Batteries

19 pages, 4837 KB

Open AccessArticle

Experimental Study of Pouch-Type Battery Cell Thermal Characteristics Operated at High C-Rates

by Marius Vasylius, Deivydas Šapalas, Benas Dumbrauskas, Valentinas Kartašovas, Audrius Senulis, Artūras Tadžijevas, Pranas Mažeika, Rimantas Didžiokas, Ernestas Šimkutis and Lukas Januta

Batteries 2026, 12(1), 14; https://doi.org/10.3390/batteries12010014 - 28 Dec 2025

Abstract

This paper investigates pouch-type lithium-ion battery cells with a nominal voltage of 3.7 V and a nominal capacity of 57 Ah. A numerical model of the cell was developed and implemented using the NTGK method, which accurately predicts electrochemical and thermal processes. The [...] Read more.

This paper investigates pouch-type lithium-ion battery cells with a nominal voltage of 3.7 V and a nominal capacity of 57 Ah. A numerical model of the cell was developed and implemented using the NTGK method, which accurately predicts electrochemical and thermal processes. The results of numerical modeling matched with the experimental results of battery cell temperature measurements—the average deviation was about 4.5%; therefore, it can be considered reliable for further engineering research and construction of battery modules. In the experimental part of the paper, the battery cell was loaded in various C-rates (from 0.5 to 2 C), using heat flux sensors, thermocouples, and a thermal imaging camera. The studies revealed that the highest temperature is in the tabs area of cells. The temperature on the face of the cell surface exceeds 35 °C already from a load of 1.35 C, which accelerates cell degradation and reduces the number of cycles. Thermal imaging revealed uneven temperature distribution, whereby the top of the cell heats up more than the bottom of the cell and the temperature gradient can reach 2–4 °C. It was observed that during faster charge/discharge modes, the temperature rises from the tabs of the cell, and during slower ones, more in the middle face surface of the cell. The studies highlight the need to apply additional cooling solutions, especially cooling of the upper cell face, to ensure durability and uniform heat distribution. Full article

(This article belongs to the Special Issue Advances in Charging Systems and Charging Management Strategies for Battery Electric Vehicles)

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18 pages, 8803 KB

Open AccessArticle

Tailoring Primary Particle Growth via Controlled Ammonia Feeding for Enhanced Electrochemical Stability of Hierarchical NCM622 Cathodes

by Khaja Hussain Shaik, Hyeon Jun Choi and Joo-Hyung Kim

Batteries 2026, 12(1), 13; https://doi.org/10.3390/batteries12010013 - 27 Dec 2025

Abstract

Ni-rich layered LiNi_0.6Co_0.2Mn_0.2O₂ (NCM622) cathodes are the most promising candidates for high-energy lithium-ion batteries, but their performance is often limited by structural instability and capacity fading due to large primary particle sizes and surface degradation. Precise [...] Read more.

Ni-rich layered LiNi_0.6Co_0.2Mn_0.2O₂ (NCM622) cathodes are the most promising candidates for high-energy lithium-ion batteries, but their performance is often limited by structural instability and capacity fading due to large primary particle sizes and surface degradation. Precise control of the primary particle size significantly impacts the performance of NCM622 cathodes and can mitigate fatigue mechanisms, but the underlying processes remain unclear. In this study, NCM622 cathodes with various primary particle sizes were synthesized by applying a controlled co-precipitation strategy by systematically controlling the ammonia feed rate and solution pH during precursor formation. Interestingly, higher ammonia feed rates promoted the formation of smaller, more ordered primary particles, whereas lower feed rates and reduced pH produced larger primary particles in spherical secondary structures. Electrochemical evaluation revealed that cathodes composed of smaller primary particles exhibited enhanced Li⁺ diffusion kinetics and superior electrochemical performance compared to those synthesized under lower ammonia feeding or reduced pH conditions. Moreover, the optimized NCM622 electrode demonstrated excellent rate capability and maintained a stable layered microstructure during cycling, retaining ~86% of its initial capacity. These results demonstrate that fine-tuning the ammonia feeding conditions during co-precipitation provides a simple and effective approach to control primary particle growth, thereby improving the structural integrity and electrochemical durability of NCM622 cathode materials. Full article

(This article belongs to the Section Battery Processing, Manufacturing and Recycling)

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28 pages, 26223 KB

Open AccessArticle

Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on the Optimized TTAO-VMD-BiLSTM

by Pengcheng Wang, Lu Liu, Qun Yu, Dongdong Hou, Enjie Li, Haijun Yu, Shumin Liu, Lizhen Qin and Yunhai Zhu

Batteries 2026, 12(1), 12; https://doi.org/10.3390/batteries12010012 (registering DOI) - 26 Dec 2025

Abstract

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for ensuring the safe operation of equipment, optimizing industrial cost management, and promoting the sustainable development of the renewable energy sector. Although various deep learning-based approaches for RUL prediction have been [...] Read more.

