Batteries

32 pages, 8473 KiB

Open AccessReview

Application of Defect Engineering via ALD in Supercapacitors

by Tiange Gao, Xiaoyang Xiao, Zhenliang Dong, Xilong Lu, Liwen Mao, Jinzheng Wang, Yiming Liu, Qingmin Hu and Jiaqiang Xu

Batteries 2024, 10(12), 438; https://doi.org/10.3390/batteries10120438 - 10 Dec 2024

Abstract

Supercapacitors are a kind of energy storage device that lie between traditional capacitors and batteries, characterized by high power density, long cycle life, and rapid charging and discharging capabilities. The energy storage mechanism of supercapacitors mainly includes electrical double-layer capacitance and pseudocapacitance. In [...] Read more.

Supercapacitors are a kind of energy storage device that lie between traditional capacitors and batteries, characterized by high power density, long cycle life, and rapid charging and discharging capabilities. The energy storage mechanism of supercapacitors mainly includes electrical double-layer capacitance and pseudocapacitance. In addition to constructing multi-level pore structures to increase the specific surface area of electrode materials, defect engineering is essential for enhancing electrochemical active sites and achieving additional extrinsic pseudocapacitance. Therefore, developing a simple and efficient method for defect engineering is essential. Atomic layer deposition (ALD) technology enables precise control over thin film thickness at the atomic level through layer-by-layer deposition. This capability allows the intentional introduction of defects, such as vacancies, heteroatom doping, or misalignment, at specific sites within the material. The ALD process can regulate the defects in materials without altering the overall structure, thereby optimizing both the electrochemical and physical properties of the materials. Its self-limiting surface reaction mechanism also ensures that defects and doping sites are introduced uniformly across the material surface. This uniform defect distribution is particularly profitable for high surface area electrodes in supercapacitor applications, as it promotes consistent performance across the entire electrode. This review systematically summarizes the latest advancements in defect engineering via ALD technology in supercapacitors, including the enhancement of conductivity and the increase of active sites in supercapacitor electrode materials through ALD, thereby improving specific capacitance and energy density of the supercapacitor device. Furthermore, we discuss the underlying mechanisms, advantages, and future directions for ALD in this field. Full article

(This article belongs to the Special Issue High-Performance Super-capacitors: Preparation and Application)

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20 pages, 4254 KiB

Open AccessArticle

An AI-Driven Particle Filter Technology for Battery System State Estimation and RUL Prediction

by Mohamed Ahwiadi and Wilson Wang

Batteries 2024, 10(12), 437; https://doi.org/10.3390/batteries10120437 - 8 Dec 2024

Abstract

The increasing demand for reliable and safe Lithium-ion (Li-ion) batteries requires more accurate estimation of state of health (SOH) and remaining useful life (RUL) prediction. However, the inherent complexity and non-linear dynamics of Li-ion batteries present specific challenges to traditional methods of SOH [...] Read more.

The increasing demand for reliable and safe Lithium-ion (Li-ion) batteries requires more accurate estimation of state of health (SOH) and remaining useful life (RUL) prediction. However, the inherent complexity and non-linear dynamics of Li-ion batteries present specific challenges to traditional methods of SOH modeling. Although particle filter (PF) techniques can handle nonlinear dynamics, they still face challenges, including particle degeneracy and loss of diversity, that reduce their ability to effectively model the nonlinear degradation mechanisms of batteries. To tackle these limitations, this paper presents a novel artificial intelligence-driven PF (AI-PF) technology for battery health modeling and prognosis. The main contributions of the AI-PF technique are as follows: (1) A novel dynamic sample degeneracy detection method is proposed to provide real-time assessment of particle weights so as to promptly identify degeneracy and improve computational efficiency. (2) An adaptive crossover and mutation strategy is proposed to reallocate low-weight particles and maintain particle diversity to improve modeling and RUL forecasting accuracy. The effectiveness of the AI-PF framework is validated through systematic evaluations carried out using benchmark models and well-recognized battery datasets. Full article

(This article belongs to the Special Issue Artificial Intelligence and Batteries: AI-Powered Innovations in Battery Technology)

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34 pages, 7437 KiB

Open AccessReview

Beyond Organic Electrolytes: An Analysis of Ionic Liquids for Advanced Lithium Rechargeable Batteries

by Karthik Vishweswariah, Anil Kumar Madikere Raghunatha Reddy and Karim Zaghib

Batteries 2024, 10(12), 436; https://doi.org/10.3390/batteries10120436 - 7 Dec 2024

Abstract

The fast-growing area of battery technology requires the availability of highly stable, energy-efficient batteries for everyday applications. This, in turn, calls for research into new battery materials, especially with regard to a battery’s main component: the electrolytes. Besides the demands associated with solid [...] Read more.

