Batteries

21 pages, 5242 KB

Open AccessArticle

A Three-Stage Reaction-Process-Corrected Equivalent Circuit Model for Predicting External Short-Circuit Current in Lithium-Ion Batteries

by Xingzhen Zhou, Chenhui Gao, Weige Zhang, Caiping Zhang, Qinhe Huang, Lei Zhang, Yusheng Li, Ling Chen, Dongzhong Hu and Jinhan Qiu

Batteries 2026, 12(6), 224; https://doi.org/10.3390/batteries12060224 - 21 Jun 2026

Viewed by 427

Abstract

Accurate prediction of external short-circuit (ESC) current is important for battery safety analysis and protection design, but conventional equivalent circuit models have difficulty reproducing the strongly nonlinear current evolution under ESC conditions. This study proposes a reaction-process-corrected second-order RC model for ESC current [...] Read more.

Accurate prediction of external short-circuit (ESC) current is important for battery safety analysis and protection design, but conventional equivalent circuit models have difficulty reproducing the strongly nonlinear current evolution under ESC conditions. This study proposes a reaction-process-corrected second-order RC model for ESC current prediction, based on ESC experiments on a 37 Ah commercial NCM pouch cell at different initial SOCs. The ESC process is described by three successive stages: bottleneck control, concentration-difference control, and separator pore closure. To represent the transport-related resistance deviation during this process, an additional correction resistance R_x and a queued-charge descriptor Q are introduced into the equivalent circuit framework. A segmented closed-loop simulation strategy is then developed to update R_x and predict the ESC current. Using the 50% SOC case as an unseen validation case, the proposed model captures the main nonlinear characteristics of ESC current, including rapid initial decay, secondary rebound, and subsequent attenuation. The proposed framework improves the physical interpretability of equivalent-circuit-based ESC simulation while retaining engineering simplicity, providing a practical approach for safety-boundary assessment and protection-oriented battery system design. Full article

(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)

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34 pages, 4254 KB

Open AccessReview

Recent Advancements in Electrolytic Zn–MnO₂ Batteries: Mechanistic Insights into Mn²⁺/MnO₂ Deposition/Dissolution and Applications to Scalable Energy Storage

by Masaharu Nakayama, Wataru Yoshida and Yasuhiro Shioji

Batteries 2026, 12(6), 223; https://doi.org/10.3390/batteries12060223 - 19 Jun 2026

Viewed by 1705

Abstract

Aqueous zinc–manganese dioxide (Zn–MnO₂) batteries are undergoing a paradigm shift from traditional ion-insertion mechanisms to a reversible deposition/dissolution process. By leveraging a two-electron transfer (Mn²⁺/MnO₂), this electrolytic system achieves a high theoretical capacity of 616 mAh g [...] Read more.

Aqueous zinc–manganese dioxide (Zn–MnO₂) batteries are undergoing a paradigm shift from traditional ion-insertion mechanisms to a reversible deposition/dissolution process. By leveraging a two-electron transfer (Mn²⁺/MnO₂), this electrolytic system achieves a high theoretical capacity of 616 mAh g⁻¹ and a theoretical operating voltage of 1.99 V. However, the accumulation of dead Mn, electrically isolated inactive phases, and dynamic interfacial pH fluctuations remain critical barriers to cycle life and practical energy density. This review systematizes a trinitarian strategy to overcome these bottlenecks, focusing on interfacial engineering, redox mediator-assisted recovery, and advanced electrode architectures. We evaluate how anion engineering and pH-buffering stabilize reaction pathways, and how diverse mediators (e.g., halogens, metal ions, and organic molecules) chemically rescue inactive manganese. Furthermore, we examine the integration of 3D carbon networks and low-cost hybrid electrodes to sustain high-areal-capacity deposition. To elucidate these complex mechanisms, we highlight multiscale analytical approaches combining synchrotron X-ray techniques and density functional theory (DFT). Finally, we outline a roadmap for applications ranging from grid-scale flow batteries to flexible wearable electronics. This work provides a comprehensive perspective on realizing sustainable, safe, and high-performance zinc-based energy storage. Full article

(This article belongs to the Special Issue Progress in Aqueous Zinc-Based Batteries)

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21 pages, 6472 KB

Open AccessArticle

Temperature-Dependent Discharge Capability of High-Power LFP Battery Cells for Starter Battery Applications

by Florian Wätzold, Anton Schlösser, Sven Beger, Daniela Schröder and Julia Kowal

Batteries 2026, 12(6), 222; https://doi.org/10.3390/batteries12060222 - 19 Jun 2026

Viewed by 800

Abstract

This study investigates the temperature-dependence performance of high-power lithium iron phosphate (LFP) cells for automotive starter batteries. Temperature effects on high-power LFP cells are contextualised based on pertinent literature in order to compare the typical capacity behaviour of lead–acid batteries with LFP. Experiments [...] Read more.

