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Volume 11
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Batteries, Volume 11, Issue 7 (July 2025) – 25 articles

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28 pages, 18036 KiB  
Article
Artificial Neural Networks for Residual Capacity Estimation of Cycle-Aged Cylindric LFP Batteries
by Pasquale Franzese, Diego Iannuzzi, Roberta Merolla, Mattia Ribera and Ivan Spina
Batteries 2025, 11(7), 260; https://doi.org/10.3390/batteries11070260 - 10 Jul 2025
Abstract
This paper introduces a data-driven methodology for accurately estimating the residual capacity (RC) of lithium iron phosphate (LFP) batteries through a tailored artificial neural network (ANN) architecture. The proposed model integrates a long short-term memory (LSTM) layer with a fully connected layer, leveraging [...] Read more.
This paper introduces a data-driven methodology for accurately estimating the residual capacity (RC) of lithium iron phosphate (LFP) batteries through a tailored artificial neural network (ANN) architecture. The proposed model integrates a long short-term memory (LSTM) layer with a fully connected layer, leveraging their combined strengths to achieve precise RC predictions. A distinguishing feature of this study is its ability to deliver highly accurate estimates using a limited dataset that was derived from a single cylindrical LFP battery with a 40 Ah capacity and collected during a controlled experimental campaign. Despite the constraints imposed by the dataset size, the ANN demonstrates remarkable performance, underscoring the model’s capability to operate effectively with minimal data. The dataset is partitioned into the training and testing subsets to ensure a rigorous evaluation. Additionally, the robustness of the approach is validated by testing the trained ANN on data from a second battery cell subjected to a distinct aging process, which was entirely unseen during training. This critical aspect underscores the method’s applicability in estimating RC for batteries with varying aging profiles, a key requirement for real-world deployment. The proposed LSTM-based architecture was also benchmarked against a GRU-based model, yielding significantly lower prediction errors. Furthermore, beyond LFP chemistry, the method was tested on a broader NMC dataset comprising seven cells aged under different C-rates and temperatures, where it maintained high accuracy, confirming its scalability and robustness across chemistries and usage conditions. These results advance battery management systems by offering a robust, efficient modeling framework that optimizes battery utilization across diverse applications, even under data-constrained conditions. Full article
(This article belongs to the Special Issue Artificial Intelligence and Batteries: AI-Powered Innovations in Battery Technology)
19 pages, 5124 KiB  
Article
Gradient Silica Loading: Performance Analysis of PEMFCs Under Temperature-Humidity Variations
by Qiang Bai, Chuangyu Hsieh, Zhenghong Liu, Qipeng Chen and Fangbor Weng
Batteries 2025, 11(7), 259; https://doi.org/10.3390/batteries11070259 - 10 Jul 2025
Abstract
Fuel cells, as one of the most promising alternatives to lithium-ion batteries for portable power systems, still face significant challenges. A critical issue is their substantial performance degradation under low-humidity conditions. To address this, researchers commonly add silica to components. This study employs [...] Read more.
Fuel cells, as one of the most promising alternatives to lithium-ion batteries for portable power systems, still face significant challenges. A critical issue is their substantial performance degradation under low-humidity conditions. To address this, researchers commonly add silica to components. This study employs a control variable method to systematically investigate the impact of four parameters—gas stoichiometry, temperature, humidity, and silica content—on fuel cell performance. Initially, the effects of gas stoichiometry, temperature, and humidity on performance were examined. Subsequently, hydrophilic silica was incorporated into the membrane electrode assembly (MEA) to assess its potential for improving performance in low-humidity environments. Experimental results reveal that under 100% humidification, silica addition had a minimal impact on performance, particularly at high temperatures where performance improved by only 2.5%. This is attributed to increased water production at elevated temperatures, which—when combined with silica’s water retention properties—exacerbates flooding. However, when humidity was reduced to 50%, silica incorporation significantly enhanced performance. At high temperatures, silica addition resulted in a 126.2% performance improvement, demonstrating its efficacy as a rational strategy under low-humidity conditions. Full article
(This article belongs to the Special Issue Challenges, Progress, and Outlook of High-Performance Fuel Cells)
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44 pages, 7563 KiB  
Review
Green Batteries: A Sustainable Approach Towards Next-Generation Batteries
by Annu, Bairi Sri Harisha, Manesh Yewale, Bhargav Akkinepally and Dong Kil Shin
Batteries 2025, 11(7), 258; https://doi.org/10.3390/batteries11070258 - 10 Jul 2025
Abstract
The rising demand for sustainable energy storage has fueled the development of green batteries as alternatives to conventional systems. However, a major research gap lies in the unified integration of environmentally friendly materials and processes across all battery components—electrodes, electrolytes, and separators—without compromising [...] Read more.