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for ensuring the safe operation of equipment, optimizing industrial cost management, and promoting the sustainable development of the renewable energy sector. Although various deep learning-based approaches for RUL prediction have been proposed, their performance is highly dependent on the availability of large training datasets. As a result, these methods generally achieve satisfactory accuracy only when sufficient training samples are available. To address this limitation, this study proposes a novel hybrid strategy that combines a parameter-optimized signal decomposition algorithm with an enhanced neural network architecture, aiming to improve RUL prediction reliability under small-sample conditions. Specifically, we develop a lithium-ion battery capacity prediction method that integrates the Triangle Topology Aggregation Optimizer (TTAO), Variational Mode Decomposition (VMD), and a Bidirectional Long Short-Term Memory (BiLSTM) network. First, the TTAO algorithm is used to optimize the number of modes and the quadratic penalty factor in VMD, enabling the decomposition of battery capacity data into multiple intrinsic mode functions (IMFs) while minimizing the impact of phenomena such as capacity regeneration. Key features highly correlated with battery life are then extracted as inputs for prediction. Subsequently, a BiLSTM network is employed to capture subtle variations in the capacity degradation process and to predict capacity based on the decomposed sequences. The prediction results are effectively integrated, and comprehensive experiments are conducted on the NASA and CALCE lithium-ion battery aging datasets. The results show that the proposed TTAO-VMD-BiLSTM model exhibits a small number of parameters, low memory consumption, high prediction accuracy, and fast convergence. The root mean square error (RMSE) does not exceed 0.8%, and the maximum mean absolute error (MAE) is less than 0.5%. Full article

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40 pages, 5707 KB

Open AccessReview

Graph Representation Learning for Battery Energy Systems in Few-Shot Scenarios: Methods, Challenges and Outlook

by Xinyue Zhang and Shunli Wang

Batteries 2026, 12(1), 11; https://doi.org/10.3390/batteries12010011 - 26 Dec 2025

Abstract

Graph representation learning (GRL) has emerged as a unifying paradigm for modeling the relational and heterogeneous nature of battery energy storage systems (BESS), yet a systematic synthesis focused on data-scarce (few-shot) battery scenarios is still lacking. Graph representation learning offers a natural way [...] Read more.

Graph representation learning (GRL) has emerged as a unifying paradigm for modeling the relational and heterogeneous nature of battery energy storage systems (BESS), yet a systematic synthesis focused on data-scarce (few-shot) battery scenarios is still lacking. Graph representation learning offers a natural way to describe the structure and interaction of battery cells, modules and packs. At the same time, battery applications often suffer from very limited labeled data, especially for new chemistries, extreme operating conditions and second-life use. This review analyzes how graph representation learning can be combined with few-shot learning to support key battery management tasks under such data-scarce conditions. We first introduce the basic ideas of graph representation learning, including models based on neighborhood aggregation, contrastive learning, autoencoders and transfer learning, and discuss typical data, model and algorithm challenges in few-shot scenarios. We then connect these methods to battery state estimation problems, covering state of charge, state of health, remaining useful life and capacity. Particular attention is given to approaches that use graph neural models, meta-learning, semi-supervised and self-supervised learning, Bayesian deep networks, and federated learning to extract transferable features from early-cycle data, partial charge–discharge curves and large unlabeled field datasets. Reported studies show that, with only a small fraction of labeled samples or a few initial cycles, these methods can achieve state and life prediction errors that are comparable to or better than conventional models trained on full datasets, while also improving robustness and, in some cases, providing uncertainty estimates. Based on this evidence, we summarize the main technical routes for few-shot battery scenarios and identify open problems in data preparation, cross-domain generalization, uncertainty quantification and deployment on real battery management systems. The review concludes with a research outlook, highlighting the need for pack-level graph models, physics-guided and probabilistic learning, and unified benchmarks to advance reliable graph-based few-shot methods for next-generation intelligent battery management. Full article

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

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13 pages, 2011 KB

Open AccessArticle

Data-Driven State-of-Health Estimation by Reconstructing Virtual Full-Charge Segments

by Dongxu Guo, Zhenghang Zou, Xin Lai and Yuejiu Zheng

Batteries 2026, 12(1), 10; https://doi.org/10.3390/batteries12010010 - 26 Dec 2025

Abstract

The rapid growth of new energy vehicles necessitates accurate battery state of health (SOH) assessment to ensure safety and reliability. However, real-world SOH estimation is challenging because users rarely perform full charge–discharge cycles, leaving only fragmented charging segments that obscure true battery capacity. [...] Read more.

