELECTRIMACS 2021, the 14th international conference of the IMACS TC1 Committee, held in Nancy, France, on 16th-19th May 2022. The conference papers deal with modelling, simulation, analysis, control, power management, design optimization, identification and diagnostics in electrical power engineering. The main application fields include electric machines and electromagnetic devices, power electronics, transportation systems, smart grids, renewable energy systems, energy storage like batteries and supercapacitors, fuel cells, and wireless power transfer. The contributions included in Volume 1 will be particularly focused on electrical engineering simulation aspects and innovative applications.
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Table of Contents
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Microgrids and Smart Grids
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Frontmatter
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A Distributed Secondary Control for Autonomous AC Microgrid Based on Photovoltaic and Energy Storage Systems
Sidlawendé V. M. Ouoba, Azeddine Houari, Mohamed MachmoumThe chapter delves into the critical role of Distributed Energy Storage Systems (DESSs) in enhancing the reliability, flexibility, and power quality of microgrids (MGs). It introduces a fully distributed control strategy that addresses the challenges of coordinating DESSs and Renewable Energy Sources (RESs) in an AC microgrid. The proposed method, Adaptive Frequency Droop based on Virtual Power (AFDVP), ensures DESSs SoC synchronization and voltage/frequency regulation. Additionally, it includes algorithms for load shedding and PV active power curtailment, making the system resilient to communication failures. The chapter presents simulation results validating the effectiveness of the proposed strategy, highlighting its potential to maintain stable and reliable MG operation.AI Generated
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AbstractIn this paper, a distributed control is proposed for Distributed Energy Storage Systems (DESSs) and Renewable Energy Sources (RESs) power management in islanded Microgrid (MG). The power management strategy is designed to maintain generation/consumption balance, to ensure State of Charge (SoC) balancing of the DESSs and MG frequency/voltage (f & V) regulation. A fully distributed control without leader-follower strategy is used to manage the power flow between renewable generators, energy storage and consumption (critical and non-critical loads), to balance the SoC of the DESSs and to restore the frequency and voltage to their nominal value only thanks to low bandwidth communication. The strategy framework of the power management set the islanded MG in 04 operations modes (normal mode, PV active power curtailment mode and load shedding and reconnection mode) in order to provide a high quality and reliable power source in the islanded MG. A MATLAB/Simulink simulation is performed with a system of two Batteries Energy Storage Systems (BESSs), three loads (a critical/variable load and two non-critical/constant loads) and photovoltaic (PV) generator, in order to verify the effectiveness and the resilience of the proposed power management method in several operation modes. -
Behavioural Modelling of Multi-MW Hybrid PV/Diesel Modular Power Plant
Sani Moussa Kadri, Brayima Dakyo, Mamadou Baïlo Camara, Yrébégnan Moussa SoroThe chapter delves into the behavioural modelling of a multi-MW hybrid PV/diesel power plant in West Africa, highlighting the need for innovative solutions to improve electricity access in rural and mining areas. By analysing data from an actual gold mining unit in Burkina Faso, the authors develop a behavioural modelling approach that accurately captures the impact of PV production on the power system. The study classifies data into disrupted and normal irradiance frames, establishing linear relationships between PV production and solar irradiance. It also models the performance ratio of the power chain and the fuel consumption of thermoelectric generators under different operating conditions. The validated models demonstrate the potential for significant fuel savings and improved system performance. The chapter concludes by discussing the implications of these findings for the design and operation of hybrid power systems, emphasizing the importance of dynamic smoothing effects and the evaluation of capital and operational costs.AI Generated
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AbstractThis paper deals with the behavioural modelling of a multi-MW PV/Diesel hybrid power plant based on long term monitoring over years. The approach is based on the analysis of production data correlated with solar resources and fuel consumption. The links between PV power and irradiance at different points of energy conversion chain up to the AC point of common coupling are carried out. The adopted methodology is to proceed to a formulation of the behavioural model of PV production considering trends and statistics observations. The first step was data classification targeting causalities and consequences of main disturbances. The second step validates the established models with operating data. The aim is to provide relevant set for scenarios simulation that allows optimal design and energy management for such hybrid plant. -