This study investigates the temperature-dependence performance of high-power lithium iron phosphate (LFP) cells for automotive starter batteries. Temperature effects on high-power LFP cells are contextualised based on pertinent literature in order to compare the typical capacity behaviour of lead–acid batteries with LFP. Experiments were conducted on five cylindrical LFP cell types in a thermal chamber across ambient temperatures from +45 °C to −30 °C using a 9 C discharge regime aligned with automotive standards. Electrical and thermal behaviours were analysed, including energy yield, power output, and surface temperature monitored by sensors and thermal imaging for room temperature. Energy output decreased exponentially with temperature but remained above 70% for most LFP cells at −18 °C, while only one cell type was functional at −30 °C. Thermal analysis at ambient temperature confirmed homogeneous temperature distribution without hotspots and low overall heating (from 2 °C to 14 °C), indicating no need for additional cooling for starter battery applications. A conservative power analysis indicated that 4 kW at −30 °C would require a 28P4S 26650 configuration, representing a lower-bound estimate. We argue that even this conservative figure suggests a potential for weight reduction compared with lead–acid systems. Energy-based Pb-equivalence factors of approximately 1.2 at −18 °C and 3 at −30 °C were derived. A preliminary guideline for cell dimensioning based on measurements at 25 °C is proposed to address discrepancies between data sheet specifications and actual performance for pack configuration based on required power. Full article

(This article belongs to the Topic Batteries, Supercapacitors, Fuel Cells and Combined Energy/Power Supply Systems, 2nd Edition)

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28 pages, 10379 KB

Open AccessArticle

Target-Mean State-of-Charge Control for Maximum Utilization of Heterogeneous Reconfigurable Battery Systems Under Constant-Bus Constraints

by Mateusz Sztuka, Mohammad Musameh, Asma Ali, Nicholas Richardson, Alessandro Di Nuovo and Walid Issa

Batteries 2026, 12(6), 221; https://doi.org/10.3390/batteries12060221 - 18 Jun 2026

Viewed by 778

Abstract

Cell degradation in second-life battery packs introduces heterogeneous capacity and internal resistance mismatch, reducing the effectiveness of conventional balancing approaches and limiting available pack runtime. Although equal state of charge (SoC) does not necessarily imply equal usable capacity, SoC-based control remains attractive for [...] Read more.

Cell degradation in second-life battery packs introduces heterogeneous capacity and internal resistance mismatch, reducing the effectiveness of conventional balancing approaches and limiting available pack runtime. Although equal state of charge (SoC) does not necessarily imply equal usable capacity, SoC-based control remains attractive for runtime-oriented operation. This paper proposes a target-mean controller for heterogeneous reconfigurable battery packs under constant-bus constraints that aims to improve runtime and achieve the cutoff-defined theoretical maximum capacity utilization limit. Using only real-time cell SoC measurements and legal switching actions, the controller selects the configuration that best reduces deviation from the pack-average SoC while preferentially loading cells above the mean. The online action selection requires no active balancing hardware, no explicit capacity or state of health (SoH) estimation, and no offline optimization; experimentally measured capacities are used only for calibrated Coulomb-counting SoC estimation. Simulation results on a heterogeneous five-cell reconfigurable battery pack show that the proposed controller reaches the cutoff-defined 90% theoretical utilization limit in the full-initial-SoC cases, while also extending runtime and reducing switching activity by up to 11.75% relative to the comparison methods. Hardware validation on a five-cell prototype further confirms this trend, achieving 89.12% experimental utilization, zero final SoC spread, and higher delivered energy than both comparison methods. A stepped-load hardware test further achieved 88.19% utilization from current integration, corresponding to 97.99% of the cutoff-defined 90% theoretical limit. The results suggest that, for heterogeneous second-life packs, SoC-based reconfiguration control can achieve both runtime improvement and near-maximum utilization without the added complexity of explicit SoH-aware balancing. Full article