The fast-growing area of battery technology requires the availability of highly stable, energy-efficient batteries for everyday applications. This, in turn, calls for research into new battery materials, especially with regard to a battery’s main component: the electrolytes. Besides the demands associated with solid ionic conduction and appropriate electrochemical behaviour, considerable effort will be necessary to thoroughly reduce safety risks in terms of flammability, leakage, and thermal runaway. Consequently, completely new classes of electrolytes need to be developed that are compatible with energy storage systems. Despite the progress made in solid polymer electrolytes, such materials have suffered from limitations to their real-world application. Now, ionic liquids are considered a class of electrolytes with the most potential for the creation of more advanced and safer lithium–ion batteries. In recent decades, ILs have been widely explored as potential electrolytes in the search for new breakthroughs in the ESS field, such those associated with fuel cells, lithium–ion batteries, and supercapacitors. The present review will discuss ILs that present high ionic conductivity, a lower melting point below 100 °C, and which feature up to 5–6 V wide electrochemical potential windows vs. Li⁺/Li. Furthermore, ILs exhibit good thermal stability, non-flammability, and low volatility—all of which are attributes realized by appropriate cation–anion combinations. This paper seeks to review the status of research concerning ILs, along with the advantages and challenges yet to be overcome in their development. Full article

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21 pages, 13415 KiB

Open AccessArticle

Modeling Thermal Runaway Mechanisms and Pressure Dynamics in Prismatic Lithium-Ion Batteries

by Mohammad Ayayda, Ralf Benger, Timo Reichrath, Kshitij Kasturia, Jacob Klink and Ines Hauer

Batteries 2024, 10(12), 435; https://doi.org/10.3390/batteries10120435 - 6 Dec 2024

Abstract

Lithium-ion batteries play a vital role in modern energy storage systems, being widely utilized in devices such as mobile phones, electric vehicles, and stationary energy units. One of the critical challenges with their use is the thermal runaway (TR), typically characterized by a [...] Read more.

Lithium-ion batteries play a vital role in modern energy storage systems, being widely utilized in devices such as mobile phones, electric vehicles, and stationary energy units. One of the critical challenges with their use is the thermal runaway (TR), typically characterized by a sharp increase in internal pressure. A thorough understanding and accurate prediction of this behavior are crucial for improving the safety and reliability of these batteries. To achieve this, two new combined models were developed: one to simulate the thermal runaway and another to simulate the internal cell pressure. The thermal model tracks a chain of decomposition reactions that eventually lead to TR. At the same time, the pressure model simulates the proportional increase in pressure due to the evaporation of the electrolyte and the gases produced from the decomposition reactions. What sets this work apart is the validation of the pressure model through experimental data, specifically for prismatic lithium-ion cells using NMC chemistries with varying stoichiometries—NMC111 and NMC811. While the majority of the literature focuses on the simulation of temperature and pressure for cylindrical cells, studies addressing these aspects in prismatic cells are much less common. This article addresses this gap by conducting pressure validation experiments, which are hardly documented in the existing studies. Furthermore, the model’s accuracy and flexibility are tested through two experiments, conducted under diverse conditions to ensure robust and adaptive predictions of cell behavior during failure scenarios. Full article

(This article belongs to the Special Issue Modeling, Reliability and Health Management of Lithium-Ion Batteries—2nd Edition)

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15 pages, 3957 KiB

Open AccessArticle

Lithium-Ion Battery Life Prediction Using Deep Transfer Learning

by Wen Zhang, R. S. B. Pranav, Rui Wang, Cheonghwan Lee, Jie Zeng, Migyung Cho and Jaesool Shim

Batteries 2024, 10(12), 434; https://doi.org/10.3390/batteries10120434 - 6 Dec 2024

Abstract

Lithium-ion batteries are critical components of various advanced devices, including electric vehicles, drones, and medical equipment. However, their performance degrades over time, and unexpected failures or discharges can lead to abrupt operational interruptions. Therefore, accurate prediction of the remaining useful life is essential [...] Read more.