Simulation and Operation Analysis of a Smart Grid Using Simulink
Alexander Van Waeyenberge, Bruno Canizes, João Soares, Sérgio Ramos, Simon Ravyts, Juliana Chavez, Zita ValeThe chapter delves into the challenges faced by medium voltage distribution networks (MVDN) as they adapt to ecological energy alternatives and the increased demand from electric vehicles. It highlights the need for smart grid (SG) solutions to manage fluctuating power flows from renewable sources. The research employs MATLAB Simulink to model and simulate the SG, providing valuable insights into network behavior and optimal implementation strategies. The proposed model integrates data from an optimal distribution grid operation algorithm, enabling a thorough analysis of network performance over various periods. The use of a variable-step solver and continuous phasor-type simulation ensures both speed and accuracy in the analysis. The findings offer practical solutions for optimizing smart grid operations, making the chapter a valuable resource for professionals in the field.AI Generated
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AbstractChanges will be required to handle the increased power flow in the network as the distribution infrastructure ages and the number of EVs and renewable grows. Designing and operating an intelligent network that reacts to changing power flows to ensure the optimal operation is the most economical option than fortifying the network with heavier cables. This work aims to build a model of a 13 bus medium voltage distribution network with high penetration of distributed energy resources and use it to analyze network conditions. The MATLAB Simulink software is used to model and evaluate the network. The outcomes suggest the model is promising and valid even when renewable generation is at low levels. -
Modelling and Optimization of Power Allocation and Benefit Sharing in a Local Energy Community
Alyssa Diva Mustika, Rémy Rigo-Mariani, Vincent Debusschere, Amaury PachurkaThe chapter delves into the emerging concept of energy communities (EC), which integrate renewable energies and microgrids. It introduces a novel energy allocation strategy based on individual energy prices, aiming to limit the disparity among members. The study presents an optimization-based energy management strategy (EMS) that maximizes self-sufficiency ratio (SSR) and three different keys of repartition (KoR) for energy allocation. A real-world case study in Le Cailar, France, demonstrates the effectiveness of the proposed methodology, showing significant improvements in energy self-sufficiency and fairer energy distribution among community members. The chapter concludes by highlighting the potential for future work in long-term planning and ancillary services at the community level.AI Generated
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AbstractThis paper proposes a strategy for the resources management and power allocation in an energy community. Especially, the fairness of the benefit sharing is assessed thanks to a metric introduced as a monthly net energy price (in c€/kWh) from the viewpoint of each individual and computed as the individual bill over the consumed energy. The community management decouples the operational (i.e., power dispatch) from the settlement phase (i.e., monthly community billing). In particular, the investigated billing approach is based on an optimization process with an additional constraint to limit the gap between the maximum and minimum identified prices over all the community members. This study then provides a new method to better address individual’s need in the community. The results show a narrow range of the individual energy price and 11.5% collective bill reduction compared to a case where the members act individually. -
Social Data to Enhance Typical Consumer Energy Profile Estimation on a National Level
Amr Alyafi, Pierre Cauchois, Benoit Delinchant, Alain BergesThis chapter delves into the challenges of electricity market settlement, particularly the need for accurate consumer energy profile estimation on a national level. It discusses the evolution of load profiling methods, from deterministic equations to dynamic load profiling using smart meter data. The chapter introduces the use of social data, specifically Twitter, to capture events that significantly impact energy consumption. It outlines a method for data collection, treatment, and integration, highlighting the importance of anomaly detection and event identification. The proposed approach is validated through a case study on French electricity consumption, showcasing improvements in load profile estimation accuracy across various models. The chapter concludes by emphasizing the potential of social data to optimize energy grid management and enhance the reliability of load profiling models.AI Generated
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AbstractSince the electrical grid creation, assessing the electricity demand is essential as we need to match the energy production/demand at all times. Load analysis is essential in improving the reliability and efficiency of the grid. Beside regular human activities, the main impact factor which explains consumption variations is the outside temperature. But there are still unpredictable variations that are mainly coming from arising social events. To build a better understanding of these variations, this work will focus on how to detect these events from social media and how to quantify their impact on residential and professional typical profiles for energy demand. -