The rising demand for sustainable energy storage has fueled the development of green batteries as alternatives to conventional systems. However, a major research gap lies in the unified integration of environmentally friendly materials and processes across all battery components—electrodes, electrolytes, and separators—without compromising performance or scalability. This review addresses this gap by highlighting recent advances in eco-conscious battery technologies, focusing on green electrode fabrication using water-based methods, electrophoretic deposition, solvent-free dry-press coating, 3D printing, and biomass-derived materials. It also examines the shift toward safer electrolytes, including ionic liquids, deep eutectic solvents, water-based systems, and solid biopolymer matrices, which improve both environmental compatibility and safety. Additionally, biodegradable separators made from natural polymers such as cellulose and chitosan offer enhanced thermal stability and ecological benefits. The review emphasizes the importance of lifecycle considerations like recyclability and biodegradability, aligning battery design with circular economy principles. While significant progress has been made, challenges such as standardization, long-term stability, and industrial scalability remain. By identifying key strategies and future directions, this article contributes to the foundation for next-generation green batteries, promoting their adoption in environmentally sensitive applications ranging from wearable electronics to grid storage. Full article
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26 pages, 8831 KiB  
Article
Coupling Performance of Cored and Coreless Circular Coils for WPTS: Experimental Validation Under Misalignment Scenarios
by Ahmed M. Ibrahim and Osama A. Mohammed
Batteries 2025, 11(7), 257; https://doi.org/10.3390/batteries11070257 - 10 Jul 2025
Abstract
Wireless power transfer systems (WPTSs) are critical for efficient and reliable electric vehicle (EV) charging, but challenges such as misalignment and coupling variations limit their performance. This paper addresses a proposed design approach for WPTSs by optimizing the following two widely used coil [...] Read more.
Wireless power transfer systems (WPTSs) are critical for efficient and reliable electric vehicle (EV) charging, but challenges such as misalignment and coupling variations limit their performance. This paper addresses a proposed design approach for WPTSs by optimizing the following two widely used coil types: ring and spiral circular coils. An analytical estimation of inductive characteristics is conducted to establish a foundation for system optimization. The study framework focuses on coil geometrical parameters and relative placements, accounting for horizontal, vertical, and angular misalignments to ensure a consistent performance under varying coupling conditions. COMSOL simulations accurately determine inductive parameters, validating the theoretical analysis for a 200 W charging coil prototype. Experimental investigations of coupling coefficients for coreless and cored charging pads highlight the superior performance of the Square I-Core-based spiral winding configuration in enhancing the coupling coefficient while ensuring that it remains below the critical value required for stable system operation. The agreement between the analytical results, simulation data, and experimental findings underscores the reliability of the proposed design approach. Full article
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11 pages, 1302 KiB  
Article
Design of a Transformer-GRU-Based Satellite Power System Status Detection Algorithm
by Guoqi Xie, Xinhao Yang, Jiayu Zhao and Zhou Huang
Batteries 2025, 11(7), 256; https://doi.org/10.3390/batteries11070256 - 8 Jul 2025
Viewed by 28
Abstract
The health state of satellite power systems plays a critical role in ensuring the normal operation of satellite platforms. This paper proposes an improved Transformer-GRU-based algorithm for satellite power status detection, which characterizes the operational condition of power systems by utilizing voltage and [...] Read more.
The health state of satellite power systems plays a critical role in ensuring the normal operation of satellite platforms. This paper proposes an improved Transformer-GRU-based algorithm for satellite power status detection, which characterizes the operational condition of power systems by utilizing voltage and temperature data from battery packs. The proposed method enhances the original Transformer architecture through an integrated attention network mechanism that dynamically adjusts attention weights to strengthen feature spatial correlations. A gated recurrent unit (GRU) network with cyclic structures is innovatively adopted to replace the conventional Transformer decoder, enabling efficient computation while maintaining temporal dependencies. Experimental results on satellite power system status detection demonstrate that the modified Transformer-GRU model achieves superior detection performance compared to baseline approaches. This research provides an effective solution for enhancing the reliability of satellite power management systems and opens new research directions for future advancements in space power system monitoring technologies. Full article
(This article belongs to the Section Battery Modelling, Simulation, Management and Application)
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32 pages, 4753 KiB  
Review
Prospective Obstacles and Improvement Strategies of Manganese-Based Materials in Achieving High-Performance Rechargeable Zinc–Air Batteries
by Zhangli Ye, Tianjing Wu, Lanhua Yi and Mingjun Jing
Batteries 2025, 11(7), 255; https://doi.org/10.3390/batteries11070255 - 8 Jul 2025
Viewed by 40
Abstract
Zinc–air batteries (ZABs) are crucial for renewable energy conversion and storage due to their cost-effectiveness, excellent safety, and superior cycling stability. However, developing efficient and affordable bifunctional electrocatalysts for the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) at the air [...] Read more.
Zinc–air batteries (ZABs) are crucial for renewable energy conversion and storage due to their cost-effectiveness, excellent safety, and superior cycling stability. However, developing efficient and affordable bifunctional electrocatalysts for the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) at the air cathode remains a significant challenge. Manganese (Mn)-based materials, known for their tunable oxidation states, adaptable crystal structures, and environmental friendliness, are regarded as the most promising candidates. This review systematically summarizes recent advances in Mn-based bifunctional catalysts, concentrating on four primary categories: Mn–N–C electrocatalysts, manganese oxides, manganates, and other Mn-based compounds. By examining the intrinsic merits and limitations of each category, we provide a comprehensive discussion of optimization strategies, which include morphological modulation, structural engineering, carbon hybridization, heterointerface construction, heteroatom doping, and defect engineering, aimed at enhancing catalytic performance. Additionally, we critically address existing challenges and propose future research directions for Mn-based materials in rechargeable ZABs, offering theoretical insights and design principles to advance the development of next-generation energy storage systems. Full article
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117 pages, 10736 KiB  
Review
Design Principles and Engineering Strategies for Stabilizing Ni-Rich Layered Oxides in Lithium-Ion Batteries
by Alain Mauger and Christian M. Julien
Batteries 2025, 11(7), 254; https://doi.org/10.3390/batteries11070254 - 4 Jul 2025
Viewed by 134
Abstract
Nickel-rich layered oxides such as LiNixMnyCozO2 (NMC), LiNixCoyAlzO2 (NCA), and LiNixMnyCozAl(1–x–y–z)O2 (NMCA), where x [...] Read more.
