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Volume 11
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Batteries, Volume 11, Issue 10 (October 2025) – 5 articles

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19 pages, 916 KB  
Article
An Integrated Co-Simulation Framework for the Design, Analysis, and Performance Assessment of EIS-Based Measurement Systems for the Online Monitoring of Battery Cells
by Nicola Lowenthal, Roberta Ramilli, Marco Crescentini and Pier Andrea Traverso
Batteries 2025, 11(10), 351; https://doi.org/10.3390/batteries11100351 - 26 Sep 2025
Abstract
Electrochemical impedance spectroscopy (EIS) is widely used at the laboratory level for monitoring/diagnostics of battery cells, but the design and validation of in situ, online measurement systems based on EIS face challenges due to complex hardware–software interactions and non-idealities. This study aims to [...] Read more.
Electrochemical impedance spectroscopy (EIS) is widely used at the laboratory level for monitoring/diagnostics of battery cells, but the design and validation of in situ, online measurement systems based on EIS face challenges due to complex hardware–software interactions and non-idealities. This study aims to develop an integrated co-simulation framework to support the design, debugging, and validation of EIS measurement systems devoted to the online monitoring of battery cells, helping to predict experimental results and identify/correct the non-ideality effects and sources of uncertainty. The proposed framework models both the hardware and software components of an EIS-based system to simulate and analyze the impedance measurement process as a whole. It takes into consideration the effects of physical non-idealities on the hardware–software interactions and how those affect the final impedance estimate, offering a tool to refine designs and interpret test results. For validation purposes, the proposed general framework is applied to a specific EIS-based laboratory prototype, previously designed by the research group. The framework is first used to debug the prototype by uncovering hidden non-idealities, thus refining the measurement system, and then employed as a digital model of the latter for fast development of software algorithms. Finally, the results of the co-simulation framework are compared against a theoretical model, the real prototype, and a benchtop instrument to assess the global accuracy of the framework. Full article
25 pages, 6367 KB  
Article
Multiphysics Optimization of Graphite-Buffered Bilayer Anodes with Diverse Inner Materials for High-Energy Lithium-Ion Batteries
by Juan C. Rubio and Martin Bolduc
Batteries 2025, 11(10), 350; https://doi.org/10.3390/batteries11100350 - 25 Sep 2025
Abstract
This study presents a multiphysics simulation approach to optimize graphite-buffered bilayer anodes for enhanced energy density in lithium-ion batteries, assessing the electrochemical impact of diverse inner-layer materials, including silicon, hard carbon, lithium titanate (LTO), and metallic lithium, in pure and graphite-composite forms. A [...] Read more.
This study presents a multiphysics simulation approach to optimize graphite-buffered bilayer anodes for enhanced energy density in lithium-ion batteries, assessing the electrochemical impact of diverse inner-layer materials, including silicon, hard carbon, lithium titanate (LTO), and metallic lithium, in pure and graphite-composite forms. A coupled finite-element model implemented in COMSOL Multiphysics 6.2 was used to integrate spherical lithium diffusion, charge conservation, and the solid electrolyte interphase (SEI) formation kinetics. The evaluated anode structure consisted of a 60 µm-thick bilayer: a 30 µm graphite surface layer coupled with a 30 µm inner layer of alternative active materials. Simulations were performed using an NMC622 cathode, LiPF6 in EC:EMC electrolyte, at room temperature, under a charge rate of 1 C, considering realistic particle sizes (graphite: 2.5 µm; Si: 0.1 µm; hard carbon: 2.5 µm; LTO: 0.2 µm; Li metal: 0.5 µm), and evaluated over 2000 cycles. The hard carbon/graphite configuration exhibited a capacity fade of 5.8% compared with 7.1% in pure graphite. Additionally, the SEI thickness decreased to 0.20 µm (from 0.25 µm), the overpotential dropped to −17 mV (from −59 mV), and the electrolyte consumption was reduced to 20.8% (from 42.9%). The analysis highlights hard carbon and LTO inner layers as optimal trade-offs between capacity and cycle stability, whereas silicon and lithium metal significantly increased the initial capacity but accelerated SEI formation and impedance growth. These findings demonstrate the graphite-buffered bilayer’s potential to decouple interfacial degradation from high-capacity materials, providing valuable guidelines for the design of advanced lithium-ion battery anodes. Full article
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10 pages, 1673 KB  
Communication
The Origin of Improved Cycle Stability of Li-O2 Batteries Using High-Concentration Electrolytes
by Wei Fan, Xu Liu, Guangqian Li, Ke Yu, Peng Wang, Min Lei, Ce Zhen, Lei Miao, Jialiang Wang, Chun Li, Junliang Hou, Hongtao Ji and Licheng Miao
Batteries 2025, 11(10), 349; https://doi.org/10.3390/batteries11100349 - 23 Sep 2025
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Abstract
The intrinsic instability of organic electrolytes seriously impedes practical applications of lithium–oxygen (Li-O2) batteries. Recent studies have shown that the use of high-concentration electrolytes can suppress the decomposition reaction of electrolytes and help enhance cell reversibility. However, the fundamental nature of [...] Read more.
