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Batteries
Volume 10
Issue 11
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Batteries, Volume 10, Issue 11 (November 2024) – 34 articles

Cover Story (view full-size image): The growing demand for lithium is increasing the need for efficient ways to process spodumene and brine into battery-grade materials. Key methods for extracting lithium from spodumene include dense medium separation (DMS), froth flotation, pyrolysis, and hydrometallurgy. Techniques like precipitation, electrodialysis, direct lithium extraction, and membrane separation are used for brine. The traditional method of extracting lithium from spodumene is energy-intensive and produces significant CO2 emissions and liquid acid effluents. Direct lithium extraction could be an alternative to this method. Membrane technology and direct lithium extraction (DLE) methods are very useful for brine. Some methods, like electrochemical ion pumping, show promise but need to be scaled up. View this paper
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9 pages, 5523 KiB  
Article
Gravure-Printed Anodes Based on Hard Carbon for Sodium-Ion Batteries
by Maria Montanino, Claudia Paoletti, Anna De Girolamo Del Mauro and Giuliano Sico
Batteries 2024, 10(11), 407; https://doi.org/10.3390/batteries10110407 - 20 Nov 2024
Viewed by 541
Abstract
Printed batteries are increasingly being investigated for feeding small, wearable devices more and more involved in our daily lives, promoting the study of printing technologies. Among these, gravure is very attractive as a low-cost and low-waste production method for functional layers in different [...] Read more.
Printed batteries are increasingly being investigated for feeding small, wearable devices more and more involved in our daily lives, promoting the study of printing technologies. Among these, gravure is very attractive as a low-cost and low-waste production method for functional layers in different fields, such as energy, sensors, and biomedical, because it is easy to scale up industrially. Thanks to our research, the feasibility of gravure printing was recently proved for rechargeable lithium-ion batteries (LiBs) manufacturing. Such studies allowed the production of high-quality electrodes involving different active materials with high stability, reproducibility, and good performance. Going beyond lithium-based storage devices, our attention was devoted on the possibility of employing highly sustainable gravure printing for sodium-ion batteries (NaBs) manufacturing, following the trendy interest in sodium, which is more abundant, economical, and ecofriendly than lithium. Here a study on gravure printed anodes for sodium-ion batteries based on hard carbon as an active material is presented and discussed. Thanks to our methodology centered on the capillary number, a high printing quality anodic layer was produced providing typical electrochemical behavior and good performance. Such results are very innovative and relevant in the field of sodium-ion batteries and further demonstrate the high potential of gravure in printed battery manufacturing. Full article
(This article belongs to the Special Issue Electrolyte and Electrode Design for Next-Generation Rechargeable Batteries)
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15 pages, 3975 KiB  
Article
Internal Temperature Estimation for Lithium-Ion Cells Based on a Layered Electro-Thermal Equivalent Circuit Model
by Wei Shi, Wei Li and Shusheng Xiong
Batteries 2024, 10(11), 406; https://doi.org/10.3390/batteries10110406 - 18 Nov 2024
Viewed by 507
Abstract
In the domain of Battery Management System (BMS) research, the precise acquisition and estimation of internal temperature distribution within lithium-ion cells is a significant challenge. The commercial viability precludes the use of internal temperature sensors, and existing methodologies for online estimation of internal [...] Read more.
In the domain of Battery Management System (BMS) research, the precise acquisition and estimation of internal temperature distribution within lithium-ion cells is a significant challenge. The commercial viability precludes the use of internal temperature sensors, and existing methodologies for online estimation of internal temperatures under various electrical loads are constrained by computational limitations and model accuracy. This study presents a layered electro-thermal equivalent circuit model (LETECM), developed by integrating a layered second-order fractional equivalent circuit model with a layered thermal equivalent circuit model. A lithium-ion battery divided into three layers was employed to illustrate the development of this LETECM. The model’s precision was validated against a 3D Newman Finite Element Model (3DNFEM), constructed using actual battery parameters. Given that the thermal gradient inside the battery is usually more pronounced under high load conditions, a 10C direct current discharge for 60 s followed by a rest period of 240 s was adopted as the test condition in the simulation. The results indicate that at the end of the DC discharge, the temperature difference between the inner layer and the surface of the battery was the largest and the maximum temperature difference predicted by the LETECM was 3.58 °C, while the 3DNFEM exhibited a temperature difference of 3.74 °C. The trends in each layer temperature and battery surface temperature obtained by the two models are highly consistent. The proposed model offers computational efficiency and maintains notable accuracy, suggesting its potential integration into BMS for real-time online applications. This advancement could provide critical internal temperature data for refining battery charging and discharging performance assessments and lifespan predictions, thereby optimizing battery management strategies. Full article
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10 pages, 2870 KiB  
Article
Modulating Diffusion Double Layer by In Situ Constructed Ultrathin Dipole Layer Towards Uniform Lithium Deposition
by Yang Nan, Songmei Li, Wen Li, Guoke Wei and Bin Li
Batteries 2024, 10(11), 405; https://doi.org/10.3390/batteries10110405 - 18 Nov 2024
Viewed by 417
Abstract
The popularization of lithium metal anode has been limited due to uneven deposition processes and lithium dendrites. Guiding homogeneous nucleation during the initial plating stage of lithium is vital to obtain a stable lithium metal anode. Herein, an ultra-thin dipole layer that can [...] Read more.