Nickel-rich layered oxides such as LiNixMnyCozO2 (NMC), LiNixCoyAlzO2 (NCA), and LiNixMnyCozAl(1–x–y–z)O2 (NMCA), where x ≥ 0.6, have emerged as key cathode materials in lithium-ion batteries due to their high operating voltage and superior energy density. These materials, characterized by low cobalt content, offer a promising path toward sustainable and cost-effective energy storage solutions. However, their electrochemical performance remains below theoretical expectations, primarily due to challenges related to structural instability, limited thermal safety, and suboptimal cycle life. Intensive research efforts have been devoted to addressing these issues, resulting in substantial performance improvements and enabling the development of next-generation lithium-ion batteries with higher nickel content and reduced cobalt dependency. In this review, we present recent advances in material design and engineering strategies to overcome the problems limiting their electrochemical performance (cation mixing, phase stability, oxygen release, microcracks during cycling). These strategies include synthesis methods to optimize the morphology (size of the particles, core–shell and gradient structures), surface modifications of the Ni-rich particles, and doping. A detailed comparison between these strategies and the synergetic effects of their combination is presented. We also highlight the synergistic role of compatible lithium salts and electrolytes in achieving state-of-the-art nickel-rich lithium-ion batteries. Full article
(This article belongs to the Special Issue Batteries: 10th Anniversary)
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15 pages, 2182 KiB  
Article
Investigating the Thermal Runaway Characteristics of the Prismatic Lithium Iron Phosphate Battery Under a Coupled Charge Rate and Ambient Temperature
by Jikai Tian, Zhenxiong Wang, Lingrui Kong, Fengyang Xu, Xin Dong and Jun Shen
Batteries 2025, 11(7), 253; https://doi.org/10.3390/batteries11070253 - 4 Jul 2025
Viewed by 178
Abstract
Optimizing the charging rate is crucial for enhancing lithium iron phosphate (LFP) battery performance. The substantial heat generation during high C-rate charging poses a significant risk of thermal runaway, necessitating advanced thermal management strategies. This study systematically investigates the coupling mechanism between charging [...] Read more.
Optimizing the charging rate is crucial for enhancing lithium iron phosphate (LFP) battery performance. The substantial heat generation during high C-rate charging poses a significant risk of thermal runaway, necessitating advanced thermal management strategies. This study systematically investigates the coupling mechanism between charging rates and ambient temperatures in overcharge-induced thermal runaway, filling the knowledge gaps associated with multi-indicator thermal management approaches. Through experiments on prismatic LFP cells across five operational conditions (1C/35 °C, 1.5C/5 °C, 1.5C/15 °C, 1.5C/25 °C, and 1.5C/35 °C), synchronized infrared thermography and electrochemical monitoring quantitatively characterize the thermal–electric coupling dynamics throughout overcharge-to-runaway transitions. The experimental findings reveal three key observations: (1) Charge rate and temperature have synergistic amplification effects on triggering thermal runaway. (2) Contrary to intuition, while low-current/high-temperature charging enhances safety versus high-current/high-temperature conditions, low-temperature/high-current charging triggers thermal runaway faster than high-temperature/high-current scenarios. (3) Staged multi-indicator lithium battery thermal runaway warning signals would be more accurate (first peaks > 0.5 °C/s temperature rise rate + >10 V/s voltage drop rate). These findings collectively demonstrate the imperative for next-generation battery management systems integrating real-time ambient temperature compensation with adaptive C-rate control, fundamentally advancing beyond conventional single-variable thermal regulation strategies. Intelligent adaptation is critical for mitigating thermal runaway risks in LFP battery operations. Full article
(This article belongs to the Special Issue Thermal Management System for Lithium-Ion Batteries: 2nd Edition)
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21 pages, 13514 KiB  
Article
Comparative Analysis via CFD Simulation on the Impact of Graphite Anode Morphologies on the Discharge of a Lithium-Ion Battery
by Alessio Lombardo Pontillo, Agnese Marcato, Daniele Versaci, Daniele Marchisio and Gianluca Boccardo
Batteries 2025, 11(7), 252; https://doi.org/10.3390/batteries11070252 - 2 Jul 2025
Viewed by 205
Abstract
The morphology of electrode materials plays a crucial role in determining the performance of lithium-ion batteries. Traditional computational models often simplify graphite flakes as uniformly sized spheres, which limits their predictive accuracy. In this study, we present a computational workflow that overcomes these [...] Read more.