The intrinsic instability of organic electrolytes seriously impedes practical applications of lithium–oxygen (Li-O2) batteries. Recent studies have shown that the use of high-concentration electrolytes can suppress the decomposition reaction of electrolytes and help enhance cell reversibility. However, the fundamental nature of concentrated electrolytes’ ability to improve the chemical durability and stability of Li-O2 batteries remains unclear. In this work, we conducted computational studies to elucidate the origin of the enhanced oxidative/reductive stability of three representative solvents—DMSO, DME, and EC—in high-concentration electrolytes. The modeling results identify that Li+-solvent complexes, one of the solvate components, are the easiest to decompose in concentrated electrolytes. Thermodynamic and kinetic characterizations reveal that more anions in concentrated electrolytes are responsible for improving the oxidative and reductive stability of electrolytes. In addition, more Li+ ions, acting as a scavenging or stabilizing agent for superoxide anion (O2−), also improve the stability of electrolytes against oxidation in Li-O2 batteries. This work provides a mechanistic understanding of the enhanced cycle stability of a Li-O2 battery using high-concentration electrolytes. Full article
(This article belongs to the Special Issue Batteries: 10th Anniversary)
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17 pages, 5954 KB  
Article
A Hybrid RUL Prediction Framework for Lithium-Ion Batteries Based on EEMD and KAN-LSTM
by Zhao Zhang, Xin Liu, Xinyu Dong, Pengyu Jiang, Runrun Zhang, Chaolong Zhang, Jiajia Shao, Yong Xie, Yan Zhang, Xuming Liu, Kaixin Cheng, Shi Chen, Zining Wang and Jieqi Wei
Batteries 2025, 11(10), 348; https://doi.org/10.3390/batteries11100348 - 23 Sep 2025
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Abstract
Accurately estimating the remaining useful life (RUL) of lithium-ion batteries in energy storage systems is critical for ensuring both the safety and reliability of the power grid. To address the complex nonlinear degradation behavior associated with battery aging, this study proposes a novel [...] Read more.
Accurately estimating the remaining useful life (RUL) of lithium-ion batteries in energy storage systems is critical for ensuring both the safety and reliability of the power grid. To address the complex nonlinear degradation behavior associated with battery aging, this study proposes a novel RUL prediction framework that integrates ensemble empirical mode decomposition (EEMD) with an ensemble learning algorithm. The approach first applies EEMD to decompose aging data into a residual component and several intrinsic mode functions (IMFs). The residual component is then modeled using a long short-term memory (LSTM) network, while the Kolmogorov–Arnold network (KAN) focuses on learning from the IMF components. These individual predictions are subsequently combined to reconstruct the overall capacity degradation trajectory. Experimental validation on real lithium-ion battery aging datasets demonstrates that the proposed method provides highly accurate RUL predictions, exhibits strong robustness, and effectively captures nonlinear characteristics under varying operating conditions. Specifically, the method achieves R2 above 0.96 with absolute RUL errors within 2–3 cycles on NASA datasets, and maintains R2 values above 0.91 with errors within 7–15 cycles on CALCE datasets. Furthermore, the optimal KAN hyperparameters for different IMF components are identified, offering valuable insights for multi-scale modeling and future model optimization. Full article
(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Diagnostics and Prognostics)
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21 pages, 3928 KB  
Article
State of Health Estimation of Lithium-Ion Battery Based on Novel Health Indicators and Improved Support Vector Regression
by Ruoxia Li, Ning He and Fuan Cheng
Batteries 2025, 11(10), 347; https://doi.org/10.3390/batteries11100347 - 23 Sep 2025
Viewed by 139
Abstract
Accurate estimation of the state of health (SOH) is a critical function of battery management system (BMS), essential for ensuring the safe and stable operation of lithium-ion batteries. To improve estimation precision, this paper proposes a novel health indicator (HI) construction method and [...] Read more.
Accurate estimation of the state of health (SOH) is a critical function of battery management system (BMS), essential for ensuring the safe and stable operation of lithium-ion batteries. To improve estimation precision, this paper proposes a novel health indicator (HI) construction method and an improved support vector regression (SVR) approach. First, the convolution operation is applied to discharge voltage data to extract new HIs that characterize battery aging; their correlations are then verified. Second, principal component analysis (PCA) is employed to reduce input dimensionality and computational burden. Third, to address the challenge of SVR parameter selection, an improved sparrow search algorithm (ISSA) is proposed for parameter optimization. Finally, the proposed method is validated using both the NASA dataset and a laboratory experimental dataset, with comparisons against existing approaches. The results show that the method achieves accurate SOH estimation under various aging conditions, demonstrating its effectiveness, robustness, and practical potential. Full article
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