The popularization of lithium metal anode has been limited due to uneven deposition processes and lithium dendrites. Guiding homogeneous nucleation during the initial plating stage of lithium is vital to obtain a stable lithium metal anode. Herein, an ultra-thin dipole layer that can be used to regulate the diffusion layer is prepared by anodizing and strong polarization on a titanium foil collector. It is demonstrated that the vertical distributions of ionic concentration and electrostatic potential on the nBTO@Ti electrode are modulated by the ultrathin dipole layer, leading to uniform diffusion of lithium ions and reduction of overpotential. Consequently, a uniform lithium nucleation and plating process are achieved on a polarized BaTiO3 collector, which is verified by microscopy. The average coulombic efficiency of the deposition-dissolution process is as high as 98.3% for 300 cycles at 0.5 mA cm−2. Moreover, the symmetrical cell shows flat potential platforms of 25 mV for 1000 cycles at 0.5 mA cm−2. Full cell with LiFePO4 as cathode also reveals excellent electrochemical performances with a steady discharge capacity of 120 mAh g−1 at 1 C and a high capacity retention of 93.3% after 200 cycles. Full article
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14 pages, 651 KiB  
Article
Coupled Electro-Thermal-Aging Battery Pack Modeling—Part 1: Cell Level
by Hadi Pasdarshahri, Émile Veilleux, William Mooney, Luc G. Fréchette, François Grondin and David Rancourt
Batteries 2024, 10(11), 404; https://doi.org/10.3390/batteries10110404 - 17 Nov 2024
Viewed by 648
Abstract
This paper presents a modeling approach to capture the coupled effects of electrical–thermal aging in Li-ion batteries at the cell level. The proposed semi-empirical method allows for a relatively high accuracy and low computational cost compared to expensive computer simulations. This is something [...] Read more.
This paper presents a modeling approach to capture the coupled effects of electrical–thermal aging in Li-ion batteries at the cell level. The proposed semi-empirical method allows for a relatively high accuracy and low computational cost compared to expensive computer simulations. This is something current models often lack but is essential for system level simulations, relevant for electric vehicle manufacturers. The aging analysis includes both cycling and calendar effects across the lifetime of the cell and reversible and irreversible heat in a lumped-mass model to capture the temperature evolution of the cell in operation. The Thévenin equivalent circuit model with capacitance used to simulate the electrical behavior of the cell was experimentally validated, showing a high correlation with the proposed model during the charging and discharging phases. A maximum error of 3% on the voltage reading was identified during discharge with the complete model. This model was also designed to be used as a stepping stone for a comprehensive model at the module and vehicle levels that can later be used by designers. Full article
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22 pages, 7572 KiB  
Article
Assessment of a Top and Bottom Cooling Strategy for Prismatic Lithium-Ion Cells Intended for Automotive Use
by Said Madaoui, Bartlomiej Guzowski, Roman Gozdur, Zlatina Dimitrova, Nicolas Audiot, Jocelyn Sabatier, Jean-Michel Vinassa and Franck Guillemard
Batteries 2024, 10(11), 403; https://doi.org/10.3390/batteries10110403 - 15 Nov 2024
Viewed by 583
Abstract
In contemporary vehicle applications, lithium-ion batteries have become a leading option among the diverse array of battery technologies available. This preference is attributed to their advantageous properties, which include low self-discharge rates and no memory effect. Despite these benefits, lithium-ion batteries are not [...] Read more.
In contemporary vehicle applications, lithium-ion batteries have become a leading option among the diverse array of battery technologies available. This preference is attributed to their advantageous properties, which include low self-discharge rates and no memory effect. Despite these benefits, lithium-ion batteries are not without their challenges. The key issues include a restricted driving range, concerns regarding longevity, safety risks, and prolonged charging durations. Efforts aimed at minimizing the charging duration frequently entail the introduction of elevated currents into the battery, a practice that can significantly elevate its temperature and, in turn, diminish its operational lifespan. Generally, battery packs in electric vehicles are equipped with flat cooling plates located on their side or bottom surfaces, which also serve the dual purpose of providing heating in colder conditions. Nevertheless, this cooling configuration faces difficulties during fast charging and may not efficiently heat or cool the batteries. In this work, a novel thermal management approach is proposed, in which a battery module is cooled not only with a bottom cooling plate but also using another cooling plate in contact with the busbars, located on the top of the battery module. The simulations and experimental tests show that this new configuration demonstrates significant improvements. The thermal time constant is reduced by 47%, enabling faster cooling of the module. Additionally, the maximum temperature reached by the battery during charging with dual cooling is lowered by 6 °C compared to the conventional approach. In this configuration, the top cooling plate acts as a thermal bridge. This is a key advantage that promotes temperature homogenization within the battery module. As a result, it supports an even aging process of batteries, ensuring their longevity and optimal performance. Full article
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18 pages, 8024 KiB  
Article
Evaluating a Nickel–Metal Hydride (NiMH) Battery Regeneration Patent Based on a Non-Intrusive and Unsupervised Prototype
by Rafael Martínez-Sánchez, Angel Molina-García, Antonio Mateo-Aroca and Alfonso P. Ramallo-González
Batteries 2024, 10(11), 402; https://doi.org/10.3390/batteries10110402 - 14 Nov 2024
Viewed by 1136
Abstract
In the ongoing shift toward electric vehicles (EVs) primarily utilizing lithium-ion battery technology, a significant population of hybrid electric vehicles (HEVs) remains operational, which are reliant on established NiMH battery systems. Over the last twenty years, these HEVs have generated a substantial number [...] Read more.