Small Signal Stability Study for Island Distributed Generation System Controlled by IDA-PBC-IA and Power Decoupled Droop Control
Nidhal Khefifi, Azeddine Houari, Mohamed Machmoum, Malek Ghanes, Mehdi ZadehThe chapter focuses on the small signal stability analysis of islanded distributed generation systems controlled by IDA-PBC-IA and decoupled droop control. It introduces the concept of autonomous microgrids and their challenges in maintaining power quality and sharing between distributed generators. The study employs small signal analysis tools to evaluate the stability of the proposed control laws, highlighting the influence of system parameters and control coefficients on stability. The use of modal analysis provides insights into the sensitivity and impact of various elements on the system's stability, showcasing the effectiveness of the proposed control techniques. The chapter concludes with a detailed examination of the stability margins under varying system parameters, demonstrating the robustness and wide stability range achieved by the proposed control strategies.AI Generated
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AbstractThe supply of electricity to remote areas such as islands or rural areas presents many challenges. To reduce costs, the use of renewable energy resources is recommended. In these circumstances, it is always important to improve the power quality in terms of waveform and power sharing between different distributed generators. In this paper, we focus on the power sharing between different distributed generators, and for this purpose, an improved decoupled control, proposed in our previous work, is studied to prove its effectiveness in providing wide range of stability. The internal control has been assimilated to a second-order filter and then, the improved decoupled control is studied to prove its effectiveness to ensure a wide range of stability. For this purpose, a microgrid composed of two distributed generators is studied. Its small-signal model including the distributed generators, the loads and the droop control laws that ensure the interconnection between the generators is revealed. A stability study in the sense of the indirect Lyapunov theory based on the evaluation of the eigenvalues of the system is performed to show the “local stability” in presence of different types of loads, the impact of the system and the control parameters on the eigenvalue is studied using the modal analysis technique. The validation of these results is proven by simulation -
MANA-Based Load-Flow Solution for Bipolar DC Microgrids
Nasim Rashidirad, Jean Mahseredjian, Ilhan Kocar, Omar SaadThe chapter delves into the growing importance of DC power distribution systems, particularly bipolar DC microgrids, which offer superior performance in technical, environmental, and economic aspects. It introduces a MANA-based load-flow solution tailored for bipolar DC microgrids, addressing the unique challenges posed by unbalanced conditions. The proposed formulation is demonstrated through a detailed case study of a 33-bus BDCMG, showcasing its effectiveness and ease of implementation. The chapter also explores the impact of different line resistances and droop coefficients on bus voltages, providing valuable insights into optimizing power distribution in bipolar DC microgrids. This comprehensive analysis underscores the potential of MANA formulation in enhancing the efficiency and reliability of DC power systems.AI Generated
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AbstractIn this paper, a novel load-flow method for unbalanced bipolar dc microgrids (BDCMGs) is presented. The principles of this method are based on the modified augmented nodal analysis (MANA) formulation, which is generic and simple to formulate. An unbalanced BDCMG is also used to verify the validity of the proposed MANA-based formulation. The findings also substantiate that in BDCMGs, different connections of DGs can highly affect the bipolar voltage profiles, in presence of different line resistances and droop coefficients. -
Analysis and Assessment of a Commercial Microgrid Laboratory Platform
Mariem Dellaly, Sonia Moussa, Sondes Skander-Mustapha, Ilhem Slama-BelkhodjaThis chapter presents a comprehensive analysis of a commercial microgrid laboratory platform, named SMARTNESS, designed to investigate collective self-consumption and microgrid operation. The platform, funded under the European MEdECoSURE project, consists of a single-phase AC microgrid with prosumers, loads, and storage systems. The study focuses on the local and central energy management systems, validating their operations through various test protocols. The authors analyze data from the platform to establish flowcharts for the energy management systems, highlighting the complexities of managing bidirectional energy flow in aging distribution networks. The chapter also includes experimental results and discussions on the platform's performance, paving the way for future advancements in microgrid technology and energy management systems.AI Generated
