Batteries

16 pages, 1715 KiB

Open AccessArticle

Multi-Step Ageing Prediction of NMC Lithium-Ion Batteries Based on Temperature Characteristics

by Abdelilah Hammou, Boubekeur Tala-Ighil, Philippe Makany and Hamid Gualous

Batteries 2024, 10(11), 384; https://doi.org/10.3390/batteries10110384 - 31 Oct 2024

The performance of lithium-ion batteries depends strongly on their ageing state; therefore, the monitoring and the prediction of the battery state of health (SoH) is necessary for an optimized and secured functioning of battery systems. This paper evaluates and compares three artificial neural [...] Read more.

The performance of lithium-ion batteries depends strongly on their ageing state; therefore, the monitoring and the prediction of the battery state of health (SoH) is necessary for an optimized and secured functioning of battery systems. This paper evaluates and compares three artificial neural network architectures for multi-step ageing prediction of lithium-ion cells: Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). These models use the features extracted from the cell’s temperature to predict the cell’s capacity. The features are extracted from experimental measurements of the cell’s surface temperature and selected based on Spearman correlation analysis. The prediction results were evaluated and compared considering three different percentages of the training dataset: 60%, 70%, and 80%. Training and testing data were generated experimentally based on accelerated ageing cycling tests. During these experiments, four Nickel Manganese Cobalt/Graphite (NMC) cells were cycled under a controlled temperature environment based on a dynamic current profile extracted from the Worldwide Harmonized Light Vehicles Test Cycles. Full article

(This article belongs to the Special Issue The Breakthrough of Traditional Electrochemical Energy Storage Systems)

24 pages, 5707 KiB

Open AccessArticle

Revolutionizing Battery Longevity by Optimising Magnesium Alloy Anodes Performance

by Bankole I. Oladapo, Mattew A. Olawumi and Francis T. Omigbodun

Batteries 2024, 10(11), 383; https://doi.org/10.3390/batteries10110383 - 30 Oct 2024

Abstract

This research explores the enhancement of electrochemical performance in magnesium batteries by optimising magnesium alloy anodes, explicitly focusing on Mg-Al and Mg-Ag alloys. The study’s objective was to determine the impact of alloy composition on anode voltage stability and overall battery efficiency, particularly [...] Read more.

This research explores the enhancement of electrochemical performance in magnesium batteries by optimising magnesium alloy anodes, explicitly focusing on Mg-Al and Mg-Ag alloys. The study’s objective was to determine the impact of alloy composition on anode voltage stability and overall battery efficiency, particularly under extended cycling conditions. The research assessed the anodes’ voltage behaviour and internal resistance across magnesium bis(trifluoromethanesulfonyl)imide (Mg(TFSI)₂) electrolyte formulations using a systematic setup involving cyclic voltammetry on the anode and electrochemical impedance spectroscopy. The Mg-Al alloy demonstrated superior performance, with minimal voltage drop and lower resistance increase than the Mg-Ag alloy. The results showed that the Mg-Al alloy maintained over 85% energy efficiency after 100 cycles, significantly outperforming the Mg-Ag alloy, which exhibited increased degradation and efficiency reduction to approximately 80%. These findings confirm that incorporating aluminium into magnesium anodes stabilises the anode voltage and enhances the overall battery efficiency by mitigating degradation mechanisms. Consequently, the Mg-Al alloy is identified as an up-and-coming candidate for use in advanced battery technologies, offering energy density and cycle life improvements. This study lays the groundwork for future research to refine magnesium alloy compositions further to boost battery performance. Full article

(This article belongs to the Special Issue The Breakthrough of Traditional Electrochemical Energy Storage Systems)

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18 pages, 9899 KiB

Open AccessArticle

A Robotic Teleoperation System with Integrated Augmented Reality and Digital Twin Technologies for Disassembling End-of-Life Batteries

by Feifan Zhao, Wupeng Deng and Duc Truong Pham

Batteries 2024, 10(11), 382; https://doi.org/10.3390/batteries10110382 - 30 Oct 2024

Abstract

Disassembly is a key step in remanufacturing, especially for end-of-life (EoL) products such as electric vehicle (EV) batteries, which are challenging to dismantle due to uncertainties in their condition and potential risks of fire, fumes, explosions, and electrical shock. To address these challenges, [...] Read more.