In the ongoing shift toward electric vehicles (EVs) primarily utilizing lithium-ion battery technology, a significant population of hybrid electric vehicles (HEVs) remains operational, which are reliant on established NiMH battery systems. Over the last twenty years, these HEVs have generated a substantial number of NiMH batteries that are either inoperable, experiencing performance degradation, or approaching the end of their service life. This situation results in a twofold challenge: (i) a growing volume of environmentally hazardous waste due to the difficulty of NiMH battery reclamation and (ii) escalating maintenance costs for HEV owners necessitated by replacement battery purchases. To overcome this scenario, patent WO2015092107A1, published in 2015, proposed a ‘Method for regenerating NiMH batteries.’ This method claimed the ability to restore NiMH batteries to their original functionality based on a non-intrusive approach. However, a comprehensive review of the relevant scientific literature fails to identify any empirical evidence supporting the efficacy of this regeneration technique. Within this context, this study provides a detailed analysis and evaluation of the regeneration process based on an unsupervised and non-intrusive prototype. The proposed prototype can be used not only to implement and evaluate the previous patent, but also to test any other process or methodology based on controlled charging/discharging periods under certain current conditions. NiMH battery cells from a Toyota Prius were included in this work as a real case study. The experimental results from this prototype demonstrate the reduced potential for battery regeneration using the proposed method. Future contributions should offer a promising solution for mitigating the challenges associated with NiMH battery disposal, maintenance within the HEV domain, and other second-life alternative options. Full article
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15 pages, 3368 KiB  
Article
The Role of Interfacial Effects in the Impedance of Nanostructured Solid Polymer Electrolytes
by Martino Airoldi, Ullrich Steiner and Ilja Gunkel
Batteries 2024, 10(11), 401; https://doi.org/10.3390/batteries10110401 - 12 Nov 2024
Viewed by 985
Abstract
The role of interfacial effects on an ion-conducting poly(styrene-b-ethylene oxide) (PS-b-PEO or SEO) diblock copolymer doped with lithium bis(trifluoromethanesulfonyl) imide (LiTFSI) was investigated by electrochemical impedance spectroscopy (EIS). Coating the surface of commonly used stainless steel electrodes with a [...] Read more.
The role of interfacial effects on an ion-conducting poly(styrene-b-ethylene oxide) (PS-b-PEO or SEO) diblock copolymer doped with lithium bis(trifluoromethanesulfonyl) imide (LiTFSI) was investigated by electrochemical impedance spectroscopy (EIS). Coating the surface of commonly used stainless steel electrodes with a specific random copolymer brush increases the measured ionic conductivity by more than an order of magnitude compared to the uncoated electrodes. The increase in ionic conductivity is related to the interfacial structure of the block copolymer domain morphology on the electrode surface. We show that the impedance associated with the electrode–electrolyte interface can be detected using nonmetallic electrodes, allowing us to distinguish the ionic conductivity behaviors of the bulk electrolyte and the interfacial layers for both as-prepared and annealed samples. Full article
(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)
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25 pages, 1385 KiB  
Article
A Comparison of Battery Equivalent Circuit Model Parameter Extraction Approaches Based on Electrochemical Impedance Spectroscopy
by Yuchao Wu and Balakumar Balasingam
Batteries 2024, 10(11), 400; https://doi.org/10.3390/batteries10110400 - 10 Nov 2024
Viewed by 1004
Abstract
This paper presents three approaches to estimating the battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). [...] Read more.
This paper presents three approaches to estimating the battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). The ES approach is assisted by the LS method for the rough determination of the lower and upper bound of the ECM parameters, and the NLS approach is incorporated with the Monte Carlo run such that different initial guesses can be assigned to improve the goodness of EIS fitting. The proposed approaches are validated using both simulated and real EIS data. Compared to the LS approach, the ES and NLS approaches show better fitting accuracy at various noise levels, whereas in both the validation using simulated EIS data and actual EIS data collected from LG 18650 and Molicel 21700 batteries, the NLS approach shows better fitting accuracy than that of LS and ES approaches. In all cases, compared with the ES approach, the computational time of the NLS approach is significantly faster, and compared with the LS approach, the NLS approach shows a minimal difference in computational time and considerably better fitting performance. Full article
(This article belongs to the Special Issue Battery Management Systems Based on Electrochemical Impedance Spectroscopy)
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23 pages, 4462 KiB  
Article
Prediction of Lithium-Ion Battery Health Using GRU-BPP
by Sahar Qaadan, Aiman Alshare, Alexander Popp and Benedikt Schmuelling
Batteries 2024, 10(11), 399; https://doi.org/10.3390/batteries10110399 - 8 Nov 2024
Viewed by 682
Abstract
Accurate prediction of lithium-ion batteries’ (LIBs) state-of-health (SOH) is crucial for the safety and maintenance of LIB-powered systems. This study addresses the variability in degradation trajectories by applying gated recurrent unit (GRU) networks alongside principal component analysis (PCA), Granger causality, and K-means clustering [...] Read more.