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AbstractThe growth of residential rooftop solar PV has given rise to new operating concepts such as collective solar self-consumption where several prosumers come together to form a microgrid with its distributed PV generations, its storage systems, its local loads with an energy management system (EMS) to optimize the operation modes according to desired criteria. Such microgrid working is relatively complex and adapting the EMS of a commercial microgrid to meet national standards and regulations or to perform deep investigations requires first analysis and assessments. This paper deals with a commercial microgrid laboratory platform. Tests and data analysis are performed to establish the flowchart of its central EMS, and then, in a future work, to develop an accurate model of the platform to test new investigated EMS. -
A Review of Frequency Control Techniques Using Artificial Neural Network for Urban Microgrid Applications
Louise Petit, Bruno FrancoisThis chapter delves into the critical role of frequency control in modern urban microgrids, particularly in the context of increasing renewable energy integration. It begins by outlining the challenges posed by the shift towards sustainable energy sources and the need for advanced energy management techniques. The chapter then explores the fundamentals of frequency control, including primary and secondary control methods, and how these have evolved with the introduction of power electronic converters. Artificial Intelligence, particularly Artificial Neural Networks (ANNs), is highlighted as a promising solution for real-time frequency control due to their adaptation and generalization capabilities. The chapter reviews various applications of ANNs in both global system management and local control systems, showcasing their superior performance compared to classical methods. Notable case studies include the use of ANNs for reserve management and PI/PID controller tuning, demonstrating significant improvements in frequency response and system stability. The chapter concludes by emphasizing the potential of ANNs to revolutionize frequency control in microgrids, paving the way for more stable and efficient energy systems.AI Generated
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AbstractThe increasing penetration of intermittent Renewable Energy Sources (RES) induces more instability of the grid and constraints on the Energy Management (EM). Microgrids (MG) are more and more experimented to better implement local flexibilities for dynamically balancing the production and load demand inside a specific area as districts of a city, as example. New solutions like Artificial Intelligence (AI) and Artificial Neural Networks (ANN) are being developed in order to improve the real-time energy management. Specifically this paper deals with the operational management of energy resources via the tuning of the frequency control parameters to satisfy the load demand. A non-exhaustive review of ANN techniques for enhancing the frequency control in microgrids is proposed. ANN techniques are shown to be performing better than other AI techniques on the specific cases reported here. -
Stator Interturn Short-Circuits Detection in the PMSM Drive by Using Current Symmetrical Components and Selected Machine Learning Algorithms
Przemyslaw Pietrzak, Marcin WolkiewiczThe chapter delves into the critical issue of interturn short-circuits (ITSCs) in Permanent Magnet Synchronous Motor (PMSM) drives, which can lead to significant failures if not detected early. It introduces the use of symmetrical current components and machine learning algorithms for accurate fault detection. The study compares four prominent machine learning algorithms—KNN, SVM, NB, and MLP—and evaluates their performance in detecting ITSCs under various operating conditions. The authors also conduct a detailed analysis of hyperparameters, demonstrating how they influence the accuracy of fault classification. The proposed diagnostic system shows promising results, particularly with the KNN algorithm, which outperforms others in early fault detection. The chapter concludes with a call for further research into real-time, microcontroller-based PMSM stator winding diagnosis systems.AI Generated
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AbstractThe fault diagnosis of Permanent Magnet Synchronous Motors (PMSMs) has been the subject of much research in recent days. This is due to the growing safety and reliability requirements for drive systems. This paper concerns detection and classification of the PMSM stator interturn short-circuits (ITSC) by using selected machine learning algorithms. The spectral analysis of symmetrical current components is applied for ITSC symptom extraction. The utilized and compared algorithms are K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Naive Bayes (NB) and Multilayer Perceptron (MLP). Experimental results confirm that the use of the KNN, SVM and MLP classifiers allows for ITSC detection with high effectiveness. The most effective is KNN, which is simple to implement and not computationally complex.