Disassembly is a key step in remanufacturing, especially for end-of-life (EoL) products such as electric vehicle (EV) batteries, which are challenging to dismantle due to uncertainties in their condition and potential risks of fire, fumes, explosions, and electrical shock. To address these challenges, this paper presents a robotic teleoperation system that leverages augmented reality (AR) and digital twin (DT) technologies to enable a human operator to work away from the danger zone. By integrating AR and DTs, the system not only provides a real-time visual representation of the robot’s status but also enables remote control via gesture recognition. A bidirectional communication framework established within the system synchronises the virtual robot with its physical counterpart in an AR environment, which enhances the operator’s understanding of both the robot and task statuses. In the event of anomalies, the operator can interact with the virtual robot through intuitive gestures based on information displayed on the AR interface, thereby improving decision-making efficiency and operational safety. The application of this system is demonstrated through a case study involving the disassembly of a busbar from an EoL EV battery. Furthermore, the performance of the system in terms of task completion time and operator workload was evaluated and compared with that of AR-based control methods without informational cues and ‘smartpad’ controls. The findings indicate that the proposed system reduces operation time and enhances user experience, delivering its broad application potential in complex industrial settings. Full article

(This article belongs to the Section Battery Processing, Manufacturing and Recycling)

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26 pages, 13548 KiB

Open AccessReview

Synthesis Methods of Si/C Composite Materials for Lithium-Ion Batteries

by Inkyu Park, Hanbyeol Lee and Oh B. Chae

Batteries 2024, 10(11), 381; https://doi.org/10.3390/batteries10110381 - 28 Oct 2024

Abstract

Silicon anodes present a high theoretical capacity of 4200 mAh/g, positioning them as strong contenders for improving the performance of lithium-ion batteries. Despite their potential, the practical application of Si anodes is constrained by their significant volumetric expansion (up to 400%) during lithiation/delithiation, [...] Read more.

Silicon anodes present a high theoretical capacity of 4200 mAh/g, positioning them as strong contenders for improving the performance of lithium-ion batteries. Despite their potential, the practical application of Si anodes is constrained by their significant volumetric expansion (up to 400%) during lithiation/delithiation, which leads to mechanical degradation and loss of electrical contact. This issue contributes to poor cycling stability and hinders their commercial viability, and various silicon–carbon composite fabrication methods have been explored to mitigate these challenges. This review covers key techniques, including ball milling, spray drying, pyrolysis, chemical vapor deposition (CVD), and mechanofusion. Each method has unique benefits; ball milling and spray drying are effective for creating homogeneous composites, whereas pyrolysis and CVD offer high-quality coatings that enhance the mechanical stability of silicon anodes. Mechanofusion has been highlighted for its ability to integrate silicon with carbon materials, showing the potential for further optimization. In light of these advancements, future research should focus on refining these techniques to enhance the stability and performance of Si-based anodes. The optimization of the compounding process has the potential to enhance the performance of silicon anodes by addressing the significant volume change and low conductivity, while simultaneously addressing cost-related concerns. Full article

(This article belongs to the Special Issue Advanced Carbon-Based Materials for Next-Generation Batteries and Supercapacitors)

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18 pages, 6338 KiB

Open AccessArticle

State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM Model

by Jun Chen, Yan Liu, Jun Yong, Cheng Yang, Liqin Yan and Yanping Zheng

Batteries 2024, 10(11), 380; https://doi.org/10.3390/batteries10110380 - 28 Oct 2024

Abstract

The accurate estimation of the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation, as direct measurement is not feasible. This paper presents a novel SOH estimation method that integrates Particle Swarm Optimization (PSO) with an [...] Read more.