Accurate prediction of lithium-ion batteries’ (LIBs) state-of-health (SOH) is crucial for the safety and maintenance of LIB-powered systems. This study addresses the variability in degradation trajectories by applying gated recurrent unit (GRU) networks alongside principal component analysis (PCA), Granger causality, and K-means clustering to analyze the relationships between operating conditions—such as temperature and load profiles—and battery performance degradation. This paper uses a publicly accessible dataset derived by aging three prismatic LIB cells under a realistic forklift operation profile. First, we identify the features that are relevant to driving variance, then we employ the winning algorithm of K-means clustering for the classification of operational states. Granger causality later investigates the inter-group relationships. Our GRU-BPP model achieves an RMSE value of 0.167 and an MAE of 0.129 for the reference performance testing (RPT) dataset and an RMSE of 0.032 with an MAE of 0.025 for the aging dataset, thus outperformed benchmark methods such as GRU, LME, and XGBoost. These results further enhance the predictiveness and robustness of this approach and yield a holistic solution to the conventional challenges in battery management and their remaining useful life (RUL) predictions. Full article
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20 pages, 4412 KiB  
Article
Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm
by Juncheng Fu, Zhengxiang Song, Jinhao Meng and Chunling Wu
Batteries 2024, 10(11), 398; https://doi.org/10.3390/batteries10110398 - 8 Nov 2024
Viewed by 933
Abstract
The accurate prediction of lithium-ion battery state of health (SOH) can extend battery life, enhance device safety, and ensure sustained reliability in critical applications. Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is [...] Read more.
The accurate prediction of lithium-ion battery state of health (SOH) can extend battery life, enhance device safety, and ensure sustained reliability in critical applications. Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is proposed using a deep hybrid kernel extreme learning machine (DHKELM) optimized by the improved black-winged kite algorithm (IBKA). First, to address the limitations of traditional extreme learning machines (ELMs) in capturing non-linear features and their poor generalization ability, the concepts of auto encoders (AEs) and hybrid kernel functions are introduced to enhance ELM, resulting in the establishment of the DHKELM model for SOH prediction. Next, to tackle the challenge of parameter selection for DHKELM, an optimal point set strategy, the Gompertz growth model, and a Levy flight strategy are employed to optimize the parameters of DHKELM using IBKA before model training. Finally, the performance of IBKA-DHKELM is validated using two distinct datasets from NASA and CALCE, comparing it against ELM, DHKELM, and BKA-DHKELM. The results show that IBKA-DHKELM achieves the smallest error, with an RMSE of only 0.0062, demonstrating exceptional non-linear fitting capability, high predictive accuracy, and good robustness. Full article
(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)
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14 pages, 11889 KiB  
Article
Thermal Propagation Test Bench for the Study of the Paschen Curve and Lightning Arcs of Venting Gas
by Björn Mulder, Kai Peter Birke, Björn Obry, Stefan Wigger, Ruslan Kozakov, Pavel Smirnov and Jochen Schein
Batteries 2024, 10(11), 397; https://doi.org/10.3390/batteries10110397 - 8 Nov 2024
Viewed by 831
Abstract
Thermal propagation events are characterized by fire and thick black smoke, leading to propagation methods with a focus on preventing heat transfer and optimizing gas flow. Yet little attention is being paid to the electric conductivity of the gas, leading to possibly unexpected [...] Read more.
Thermal propagation events are characterized by fire and thick black smoke, leading to propagation methods with a focus on preventing heat transfer and optimizing gas flow. Yet little attention is being paid to the electric conductivity of the gas, leading to possibly unexpected battery casing openings due to lightning arcs as well as potentially providing the minimum ignition energy. This gas composition (omitting particles) was used at different temperatures and pressures in a lightning arc test bench, leading to the Paschen curve. Using a mini-module cell setup, filtered venting gas was flowed through another lightning arc test bench, allowing for in situ measurements. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire)
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22 pages, 7929 KiB  
Review
Recent Advances in High-Performance Carbon-Based Electrodes for Zinc-Ion Hybrid Capacitors
by Ying Liu, Lechun Song, Chenze Li, Caicheng Song and Xiang Wu
Batteries 2024, 10(11), 396; https://doi.org/10.3390/batteries10110396 - 7 Nov 2024
Viewed by 790
Abstract
Aqueous zinc-ion hybrid capacitors (ZIHCs) have emerged as a promising technology, showing superior energy and power densities, as well as enhanced safety, inexpensive and eco-friendly features. Although ZIHCs possess the advantages of both batteries and supercapacitors, their energy density is still unsatisfactory. Therefore, [...] Read more.