The morphology of electrode materials plays a crucial role in determining the performance of lithium-ion batteries. Traditional computational models often simplify graphite flakes as uniformly sized spheres, which limits their predictive accuracy. In this study, we present a computational workflow that overcomes these limitations by incorporating a more realistic representation of graphite morphologies. This workflow is designed to be flexible and reproducible, enabling efficient evaluation of electrochemical performance across diverse material structures. By exploring different graphite morphologies, our approach accelerates the optimization of material preparation techniques and processing conditions. Our findings reveal that incorporating greater morphological complexity leads to significant deviations from classical model predictions. Instead, our refined model offers a more accurate representation of battery discharge behavior, closely aligning with experimental data. This improvement underscores the importance of detailed morphological descriptions in advancing battery design and performance assessments. To promote accessibility and reproducibility, we provide the developed code for seamless integration with the COMSOL API, allowing researchers to implement and adapt it easily. This computational framework serves as a valuable tool for investigating the impact of graphite morphology on battery performance, bridging the gap between theoretical modeling and experimental validation to enhance lithium-ion battery technology. Full article
(This article belongs to the Special Issue Batteries: 10th Anniversary)
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15 pages, 508 KiB  
Article
Demand-Adapting Charging Strategy for Battery-Swapping Stations
by Benjamín Pla, Pau Bares, Andre Aronis and Augusto Perin
Batteries 2025, 11(7), 251; https://doi.org/10.3390/batteries11070251 - 2 Jul 2025
Viewed by 127
Abstract
This paper analyzes the control strategy for urban battery-swapping stations by optimizing the charging policy based on real-time battery demand and the time required for a full charge. The energy stored in available batteries serves as an electricity buffer, allowing energy to be [...] Read more.
This paper analyzes the control strategy for urban battery-swapping stations by optimizing the charging policy based on real-time battery demand and the time required for a full charge. The energy stored in available batteries serves as an electricity buffer, allowing energy to be drawn from the grid when costs or equivalent CO2 emissions are low. An optimized charging policy is derived using dynamic programming (DP), assuming average battery demand and accounting for both the costs and emissions associated with electricity consumption. The proposed algorithm uses a prediction of the expected traffic in the area as well as the expected cost of electricity on the net. Battery tests were conducted to assess charging time variability, and traffic density measurements were collected in the city of Valencia across multiple days to provide a realistic scenario, while real-time data of the electricity cost is integrated into the control proposal. The results show that incorporating traffic and electricity price forecasts into the control algorithm can reduce electricity costs by up to 11% and decrease associated CO2 emissions by more than 26%. Full article
(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)
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12 pages, 2634 KiB  
Article
Enhancing the Cycle Life of Silicon Oxide–Based Lithium-Ion Batteries via a Nonflammable Fluorinated Ester–Based Electrolyte
by Kihun An, Yen Hai Thi Tran, Dong Guk Kang and Seung-Wan Song
Batteries 2025, 11(7), 250; https://doi.org/10.3390/batteries11070250 - 30 Jun 2025
Viewed by 194
Abstract
Silicon oxide–graphite is a promising high-capacity anode material for next-generation lithium-ion batteries (LIBs). However, despite using a small fraction (≤5%) of Si, it suffers from a short cycle life owing to intrinsic swelling and particle pulverization during cycling, making practical application challenging. High-nickel [...] Read more.
Silicon oxide–graphite is a promising high-capacity anode material for next-generation lithium-ion batteries (LIBs). However, despite using a small fraction (≤5%) of Si, it suffers from a short cycle life owing to intrinsic swelling and particle pulverization during cycling, making practical application challenging. High-nickel (Ni ≥ 80%) oxide cathodes for high-energy-density LIBs and their operation beyond 4.2 V have been pursued, which requires the anodic stability of the electrolyte. Herein, we report a nonflammable multi-functional fluorinated ester–based liquid electrolyte that stabilizes the interfaces and suppresses the swelling of highly loaded 5 wt% SiO–graphite anode and LiNi0.88Co0.08Mn0.04O2 cathode simultaneously in a 3.5 mAh cm−2 full cell, and improves cycle life and battery safety. Surface characterization results reveal that the interfacial stabilization of both the anode and cathode by a robust and uniform solid electrolyte interphase (SEI) layer, enriched with fluorinated ester-derived inorganics, enables 80% capacity retention of the full cell after 250 cycles, even under aggressive conditions of 4.35 V, 1 C and 45 °C. This new electrolyte formulation presents a new opportunity to advance SiO-based high-energy density LIBs for their long operation and safety. Full article
(This article belongs to the Collection Feature Papers in Batteries)
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18 pages, 1972 KiB  
Article
Lithium Growth on Alloying Substrates and Effect on Volumetric Expansion
by Laura C. Merrill, Robert L. Craig, Damion P. Cummings and Julia I. Deitz
Batteries 2025, 11(7), 249; https://doi.org/10.3390/batteries11070249 - 29 Jun 2025
Viewed by 197
Abstract
The widespread implementation of next-generation Li metal anodes is limited, in part, due to the formation of dendritic and/or mossy electrodeposits during cycling. These morphologies can lead to battery failure due to the formation of short circuits and significant volumetric expansion at the [...] Read more.
The widespread implementation of next-generation Li metal anodes is limited, in part, due to the formation of dendritic and/or mossy electrodeposits during cycling. These morphologies can lead to battery failure due to the formation of short circuits and significant volumetric expansion at the anode. One strategy to control the electrodeposition of Li metal is to use lithiophilic materials at the anode. Here, we evaluate the impact of Ag and Au on the early stages of Li metal electrodeposition and cycling. The alloying substrates decrease the voltage for Li reduction and improve Li wetting/adhesion. We probe volumetric expansion directly through dilatometry measurements and find that the degree of volumetric expansion is less when lithium is cycled on an alloying substrate compared to a non-alloying substrate (Cu). Dilatometry experiments reveal that Au has the least amount of volumetric expansion and coin cell cycling experiments indicate that Ag yields more stable cycling compared to Au or Cu. The evaluation of in situ cross-sectional images of cycled coin cells shows that Ag has the lowest volumetric expansion in a coin cell format. Full article
(This article belongs to the Special Issue Batteries: 10th Anniversary)
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18 pages, 3928 KiB  
Article
Limited-Data Augmentation for Fault Diagnosis in Lithium-Ion Battery Energy Storage Systems via Transferable Conditional Diffusion
by Zhipeng Yang, Yuhao Pan, Wenchao Liu, Jinhao Meng and Zhengxiang Song
Batteries 2025, 11(7), 248; https://doi.org/10.3390/batteries11070248 - 27 Jun 2025
Viewed by 182
Abstract
Fault diagnosis accuracy in lithium-ion battery-based energy storage systems is significantly constrained by the limited availability of fault-specific datasets. This study addresses this critical issue by proposing a diffusion-based data augmentation methodology tailored explicitly for battery fault diagnosis scenarios. The proposed conditional diffusion [...] Read more.