The accurate estimation of the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation, as direct measurement is not feasible. This paper presents a novel SOH estimation method that integrates Particle Swarm Optimization (PSO) with an Extreme Learning Machine (ELM) to improve prediction accuracy. Health Indicators (HIs) are first extracted from the battery’s charging curve, and correlation analysis is conducted on seven indirect HIs using Pearson and Spearman coefficients. To reduce dimensionality and eliminate redundancy, Principal Component Analysis (PCA) is applied, with the principal component contributing over 94% used as a fusion HI to represent battery capacity degradation. PSO is then employed to optimize the weights (ε) between the input and hidden layers, as well as the hidden layer bias (u) in the ELM, treating these parameters as particles in the PSO framework. This optimization enhances the ELM’s performance, addressing instability issues in the standard algorithm. The proposed PSO-ELM model demonstrates superior accuracy in SOH prediction compared with ELM and other methods. Experimental results show that the mean absolute error (MAE) is 0.0034, the mean absolute percentage error (MAPE) is 0.467%, and the root mean square error (RMSE) is 0.0043, providing a valuable reference for battery safety and reliability assessments. Full article

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35 pages, 4388 KiB

Open AccessReview

Transformations of Critical Lithium Ores to Battery-Grade Materials: From Mine to Precursors

by Sabbir Ahmed, Anil Kumar Madikere Raghunatha Reddy and Karim Zaghib

Batteries 2024, 10(11), 379; https://doi.org/10.3390/batteries10110379 - 28 Oct 2024

Abstract

The escalating demand for lithium has intensified the need to process critical lithium ores into battery-grade materials efficiently. This review paper overviews the transformation processes and cost of converting critical lithium ores, primarily spodumene and brine, into high-purity battery-grade precursors. We systematically examine [...] Read more.

The escalating demand for lithium has intensified the need to process critical lithium ores into battery-grade materials efficiently. This review paper overviews the transformation processes and cost of converting critical lithium ores, primarily spodumene and brine, into high-purity battery-grade precursors. We systematically examine the study findings on various approaches for lithium recovery from spodumene and brine. Dense media separation (DMS) and froth flotation are the most often used processes for spodumene beneficiation. Magnetic separation (MS) and ore gravity concentration techniques in spodumene processing have also been considered. To produce battery-grade lithium salts, the beneficiated-concentrated spodumene must be treated further, with or without heat, in the presence of acidic or alkaline media. As a result, various pyro and hydrometallurgical techniques have been explored. Moreover, the process of extracting lithium from brine through precipitation, liquid–liquid extraction, and polymer inclusion membrane separation employing different organic, inorganic, and composite polymer sorbents has also been reviewed. Full article

(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)

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21 pages, 6398 KiB

Open AccessArticle

Early Internal Short Circuit Diagnosis for Lithium-Ion Battery Packs Based on Dynamic Time Warping of Incremental Capacity

by Meng Zhang, Qiang Guo, Ke Fu, Xiaogang Du, Hao Zhang, Qi Zuo, Qi Yang and Chao Lyu

Batteries 2024, 10(11), 378; https://doi.org/10.3390/batteries10110378 - 28 Oct 2024

Abstract

Timely identification of early internal short circuit faults, commonly referred to as micro short circuits (MSCs), is essential yet poses significant challenges for the safe and reliable operation of lithium-ion battery (LIB) energy storage systems. This paper introduces an innovative diagnostic method for [...] Read more.

Timely identification of early internal short circuit faults, commonly referred to as micro short circuits (MSCs), is essential yet poses significant challenges for the safe and reliable operation of lithium-ion battery (LIB) energy storage systems. This paper introduces an innovative diagnostic method for early internal short circuits in LIB packs, utilizing dynamic time warping (DTW) applied to incremental capacity (IC). Initially, the terminal voltages of all cells within the LIB pack are ordered at any moment to determine the median terminal voltage, which is then used to generate the median IC curve. This curve acts as a reference benchmark that represents the condition of healthy cells in the pack. Subsequently, the DTW algorithm is utilized to measure the similarity between each cell’s IC curve and the median IC curve. Cells exhibiting similarity scores that exceed a specified threshold are identified as having MSC faults. Lastly, for the cells diagnosed with MSC conditions, a method for estimating short-circuit resistance (SR) based on variations in maximum charging voltage is devised to quantitatively evaluate the severity and evolution of the MSC. Experimental findings reveal that the proposed method effectively identifies MSC cells in the LIB pack and estimates their SRs without the necessity of a battery model, thereby affirming the method’s validity. Full article

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42 pages, 10682 KiB

Open AccessReview

North America’s Potential for an Environmentally Sustainable Nickel, Manganese, and Cobalt Battery Value Chain

by Gary Vegh, Anil Kumar Madikere Raghunatha Reddy, Xia Li, Sixu Deng, Khalil Amine and Karim Zaghib

Batteries 2024, 10(11), 377; https://doi.org/10.3390/batteries10110377 - 27 Oct 2024

Abstract

The Detroit Big Three General Motors (GMs), Ford, and Stellantis predict that electric vehicle (EV) sales will comprise 40–50% of the annual vehicle sales by 2030. Among the key components of LIBs, the LiNixMnyCo_1−x−yO₂ cathode, which comprises nickel, manganese, and [...] Read more.