The rapid growth of new energy vehicles necessitates accurate battery state of health (SOH) assessment to ensure safety and reliability. However, real-world SOH estimation is challenging because users rarely perform full charge–discharge cycles, leaving only fragmented charging segments that obscure true battery capacity. To address this, we propose a data-driven method that reconstructs a virtual full-charge cycle. By clustering charging segments based on temperature and current, the approach creatively splices multiple incomplete curves from similar mileages and conditions into a complete charging profile. This enables robust full-capacity estimation on a large-scale real-world vehicle dataset, achieving estimation errors below 2% when compared with offline validation tests. The method offers a practical and scalable solution for SOH monitoring and fleet management using field data. Full article

(This article belongs to the Special Issue Physics-Informed Artificial Intelligence for Battery Energy Storage Systems)

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21 pages, 2857 KB

Open AccessArticle

Distributed Energy Storage Configuration Method for AC/DC Hybrid Distribution Network Based on Bi-Level Optimization

by Jianjun Zhao, Jianqi Wang, Mengke Gao, Yinfeng Sun, Yang Li, Zhenhao Wang and Xu Zhao

Batteries 2026, 12(1), 9; https://doi.org/10.3390/batteries12010009 - 26 Dec 2025

Abstract

Aiming at prominent voltage quality problems in AC/DC hybrid distribution networks with a high proportion of distributed energy and diversified loads, this paper proposes a bi-level energy storage system (ESS) optimization model. The upper level optimizes the ESS configuration with the goal of [...] Read more.

Aiming at prominent voltage quality problems in AC/DC hybrid distribution networks with a high proportion of distributed energy and diversified loads, this paper proposes a bi-level energy storage system (ESS) optimization model. The upper level optimizes the ESS configuration with the goal of minimizing the cost, and the lower level optimizes the real-time running state of the ESS. Considering multiple constraints, the improved PSO algorithm and the Gurobi solver are used to solve the problem. The test on the modified IEEE-33 node system verified that the model effectively improved voltage quality and reduced power system costs, which provides theoretical and engineering support for the scientific configuration of the ESS. Full article

(This article belongs to the Special Issue Advances in Charging Systems and Charging Management Strategies for Battery Electric Vehicles)

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36 pages, 117874 KB

Open AccessReview

Synergistic Experimental and Computational Strategies for MXene-Based Zinc-Ion Batteries

by Man Li and Seunghyun Song

Batteries 2026, 12(1), 8; https://doi.org/10.3390/batteries12010008 - 26 Dec 2025

Abstract

Zinc-ion batteries (ZIBs) are regarded as one of the promising next-generation energy storage technologies due to their high volumetric capacity, cost-effectiveness, and high safety. MXene materials, featuring a unique two-dimensional (2D) layered structure, excellent conductivity, and tunable surface chemistry, have been widely applied [...] Read more.

Zinc-ion batteries (ZIBs) are regarded as one of the promising next-generation energy storage technologies due to their high volumetric capacity, cost-effectiveness, and high safety. MXene materials, featuring a unique two-dimensional (2D) layered structure, excellent conductivity, and tunable surface chemistry, have been widely applied in energy storage systems. This review summarizes the recent progress in experimental and computational strategies for MXene-based ZIBs. The construction of MXene-based electrodes and the effect mechanisms of Zn-ion transport facilitation, electrode cycling stability, and anode dendrite suppression are discussed. Subsequently, the theoretical simulation strategies for MXene performance investigation are analyzed, including surface chemistry and defect engineering of MXene-based electrodes and the rational design of heterostructure interfaces for enhancing conductivity and suppressing Zn dendrite growth. Finally, the review outlines the major challenges that currently hinder the applications of MXene in ZIBs and proposes future research directions, offering insights that may guide the continued advancement of next-generation MXene-based energy storage systems. Full article

(This article belongs to the Special Issue Two-Dimensional Materials for Advanced Batteries)

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16 pages, 3861 KB

Open AccessArticle

Nitrogen Plasma-Assisted Surface Engineering on Multilayer Ti₃C₂T_x Electrodes for Enhanced Interfacial Charge Dynamics and Energy Storage in Ascorbic Acid Electrolyte

by Yu-Jie Liu and Chun-Pei Cho

Batteries 2026, 12(1), 7; https://doi.org/10.3390/batteries12010007 - 25 Dec 2025

Abstract

The intrinsic limitations of Ti₃C₂T_x electrodes, specifically low interfacial charge-transfer efficiency and structural degradation in strongly acidic environments, hinder their performance in high-rate aqueous supercapacitors. Herein, we report a synergistic strategy combining nitrogen plasma surface engineering with a [...] Read more.