Lithium-ion batteries are critical components of various advanced devices, including electric vehicles, drones, and medical equipment. However, their performance degrades over time, and unexpected failures or discharges can lead to abrupt operational interruptions. Therefore, accurate prediction of the remaining useful life is essential to ensure device safety and reliability. Conventional RUL prediction methods typically rely on regression analysis, signal processing, and machine learning techniques to assess battery conditions such as charge/discharge cycles, voltage, temperature, and durability. Although effective, these approaches are constrained by their dependence on large amounts of labeled data and the necessity for complex feature engineering to capture battery physical characteristics. In this study, we propose an approach that employs deep transfer learning to address these limitations. By leveraging pretrained model weights, the proposed method significantly improves the efficiency and accuracy of RUL prediction even under limited training data conditions. Furthermore, we investigate the impact of external environmental factors and physical battery characteristics on RUL prediction precision, thereby contributing to a more robust and reliable prediction framework. Full article

(This article belongs to the Topic Design Strategies for Battery Energy Storage and Electric Vehicles to Promote Circular Economy)

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24 pages, 11173 KiB

Open AccessArticle

Advanced State-of-Health Estimation for Lithium-Ion Batteries Using Multi-Feature Fusion and KAN-LSTM Hybrid Model

by Zhao Zhang, Runrun Zhang, Xin Liu, Chaolong Zhang, Gengzhi Sun, Yujie Zhou, Zhong Yang, Xuming Liu, Shi Chen, Xinyu Dong, Pengyu Jiang and Zhexuan Sun

Batteries 2024, 10(12), 433; https://doi.org/10.3390/batteries10120433 - 6 Dec 2024

Abstract

Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks [...] Read more.

Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks (KAN) and Long Short-Term Memory (LSTM) networks. The method is based on fully charged battery characteristics, extracting key parameters such as voltage, temperature, and charging data collected during cycles. Validation was conducted under a temperature range of 10 °C to 30 °C and different charge–discharge current rates. Notably, temperature variations were primarily caused by seasonal changes, enabling the experiments to more realistically simulate the battery’s performance in real-world applications. By enhancing dynamic modeling capabilities and capturing long-term temporal associations, experimental results demonstrate that the method achieves highly accurate SOH estimation under various charging conditions, with low mean absolute error (MAE) and root mean square error (RMSE) values and a coefficient of determination (

R^{2}

) exceeding 97%, significantly improving prediction accuracy and efficiency. Full article

(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)

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15 pages, 1452 KiB

Open AccessArticle

Learning the Ageing Behaviour of Lithium-Ion Batteries Using Electric Vehicle Fleet Analysis

by Thomas Lehmann, Erik Berendes, Richard Kratzing and Gautam Sethia

Batteries 2024, 10(12), 432; https://doi.org/10.3390/batteries10120432 - 5 Dec 2024

Abstract

This article presents the results of the Febal project, where the aim was to parametrize a stress-factor-based ageing model for Lithium-ion batteries using operation data of an electric fleet. Contrary to state-of-the-art methods, this approach does not rely on laboratory ageing tests only. [...] Read more.

This article presents the results of the Febal project, where the aim was to parametrize a stress-factor-based ageing model for Lithium-ion batteries using operation data of an electric fleet. Contrary to state-of-the-art methods, this approach does not rely on laboratory ageing tests only. Instead, a novel physics-informed learning procedure is used to combine the accuracy and flexibility of data-driven approaches with the extrapolation properties of physical models. The ageing model is parameterized in a two-stage process. In order to cover data ranges not present in operation, a laboratory ageing test campaign is used as a baseline. In the second stage, transfer learning is used to adjust a subset of the model parameters to fit data of different cells. This approach is not only applied to laboratory measurements but also validated by a series of capacity checkup tests performed with a fleet of electric vehicles. Results show the improved state-of-health (SOH) prediction of the proposed model parameterization method. 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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12 pages, 1657 KiB

Open AccessArticle

Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations

by Claudio Ronchetti, Sara Marchio, Francesco Buonocore, Simone Giusepponi, Sergio Ferlito and Massimo Celino

Batteries 2024, 10(12), 431; https://doi.org/10.3390/batteries10120431 - 5 Dec 2024

Abstract

Energy storage technologies have experienced significant advancements in recent decades, driven by the growing demand for efficient and sustainable energy solutions. The limitations associated with lithium’s supply chain, cost, and safety concerns have prompted the exploration of alternative battery chemistries. For this reason, [...] Read more.