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Energy Storage Systems
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Frontmatter
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Potential Operation of Battery Systems to Provide Automatic Frequency Reserve Restoration (aFRR) Service
J. Cardo-Miota, E. Pérez, H. BeltranThe chapter delves into the operational challenges posed by the growing integration of renewable energy sources (RES) in power grids. It highlights the role of energy storage systems, particularly Li-ion based Battery Energy Storage Systems (BESS), in providing ancillary services such as Automatic Frequency Reserve Restoration (aFRR). The chapter introduces a simulation model of the Spanish Automatic Generation Control (AGC) system and conducts an economic feasibility study to determine the optimal BESS size for participating in the aFRR market. The study also considers the participation of BESS in the continuous intraday energy market, making it a valuable resource for understanding the complexities and potential solutions in modern power systems.AI Generated
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AbstractAs a consequence of the enormous growth being experienced by renewable energy systems (RES), conventional technologies such as coal or gas, which unlike RES are dispatchable, are reducing their participation in energy markets, increasing the instability of the electric energy systems. Therefore, there is a need for RES or other converter-based technologies to replace the traditional ancillary service providers. In this sense, battery energy storage systems (BESS) are considered the best candidates. This paper defines a model to simulate the Spanish Automatic Generation Control (AGC). This simulator is used to provide inputs to the operation of a BESS that participates in the secondary frequency regulation market and in the continuous intraday energy market. Subsequently, the paper introduces an economic feasibility study to determine the best BESS size to operate simultaneously in both markets. The results obtained show that BESS with energy capacities of 2 h are the best option (from both a technical and an economic point of view) to be part of a regulation zone. -
Incremental Capacity Analysis as a Diagnostic Method Applied to Second Life Li-ion Batteries
Lucas Albuquerque, Fabien Lacressonnière, Xavier Roboam, Christophe ForgezThe chapter delves into the growing challenge of disposing of electric vehicle (EV) batteries and the innovative solutions aimed at giving these batteries a second life. It introduces the Incremental Capacity Analysis (ICA) method as a precise diagnostic tool for characterizing the State of Health (SoH) and degradation modes of Li-ion batteries. The study compares ICA with other diagnostic methods and demonstrates its effectiveness through experimental tests and simulations. The chapter also discusses the impact of different charge rates on the accuracy of ICA and highlights the potential of this method for creating more homogeneous and efficient second life batteries.AI Generated
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AbstractThis work is inserted in the context of second life Li-ion batteries: for such storage devices, their first life characteristics are unknown and a simple capacity measurement might not be sufficient to fully characterize and get it ready for its second life. The Incremental Capacity Analysis (ICA) was used in this study to give a more intimate diagnosis of the batteries’ Degradation Modes (DMs), providing a link with physical degradation phenomena. This method was applied to a lithium-ion battery module (NMC/Graphite) which was used in an electrical vehicle and to a single cell from a similar module in order to verify its potential use in this context. Both IC curves were then compared to a DM simulation using the ′Alawa software, capable of simulating different ageing phenomena and their effects on the IC curves. Moreover, this work gives an intrinsic view and explanation of the IC signature for the mentioned battery technology. -
A Li-Ion Battery Charger with Embedded Signal Generator for On-Board Electrochemical Impedance Spectroscopy
Luigi Mattia, Giovanni Petrone, Walter ZamboniThis chapter explores the development of a low-cost on-board Electrochemical Impedance Spectroscopy (EIS) stimulation system for Li-ion batteries. The system is based on a commercial battery charger modified with an FPGA to generate high-quality sinusoidal stimuli. The implementation details, including hardware layout and FPGA architecture, are discussed. Experimental results demonstrate the system's capability to perform EIS analysis over a wide frequency range, with THD values consistently below 5%. The chapter highlights the simplicity and scalability of the approach, making it suitable for various applications from automotive to consumer electronics.AI Generated