The intrinsic limitations of Ti₃C₂T_x electrodes, specifically low interfacial charge-transfer efficiency and structural degradation in strongly acidic environments, hinder their performance in high-rate aqueous supercapacitors. Herein, we report a synergistic strategy combining nitrogen plasma surface engineering with a redox-active ascorbic acid electrolyte to optimize the electrode/electrolyte interfacial kinetics. By systematic investigation, the Ti₃C₂T_x supercapacitor obtained by a 10-min plasma duration (N10P-AA) achieved the optimal balance between activating surface sites and preserving the conductive Ti–C framework integrity. The ascorbic acid electrolyte broadened the potential window to approximately 0.7 V, and N10P-AA exhibited the lowest charge-transfer impedance and superior rate capability, retaining a relatively high Coulombic efficiency (>72%) even at a high scan rate of 10,000 mV·s⁻¹. The EIS results and kinetics analysis (b values) confirmed that the moderate plasma activation effectively promoted more surface-dominated charge storage kinetics and mitigated diffusion limitation, consistent with reduced charge-transfer resistance and a smaller Warburg slope. The XPS results revealed that the 10-min treatment suppressed detrimental oxidation during cyclings and facilitated the formation of electrochemically favorable hydroxylated surface functional groups. This work demonstrates a feasible surface electrolyte co-engineering strategy for modulating the interfacial behavior of MXene, which is of great significance for future high-efficiency aqueous electrochemical energy storage and potential biosensing applications. Full article

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

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22 pages, 8743 KB

Open AccessArticle

Deep Learning-Based State Estimation for Sodium-Ion Batteries Using Long Short-Term Memory Network

by Yunzhe Li, Yuhao Li, Jiangong Zhu, Haifeng Dai, Zhi Li and Bo Jiang

Batteries 2026, 12(1), 6; https://doi.org/10.3390/batteries12010006 - 25 Dec 2025

Abstract

Sodium-ion batteries (SIBs) have attracted growing attention as an alternative to lithium-ion technologies for electric mobility and stationary energy-storage applications, owing to the wide availability of sodium resources, cost advantages, and comparatively favorable safety characteristics. Accurate state-of-health (SOH) estimation is essential for safe [...] Read more.

Sodium-ion batteries (SIBs) have attracted growing attention as an alternative to lithium-ion technologies for electric mobility and stationary energy-storage applications, owing to the wide availability of sodium resources, cost advantages, and comparatively favorable safety characteristics. Accurate state-of-health (SOH) estimation is essential for safe and reliable SIB deployment, yet existing data-driven methods still suffer from limited accuracy and interpretability, as well as a lack of dedicated aging datasets. This study proposes an explainable SOH estimation methodology based on a long short-term memory (LSTM) network combined with model-agnostic KernelSHAP analysis. Thirteen health indicators (HIs) are extracted from charge/discharge data and post-charge relaxation segments, and the most relevant indicators are selected via Pearson correlation screening as model inputs. Built on these HIs, an LSTM-based multi-step framework is developed to take HI sequences as input and forecast the SOH trajectory over the subsequent 20 cycles. Experimental results show that the proposed method achieves high accuracy and robust cross-cell generalization, with mean absolute error (MAE) below 1.0%, root-mean-square error (RMSE) below 1.2% across all cells, and an average RMSE of about 0.75% in the main cross-cell setting. KernelSHAP-based global and temporal analyses further clarify how different HIs and time positions influence SOH estimates, enhancing model transparency and physical interpretability. Full article

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

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21 pages, 5487 KB

Open AccessArticle

A Health-Aware Hybrid Reinforcement–Predictive Control Framework for Sustainable Energy Management in Photovoltaic–Electric Vehicle Microgrids

by Muhammed Cavus and Margaret Bell

Batteries 2026, 12(1), 5; https://doi.org/10.3390/batteries12010005 - 24 Dec 2025

Abstract

The increasing electrification of mobility within smart cities has accelerated the need for intelligent energy management strategies that jointly address cost, emissions, and battery health. This study develops a health-aware hybrid reinforcement–predictive energy manager (H-RPEM) designed for photovoltaic–electric vehicle (PV-EV) microgrids. The proposed [...] Read more.