Fault diagnosis accuracy in lithium-ion battery-based energy storage systems is significantly constrained by the limited availability of fault-specific datasets. This study addresses this critical issue by proposing a diffusion-based data augmentation methodology tailored explicitly for battery fault diagnosis scenarios. The proposed conditional diffusion model leverages transfer learning and attention-enhanced fine-tuning strategies to generate high-quality synthetic fault data, ensuring targeted representation of rare fault conditions. By integrating condition-aware sampling strategies, the approach effectively mitigates mode collapse issues frequently encountered in adversarial generative methods, thus substantially enriching the diversity and quality of fault representations. Comprehensive evaluation using statistical similarity measures and downstream classification tasks demonstrates notable improvements. After the integration of attention mechanisms, the Pearson correlation coefficient between the synthetic and real samples increases from 0.29 to 0.91. In downstream diagnostic tasks, models trained on augmented datasets exhibit substantial gains in regards to the recall and F1-score, which increase from near-zero levels to values exceeding 0.91 for subtle overcharge and overdischarge faults. These results confirm the effectiveness and practical utility of the proposed augmentation approach in enhancing diagnostic performance under data-scarce conditions. Full article
(This article belongs to the Special Issue Advanced Lithium-Ion Battery Management in Renewable Energy Systems: 2nd Edition)
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14 pages, 4052 KiB  
Article
Analysis of Hydrogen Leakage and Influencing Factors of Fuel Cell Vehicles in Enclosed Spaces
by Congxin Li and Zhang Xin
Batteries 2025, 11(7), 247; https://doi.org/10.3390/batteries11070247 - 26 Jun 2025
Viewed by 217
Abstract
A simulation study was conducted on the hydrogen leakage diffusion process and influencing factors of fuel cell vehicles in enclosed spaces. The results indicate that when hydrogen leakage flows towards the rear of the vehicle, it mainly flows along the rear wall of [...] Read more.
A simulation study was conducted on the hydrogen leakage diffusion process and influencing factors of fuel cell vehicles in enclosed spaces. The results indicate that when hydrogen leakage flows towards the rear of the vehicle, it mainly flows along the rear wall of the space and diffuses to the surrounding areas. Setting ventilation openings of different areas on the top of the carriage did not significantly improve the spatial diffusion speed of the leaked hydrogen, and the impact on the concentration of leaked hydrogen was limited to the vicinity of the ventilation openings. The ventilation opening at the rear can accelerate the diffusion of hydrogen gas to the external environment, significantly reducing the concentration of hydrogen and rate of gas rise. When the leaked hydrogen gas flows towards the front of the vehicle and above the space, the concentration of hydrogen mainly increases along the height direction of the space. The research results have significant safety implications for the use of fuel cell semi-trailer trucks. Full article
(This article belongs to the Special Issue Challenges, Progress, and Outlook of High-Performance Fuel Cells)
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29 pages, 4054 KiB  
Article
Investigation of Convective and Radiative Heat Transfer of 21700 Lithium-Ion Battery Cells
by Gábor Kovács, Szabolcs Kocsis Szürke and Szabolcs Fischer
Batteries 2025, 11(7), 246; https://doi.org/10.3390/batteries11070246 - 26 Jun 2025
Viewed by 347
Abstract
Due to their high energy density and power potential, 21700 lithium-ion battery cells are a widely used technology in hybrid and electric vehicles. Efficient thermal management is essential for maximizing the performance and capacity of Li-ion cells in both low- and high-temperature operating [...] Read more.
Due to their high energy density and power potential, 21700 lithium-ion battery cells are a widely used technology in hybrid and electric vehicles. Efficient thermal management is essential for maximizing the performance and capacity of Li-ion cells in both low- and high-temperature operating conditions. Optimizing thermal management systems remains critical, particularly for long-range and weight-sensitive applications. In these contexts, passive heat dissipation emerges as an ideal solution, offering effective thermal regulation with minimal additional system weight. This study aims to deepen the understanding of passive heat dissipation in 21700 battery cells and optimize their performance. Special emphasis is placed on analyzing heat transfer and the relative contributions of convective and radiative mechanisms under varying temperature and discharge conditions. Laboratory experiments were conducted under controlled environmental conditions at various discharge rates, ranging from 0.5×C to 5×C. A 3D-printed polymer casing was applied to the cell to enhance thermal dissipation, designed specifically to increase radiative heat transfer while minimizing system weight and reliance on active cooling solutions. Additionally, a numerical model was developed and optimized using experimental data. This model simulates convective and radiative heat transfer mechanisms with minimal computational demand. The optimized numerical model is intended to facilitate further investigation of the cell envelope strategy at the module and battery pack levels in future studies. Full article
(This article belongs to the Special Issue Rechargeable Batteries)
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17 pages, 2795 KiB  
Article
Coordinated Control Strategy-Based Energy Management of a Hybrid AC-DC Microgrid Using a Battery–Supercapacitor
by Zineb Cabrane, Donghee Choi and Soo Hyoung Lee
Batteries 2025, 11(7), 245; https://doi.org/10.3390/batteries11070245 - 25 Jun 2025
Viewed by 259
Abstract
The need for electrical energy is dramatically increasing, pushing researchers and industrial communities towards the development and improvement of microgrids (MGs). It also encourages the use of renewable energies to benefit from available sources. Thereby, the implementation of a photovoltaic (PV) system with [...] Read more.
