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Batteries, Volume 11, Issue 10 (October 2025) – 3 articles

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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
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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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
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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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
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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