The increasing electrification of mobility within smart cities has accelerated the need for intelligent energy management strategies that jointly address cost, emissions, and battery health. This study develops a health-aware hybrid reinforcement–predictive energy manager (H-RPEM) designed for photovoltaic–electric vehicle (PV-EV) microgrids. The proposed controller unifies model-based predictive optimisation with adaptive reinforcement learning to achieve both short-term operational efficiency and long-term asset preservation. A comprehensive dataset of solar generation, EV charging behaviour, and stochastic load profiles was employed to train and validate the hybrid control framework under realistic operating conditions. Quantitative results indicate that the proposed H-RPEM controller achieves an 18.7% reduction in total operating cost and a 22.5% decrease in carbon emissions, whilst maintaining the battery state-of-health above 0.95 throughout a 24 h operational cycle. When benchmarked against standard predictive control, the hybrid strategy converges 30–40 episodes faster and delivers a 25% improvement in reward stability, demonstrating enhanced robustness and learning efficiency. The results confirm that H-RPEM achieves robust and balanced performance across economic, environmental, and technical domains, establishing it as a scalable and health-conscious control solution for next-generation smart city microgrids. Full article

(This article belongs to the Special Issue AI-Powered Battery Management and Grid Integration for Smart Cities)

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4 pages, 136 KB

Open AccessEditorial

Beyond Traditional Batteries—Emerging Systems for Next-Generation Energy Storage

by Xiaolin Tian, Yuqi Qin and Xiaoyuan Shi

Batteries 2026, 12(1), 4; https://doi.org/10.3390/batteries12010004 - 23 Dec 2025

Abstract

Against the backdrop of the global energy transition, new energy battery technology plays an increasingly vital role in modern society, effectively addressing the intermittency of energy sources [...] Full article

(This article belongs to the Special Issue The Breakthrough of Traditional Electrochemical Energy Storage Systems)

10 pages, 1360 KB

Open AccessArticle

An Experimental and Modeling Study on Commercial Lithium Titanate Batteries with Different Cathode Materials

by Hao Li

Batteries 2026, 12(1), 3; https://doi.org/10.3390/batteries12010003 - 22 Dec 2025

Abstract

This study presents a comparative analysis of the performance and modeling differences among lithium titanate oxide (LTO) batteries with three different cathode materials. An evaluation was conducted by performing performance tests over −20 °C to 25 °C at various current rates. Differences in [...] Read more.

This study presents a comparative analysis of the performance and modeling differences among lithium titanate oxide (LTO) batteries with three different cathode materials. An evaluation was conducted by performing performance tests over −20 °C to 25 °C at various current rates. Differences in open-circuit voltage curves, as well as charge and discharge capacities under different temperatures and C-rates, were systematically compared. At 25 °C, the NCM cathode enabled superior rate capability, retaining over 90% of its capacity at 8 C discharge, whereas the LCO-based cells exhibited significant capacity fade. Conversely, at −20 °C, the LCO cathode demonstrated better low-temperature performance, delivering almost 80% of its room-temperature capacity at 4 C, compared to less than 5% for the NCM cathode. The batteries were modeled using a second-order equivalent circuit model, and variations in model parameters were analyzed from the perspectives of internal resistance and electrode kinetics. The second-order equivalent circuit model revealed that the NCM-based cells had lower ohmic resistance and faster electrode kinetics. By correlating battery performance with cathode materials, this study evaluates the suitability of LTO batteries with different cathodes for various application scenarios, providing valuable insights for battery application and management. Full article

(This article belongs to the Special Issue Batteries: 10th Anniversary)

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20 pages, 8145 KB

Open AccessArticle

State of Health Estimation of Lithium Cobalt Oxide Batteries Based on ARX Identification Across Different Temperatures

by Simone Barcellona, Mattia Ribera, Emanuele Fedele, Pasquale Franzese, Luigi Piegari, Lorenzo Codecasa and Diego Iannuzzi

Batteries 2026, 12(1), 2; https://doi.org/10.3390/batteries12010002 - 20 Dec 2025

Abstract

Lithium-ion batteries (LiBs) undergo degradation influenced by storage and cycling conditions. Accurate state of health (SOH) assessment is crucial for predicting battery aging, which is generally marked by a decline in capacity (energy fade) or an increase in internal resistance (power fade). This [...] Read more.