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AbstractThe development of a battery monitoring system is one of main tasks for applications needing an efficient and well-designed battery storage system. In this framework, a fast, on-board, non-invasive and low-cost diagnosis system has a primary importance. Among the large number of diagnosis techniques, the Electrochemical Impedance Spectroscopy (EIS) is one of the most powerful. It allows one to extract information about the overall state of an electrochemical cell by stimulating it with current or voltage signals with appropriate shapes and frequency. In this work, we present the changes made to a commercial Lithium-ion battery charger to implement a system for the generation of EIS stimuli, preserving large part of the native functions of the battery charger. The stimulation functions are implemented using a field-programmable gate array (FPGA) board, which ensures a good voltage resolution and an optimal frequency range for this kind of applications. -
A Survey of Energy Management Systems Considering Battery State of Health Preservation in Microgrid Applications
Maria Carmela Di Piazza, Massimiliano Luna, Giuseppe La TonaThis chapter delves into the critical role of energy management systems (EMSs) in microgrid applications, emphasizing the need to consider battery state of health (SOH) preservation. It surveys various control-oriented battery degradation models, categorizing them into electrochemical, semi-empirical/empirical, and physics-based non-electrochemical models. The chapter also highlights recent literature contributions that integrate these models into EMSs to extend battery life and enhance grid efficiency. Additionally, it discusses open challenges and future directions in the field, such as the need for more accurate and computationally efficient battery models and the optimization of microgrid operations for better return on investment.AI Generated
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AbstractElectrochemical storage systems play an increasingly central role in microgrids, providing several services which allow for more flexible and reliable operation. Lifetime of battery storage systems is a critical aspect to consider for their sustainable and cost-effective employment. In this paper a survey of energy management systems (EMSs) designed to contribute to battery lifetime extension is presented. To pursue this objective, the design of EMSs must rely on suitable battery degradation models, the most significant of which have been retrieved from the technical literature and described as well. -
Impedance Modeling for Multichannel EIS in Industrial Scale Vanadium Redox Flow Batteries
Andrea Trovò, Walter Zamboni, Massimi GuarnieriThe chapter focuses on the application of Electrochemical Impedance Spectroscopy (EIS) for monitoring and characterizing Vanadium Redox Flow Batteries (VRFBs) at an industrial scale. It introduces advanced impedance models, such as the ZARC and ZARC+W models, which offer improved fitting accuracy compared to traditional RRC models. The study emphasizes the importance of multichannel measurements for detecting imbalances and performance issues within the battery stack. The results demonstrate that these enhanced models can significantly reduce errors in impedance data fitting, providing valuable insights into the battery's state of health. The research highlights the potential of these techniques for developing advanced online monitoring systems, essential for maintaining the efficiency and longevity of industrial-scale energy storage solutions.AI Generated
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AbstractThe work provides early results obtained with a multichannel EIS system, which were used to identify an equivalent circuit of an Industrial Scale Vanadium Redox Flow Battery (IS-VRFB) stack with a rated power/energy of 9 kW/27 kWh. The single cell impedance is represented with three different models, including a series resistance and an RC loop (RRC model), or a constant phase element (CPE) loop (a ZARC element), or a ZARC element including also a Warburg impedance. The inclusion of the CPE constitutes a substantial improvement in the fit. Conversely, the addition of the Warburg element, which aims to model the mass transfer in the electrochemical process, does not produce significant effects for the frequencies at which we have experimental data. This numerical results are validated against EIS measurements taken on IS-VRFB. Very few analyses of this type are reported in the literature for such batteries. This study set the stage for developing advanced online State of Health (SOH) management for IS-VRFB. -
Numerical Assessment of Cooling Systems for Thermal Management of Lithium-Ion Batteries