The Detroit Big Three General Motors (GMs), Ford, and Stellantis predict that electric vehicle (EV) sales will comprise 40–50% of the annual vehicle sales by 2030. Among the key components of LIBs, the LiNixMnyCo_1−x−yO₂ cathode, which comprises nickel, manganese, and cobalt (NMC) in various stoichiometric ratios, is widely used in EV batteries. This review reveals NMC cathodes from laboratory research. Furthermore, this study examines the environmental effect of NMC cathode production for EV batteries (including coating technologies), encompassing aspects such as energy consumption, water usage, and air emissions. Although gaps persist in NMC cathode environmental assessments (NMC111, NMC532, NMC622, and NMC811), limited life cycle assessments “(LCA)” have been conducted. Most available data originate from Asia (primarily China), accounting for 85% of the production of EV LIB cathode materials. The concept of battery passports for data collection on LIB components has been proposed to facilitate material traceability as a system for ensuring a sustainable supply chain for critical minerals. The automotive industry’s shift to electrification necessitates a sustainable supply chain from mine to vehicle end-of-life. As the critical mineral supply moves from Asia to North America, environmentally friendly industrial methods must be studied to provide this supply chain direction. Full article

(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)

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21 pages, 2909 KiB

Open AccessArticle

Optimization of Retired Lithium-Ion Battery Pack Reorganization and Recycling Using 3D Assessment Technology

by Wan Chen, Jiaoyue Su, Lei Shen, Xinfa Gu, Junjie Xie, Na Sun, Hui Huang and Jie Ji

Batteries 2024, 10(11), 376; https://doi.org/10.3390/batteries10110376 - 24 Oct 2024

Abstract

This study introduces a sophisticated methodology that integrates 3D assessment technology for the reorganization and recycling of retired lithium-ion battery packs, aiming to mitigate environmental challenges and enhance sustainability in the electric vehicle sector. By deploying a kernel extreme learning machine (KELM), variational [...] Read more.

This study introduces a sophisticated methodology that integrates 3D assessment technology for the reorganization and recycling of retired lithium-ion battery packs, aiming to mitigate environmental challenges and enhance sustainability in the electric vehicle sector. By deploying a kernel extreme learning machine (KELM), variational mode decomposition (VMD), and an advanced sparrow search algorithm (SSA), the research achieves a marked increase in the precision of battery classification and performance forecasting. Implementing a three-dimensional dynamic evaluation model, the study optimizes battery pack grouping strategies, culminating in superior secondary utilization rates, extended operational lifespans, and minimized ecological footprints. The research demonstrates that balanced weight distribution strategies, which maximize energy density to 61.37571 Wh/L and cycle counts up to 947 cycles, are pivotal for the efficient reorganization of battery packs, substantiating the economic feasibility and environmental sustainability of recycling initiatives. Future endeavors will extend this research to investigate the influence of diverse battery materials and morphologies on reorganization efficacy, with the aim of broadening the application horizons to include real-world scenarios, thereby refining battery performance and lifespan predictions and propelling forward the frontiers of recycling technology and policy development. Full article

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11 pages, 1232 KiB

Open AccessArticle

European Transition to Electric Vehicles: Italy as a Case of Study

by Mattia Bartoli, Carlo Rosso and Alberto Tagliaferro

Batteries 2024, 10(11), 375; https://doi.org/10.3390/batteries10110375 - 22 Oct 2024

Abstract

The European ban on internal combustion engines has raised several questions from both the automotive players and society. The proliferation of electric vehicles is struggling as it is facing issues related to energy supply and distribution and to infrastructure availability. These problems are [...] Read more.