Lithium-ion batteries (LiBs) undergo degradation influenced by storage and cycling conditions. Accurate state of health (SOH) assessment is crucial for predicting battery aging, which is generally marked by a decline in capacity (energy fade) or an increase in internal resistance (power fade). This study investigates the impulse response (IR) technique for assessing the SOH of lithium cobalt oxide batteries, addressing both capacity fade and rising internal resistance. The IR method relies on a predefined dataset that records the voltage response of the LiB to pulse current inputs across various states of charge (SOC), temperatures, and aging conditions to train a series of linear auto-regressive exogenous (ARX) models. This dataset is then used as a look-up table for subsequent SOH estimation under new operating conditions. The results demonstrate that the method can capture trends in capacity fade and resistance increase only when multiple battery temperatures are incorporated into the look-up table. In contrast, estimations based on ARX models trained at a single fixed temperature fail to provide reliable predictions of battery SOH. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)

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38 pages, 1295 KB

Open AccessReview

Secondary Use of Retired Lithium-Ion Traction Batteries: A Review of Health Assessment, Interface Technology, and Supply Chain Management

by Wen Gao, Ai Chin Thoo, Moniruzzaman Sarker, Noven Lee, Xiaojun Deng and Yun Yang

Batteries 2026, 12(1), 1; https://doi.org/10.3390/batteries12010001 - 19 Dec 2025

Abstract

Lithium-ion batteries (LIBs) dominate energy storage for electric vehicles (EVs) due to their high energy density, long cycle life, and low self-discharge. However, high costs, complex manufacturing, and the requirement for advanced battery management systems (BMSs) constrain their broader deployment. Therefore, extending the [...] Read more.

Lithium-ion batteries (LIBs) dominate energy storage for electric vehicles (EVs) due to their high energy density, long cycle life, and low self-discharge. However, high costs, complex manufacturing, and the requirement for advanced battery management systems (BMSs) constrain their broader deployment. Therefore, extending the utility of LIBs through reuse is essential for economic and environmental sustainability. Retired EV batteries with 70–80% state-of-health (SOH) can be repurposed in battery energy storage systems (BESSs) to support power grids. Effective reuse depends on accurate and rapid assessment of SOH and state-of-safety (SOS), which relies on precise state-of-charge (SOC) detection, particularly for aged LIBs with elevated thermal and electrochemical risks. This review systematically surveys SOC, SOH, and SOS detection methods for second-life LIBs, covering model-based, data-driven, and hybrid approaches, and highlights strategies for a fast and reliable evaluation. It further examines power electronics topologies and control strategies for integrating second-life LIBs into power grids, focusing on safety, efficiency, and operational performance. Finally, it analyzes key factors within the closed-loop supply chain, particularly reverse logistics, and provides guidance on enhancing adoption and supporting the establishment of circular battery ecosystems. This review serves as a comprehensive resource for researchers, industry stakeholders, and policymakers aiming to optimize second-life utilization of traction LIBs. Full article

(This article belongs to the Special Issue Industrialization of Second-Life Batteries)

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17 pages, 4693 KB

Open AccessArticle

From Waste to Cathode: A Comparative Evaluation of Sol–Gel and Co-Precipitation Routes for Closed-Loop Recycling of Lithium-Ion Battery Cathodes

by Alexandra Kosenko, Konstantin Pushnitsa, Pavel Novikov and Anatoliy A. Popovich

Batteries 2025, 11(12), 466; https://doi.org/10.3390/batteries11120466 - 18 Dec 2025

Abstract

The exponential growth of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems has amplified the urgent need for sustainable recycling strategies. Conventional pyrometallurgical and hydrometallurgical methods for LIB recycling are energy-intensive, chemically demanding, and fail to preserve the structural integrity of [...] Read more.

The exponential growth of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems has amplified the urgent need for sustainable recycling strategies. Conventional pyrometallurgical and hydrometallurgical methods for LIB recycling are energy-intensive, chemically demanding, and fail to preserve the structural integrity of cath-ode materials. Closed-loop recycling, in contrast, enables the recovery of layered oxides with minimal processing steps, reducing environmental footprint and supporting a circular economy. This study provides a systematic comparison of two regeneration approaches—sol–gel synthesis and hydroxide co-precipitation—for closed-loop recycling of layered NCM (LiNi_xCo_yMn_zO₂) cathode materials recovered from spent LIBs. Spent cells were mechani-cally processed and leached using malic acid to recover Ni, Co, Mn, which were subsequently used to synthesize NCM622 cathode powders. The regenerated materials were characterized using SEM/EDX, XRD, and electrochemical testing in CR2032 coin cells. Both methods successfully produced phase-pure layered oxides with the R-3m structure, with distinct differences in structural ordering and electrochemical behavior. The sol–gel-derived NCM622 displayed higher crystallinity and reduced cation mixing, evidenced by an I(003)/I(104) ratio of 1.896 compared to 1.720 for the co-precipitated sample, and delivered a high initial discharge capacity of 170 mAh/g at 0.1 C. However, it exhibited significant capacity fade, retaining only 60 mAh/g after 40 cycles. In contrast, the co-precipitation route produced hierarchical porous spherical agglomerates that offered superior cycling stability, maintaining ~150 mAh/g after 40 cycles with lower polarization (ΔE_p = 0.16 V). Both materials demonstrated electrochemical performance comparable to commercial NCM. Overall, hydroxide co-precipitation emerged as the most industrially viable method due to scalable processing, compositional robustness, and improved long-term stability of regenerated cathodes. This work highlights the critical influence of synthesis route selection in LIB closed-loop recycling and provides a technological framework for industrial recovery of high-value NCM cathode materials. Full article