(This article belongs to the Special Issue Research on Advanced Battery Technologies for Future Integrated Transportation–Energy Systems)

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14 pages, 2882 KB

Open AccessArticle

Single-Walled Carbon Nanotube Templated Three-Dimensional Porous Si/SiO₂ Core–Shell Cylindrical Hybrid Anode Material for Lithium-Ion Batteries

by SeYi Kwon and Jun-Ki Lee

Batteries 2026, 12(6), 220; https://doi.org/10.3390/batteries12060220 - 18 Jun 2026

Viewed by 1254

Abstract

Silicon (Si) is a leading anode candidate for next-generation lithium-ion batteries owing to its high theoretical capacity (~4200 mAh/g), but its >300% volumetric expansion during lithiation causes particle pulverization, loss of electrical contact, and continuous solid electrolyte interphase (SEI) reformation, resulting in rapid [...] Read more.

Silicon (Si) is a leading anode candidate for next-generation lithium-ion batteries owing to its high theoretical capacity (~4200 mAh/g), but its >300% volumetric expansion during lithiation causes particle pulverization, loss of electrical contact, and continuous solid electrolyte interphase (SEI) reformation, resulting in rapid capacity fade. Here, we report a single-walled carbon nanotube (SWNT)-templated porous Si/SiO₂ core–shell cylindrical hybrid anode synthesized by combining block copolymer-directed sol–gel assembly with controlled magnesiothermic reduction. SWNT bundles act as a three-dimensional structural template that directs the formation of a continuously interconnected cylindrical porous network, a geometry difficult to obtain by conventional particle-based compositing. The controlled, partial magnesiothermic reduction intentionally preserves residual amorphous SiO₂ within the porous shell as an electrochemically inactive mechanical buffer that suppresses Si volume expansion and stabilizes the electrode. A side-by-side comparison with a fully reduced, SiO₂-free counterpart of identical architecture isolates the role of the SiO₂ buffer in achieving long-term cycling stability. The SWNT-porous Si/SiO₂ hybrid delivers a reversible capacity of 1133 mAh/g in the first cycle and retains 90% of its initial capacity after 200 cycles at 1 C with 99.7% Coulombic efficiency, together with a rate capability of 482 mAh/g at 5 C. Post-cycling cross-sectional analysis confirms minimal electrode-level swelling (~2 μm) after 200 cycles, demonstrating the structural efficacy of the SWNT-templated porous architecture combined with the SiO₂ buffer for structurally stable Si anodes. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries—Silicon Anodes for Next-Generation Batteries: Materials, Design Strategies, Performance, and Future Directions)

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18 pages, 2468 KB

Open AccessArticle

Analysis of Safety Characteristics for Prismatic Lithium-Ion Batteries Based on a Refined Model

by Pengfei Yan, Fang Wang, Tianyi Ma, Liduo Chen, Gaiyun He, Liqiong Han and Zhipeng Sun

Batteries 2026, 12(6), 219; https://doi.org/10.3390/batteries12060219 - 17 Jun 2026

Viewed by 313

Abstract

As the global automotive industry is transitioning toward sustainable development, new energy vehicles (NEVs) have experienced rapid global growth due to their environmental friendliness and high efficiency. Global sales of NEVs are projected to reach 50 million units by 2030. Nevertheless, safety incidents [...] Read more.

As the global automotive industry is transitioning toward sustainable development, new energy vehicles (NEVs) have experienced rapid global growth due to their environmental friendliness and high efficiency. Global sales of NEVs are projected to reach 50 million units by 2030. Nevertheless, safety incidents caused by impacts on traction batteries remain a major factor restricting the development of NEVs. Prismatic batteries, which account for over 90% of the traction battery market owing to their high energy density and structural robustness, nevertheless continue to face significant safety challenges under mechanical loading conditions. Typical failure modes involve structural damage induced by external compressive forces during severe vehicular collisions, which can subsequently result in the tearing of internal electrode layers and rupture of the separator, thereby initiating internal short circuits and leading to severe incidents. Accordingly, this research focuses on the mechanism of structural damage transmission for prismatic lithium-ion batteries under compression conditions. By integrating a refined mechanical model, it further elucidates the structural failure mechanisms and conducts a microscopic analysis of the damaged battery structure to investigate the effects of varying damage levels on battery safety performance, providing significant guidance for the safety and reliability of new energy vehicles. Full article