The European ban on internal combustion engines has raised several questions from both the automotive players and society. The proliferation of electric vehicles is struggling as it is facing issues related to energy supply and distribution and to infrastructure availability. These problems are considerably different across the various countries. It is consequently a tough challenge to provide a worldwide comprehensive evaluation. In this paper we first outline the common problems to later move to provide a clear picture of the Italian scenario, starting from the available data related to mobility for the period 2018–2022. We outline the main problems to be tackled and the related costs. Italy is an interesting and representative case as it is bound to the timeline set by the European green transition. Full article

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32 pages, 2891 KiB

Open AccessReview

Review of Cell Level Battery (Calendar and Cycling) Aging Models: Electric Vehicles

by Gulsah Yarimca and Erdal Cetkin

Batteries 2024, 10(11), 374; https://doi.org/10.3390/batteries10110374 - 22 Oct 2024

Abstract

Electrochemical battery cells have been a focus of attention due to their numerous advantages in distinct applications recently, such as electric vehicles. A limiting factor for adaptation by the industry is related to the aging of batteries over time. Characteristics of battery aging [...] Read more.

Electrochemical battery cells have been a focus of attention due to their numerous advantages in distinct applications recently, such as electric vehicles. A limiting factor for adaptation by the industry is related to the aging of batteries over time. Characteristics of battery aging vary depending on many factors such as battery type, electrochemical reactions, and operation conditions. Aging could be considered in two sections according to its type: calendar and cycling. We examine the stress factors affecting these two types of aging in detail under subheadings and review the battery aging literature with a comprehensive approach. This article presents a review of empirical and semi-empirical modeling techniques and aging studies, focusing on the trends observed between different studies and highlighting the limitations and challenges of the various models. Full article

(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)

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18 pages, 3442 KiB

Open AccessArticle

Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation

by Hongyu Shen, Yuefeng Liu, Qiyan Zhao, Guoyue Xue, Tiange Zhang and Xiuying Tan

Batteries 2024, 10(10), 373; https://doi.org/10.3390/batteries10100373 - 21 Oct 2024

Abstract

The battery systems of electric vehicles (EVs) are directly impacted by battery temperature in terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic conditions, and a dearth of high-quality data sets make modeling and predicting nonlinear multiscale electrochemical systems challenging. [...] Read more.

The battery systems of electric vehicles (EVs) are directly impacted by battery temperature in terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic conditions, and a dearth of high-quality data sets make modeling and predicting nonlinear multiscale electrochemical systems challenging. In this work, a novel Mamba network architecture called BMPTtery (Bidirectional Mamba Predictive Battery Temperature Representation) is proposed to overcome these challenges. First, a two-step hybrid model of trajectory piecewise–polynomial regression and exponentially weighted moving average is created and used to an operational dataset of EVs in order to handle the problem of noisy and incomplete time-series data. Each time series is then individually labeled to learn the representation and adaptive correlation of the multivariate series to capture battery performance variations in complex dynamic operating environments. Next, a prediction method with multiple steps based on the bidirectional Mamba is suggested. When combined with a failure diagnosis approach, this scheme can accurately detect heat failures due to excessive temperature, rapid temperature rise, and significant temperature differences. The experimental results demonstrate that the technique can accurately detect battery failures on a dataset of real operational EVs and predict the battery temperature one minute ahead of time with an MRE of 0.273%. Full article

(This article belongs to the Topic Advanced Battery Thermal Management Solution for Electric Vehicles)

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18 pages, 7064 KiB

Open AccessReview

A Review on Advanced Battery Thermal Management Systems for Fast Charging in Electric Vehicles

by Le Duc Tai, Kunal Sandip Garud, Seong-Guk Hwang and Moo-Yeon Lee

Batteries 2024, 10(10), 372; https://doi.org/10.3390/batteries10100372 - 20 Oct 2024

Abstract

To protect the environment and reduce dependence on fossil fuels, the world is shifting towards electric vehicles (EVs) as a sustainable solution. The development of fast charging technologies for EVs to reduce charging time and increase operating range is essential to replace traditional [...] Read more.