Energy storage technologies have experienced significant advancements in recent decades, driven by the growing demand for efficient and sustainable energy solutions. The limitations associated with lithium’s supply chain, cost, and safety concerns have prompted the exploration of alternative battery chemistries. For this reason, research to replace widespread lithium batteries with sodium-ion batteries has received more and more attention. In the present work, we report cutting-edge research, where we explored a wide range of compositions of cathode materials for Na-ion batteries by first-principles calculations using workflow chains developed within the AiiDA framework. We trained crystal graph convolutional neural networks and geometric crystal graph neural networks, and we demonstrate the ability of the machine learning algorithms to predict the formation energy of the candidate materials as calculated by the density functional theory. This materials discovery approach is disruptive and significantly faster than traditional physics-based computational methods. Full article

(This article belongs to the Special Issue Electrolyte and Electrode Design for Next-Generation Rechargeable Batteries)

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9 pages, 3963 KiB

Open AccessArticle

Facile Synthesis of S/Ti₃C₂T_x Mxene@Se Cathode for High-Sulfur-Loading Lithium–Sulfur Batteries

by Yupu Shi, Jianbin Xu, Xian Du, Yi Zhang, Fan Zhao, Ziwei Tang, Le Kang and Huiling Du

Batteries 2024, 10(12), 430; https://doi.org/10.3390/batteries10120430 - 3 Dec 2024

Abstract

Lithium–sulfur batteries (LSBs) are gaining much attention because they offer a much higher theoretical energy density compared to traditional lithium-ion batteries. However, the cycling performance of LSBs with high sulfur mass loading is poor due to the shuttle effect, limiting the practical application [...] Read more.

Lithium–sulfur batteries (LSBs) are gaining much attention because they offer a much higher theoretical energy density compared to traditional lithium-ion batteries. However, the cycling performance of LSBs with high sulfur mass loading is poor due to the shuttle effect, limiting the practical application of LSBs. In this work, a unique porous sulfur/Ti₃C₂T_x Mxene@selenium (S/Ti₃C₂T_x@Se) cathode of a LSB is synthesized by a simple hydrothermal method to address these challenges. In this composite, Ti₃C₂T_x forms a conductive framework and Se is tightly anchored on the framework. The Se inhibits the agglomeration of Ti₃C₂T_x and prevents the collapse of Ti₃C₂T_x. The S/Ti₃C₂T_x@Se composite can adsorb lithium polysulfides (LiPSs) and suppresses the shuttle effect and volume changes during cycling, improving the cycling stability of LSBs with high S loading. A high capacity of 812.2 mAh g⁻¹ at 0.1 C with 5.0 mg cm⁻² sulfur mass loading after 100 cycles is obtained. This work could inspire further research into high-performance S host materials for high-S-loading LSBs. Full article

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21 pages, 15469 KiB

Open AccessArticle

Synthesis and Characterization of Lithium Phosphate (Li₃PO₄) as a Solid Electrolyte

by Seybou Yacouba Zakariyaou, Hua Ye and Chongwen Jiang

Batteries 2024, 10(12), 429; https://doi.org/10.3390/batteries10120429 - 3 Dec 2024

Abstract

Due to its high thermal stability, environmental friendliness, and safety, lithium phosphate (Li₃PO₄) is used as a solid electrolyte in battery applications, but it is usually used with dopants due to its lower ionic conductivity, which is required for [...] Read more.

Due to its high thermal stability, environmental friendliness, and safety, lithium phosphate (Li₃PO₄) is used as a solid electrolyte in battery applications, but it is usually used with dopants due to its lower ionic conductivity, which is required for ion transport. However, due to its stability and environmentally friendly aspect, lithium phosphate is still a hot topic among suitable energy materials that need further research to improve its electrochemical properties. In the current work, a novel synthesis of lithium phosphate was proposed from the raw materials lithium carbonate (Li₂CO₃) and trisodium phosphate dodecahydrate (Na₃PO₄*12H₂O) under suitable stoichiometric conditions using the co-precipitation method. In the set of synthesized samples, a single-phase β-Li₃PO₄ (named LPO-4) with 99.7% purity and 93.49% yield was successfully prepared under appropriate stoichiometric conditions and pH 13 at 90 °C. The average particle size was 10 nm with a large surface area of 9.02 m²g⁻¹. Electrochemical impedance spectroscopy (EIS) of LPO-4 revealed a conductivity of 7.1 × 10⁻⁶ S.cm⁻¹ at room temperature and 2.7 × 10⁻⁵ S.cm⁻¹ at 80 °C with a low activation energy of 0.38 eV. This performance is attributed to the morphology of the nanotubes and the smaller particle size, which enlarge the reaction interfaces and shorten the diffusion distance of lithium ions. The kinetic and thermodynamic key parameters showed that the β-Li₃PO₄ exhibits thermal stability in the room temperature range up to 208.8 °C. All these property values indicate a promising application of lithium phosphate as a solid electrolyte in solid-state batteries and a new route for further investigation. Full article