Girolama Airò Farulla, Davide Aloisio, Valeria Palomba, Andrea Frazzica, Giovanni Brunaccini, Francesco SergiThe chapter delves into the critical role of thermal management in lithium-ion batteries, highlighting the importance of maintaining optimal operating temperatures to prevent thermal runaway and extend battery life. It reviews various thermal management systems and their advantages, with a particular focus on phase change material-based passive cooling and hybrid PCM/liquid configurations. The authors develop a simplified thermal model using COMSOL Multi-physics® to simulate the thermal distribution of lithium-ion batteries, validating their results with experimental data from charging/discharging cycles. The model takes into account key physical properties and heat generation mechanisms, providing a detailed analysis of temperature distribution under different operating conditions. The chapter concludes by proposing novel materials and hybrid systems for effective thermal management under high current conditions, offering valuable insights for professionals in the field.AI Generated
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AbstractLithium-ion batteries have the advantages of high energy density, high charge-discharge efficiency, low self-discharge effect and long cycle life that make them suitable in both stationary and mobile applications. They are the most widely used solution in the field of electric vehicles and are increasing their application for stationary applications. Both the life-time and performances are negatively affected by high temperatures so the prevision of the thermal behaviour is a crucial step in the battery modelling.Based on an experimental setup, a simplified thermal model was developed to estimate the surface temperatures of a lithium titanate cell from current and voltage measurements.The model was implemented in the COMSOL Multiphysics® Finite Element code. Charge and discharge cycles of the cell were performed and the predicted heat generation used as input of the thermal model. The calibrated model was lastly used to assess two thermal battery management (TBM) cooling systems, in this case applied to a single cell: a passive phase change material (PCM) system and a hybrid PCM/water system. The effects of the PCM thickness and velocity inlet of the water on the cell temperature were investigated. Results showed that, in comparison to the passively air cooled cell, both systems decreased the maximum surface temperatures, thus improving the uniformity of the temperature distribution and keeping the battery in a safe temperature range. -
Modeling of the Thermal Runaway Phenomenon of Cylindrical 18650 Li-Ion Cells
Paola Russo, Sofia Ubaldi, Maria Luisa MeleThe chapter delves into the modeling of thermal runaway in cylindrical 18650 Li-ion cells, highlighting the complex interplay between chemical reactions and thermal processes. It introduces a mathematical model that predicts the thermal behavior of cells under normal use and abuse conditions, with a focus on the critical exothermic reactions that cause heat generation. The model is validated through comparisons with experimental data obtained from cone calorimeter tests, demonstrating its reliability in predicting battery performance and safety. The chapter also discusses the challenges and controversies in existing kinetics studies, emphasizing the need for more precise models to ensure better battery design and performance.AI Generated
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AbstractThe thermal runaway (TR) is the main safety concern of lithium-ion batteries (LIBs). Methods for predicting and preventing TR are critical to achieve greater battery safety. Many researchers have studied the reactions that take place inside the cell and that because of their exothermicity trigger the TR. In this work the coupled electrochemical-thermal model for a lithium-ion cell was extended with contributions from exothermic reactions based on an Arrhenius law to model mechanisms of abuse, which could lead to a thermal runaway. Firstly, differential scanning calorimetry (DSC) tests were conducted on the individual components of the cell to characterize the reactions of the TR process in terms of onset temperature, thermal and kinetic parameters. The kinetic parameters of each reaction were identified by the Kissinger method. Then the thermal and kinetics parameters of the reactions occurring during the thermal runaway together with the phenomena involving the electrolyte (i.e., evaporation, boiling and venting) were included in the Battery and Fuel Cell Module of COMSOL Multiphysics simulator, to simulate the behaviour of a cylindrical 18650 cell under thermal abuse conditions. In particular, the results of the model appear to agree with the experimental data, concerning to a NCA 18650 cell subjected to radiative heat flux in a cone calorimeter.
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- Title
- ELECTRIMACS 2022
- Editors
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Serge Pierfederici
Jean-Philippe Martin
- Copyright Year
- 2023
- Publisher
- Springer International Publishing
- Electronic ISBN
- 978-3-031-24837-5
- Print ISBN
- 978-3-031-24836-8
- DOI
- https://doi.org/10.1007/978-3-031-24837-5
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