To protect the environment and reduce dependence on fossil fuels, the world is shifting towards electric vehicles (EVs) as a sustainable solution. The development of fast charging technologies for EVs to reduce charging time and increase operating range is essential to replace traditional internal combustion engine (ICE) vehicles. Lithium-ion batteries (LIBs) are efficient energy storage systems in EVs. However, the efficiency of LIBs depends significantly on their working temperature range. However, the huge amount of heat generated during fast charging increases battery temperature uncontrollably and may lead to thermal runaway, which poses serious hazards during the operation of EVs. In addition, fast charging with high current accelerates battery aging and seriously reduces battery capacity. Therefore, an effective and advanced battery thermal management system (BTMS) is essential to ensure the performance, lifetime, and safety of LIBs, particularly under extreme charging conditions. In this perspective, the current review presents the state-of-the-art thermal management strategies for LIBs during fast charging. The serious thermal problems owing to heat generated during fast charging and its impacts on LIBs are discussed. The core part of this review presents advanced cooling strategies such as indirect liquid cooling, immersion cooling, and hybrid cooling for the thermal management of batteries during fast charging based on recently published research studies in the period of 2019–2024 (5 years). Finally, the key findings and potential directions for next-generation BTMSs toward fast charging are proposed. This review offers an in-depth analysis by providing recommendations and potential solutions to develop reliable and efficient BTMSs for LIBs during fast charging. Full article

(This article belongs to the Special Issue Advances in Thermal Management for Batteries)

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16 pages, 1133 KiB

Open AccessArticle

The Role of Machine Learning in Enhancing Battery Management for Drone Operations: A Focus on SoH Prediction Using Ensemble Learning Techniques

by Büşra Çetinus, Saadin Oyucu, Ahmet Aksöz and Emre Biçer

Batteries 2024, 10(10), 371; https://doi.org/10.3390/batteries10100371 - 18 Oct 2024

Abstract

This study considers the significance of drones in various civilian applications, emphasizing battery-operated drones and their advantages and limitations, and highlights the importance of energy consumption, battery capacity, and the state of health of batteries in ensuring efficient drone operation and endurance. It [...] Read more.

This study considers the significance of drones in various civilian applications, emphasizing battery-operated drones and their advantages and limitations, and highlights the importance of energy consumption, battery capacity, and the state of health of batteries in ensuring efficient drone operation and endurance. It also describes a robust testing methodology used to determine battery SoH accurately, considering discharge rates and using machine learning algorithms for analysis. Machine learning techniques, including classical regression models and Ensemble Learning methods, were developed and calibrated using experimental UAV data to predict SoH accurately. Evaluation metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) assess model performance, highlighting the balance between model complexity and generalization. The results demonstrated improved SoH predictions with machine learning models, though complexities may lead to overfitting challenges. The transition from simpler regression models to intricate Ensemble Learning methods is meticulously described, including an assessment of each model’s strengths and limitations. Among the Ensemble Learning methods, Bagging, GBR, XGBoost, LightGBM, and stacking were studied. The stacking technique demonstrated promising results: for Flight 92 an RMSE of 0.03% and an MAE of 1.64% were observed, while for Flight 129 the RMSE was 0.66% and the MAE stood at 1.46%. Full article

(This article belongs to the Special Issue Machine Learning for Advanced Battery Systems)

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35 pages, 17707 KiB

Open AccessReview

Safety Aspects of Sodium-Ion Batteries: Prospective Analysis from First Generation Towards More Advanced Systems

by Pempa Tshering Bhutia, Sylvie Grugeon, Asmae El Mejdoubi, Stéphane Laruelle and Guy Marlair

Batteries 2024, 10(10), 370; https://doi.org/10.3390/batteries10100370 - 17 Oct 2024

Abstract

After an introductory reminder of safety concerns pertaining to early rechargeable battery technologies, this review discusses current understandings and challenges of advanced sodium-ion batteries. Sodium-ion technology is now being marketed by industrial promoters who are advocating its workable capacity, as well as its [...] Read more.