The need for electrical energy is dramatically increasing, pushing researchers and industrial communities towards the development and improvement of microgrids (MGs). It also encourages the use of renewable energies to benefit from available sources. Thereby, the implementation of a photovoltaic (PV) system with a hybrid energy storage system (HESS) can create a standalone MG. This paper presents an MG that uses photovoltaic energy as a principal source. An HESS is required, combining batteries and supercapacitors. This MG responds “insure” both alternating current (AC) and direct current (DC) loads. The batteries and supercapacitors have separate parallel connections to the DC bus through bidirectional converters. The DC loads are directly connected to the DC bus where the AC loads use a DC-AC inverter. A control strategy is implemented to manage the fluctuation of solar irradiation and the load variation. This strategy was implemented with a new logic control based on Boolean analysis. The logic analysis was implemented for analyzing binary data by using Boolean functions (‘0’ or ‘1’). The methodology presented in this paper reduces the stress and the faults of analyzing a flowchart and does not require a large concentration. It is used in this paper in order to simplify the control of the EMS. It permits the flowchart to be translated to a real application. This analysis is based on logic functions: “Or” corresponds to the addition and “And” corresponds to the multiplication. The simulation tests were executed at Tau  =  6 s of the low-pass filter and conducted in 60 s. The DC bus voltage was 400 V. It demonstrates that the proposed management strategy can respond to the AC and DC loads. Full article
(This article belongs to the Section Battery Modelling, Simulation, Management and Application)
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21 pages, 6959 KiB  
Article
Design and Implementation of an Electrolyte Temperature Control System for AgO-Al Batteries
by Zhaoliang Dou, Qingyan Tang, Zhuangzhuang Du, Yue Du, Shuang Li and Fengbin Liu
Batteries 2025, 11(7), 244; https://doi.org/10.3390/batteries11070244 - 24 Jun 2025
Viewed by 384
Abstract
AgO-Al batteries generate substantial heat during discharge, and inadequate heat dissipation can degrade battery performance and pose thermal runaway risks. To meet thermal control requirements for experimental scenarios, a feedback-controlled thermal management system was developed. Computational fluid dynamics was employed to analyze the [...] Read more.
AgO-Al batteries generate substantial heat during discharge, and inadequate heat dissipation can degrade battery performance and pose thermal runaway risks. To meet thermal control requirements for experimental scenarios, a feedback-controlled thermal management system was developed. Computational fluid dynamics was employed to analyze the effects of seawater flow rate, seawater temperature, electrolyte flow rate, and initial electrolyte temperature on electrolyte outlet temperature and heat dissipation capacity. Results indicate that heat dissipation capacity is negatively correlated with seawater temperature and positively correlated with electrolyte inlet temperature. It increases with higher seawater and electrolyte flow rates, though the increase becomes negligible when the seawater flow rate exceeds 10 m/s. The designed system adapts to dynamic operating conditions via real-time parameter tuning. Experimental validation confirms its effectiveness in regulating electrolyte outlet temperature, achieving steady-state control accuracy within ±3 °C and a dynamic response time of less than 7 min—meeting thermal management requirements for battery test benches. This study provides critical data and technical support for developing temperature control technologies and performance testing of seawater-activated batteries. Full article
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22 pages, 7195 KiB  
Article
Bayesian Optimization-Based State-of-Charge Estimation with Temperature Drift Compensation for Lithium-Ion Batteries
by Zhen-Rong Yuan, Ke-Feng Huang, Cai-Hua Xu, Jun-Chao Zou and Jun Yan
Batteries 2025, 11(7), 243; https://doi.org/10.3390/batteries11070243 - 24 Jun 2025
Viewed by 526
Abstract
With the widespread application of electric vehicles and electrical energy storage systems, the accurate monitoring of lithium battery states has become crucial for ensuring safety and improving efficiency in terms of the applications. For this reason, this study proposes an algorithm focusing on [...] Read more.