(This article belongs to the Special Issue Second Life and Recycling: Perspectives for High-Performance Batteries)

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18 pages, 1484 KB

Open AccessArticle

Insights into Chemo-Mechanical Yielding and Eigenstrains in Lithium-Ion Battery Degradation

by Fatih Uzun

Batteries 2025, 11(12), 465; https://doi.org/10.3390/batteries11120465 - 18 Dec 2025

Abstract

In lithium-ion battery electrodes, repeated lithium insertion and extraction generate compositional gradients and volumetric changes that produce evolving stress fields and eigenstrains, accelerating mechanical degradation. While existing diffusion-induced stress models often capture only elastic behavior, they rarely provide a closed-form analytical treatment of [...] Read more.

In lithium-ion battery electrodes, repeated lithium insertion and extraction generate compositional gradients and volumetric changes that produce evolving stress fields and eigenstrains, accelerating mechanical degradation. While existing diffusion-induced stress models often capture only elastic behavior, they rarely provide a closed-form analytical treatment of irreversible deformation or its connection to cyclic degradation. In this work, a transparent analytical framework is developed for a planar electrode that explicitly couples lithium diffusion with elastic-plastic deformation, eigenstrain formation, and fracture-aware stress relaxation. The framework provides a means to quantitatively model the evolution of residual stress gradients, revealing the formation of a damaging tensile state at the electrode surface after delithiation and demonstrating how path-dependent irreversible deformation establishes a degradation memory. A parametric study is used to demonstrate the framework’s capability to clarify the influence of diffusivity and yield strength on residual stress development. This framework, which unifies diffusion, plasticity, and fracture in closed-form mechanical relations, provides new physical insight into the origins of chemo-mechanical degradation and offers a computationally efficient tool for guiding the design of durable next-generation electrode materials where chemo-mechanical strains are moderate. Full article

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Graphical abstract

24 pages, 9272 KB

Open AccessArticle

Cleaning and Cross-Contamination in Continuous Twin-Screw Extrusion of Battery Slurries

by Kevin Raczka, Furkan Öksüz, Nooshin Galahroudi, Emma Schiessl, Hermann Nirschl and Frank Rhein

Batteries 2025, 11(12), 464; https://doi.org/10.3390/batteries11120464 - 18 Dec 2025

Abstract

In the current industry standard of batch processing electrode slurry, manual cleaning processes pose significant challenges due to their labor intensive nature. The long-term objective is to expand the existing mixing process to create an intelligent, autonomous, and continuous slurry production process. This [...] Read more.

In the current industry standard of batch processing electrode slurry, manual cleaning processes pose significant challenges due to their labor intensive nature. The long-term objective is to expand the existing mixing process to create an intelligent, autonomous, and continuous slurry production process. This will result in a reduction in downtime and setup times, as well as an increase in the degree of automation. Additionally, the implementation of complex parameter selection in the mixing process is intended to make it manageable for variable recipes, ensuring efficient, resource-saving process control. This study aims to address this issue by investigating the continuous production of anode slurry and its subsequent cleaning in a laboratory extruder, with a focus on optimizing the cleaning conditions and analyzing the residual slurry. Several samples were taken during the cleaning of the process area and analyzed by UV-Vis spectroscopy, while also quantifying the residual slurry on the screw elements. The effectiveness of the cleaning was evaluated using Sinner’s Circle parameters, i.e., the effects of time, mechanical, chemical and thermal treatment on the effectiveness of the cleaning process are evaluated and discussed. Several detergents were tested, including deionized water, alcohol, and industrial detergents. Deionized water proved to be the most effective in terms of cleaning rate and residual slurry. In addition, higher screw speeds and flow rates improved cleaning efficiency. The effect of temperature was significant, with better cleaning rate results at higher temperatures. This indicates that mechanical and thermal factors play a critical role in improving cleaning kinetics. For a more in-depth knowledge of the resulting cell chemistry, successive cross-contamination of cathode materials in anode half-cells was examined. As a result, an indicator was identified in the first cycle that displays a voltage increase during delithiation with regard to electrochemical properties. Full article