After an introductory reminder of safety concerns pertaining to early rechargeable battery technologies, this review discusses current understandings and challenges of advanced sodium-ion batteries. Sodium-ion technology is now being marketed by industrial promoters who are advocating its workable capacity, as well as its use of readily accessible and cheaper key cell components. Often claimed to be safer than lithium-ion cells, currently only limited scientifically sound safety assessments of sodium-ion cells have been performed. However, the predicted sodium-ion development roadmap reveals that significant variants of sodium-ion batteries have entered or will potentially enter the market soon. With recent experiences of lithium-ion battery failures, sodium-ion battery safety management will constitute a key aspect of successful market penetration. As such, this review discusses the safety issues of sodium-ion batteries, presenting a twofold innovative perspective: (i) in terms of comparison with the parent lithium-ion technology making use of the same working principle and similar flammable non-aqueous solvent basis, and (ii) anticipating the arrival of innovative sub-chemistries at least partially inspired from successive generations of lithium-ion cells. The authors hope that the analysis provided will assist concerned stakeholders in the quest for safe marketing of sodium-ion batteries. Full article

(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)

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13 pages, 4592 KiB

Open AccessArticle

Inkjet-Printed Silver Lithiophilic Sites on Copper Current Collectors: Tuning the Interfacial Electrochemistry for Anode-Free Lithium Batteries

by Seyedalireza Mirbagheri, Eugenio Gibertini and Luca Magagnin

Batteries 2024, 10(10), 369; https://doi.org/10.3390/batteries10100369 - 17 Oct 2024

Abstract

Anode-free lithium batteries (AFLBs) present an opportunity to eliminate the need for conventional graphite electrodes or excess lithium–metal anodes, thus increasing the cell energy density and streamlining the manufacturing process. However, their attributed poor coulombic efficiency leads to rapid capacity decay, underscoring the [...] Read more.

Anode-free lithium batteries (AFLBs) present an opportunity to eliminate the need for conventional graphite electrodes or excess lithium–metal anodes, thus increasing the cell energy density and streamlining the manufacturing process. However, their attributed poor coulombic efficiency leads to rapid capacity decay, underscoring the importance of achieving stable plating and stripping of Li on the negative electrode for the success of this cell configuration. A promising approach is the utilization of lithiophilic coatings such as silver to mitigate the Li nucleation overpotential on the Cu current collector, thereby improving the process of Li plating/stripping. On the other hand, inkjet printing (IJP) emerges as a promising technique for electrode modification in the manufacturing process of lithium batteries, offering a fast and scalable technology capable of depositing both thin films and patterned structures. In this work, a Fujifilm Dimatix inkjet printer was used to deposit Ag sites on a Cu current collector, aiming to modulate the interfacial electrochemistry of the system. Samples were fabricated with varying areas of coverage and the electrochemical performance of the system was systematically evaluated from bare Cu (non-lithiophilic) to a designed pattern (partially lithiophilic) and the fully coated thin film case (lithiophilic). Increasing lithiophilicity resulted in lower charge transfer resistance, higher exchange current density and reduced Li nucleation overpotential (from 55.75 mV for bare Cu to 13.5 mV for the fully coated case). Enhanced half-cell cyclability and higher coulombic efficiency were also achieved (91.22% CE over 76 cycles for bare Cu, 97.01% CE over 250 cycles for the fully coated case), alongside more uniform lithium deposition and fewer macroscopic irregularities. Moreover, our observations demonstrated that surface patterning through inkjet printing could represent an innovative, easy and scalable strategy to provide preferential Li nucleation sites to guide the subsequent Li deposition. Full article

(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)

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12 pages, 6221 KiB

Open AccessArticle

An Electrochemical Impedance Spectrum-Based State of Health Differential Indicator with Reduced Sensitivity to Measurement Errors for Lithium–Ion Batteries

by Jaber Abu Qahouq

Batteries 2024, 10(10), 368; https://doi.org/10.3390/batteries10100368 - 16 Oct 2024

Abstract

As the use of electrochemical batteries, especially lithium–ion (Li-Ion) batteries, increases due to emerging applications and expanding markets, the accurate and fast estimation of their state of health (SOH) is becoming increasingly important. The accuracy of the SOH estimation is highly dependent on [...] Read more.

As the use of electrochemical batteries, especially lithium–ion (Li-Ion) batteries, increases due to emerging applications and expanding markets, the accurate and fast estimation of their state of health (SOH) is becoming increasingly important. The accuracy of the SOH estimation is highly dependent on the correlation strength between the used indicator and SOH and the accuracy of the SOH indicator measurement. This paper presents a new differential indicator which has a strong and consistent correlation with the SOH of Li-Ion batteries, based on a new Electrochemical Impedance Spectrum (EIS) Phase–Magnitude relationship. It is shown in this paper that the EIS Phase–Magnitude relationship exhibits a phase-based differential impedance magnitude SOH indicator between a first-phase peak point and a last-phase valley point. Because of the differential nature of this SOH indicator and because the two impedance values are measured at a phase peak point and a valley phase point regardless of the phase absolute values, the effect of impedance measurement shift/offset (error) on SOH estimation is reduced. This supports the future development of more accurate and faster online and offline SOH estimation algorithms and systems that have a higher immunity to impedance measurement shift/offset (error). Furthermore, in this work, the EIS was measured for a lithium–ion battery that was down to a ~15% SOH, which was not only used to support the conclusions of this paper, but also helped in filling a gap in the literature for EIS data under deep/high degradation levels. Full article