Aqueous zinc-ion hybrid capacitors (ZIHCs) have emerged as a promising technology, showing superior energy and power densities, as well as enhanced safety, inexpensive and eco-friendly features. Although ZIHCs possess the advantages of both batteries and supercapacitors, their energy density is still unsatisfactory. Therefore, it is extremely crucial to develop reasonably matched electrode materials. Based on this challenge, a surge of studies has been conducted on the modification of carbon-based electrode materials. Herein, we first summarize the progress of the related research and elucidate the energy storage mechanism associated with carbon-based electrodes for ZIHCs. Then, we investigate the influence of the synthesis routes and modification strategies of the electrode materials on electrochemical stability. Finally, we summarize the current research challenges facing ZIHCs and predict potential future research pathways. In addition, we suggest key scientific questions to focus on and potential directions for further exploration. Full article
(This article belongs to the Special Issue Advanced Studies on High-Performance Metal-Ion Capacitors: Technologies, Systems and Applications)
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25 pages, 5058 KiB  
Review
Research Progress and Challenges of Carbon/MXene Composites for Supercapacitors
by Li Sun, Yu Dong, Hangyu Li, Hanqi Meng, Jianfei Liu, Qigao Cao and Chunxu Pan
Batteries 2024, 10(11), 395; https://doi.org/10.3390/batteries10110395 - 7 Nov 2024
Viewed by 798
Abstract
Carbon materials/MXenes composite materials have gained widespread attention in the field of supercapacitors due to their excellent electrochemical performance. Carbon materials are considered ideal electrode materials for supercapacitors due to their high specific surface area, good conductivity, and outstanding electrochemical stability. MXenes, as [...] Read more.
Carbon materials/MXenes composite materials have gained widespread attention in the field of supercapacitors due to their excellent electrochemical performance. Carbon materials are considered ideal electrode materials for supercapacitors due to their high specific surface area, good conductivity, and outstanding electrochemical stability. MXenes, as a novel two-dimensional material, exhibit prominent conductivity, mechanical properties, and ionic conductivity, thereby showing great potential for applications in energy storage devices. The combination of carbon materials and MXenes is expected to fully leverage the advantages of both, optimizing electrode conductivity, enhancing the energy density and power density, and improving the charge–discharge performance. This article reviews the key research progress of carbon/MXenes composite materials in supercapacitors in recent years, including their synthesis methods, structural tuning, and improvements in their electrochemical performance. Finally, the article looks forward to future research directions and proposes potential strategies to enhance the overall performance of the composite materials and achieve large-scale applications. By addressing the existing challenges, carbon/MXenes composite materials are anticipated to achieve higher energy and power outputs for the supercapacitor field in the future, providing strong support for the development of new energy storage technologies such as electric vehicles and wearable devices. Full article
(This article belongs to the Special Issue Advanced Carbon-Based Materials for Next-Generation Batteries and Supercapacitors)
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22 pages, 11094 KiB  
Article
State of Health Estimation for Lithium-Ion Batteries Using an Explainable XGBoost Model with Parameter Optimization
by Zhenghao Xiao, Bo Jiang, Jiangong Zhu, Xuezhe Wei and Haifeng Dai
Batteries 2024, 10(11), 394; https://doi.org/10.3390/batteries10110394 - 7 Nov 2024
Viewed by 975
Abstract
Accurate and reliable estimation of the state of health (SOH) of lithium-ion batteries is crucial for ensuring safety and preventing potential failures of power sources in electric vehicles. However, current data-driven SOH estimation methods face challenges related to adaptiveness and interpretability. This paper [...] Read more.
Accurate and reliable estimation of the state of health (SOH) of lithium-ion batteries is crucial for ensuring safety and preventing potential failures of power sources in electric vehicles. However, current data-driven SOH estimation methods face challenges related to adaptiveness and interpretability. This paper investigates an adaptive and explainable battery SOH estimation approach using the eXtreme Gradient Boosting (XGBoost) model. First, several battery health features extracted from various charging and relaxation processes are identified, and their correlation with battery aging is analyzed. Then, a SOH estimation method based on the XGBoost algorithm is established, and the model’s hyper-parameters are tuned using the Bayesian optimization algorithm (BOA) to enhance the adaptiveness of the proposed estimation model. Additionally, the Tree SHapley Additive exPlanation (TreeSHAP) technique is employed to analyze the explainability of the estimation model and reveal the influence of different features on SOH evaluation. Experiments involving two types of batteries under various aging conditions are conducted to obtain battery cycling aging data for model training and validation. The quantitative results demonstrate that the proposed method achieves an estimation accuracy with a mean absolute error of less than 2.7% and a root mean squared error of less than 3.2%. Moreover, the proposed method shows superior estimation accuracy and performance compared to existing machine learning models. Full article
(This article belongs to the Special Issue State-of-Health Estimation of Batteries)
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12 pages, 5238 KiB  
Article
Simulation and Optimization of a Hybrid Photovoltaic/Li-Ion Battery System
by Xiaoxiao Yu, Juntao Fan, Zihua Wu, Haiping Hong, Huaqing Xie, Lan Dong and Yihuai Li
Batteries 2024, 10(11), 393; https://doi.org/10.3390/batteries10110393 - 6 Nov 2024
Viewed by 739
Abstract
The coupling of solar cells and Li-ion batteries is an efficient method of energy storage, but solar power suffers from the disadvantages of randomness, intermittency and fluctuation, which cause the low conversion efficiency from solar energy into electric energy. In this paper, a [...] Read more.