(This article belongs to the Special Issue Electrolyte and Electrode Design for Next-Generation Rechargeable Batteries)

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12 pages, 3778 KiB

Open AccessArticle

Synthesis of Three Ternary NiPP@PDA@DTA by Bridging Polydopamine and Its Flame Retardancy in Epoxy Resin

by Wenxin Zhu, Huiyu Chai, Yue Lu, Wang Zhan and Qinghong Kong

Batteries 2024, 10(12), 428; https://doi.org/10.3390/batteries10120428 - 3 Dec 2024

Abstract

Epoxy resin (EP) is an indispensable packaging material for batteries. Excellent thermal and flame-retardant properties of EP can ensure the safety performance of batteries. To solve the low-efficiency flame retardant of EP, nickel phenyl phosphate (NiPP) was synthesized and its surface was modified [...] Read more.

Epoxy resin (EP) is an indispensable packaging material for batteries. Excellent thermal and flame-retardant properties of EP can ensure the safety performance of batteries. To solve the low-efficiency flame retardant of EP, nickel phenyl phosphate (NiPP) was synthesized and its surface was modified by polymerization of dopamine (PDA). [3-(hydroxy-phenyl-methylidene) imimine] triazole (DTA) was synthesized using 9,10-dihydro-9-oxygen-10-phosphophene-10-oxide (DOPO), 3-amino-1,2,4-triazole and p-hydroxybenzaldehyde. The hybrid flame retardance NiPP@PDA@DTA was further synthesized by self-assembly between the negative charge on the surface of DTA and the positive charge on the surface of modified NiPP@PDA. Then, NiPP@PDA@DTA was added to EP to prepare EP/NiPP@PDA@DTA composites. The results showed that the incorporation of NiPP@PDA@DTA promoted the residual yield at high temperatures. Furthermore, EP composites showed excellent flame retardancy when NiPP@PDA@DTA was added. The EP/4 wt% NiPP@PDA@DTA composites can reach UL-94 V0 grade with a limit oxygen index (LOI) of 33.7%. While the heat release rate (HRR), total release rate (THR), CO₂ production (CO₂P) and total smoke release (TSR) of EP/4 wt% NiPP@PDA@DTA composites decreased by 16.9%, 30.8%, 16.9% and 27.7% compared with those of EP. These improvements are mainly due to the excellent catalytic carbonization performance of Ni metal and P compounds. The azazole and phosphaphenanthrene groups have the effects of dilution quenching in the gas phase and cross-linking network blocking, as well as enhanced blowing-out effects. Full article

(This article belongs to the Special Issue Thermal Safety of Lithium Ion Batteries—2nd Edition)

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30 pages, 5586 KiB

Open AccessArticle

Optimising Grid-Connected PV-Battery Systems for Energy Arbitrage and Frequency Containment Reserve

by Rodolfo Dufo-López, Juan M. Lujano-Rojas, Jesús S. Artal-Sevil and José L. Bernal-Agustín

Batteries 2024, 10(12), 427; https://doi.org/10.3390/batteries10120427 - 2 Dec 2024

Abstract

This study introduces a novel method for optimising the size and control strategy of grid-connected, utility-scale photovoltaic (PV) systems with battery storage aimed at energy arbitrage and frequency containment reserve (FCR) services. By applying genetic algorithms (GA), the optimal configurations of PV generators, [...] Read more.

This study introduces a novel method for optimising the size and control strategy of grid-connected, utility-scale photovoltaic (PV) systems with battery storage aimed at energy arbitrage and frequency containment reserve (FCR) services. By applying genetic algorithms (GA), the optimal configurations of PV generators, inverters/chargers, and batteries were determined, focusing on maximising the net present value (NPV). Both DC- and AC-coupled systems were explored. The performance of each configuration was simulated over a 25-year lifespan, considering varying pricing, solar resources, battery ageing, and PV degradation. Constraints included investment costs, capacity factors, and land use. A case study conducted in Wiesenthal, Germany, was followed by sensitivity analyses, revealing that a 75% reduction in battery costs is needed to make AC-coupled PV-plus-battery systems as profitable as PV-only systems. Further analysis shows that changes in electricity and FCR pricing as well as limits on FCR charging can significantly impact NPV. The study confirms that integrating arbitrage and FCR services can optimize system profitability. Full article

(This article belongs to the Section Battery Modelling, Simulation, Management and Application)