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12 pages, 2912 KB

Open AccessArticle

KI-Assisted MnO₂ Electrocatalysis Enables Low-Charging Voltage, Long-Life Rechargeable Zinc–Air Batteries

by Francesco Biscaglia, Sabrina Di Masi, Marco Milanese, Claudio Mele, Giuseppe Gigli, Arturo De Risi and Luisa De Marco

Batteries 2025, 11(12), 463; https://doi.org/10.3390/batteries11120463 - 16 Dec 2025

Abstract

Rechargeable zinc–air batteries (ZABs) are promising candidates for sustainable energy storage owing to their high theoretical energy density, safety, and environmental compatibility. However, their practical application is hindered by sluggish oxygen evolution reaction (OER) kinetics and the high charging voltage required, which reduce [...] Read more.

Rechargeable zinc–air batteries (ZABs) are promising candidates for sustainable energy storage owing to their high theoretical energy density, safety, and environmental compatibility. However, their practical application is hindered by sluggish oxygen evolution reaction (OER) kinetics and the high charging voltage required, which reduce energy efficiency and accelerate electrode degradation. Here, we report for the first time the beneficial role of potassium iodide (KI) as a reaction modifier in ZABs employing manganese dioxide (MnO₂) as a bifunctional catalyst. MnO₂ not only exhibits remarkable electrocatalytic activity toward the oxygen reduction reaction (ORR) but also catalyzes the iodide oxidation reaction (IOR), which proceeds at significantly lower potentials than the OER. As a result, KI-modified MnO₂ ZABs achieve a remarkably low charging voltage of ≈1.8 V and an energy efficiency of 69.9% at 5 mA/cm². Although the IOR is not fully reversible in alkaline media and its effectiveness depends on the iodide concentration in the electrolyte—which may decrease upon repeated discharge–charge cycling—the suppression of electrode degradation enables stable operation for more than 200 charge–discharge cycles. These findings demonstrate the synergistic effect of KI and MnO₂ in enabling an efficient ORR/IOR pathway, providing a sustainable and cost-effective alternative to noble metal catalysts and opening new perspectives for the practical development of high-performance ZABs. Full article

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Open AccessArticle

In Situ Growth of Cross-Linked Ti₂Nb₁₀O₂₉ Nanoparticles on Inner/Outer Surfaces of Carbon Microtubes for High-Efficiency Lithium Storage

by Zhi Nie, Hualin Xiong, Changlong Du, Lei Yu, Lianrui Li, Gengping Wan and Guizhen Wang

Batteries 2025, 11(12), 462; https://doi.org/10.3390/batteries11120462 - 16 Dec 2025

Abstract

Improving electronic and ionic transport and the structural stability of electrode materials is essential for the development of advanced lithium-ion batteries. Despite its great potential as a high-power anode, Ti₂Nb₁₀O₂₉ (TNO) still underperforms due to its unsatisfactory electronic [...] Read more.

Improving electronic and ionic transport and the structural stability of electrode materials is essential for the development of advanced lithium-ion batteries. Despite its great potential as a high-power anode, Ti₂Nb₁₀O₂₉ (TNO) still underperforms due to its unsatisfactory electronic and ionic conductivity. Here, a TNO/carbon microtube (TNO@CMT) composite is constructed via an ethanol-assisted solvothermal process and controlled annealing. The hollow carbon framework derived from kapok fibers provides a lightweight conductive skeleton and abundant nucleation sites for uniform TNO growth. By tuning precursor concentration, the interfacial structure and loading are precisely regulated, optimizing electron/ion transport. The optimized TNO@CMT-2 exhibits uniformly dispersed TNO nanoparticles anchored on both inner and outer CMT surfaces, enabling rapid electron transfer, short Li⁺ diffusion paths, and high structural stability. Consequently, it delivers a reversible capacity of 314.9 mAh g⁻¹ at 0.5 C, retains 75.8% capacity after 1000 cycles at 10 C, and maintains 147.96 mAh g⁻¹ at 40 C. Furthermore, the Li⁺ diffusion coefficient of TNO/CMT-2 is 5.4 × 10⁻¹¹ cm² s⁻¹, which is nearly four times higher than that of pure TNO. This work presents a promising approach to designing multi-cation oxide/carbon heterostructures that synergistically enhance charge and ion transport, offering valuable insights for next-generation high-rate lithium-ion batteries. Full article

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