The coupling of solar cells and Li-ion batteries is an efficient method of energy storage, but solar power suffers from the disadvantages of randomness, intermittency and fluctuation, which cause the low conversion efficiency from solar energy into electric energy. In this paper, a circuit model for the coupling system with PV cells and a charge controller for a Li-ion battery is presented in the MATLAB/Simulink environment. A new three-stage charging strategy is proposed to explore the changing performance of the Li-ion battery, comprising constant-current charging, maximum power point tracker (MPPT) charging and constant-voltage charging stages, among which the MPPT charging stage can achieve the fastest maximum power point (MPP) capture and, therefore, improve battery charging efficiency. Furthermore, the charge controller can improve the lifetime of the battery through the constant-current and constant-voltage charging scheme. The simulation results indicate that the three-stage charging strategy can achieve an improvement in the maximum power tracking efficiency of 99.9%, and the average charge controller efficiency can reach 96.25%, which is higher than that of commercial chargers. This work efficiently matches PV cells and Li-ion batteries to enhance solar energy storages, and provides a new optimization idea for hybrid PV/Li-ion systems. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries: Design, Preparation, Reaction Mechanisms of Electrode Materials, and Battery Life Evaluation)
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29 pages, 1273 KiB  
Article
Model-Based Performance Evaluation of Hybrid Solid-State Batteries: Impact of Laser-Ablated Geometrical Structures
by Maximilian Scheller, Axel Durdel, Alexander Frank and Andreas Jossen
Batteries 2024, 10(11), 392; https://doi.org/10.3390/batteries10110392 - 5 Nov 2024
Viewed by 889
Abstract
Due to challenges in manufacturing composite cathodes with oxide solid electrolytes, new cell concepts are emerging in which the infiltration of solid-polymer electrolyte (SPE) into 3D cathode pore structures improves capacity retention and cycling stability. However, the performance limitation and the resulting practical [...] Read more.
Due to challenges in manufacturing composite cathodes with oxide solid electrolytes, new cell concepts are emerging in which the infiltration of solid-polymer electrolyte (SPE) into 3D cathode pore structures improves capacity retention and cycling stability. However, the performance limitation and the resulting practical relevance of such a hybrid concept have not yet been analyzed and discussed. This study investigates the impact of laser-ablated geometric structures on the performance of hybrid solid-state batteries (SSBs). A Doyle–Fuller–Newman modeling approach is developed and parameterized for structured hybrid SSBs that incorporate a PEO/LiTFSI SPE and an LLZO ceramic separator, as well as NMC-811 and Li-metal for the positive- and negative-electrode active materials. Comparison between structured and planar cell designs reveals significant rate capability improvements in structured designs due to reduced diffusion and interfacial charge transfer polarization. A sensitivity analysis of geometric structure parameters shows further potential for performance improvement in terms of specific capacity and energy density. However, current constriction effects in the LLZO separator can deteriorate the rate capability. A more general perspective is then taken by analyzing the impact of changing SPE parameters. An energy density of 128 Wh kg−1 at 1C, and 220 Wh kg−1 at 1C with improved SPE parameters is achieved in the best case, approaching the target of 250 Wh kg−1, which is currently achieved for conventional Li-ion batteries. Full article
(This article belongs to the Section Battery Modelling, Simulation, Management and Application)
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27 pages, 20905 KiB  
Tutorial
Teaching Aid Regarding the Application of Advanced Organic Petrography in Recycling End-of-Life Lithium-Ion Batteries
by Bruno Valentim
Batteries 2024, 10(11), 391; https://doi.org/10.3390/batteries10110391 - 5 Nov 2024
Viewed by 664
Abstract
This teaching aid aims to illustrate a range of the most common materials in end-of-life (EoL) lithium-ion batteries (LIBs) to demonstrate the usefulness of advanced organic petrography in the characterization of EoL LIB materials and to assess the efficiency of LIB recycling processes [...] Read more.
This teaching aid aims to illustrate a range of the most common materials in end-of-life (EoL) lithium-ion batteries (LIBs) to demonstrate the usefulness of advanced organic petrography in the characterization of EoL LIB materials and to assess the efficiency of LIB recycling processes from the pre-processing stage up to the impurities of the metallurgical processes. Additionally, it may be useful for students, petrographers, and professionals in battery development and recycling. Full article
(This article belongs to the Section Battery Processing, Manufacturing and Recycling)
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16 pages, 3553 KiB  
Article
Thermal Runaway Warning of Lithium Battery Based on Electronic Nose and Machine Learning Algorithms
by Zilong Pu, Miaomiao Yang, Mingzhi Jiao, Duan Zhao, Yu Huo and Zhi Wang
Batteries 2024, 10(11), 390; https://doi.org/10.3390/batteries10110390 - 5 Nov 2024
Viewed by 829
Abstract
Characteristic gas detection can be an efficient way to predict the degree of thermal runaway of a lithium battery. In this work, a sensor array consisting of three commercial MOS sensors was employed to discriminate between three target gases, CO, H2 and [...] Read more.