(This article belongs to the Special Issue Toward Next-Generation Rechargeable Lithium-Ion Batteries: Current Status and Future Prospects)

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25 pages, 8971 KiB

Open AccessArticle

General Machine Learning Approaches for Lithium-Ion Battery Capacity Fade Compared to Empirical Models

by Quentin Mayemba, Gabriel Ducret, An Li, Rémy Mingant and Pascal Venet

Batteries 2024, 10(10), 367; https://doi.org/10.3390/batteries10100367 - 16 Oct 2024

Abstract

Today’s growing demand for lithium-ion batteries across various industrial sectors has introduced a new concern: battery aging. This issue necessitates the development of tools and models that can accurately predict battery aging. This study proposes a general framework for constructing battery aging models [...] Read more.

Today’s growing demand for lithium-ion batteries across various industrial sectors has introduced a new concern: battery aging. This issue necessitates the development of tools and models that can accurately predict battery aging. This study proposes a general framework for constructing battery aging models using machine learning techniques and compares these models with two existing empirical models, including a commercial one. To build the models, the databases produced by EVERLASTING and Bills et al. were utilized. The aim is to create universally applicable models that can address any battery-aging scenario. In this study, three types of models were developed: a vanilla neural network, a neural network inspired by extreme learning machines, and an encoder coupled with a neural network. The inputs for these models are derived from established knowledge in battery science, allowing the models to capture aging effects across different use cases. The models were trained on cells subjected to specific aging conditions and they were tested on other cells from the same database that experienced different aging conditions. The results obtained during the test for the vanilla neural network showed an RMSE of 1.3% on the Bills et al. test data and an RMSE of 2.7% on the EVERLASTING data, demonstrating similar or superior performance compared to the empirical models and proving the ability of the models to capture battery aging. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries—2nd Edition)

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16 pages, 3920 KiB

Open AccessArticle

Characterization of Lithium-Ion Battery Fire Emissions—Part 2: Particle Size Distributions and Emission Factors

by Matthew Claassen, Bjoern Bingham, Judith C. Chow, John G. Watson, Pengbo Chu, Yan Wang and Xiaoliang Wang

Batteries 2024, 10(10), 366; https://doi.org/10.3390/batteries10100366 - 16 Oct 2024

Abstract

The lithium-ion battery (LIB) thermal runaway (TR) emits a wide size range of particles with diverse chemical compositions. When inhaled, these particles can cause serious adverse health effects. This study measured the size distributions of particles with diameters less than 10 µm released [...] Read more.

The lithium-ion battery (LIB) thermal runaway (TR) emits a wide size range of particles with diverse chemical compositions. When inhaled, these particles can cause serious adverse health effects. This study measured the size distributions of particles with diameters less than 10 µm released throughout the TR-driven combustion of cylindrical lithium iron phosphate (LFP) and pouch-style lithium cobalt oxide (LCO) LIB cells. The chemical composition of fine particles (PM_2.5) and some acidic gases were also characterized from filter samples. The emission factors of particle number and mass as well as chemical components were calculated. Particle number concentrations were dominated by those smaller than 500 nm with geometric number mean diameters below 130 nm. Mass concentrations were also dominated by smaller particles, with PM₁ particles making up 81–95% of the measured PM₁₀ mass. A significant amount of organic and elemental carbon, phosphate, and fluoride was released as PM_2.5 constituents. The emission factor of gaseous hydrogen fluoride was 10–81 mg/Wh, posing the most immediate danger to human health. The tested LFP cells had higher emission factors of particles and HF than the LCO cells. Full article

(This article belongs to the Special Issue Thermal Safety of Lithium Ion Batteries)

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