With the widespread application of electric vehicles and electrical energy storage systems, the accurate monitoring of lithium battery states has become crucial for ensuring safety and improving efficiency in terms of the applications. For this reason, this study proposes an algorithm focusing on Bayesian optimization-based adaptive extended Kalman filter (BO-AEKF) to enhance the numerical accuracy and stability of state-of-charge (SOC) estimation for lithium batteries under various operating conditions. By comparing with traditional methods, the proposed algorithm, introducing a temperature-adaptive mechanism and a dynamic parameter updating strategy, can effectively address the estimation limitations under severe temperature variations and initial SOC uncertainties. Experimental results demonstrate that the proposed algorithm exhibits superior estimation performance at different temperatures, including −10 °C, 0 °C, 25 °C, and 50 °C; particularly under dynamic operating conditions, the maximum error (MAX) and root mean square error (RMSE) are reduced by 51.9% and 74.5%, respectively, compared to the extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) algorithms. Furthermore, the BO-AEKF achieves rapid convergence even with unknown initial SOC values, demonstrating its robustness and adaptability. These findings provide more reliable technical support for the development of battery management systems, making them suitable for state estimation in electric vehicles and renewable energy storage systems. Full article
(This article belongs to the Special Issue Advanced Control and Optimization of Battery Energy Storage Systems—2nd Edition)
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14 pages, 1961 KiB  
Article
Characteristic Differences of Thermal Runaway Triggered by Overheating and Overcharging in Lithium-Ion Batteries and Multi-Dimensional Safety Protection Strategies
by Yao Yao, Lu Liu, Juan Gu, Haozhe Xing, Huachao Liu, Yihao Cheng, Youning Wang, Songlin Yue, Yanyu Qiu and Zhi Zhang
Batteries 2025, 11(7), 242; https://doi.org/10.3390/batteries11070242 - 24 Jun 2025
Viewed by 636
Abstract
Overheating and overcharging are the core triggering conditions for the thermal runaway of lithium-ion batteries. Studying the behavioral differences of thermal runaway of lithium-ion batteries under these two conditions is crucial for the safety design and protection of lithium-ion batteries. In this study, [...] Read more.
Overheating and overcharging are the core triggering conditions for the thermal runaway of lithium-ion batteries. Studying the behavioral differences of thermal runaway of lithium-ion batteries under these two conditions is crucial for the safety design and protection of lithium-ion batteries. In this study, we investigated the temperature, pressure, gas generation, and heat generation characteristics of lithium batteries under these two conditions. Under overheating conditions, the release of lattice oxygen in the cathode and the decomposition of the electrolyte trigger a self-catalytic reaction, generating CO2 (54.7%) and H2 (29.7%), with a total heat release of 17.6 kJ and a heat accumulation rate of 24.3 W, forming a local high-temperature core area. Under overcharging conditions, the voltage drop, capacity attenuation of 21.1% (2230→1762 mAh), and internal resistance surge (6→21 mΩ) reflect severe damage to the electrode. Accompanied by the oxygenation of the EC electrolyte (CO32− + C2H4↑), the gas production rate is faster. The middle pressure was 0.601 MPa, and the proportion of CO2 was 67.4%. However, the triggering of thermal runaway relies on the synergistic effect of internal electrochemical reactions and ohmic heat accumulation, resulting in a relatively low rate of energy accumulation. Full article
(This article belongs to the Special Issue Thermal Management in Lithium-Ion Batteries: Latest Advances and Prospects)
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16 pages, 3158 KiB  
Article
Interpretable Deep Learning Using Temporal Transformers for Battery Degradation Prediction
by James Sadler, Rizwaan Mohammed and Kotub Uddin
Batteries 2025, 11(7), 241; https://doi.org/10.3390/batteries11070241 - 23 Jun 2025
Viewed by 493
Abstract
Accurate modelling of lithium-ion battery degradation is a complex problem, dependent on multiple internal mechanisms that can be affected by a multitude of external conditions. In this study, a transformer-based approach, capable of leveraging historical conditions and known-future inputs is introduced. The model [...] Read more.
Accurate modelling of lithium-ion battery degradation is a complex problem, dependent on multiple internal mechanisms that can be affected by a multitude of external conditions. In this study, a transformer-based approach, capable of leveraging historical conditions and known-future inputs is introduced. The model can make predictions from as few as 100 input cycles, and compared to other state-of-the-art techniques, our approach shows an increase in accuracy. The model utilises specialised components within its architecture to provide interpretable results, introducing the possibility of understanding path-dependency in Li-Ion battery degradation. The ability to incorporate static metadata opens the door for a foundational deep learning model for battery degradation forecasting. Full article
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28 pages, 1412 KiB  
Article
The Collisional Charging of a Transmon Quantum Battery
by Nicolò Massa, Fabio Cavaliere and Dario Ferraro
Batteries 2025, 11(7), 240; https://doi.org/10.3390/batteries11070240 - 23 Jun 2025
Viewed by 352
Abstract
Motivated by recent developments in the field of multilevel quantum batteries, we present the model of a quantum device for energy storage with anharmonic level spacing, based on a superconducting circuit in the transmon regime. It is charged via the sequential interaction with [...] Read more.
Motivated by recent developments in the field of multilevel quantum batteries, we present the model of a quantum device for energy storage with anharmonic level spacing, based on a superconducting circuit in the transmon regime. It is charged via the sequential interaction with a collection of identical and independent ancillary two-level systems. By means of a numerical analysis, we show that, in case these ancillas are coherent, this kind of quantum battery can achieve remarkable performances in terms of the control of the stored energy and its extraction in regimes of parameters within reach in nowadays quantum circuits. Full article
(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)
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30 pages, 11102 KiB  
Article
Impact of Temperature and Depth of Discharge on Commercial Nickel Manganese Oxide and Lithium Iron Phosphate Batteries After Three Years of Aging
by Matthieu Dubarry, Andrew Pearson, Keiran Pringle, Youssof Shekibi and Steven Pas
Batteries 2025, 11(7), 239; https://doi.org/10.3390/batteries11070239 - 22 Jun 2025
Viewed by 411
Abstract
Accurate cell selection is primordial to ensure battery safety and longevity. Unfortunately, because of path dependence, finding out which cells are best adapted to a specific application is not straightforward and might require significant testing. This work provides the analysis of three years [...] Read more.