Characteristic gas detection can be an efficient way to predict the degree of thermal runaway of a lithium battery. In this work, a sensor array consisting of three commercial MOS sensors was employed to discriminate between three target gases, CO, H2 and a mixture of the two, which are characteristic gases released during the thermal runaway of lithium batteries. In this work, an integrated model that makes the classification stage results one of the feature inputs for the concentration regression stage was employed, successfully reducing the RMSE of the concentration regression results. In addition, we also explored the influence of the selection of the response time length on the classification and regression tasks, achieving the best results in a short time through the optimum algorithm. To assess the impact of time duration sensor data on the results, we selected four time windows of different length and extracted the corresponding sensor response data for subsequent processing. Initially, principal component analysis (PCA) was used to visualise the clustering of the three target gas samples at room temperature, providing a preliminary data analysis. For the classification phase, we chose three classification algorithms—MLP (Multilayer Perceptron), ELM (Extreme Learning Machine), and SVM (Support Vector Machine)—and performed a comprehensive comparison of their classification and generalisation abilities using grid search for hyperparameter optimisation and five-fold cross-validation. The results demonstrated that MLP achieved 99.23% classification accuracy during the 20 s response period. In the concentration regression phase, we combined the classification results with the raw features to create a new feature set, which was then input into a multi-output MLP regression model. The root mean square error (RMSE) employing the new feature set was used to measure the prediction error. Ultimately, the findings showed that the input of combined features significantly reduced the regression error for the mixed gas. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire)
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13 pages, 3877 KiB  
Article
Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data
by Yong Seok Bae, Sungwon Lee and Janghyuk Moon
Batteries 2024, 10(11), 389; https://doi.org/10.3390/batteries10110389 - 5 Nov 2024
Viewed by 927
Abstract
This study introduces a novel Sequence-to-Sequence (Seq2Seq) deep learning model for predicting lithium-ion batteries’ remaining useful life. We address the challenge of extrapolating battery performance from high-rate to low-rate charging conditions, a significant limitation in previous studies. Experiments were also conducted on commercial [...] Read more.
This study introduces a novel Sequence-to-Sequence (Seq2Seq) deep learning model for predicting lithium-ion batteries’ remaining useful life. We address the challenge of extrapolating battery performance from high-rate to low-rate charging conditions, a significant limitation in previous studies. Experiments were also conducted on commercial cells using charge rates from 1C to 3C. Comparative analysis of fully connected neural networks, convolutional neural networks, and long short-term memory networks revealed their limitations in extrapolating to untrained conditions. Our Seq2Seq model overcomes these limitations, predicting charging profiles and discharge capacity for untrained, low-rate conditions using only high-rate charging data. The Seq2Seq model demonstrated superior performance with low error and high curve-fitting accuracy for 1C and 1.2C untrained data. Unlike traditional models, it predicts complete charging profiles (voltage, current, temperature) for subsequent cycles, offering a comprehensive view of battery degradation. This method significantly reduces battery life testing time while maintaining high prediction accuracy. The findings have important implications for lithium-ion battery development, potentially accelerating advancements in electric vehicle technology and energy storage. Full article
(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)
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15 pages, 7772 KiB  
Article
State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm
by Tianqing Yuan, Yang Liu, Jing Bai and Hao Sun
Batteries 2024, 10(11), 388; https://doi.org/10.3390/batteries10110388 - 4 Nov 2024
Viewed by 729
Abstract
The ability to quickly and accurately estimate the state of charge (SOC) of lithium batteries is a key function of the battery management system (BMS). To enhance the accuracy of SOC estimation for lithium batteries, we propose a method that combines the dynamic [...] Read more.
The ability to quickly and accurately estimate the state of charge (SOC) of lithium batteries is a key function of the battery management system (BMS). To enhance the accuracy of SOC estimation for lithium batteries, we propose a method that combines the dynamic factor recursive least squares (DFFRLS) algorithm and the strong tracking H-infinity filtering (STF-HIF) algorithm. To address the issue of fixed forgetting factors in recursive least squares (RLS) that struggle to maintain both fast convergence and stability in battery parameter identification, we introduce dynamic forgetting factors. This approach adjusts the forgetting factor based on the residuals between the model’s estimated and actual values. To improve the H-infinity filtering (HIF) algorithm’s poor performance in tracking sudden state changes, we propose a combined STF-HIF algorithm, integrating HIF with strong tracking filtering (STF). Simulation experiments indicate that, compared to the HIF algorithm, the STF-HIF algorithm achieves a maximum absolute SOC estimation error (MaxAE) of 0.69%, 0.72%, and 1.22%, with mean absolute errors (MAE) of 0.27%, 0.25%, and 0.38%, and root mean square errors (RMSE) of 0.33%, 0.30%, and 0.46% under dynamic stress testing (DST), federal urban driving schedules (FUDS), and Beijing dynamic stress testing (BJDST) conditions, respectively. Full article
(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)
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17 pages, 5682 KiB  
Article
Tuning Molten-Salt-Mediated Calcination in Promoting Single-Crystal Synthesis of Ni-Rich LiNixMnyCozO2 Cathode Materials
by Joon Kyung Koong and George P. Demopoulos
Batteries 2024, 10(11), 387; https://doi.org/10.3390/batteries10110387 - 2 Nov 2024
Viewed by 1035
Abstract
High Ni-content LiNixMnyCozO2 (NMC) cathodes (with x ≥ 0.8, x + y + z = 1) have gained attention recently for their high energy density in electric vehicle (EV) Li-ion batteries. However, Ni-rich cathodes pose challenges [...] Read more.