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23 pages, 5556 KiB

Open AccessArticle

SOC Estimation of a Lithium-Ion Battery at Low Temperatures Based on a CNN-Transformer and SRUKF

by Xun Gong, Tianzhu Jiang, Bosong Zou, Huijie Wang, Kaiyi Yang, Xinhua Liu, Bin Ma and Jiamei Lin

Batteries 2024, 10(12), 426; https://doi.org/10.3390/batteries10120426 - 1 Dec 2024

Abstract

As environmental regulations become stricter, the advantages of pure electric vehicles over fuel vehicles are becoming more and more significant. Due to the uncertainty of the actual operating conditions of the vehicle, accurate estimation of the state-of-charge (SOC) of the power battery under [...] Read more.

As environmental regulations become stricter, the advantages of pure electric vehicles over fuel vehicles are becoming more and more significant. Due to the uncertainty of the actual operating conditions of the vehicle, accurate estimation of the state-of-charge (SOC) of the power battery under multi-temperature scenarios plays an important role in guaranteeing the safety, economy, and reliability of electric vehicles. In this paper, a SOC estimation method based on the fusion of convolutional neural network-transformer (CNN-Transformer) and square root unscented Kalman filter (SRUKF) for lithium-ion batteries in low-temperature scenarios is proposed. First, the CNN-Transformer base model is established. Then, the SRUKF algorithm is used to update the state of the Coulomb counting method results based on the base model results. Finally, ensemble learning theory is applied to estimate SOC in multi-temperature scenarios. Data is obtained from laboratory conditions at −20 °C, −7 °C, and 0 °C. The experimental results show that the SOC estimation method proposed in this study is stable in terms of the root mean square error (RMSE) being between 2.69% and 4.22%. The proposed base model is also compared with the long short-term memory (LSTM) network and gated recurrent unit (GRU) network to demonstrate its relative advantages. Full article

(This article belongs to the Special Issue Battery Energy Storage Management by Integrating Omni-Channel Information: Battery Physics, Machine Learning, Force/Thermal/Electrical/Gas Sensors)

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23 pages, 4970 KiB

Open AccessArticle

Sequential Multi-Scale Modeling Using an Artificial Neural Network-Based Surrogate Material Model for Predicting the Mechanical Behavior of a Li-Ion Pouch Cell Under Abuse Conditions

by Alexander Schmid, Christian Ellersdorfer, Eduard Ewert and Florian Feist

Batteries 2024, 10(12), 425; https://doi.org/10.3390/batteries10120425 - 1 Dec 2024

Abstract

To analyze the safety behavior of electric vehicles, mechanical simulation models of their battery cells are essential. To ensure computational efficiency, the heterogeneous cell structure is represented by homogenized material models. The required parameters are calibrated against several characteristic cell experiments. As a [...] Read more.

To analyze the safety behavior of electric vehicles, mechanical simulation models of their battery cells are essential. To ensure computational efficiency, the heterogeneous cell structure is represented by homogenized material models. The required parameters are calibrated against several characteristic cell experiments. As a result, it is hardly possible to describe the behavior of the individual battery components, which reduces the level of detail. In this work, a new data-driven material model is presented, which not only provides the homogenized behavior but also information about the components. For this purpose, a representative volume element (RVE) of the cell structure is created. To determine the constitutive material models of the individual components, different characterization tests are performed. A novel method for carrying out single-layer compression tests is presented for the characterization in the thickness direction. The parameterized RVE is subjected to a large number of load cases using first-order homogenization theory. This data basis is used to train an artificial neural network (ANN), which is then implemented in commercial FEA software LS-DYNA R9.3.1 and is thus available as a material model. This novel data-driven material model not only provides the stress–strain relationship, but also outputs information about the condition of the components, such as the thinning of the separator. The material model is validated against two characteristic cell experiments. A three-point-bending test and an indentation test of the cell is used for this purpose. Finally, the influence of the architecture of the neural network on the computational effort is discussed. Full article

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36 pages, 10088 KiB

Open AccessReview

Recent Advances in Lithium Iron Phosphate Battery Technology: A Comprehensive Review

by Tao Chen, Man Li and Joonho Bae

Batteries 2024, 10(12), 424; https://doi.org/10.3390/batteries10120424 - 1 Dec 2024

Abstract

Lithium iron phosphate (LFP) batteries have emerged as one of the most promising energy storage solutions due to their high safety, long cycle life, and environmental friendliness. In recent years, significant progress has been made in enhancing the performance and expanding the applications [...] Read more.