Accurate cell selection is primordial to ensure battery safety and longevity. Unfortunately, because of path dependence, finding out which cells are best adapted to a specific application is not straightforward and might require significant testing. This work provides the analysis of three years of aging, both cycling and calendar, for two batches of commercial cells of different chemistries. Using design of experiments and analysis of variance, this work showed that the impact of temperature and depth of discharge, both at the beginning and end of discharge, are chemistry dependent. Moreover, an analysis of the cells’ degradation modes also showcased different pathways depending on the positive electrode chemistry and the type of aging. Full article
(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Diagnostics and Prognostics)
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22 pages, 5241 KiB  
Article
A SOH Estimation Method for Lithium-Ion Batteries Based on CPA and CNN-KAN
by Kaixin Cheng, Chaolong Zhang, Kui Shao, Jin Tong, Anxiang Wang, Yujie Zhou, Zhao Zhang and Yan Zhang
Batteries 2025, 11(7), 238; https://doi.org/10.3390/batteries11070238 - 20 Jun 2025
Viewed by 318
Abstract
Lithium-ion batteries are the primary power source for new energy vehicles, making accurate estimation of their state of health (SOH) essential for ensuring the safe operation of battery systems. This paper proposes a Capacity–Power Analysis (CPA) method that incorporates temperature features to enhance [...] Read more.
Lithium-ion batteries are the primary power source for new energy vehicles, making accurate estimation of their state of health (SOH) essential for ensuring the safe operation of battery systems. This paper proposes a Capacity–Power Analysis (CPA) method that incorporates temperature features to enhance feature extraction across a broader range. Additionally, we introduce an SOH estimation method for lithium batteries based on a Convolutional Neural Network (CNN) and a Kolmogorov–Arnold Network (KAN). By extracting the capacity–power curve and average temperature features during constant-current and constant-voltage charging, the CNN-KAN model establishes a nonlinear mapping relationship between the extracted features and SOH, enabling high-precision SOH estimation for lithium-ion batteries. Four 18650 batteries were tested under various charging and discharging conditions in a laboratory setting. The coefficient of determination (R2) exceeded 96.4%, the root mean square error (RMSE) was below 0.86%, and the mean absolute error (MAE) was under 0.7%, confirming that the proposed method demonstrates excellent estimation performance. Full article
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19 pages, 6671 KiB  
Article
Optimized Flow Field Design with Dead-Zone Compensation for Enhanced Performance in Aqueous AgO-Al Batteries
by Peiqiang Chen, Qun Zheng, Chunhua Xiong, Jinmao Chen, Xudong Wang, Xing Su, Long Huang, Pan Li, Wanli Xu and Man Ruan
Batteries 2025, 11(7), 237; https://doi.org/10.3390/batteries11070237 - 20 Jun 2025
Viewed by 662
Abstract
The electrolyte flow field plays a pivotal role in determining the electrochemical performance of aqueous AgO-Al batteries. However, traditional flow field structures often suffer from the formation of dead zones, leading to uneven mass transport and side reactions. In this study, a flow [...] Read more.
The electrolyte flow field plays a pivotal role in determining the electrochemical performance of aqueous AgO-Al batteries. However, traditional flow field structures often suffer from the formation of dead zones, leading to uneven mass transport and side reactions. In this study, a flow field optimization strategy incorporating dead-zone compensation is proposed, which identifies localized dead zones and implements structural corrections to enhance electrolyte distribution. Numerical simulations reveal improved flow uniformity and reduced concentration polarization, while experimental validation confirms enhanced battery performance under the optimized configuration. This work provides a generalizable approach for electrolyte flow field design that improves mass transfer and electrochemical efficiency, offering practical insights for the development of high-performance aqueous batteries. Full article
(This article belongs to the Section Aqueous Batteries)
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8 pages, 2364 KiB  
Article
Machine Learning-Based Methodology for Fast Assessment of Battery Health Status
by Woongchul Choi
Batteries 2025, 11(7), 236; https://doi.org/10.3390/batteries11070236 - 20 Jun 2025
Viewed by 287
Abstract
Global electric vehicle (EV) markets are rapidly expanding, and the efficient management of batteries has become increasingly important due to supply constraints of rare metals and other raw materials required for lithium-ion batteries. Accordingly, the reuse and recycling of used batteries from early [...] Read more.
Global electric vehicle (EV) markets are rapidly expanding, and the efficient management of batteries has become increasingly important due to supply constraints of rare metals and other raw materials required for lithium-ion batteries. Accordingly, the reuse and recycling of used batteries from early EVs are emerging as key solutions. This study proposes a machine learning-based approach to rapidly and reliably estimate the static capacity of used batteries. While conventional methods require significant measurement time, this study demonstrates that accurate static capacity estimation is possible using only short-term partial discharge data (6 min under 1C-rate CC conditions) by leveraging an RNN (recurrent neural network) architecture specialized for time-series data processing. The proposed model achieves high prediction accuracy, with an average RMSE of 28.439 mAh, average MSE of 808.799 mAh2, average MAE of 13.049 mAh, and average R2 of 0.9993, while significantly reducing the evaluation time compared to conventional methods. This is expected to greatly enhance the efficiency and practicality of battery reuse and recycling processes. Full article
(This article belongs to the Special Issue Artificial Intelligence and Batteries: AI-Powered Innovations in Battery Technology)
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