High Ni-content LiNixMnyCozO2 (NMC) cathodes (with x ≥ 0.8, x + y + z = 1) have gained attention recently for their high energy density in electric vehicle (EV) Li-ion batteries. However, Ni-rich cathodes pose challenges in capacity retention due to inherent structural and surface redox instabilities. One promising strategy is to make the Ni-rich NMC material in the form of single-crystal micron-sized particles, as they resist intergranular and surface degradation during cycling. Among various methods to synthesize single-crystal NMC (SC-NMC) particles, molten-salt-assisted calcination offers distinct processing advantages but at present, is not yet optimized or mechanistically clarified to yield the desired control over crystal growth and morphology. In this project, molten-salt-mediated transformation of Ni0.85Mn0.05Co0.15(OH)2 precursor (P-NMC) particles to LiNi0.85Mn0.05Co0.15O2 particles is investigated in terms of the crystal growth mechanism and its electrochemical response. Unlike previous studies that involved large volumes of molten salt, using a smaller volume of molten KCl is found to result in larger primary particles with improved cycling performance achieved via partial reactive dissolution and heterogeneous nucleation growth, suggesting that the ratio of molten salt volume to NMC mass is an important parameter in the synthesis of single-crystal Ni-rich NMC materials. Full article
(This article belongs to the Section Battery Mechanisms and Fundamental Electrochemistry Aspects)
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10 pages, 3297 KiB  
Article
Novel One-Step Production of Carbon-Coated Sn Nanoparticles for High-Capacity Anodes in Lithium-Ion Batteries
by Emma M. H. White, Lisa M. Rueschhoff, Steve W. Martin and Iver E. Anderson
Batteries 2024, 10(11), 386; https://doi.org/10.3390/batteries10110386 - 1 Nov 2024
Viewed by 923
Abstract
Lithium-ion batteries offer the highest energy density of any currently available portable energy storage technology. By using different anode materials, these batteries could have an even greater energy density. One material, tin, has a theoretical lithium capacity (994 mAh/g) over three-times higher than [...] Read more.
Lithium-ion batteries offer the highest energy density of any currently available portable energy storage technology. By using different anode materials, these batteries could have an even greater energy density. One material, tin, has a theoretical lithium capacity (994 mAh/g) over three-times higher than commercial carbon anode materials. Unfortunately, to achieve this high capacity, bulk tin undergoes a large volume expansion, and the material pulverizes during cycling, giving a rapid capacity fade. To mitigate this issue, tin must be scaled down to the nano-level to take advantage of unique micromechanics at the nanoscale. Synthesis techniques for Sn nanoparticle anodes are costly and overly complicated for commercial production. A novel one-step process for producing carbon-coated Sn nanoparticles via spark plasma erosion (SPE) shows great promise as a simple, inexpensive production method. The SPE method, characterization of the resulting particles, and their high-capacity reversible electrochemical performance as anodes are described. With only a 10% addition of these novel SPE carbon-coated Sn particles, one anode composition demonstrated a reversible capacity of ~460 mAh/g, achieving the theoretical capacity of that particular electrode formulation. These SPE carbon-coated Sn nanoparticles are drop-in ready for present commercial lithium-ion anode processing and would provide a ~10% increase in the total capacity of current commercial lithium-ion cells. Full article
(This article belongs to the Special Issue High Capacity Anode Materials for Lithium-Ion Batteries)
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14 pages, 4573 KiB  
Article
Stabilizing the Solid Electrolyte Interphase of SiOx Negative Electrodes: The Role of Fluoroethylene Carbonate in Enhancing Electrochemical Performance
by Paul Maldonado Nogales, Sangyup Lee, Seunga Yang, Inchan Yang, Soen Hui Choi, Sei-Min Park, Jae Ho Lee, Chan Jung Kim, Jung-Chul An and Soon-Ki Jeong
Batteries 2024, 10(11), 385; https://doi.org/10.3390/batteries10110385 - 31 Oct 2024
Viewed by 666
Abstract
This study examined the role of fluoroethylene carbonate (FEC) in stabilizing the solid electrolyte interphase (SEI) and enhancing the electrochemical performance of SiOx-based composite negative electrodes in lithium-ion batteries. Two electrolyte systems were used: 1.0 mol dm−3 (M) LiPF6 in a [...] Read more.
This study examined the role of fluoroethylene carbonate (FEC) in stabilizing the solid electrolyte interphase (SEI) and enhancing the electrochemical performance of SiOx-based composite negative electrodes in lithium-ion batteries. Two electrolyte systems were used: 1.0 mol dm−3 (M) LiPF6 in a mixture of ethylene carbonate (EC) and ethyl methyl carbonate (EMC) with 0.5 wt.% VC, and 1.0 M LiPF6 in a mixture of EC and EMC with 1.0 wt.% VC and 10 wt.% FEC. These systems enabled the investigation of how FEC contributes to SEI stabilization and cycling stability. FEC promotes the formation of a LiF-rich SEI layer, which mitigates volume expansion and enhances capacity retention. Additionally, the accumulation of Li2CO3 and Li2O in the SEI was found to increase interfacial resistance, as observed through electrochemical impedance spectroscopy (EIS). Among the SiOx contents tested (0%, 3%, and 7.8%), the 3% SiOx content exhibited the best balance between SiOx and carbon nanotubes, resulting in improved SEI formation and enhanced electrochemical performance. These results offer insights into the optimization of electrolyte formulations for long-term cycling stability in SiOx-based lithium-ion batteries. Full article
(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)
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16 pages, 6180 KiB  
Article
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
Viewed by 769
Abstract
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)
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24 pages, 5707 KiB  
Article
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
Cited by 1 | Viewed by 1133
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  
Article
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
Viewed by 1067
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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23 pages, 13548 KiB  
Review
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
Viewed by 1157
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  
Article
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
Viewed by 1053
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  
Review
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
Viewed by 2912
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  
Article
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
Viewed by 829
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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