Lithium iron phosphate (LFP) batteries have emerged as one of the most promising energy storage solutions due to their high safety, long cycle life, and environmental friendliness. In recent years, significant progress has been made in enhancing the performance and expanding the applications of LFP batteries through innovative materials design, electrode engineering, and manufacturing techniques. This review paper provides a comprehensive overview of the recent advances in LFP battery technology, covering key developments in materials synthesis, electrode architectures, electrolytes, cell design, and system integration. This review paper aims to provide a comprehensive overview of the recent advances in lithium iron phosphate (LFP) battery technology, encompassing materials development, electrode engineering, electrolytes, cell design, and applications. By highlighting the latest research findings and technological innovations, this paper seeks to contribute to the continued advancement and widespread adoption of LFP batteries as sustainable and reliable energy storage solutions for various applications. We also discuss the current challenges and future prospects for LFP batteries, emphasizing their potential role in sustainable energy storage solutions for various applications, including electric vehicles, renewable energy integration, and grid-scale energy storage. Full article

(This article belongs to the Special Issue Emerging Materials and Technologies for Post-Lithium-Ion Batteries—2nd Edition)

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17 pages, 3461 KiB

Open AccessArticle

Experimental Research on the Ignition Characteristics and Inhibition Strategy for Venting Emissions Mixture of Failure LiFePO₄ Battery

by Yan Wang, Zhaozhi Zhang, Ruiguang Yu, Yalun Li, Hewu Wang, Languang Lu, Xuning Feng and Minggao Ouyang

Batteries 2024, 10(12), 423; https://doi.org/10.3390/batteries10120423 - 30 Nov 2024

Abstract

When the concentration of a gas is below its lower flammable limit and the content of a liquid is below its minimum explosible concentration, their combined fuel mixture can be ignitable. The flammability characteristics and inhibition strategies for battery emission mixtures deserve further [...] Read more.

When the concentration of a gas is below its lower flammable limit and the content of a liquid is below its minimum explosible concentration, their combined fuel mixture can be ignitable. The flammability characteristics and inhibition strategies for battery emission mixtures deserve further in-depth research attention. This article presents experimental research on the ignition characteristics and inhibition strategy for a venting emission mixture of a failure LiFePO₄ battery. By identifying the components of venting emissions, ignition experiments for gases, electrolyte mist, their combination fuels, and mixtures with additives are performed to determine the flammable parameters, including ignition sensitivity and severity. The hybrid combination of non-flammable venting gases and electrolyte mist has the potential to induce ignition. However, there still exists a non-ignition region, where the gas concentration ratio (m_g) is below 0.15 and the liquid concentration ratio (m_l) is below 0.1. A safety design principle can be proposed: increasing ignition temperature, prolonging ignition time, and reducing maximum pressure. Adhering to this principle, a non-flammable electrolyte consisting of 1 mol LiPF₆ in EC:DEC = 1:1 vol%, with FEC at 10% and VC at 1%, can be considered as an optimization strategy. In comparison to the original gas–liquid mixtures, the region where no ignition occurs becomes wider when both the m_g is below 0.45 and the m_l is below 0.3. The new two-phase mixture has an ignition temperature of 835 °C, which is, respectively, 50% higher than that of the original mixture. Overall, this experimental research demonstrates an innovative methodology for assessing the battery venting emission mixture safety while proposing a design principle for modifying non-flammable electrolyte functional materials. Consequently, these findings can contribute to formulating more suitable preventive and protective measures for commercial electric vehicles and battery energy storage systems’ thermal safety designs. Full article

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30 pages, 11169 KiB

Open AccessArticle

Mechanical Characterization and Modeling of Large-Format Lithium-Ion Battery Cell Electrodes and Separators for Real Operating Scenarios

by Johannes Brehm, Axel Durdel, Tobias Kussinger, Philip Kotter, Maximilian Altmann and Andreas Jossen

Batteries 2024, 10(12), 422; https://doi.org/10.3390/batteries10120422 - 29 Nov 2024

Abstract

This study presents a novel application-oriented approach to the mechanical characterization and subsequent modeling of porous electrodes and separators in lithium-ion cells to gain a better understanding of their real mechanical operating behavior. An experimental study was conducted on the non-linear stiffness of [...] Read more.

This study presents a novel application-oriented approach to the mechanical characterization and subsequent modeling of porous electrodes and separators in lithium-ion cells to gain a better understanding of their real mechanical operating behavior. An experimental study was conducted on the non-linear stiffness of LiNi_0.8Co_0.15Al_0.05O₂ and graphite electrodes as well as PE separators, harvested from large-format lithium-ion cells, using compression tests. The mechanical response of the components was determined for different operating conditions, including nominal stress levels, mechanical loading rates, and mechanical cycles. The presented work describes the test procedure, the experimental setup, and an objective evaluation method, allowing for a detailed summary of the observed mechanical behavior. A distinct nominal stress level and mechanical cycle dependency of the non-linear stiffnesses of the porous materials were found. However, no clear dependency on compression rate was observed. Based on the experimental data, a poroelastic mechanical model was utilized to predict the non-linear behavior of these porous materials under real mechanical operating scenarios with a normalized root-mean-squared error less than 5.5%. The results provide essential new insights into the mechanical behavior of porous electrodes and separators in lithium-ion cells under real operating conditions, enabling the accelerated development of high-performing and safe batteries for various applications. Full article

(This article belongs to the Special Issue Recent Progress of Battery Design, Modeling and Testing in Electric Vehicles)

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32 pages, 4109 KiB

Open AccessReview

Critical Review of Temperature Prediction for Lithium-Ion Batteries in Electric Vehicles

by Junting Bao, Yuan Mao, Youbing Zhang, Hao Xu, Yajie Jiang and Yun Yang

Batteries 2024, 10(12), 421; https://doi.org/10.3390/batteries10120421 - 29 Nov 2024

Abstract

This paper reviews recent advancements in predicting the temperature of lithium-ion batteries in electric vehicles. As environmental and energy concerns grow, the development of new energy vehicles, particularly electric vehicles, has become a significant trend. Lithium-ion batteries, as the core component of electric [...] Read more.

This paper reviews recent advancements in predicting the temperature of lithium-ion batteries in electric vehicles. As environmental and energy concerns grow, the development of new energy vehicles, particularly electric vehicles, has become a significant trend. Lithium-ion batteries, as the core component of electric vehicles, have their performance and safety significantly impacted by temperature. This paper begins by introducing the fundamental components and operating principles of lithium-ion batteries, followed by an analysis of how temperature affects battery performance and safety. Next, the methods for measuring and predicting battery temperature are categorized and discussed, including model-based methods, data-driven methods, and hybrid approaches that combine both. Finally, the paper summarizes the application of temperature prediction in a BMS and provides an outlook on future research directions. Full article

(This article belongs to the Special Issue Advances in Thermal Management for Batteries: 2nd Edition)

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11 pages, 2929 KiB

Open AccessArticle

Dendrite-Free Zn Anode Modified by Organic Coating for Stable Aqueous Zinc Ion Batteries

by Fujie Li, Hongfei Zhang, Xuehua Liu, Binghui Xu and Chao Wang

Batteries 2024, 10(12), 420; https://doi.org/10.3390/batteries10120420 - 29 Nov 2024

Abstract

Aqueous zinc-ion batteries (AZIBs) have emerged as highly promising options for large-scale energy storage systems due to their cost-effectiveness, substantial energy capacity, and improved safety features. However, the Zn anode faces challenges such as self-corrosion and dendrite formation, which limit its practical use [...] Read more.

Aqueous zinc-ion batteries (AZIBs) have emerged as highly promising options for large-scale energy storage systems due to their cost-effectiveness, substantial energy capacity, and improved safety features. However, the Zn anode faces challenges such as self-corrosion and dendrite formation, which limit its practical use in AZIB applications. In this work, a simple blade-coating method was used to successfully coat poly (vinylidene fluoride–hexafluoro propylene) (PVDF-HFP) on the Zn anode. The coated Zn anode (P-Zn) displayed a stable cycling performance (700 h) at 1 mA cm⁻² current density in the symmetric cell. In addition, the full cell using MnO₂ as the cathode and P-Zn as the anode retained almost full capacity even after 1400 cycles at 2C, far outperforming the full cell using the unmodified Zn anode with only 50% capacity retention after 600 cycles. In situ optical observations of Zn deposition demonstrate that the special organic coating significantly enhances the uniform deposition of Zn²⁺, thus effectively mitigating corrosion and hydrogen evolution. Density Functional Theory (DFT) calculations show that the PVDF-HFP coating effectively narrows the adsorption energy gap between the P-Zn (002) and (101) planes, leading to the homogeneous deposition of Zn²⁺ with fewer Zn dendrites. A simple and feasible strategy for designing ultra-stable AZIBs by coating an organic protective layer on the Zn surface is provided by this work. Full article

(This article belongs to the Special Issue Electrolyte and Electrode Design for Next-Generation Rechargeable Batteries)

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