(This article belongs to the Special Issue Thermal Management in Lithium-Ion and Emerging Batteries: Latest Advances and Prospects)

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16 pages, 4815 KB

Open AccessEditor’s ChoiceArticle

Metal-Organic Frameworks (MOFs)-Integrated Separator for Improving the Cycle Stability of Lithium–Ion Batteries

by Apurba Ray, Neil Wood, Emre Guney, Bilal Tasdemir, Kamil Burak Dermenci, Maitane Berecibar and Bilge Saruhan

Batteries 2026, 12(6), 218; https://doi.org/10.3390/batteries12060218 - 16 Jun 2026

Viewed by 1972

Abstract

To date, lithium–ion batteries (LIBs) are considered one of the most promising and market-leading energy storage systems due to their high theoretical capacity and energy density. However, poor thermal and cyclic stability, low electrolyte uptake, and the possibility for frequent short circuits of [...] Read more.

To date, lithium–ion batteries (LIBs) are considered one of the most promising and market-leading energy storage systems due to their high theoretical capacity and energy density. However, poor thermal and cyclic stability, low electrolyte uptake, and the possibility for frequent short circuits of typical separators and evolution of several gases during long cycle operation pose several problems for LIBs. Metal-organic frameworks (MOFs) have attracted widespread interest as a promising material for improving the cycle stability and safety of rechargeable batteries due to their inherent surface and structural properties such as high specific surface area, high porosity, and ionic conductivity. In this work, the aim is to provide detailed descriptions of the synthesis routes and parameters for obtaining various MOFs such as Zr-MOF-808 and Ni-MOF-74 nanoparticles and the fabrication of those MOF-integrated separators. To optimize the crystallinity, morphological and compositional characteristics, and several material characterizations such as XRD, SEM, and EDX have been applied. Afterwards, the synthesized MOF-integrated glass fiber (GF) separators have been developed for lithium–ion battery (LIB) applications. To investigate the electrochemical performance and the effect of MOF integration into the separators, electrochemical studies in the form of galvanostatic charge–discharge (GCD), electrochemical impedance spectroscopy (EIS) have been evaluated by preparing CR2032-type half-coin cells. This MOFs-integrated GF-separators and synthesized LiNi_0.6Mn_0.2Co_0.2O₂ (NMC622) cathode materials-based coin cell LIB exhibited higher cycle stability than bare GF-separator based LIB. This novel approach and extensive research suggest that development of MOF-integrated separators could significantly improve cycle stability by reducing the internal cell degradation for next generation energy storage devices. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Interface Science in Batteries)

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14 pages, 6584 KB

Open AccessArticle

Investigating the Correlation Between Mechanical Impact and Long Term Performance Degradation in Li-Ion Batteries

by John Sherman and Anthony Bombik

Batteries 2026, 12(6), 217; https://doi.org/10.3390/batteries12060217 - 15 Jun 2026

Viewed by 336

Abstract

Lithium-ion batteries (LIBs) are subject to mechanical abuse both in electric vehicles and consumer electronic applications when dropped, which can lead to capacity degradation even if the cells survive the impact. This study investigates the impact of mechanical damage on the electrochemical performance [...] Read more.

Lithium-ion batteries (LIBs) are subject to mechanical abuse both in electric vehicles and consumer electronic applications when dropped, which can lead to capacity degradation even if the cells survive the impact. This study investigates the impact of mechanical damage on the electrochemical performance of LIBs, focusing on capacity retention and internal resistance changes. The batteries were subjected to dynamic mechanical impact using varying impact energies (3J, 5J, and 7J) while measuring internal resistance and capacity before and after the impact. Hybrid Pulse Power Characterization (HPPC) was employed to assess internal resistance and capacity degradation across multiple cycles. Our results demonstrate that even minor mechanical damage can cause significant performance decay, especially after several cycles. The study also reveals that the state of charge (SOC) prior to impact has a minimal effect on the survival rate of the cells but influences the extent of damage observed. Post-impact analysis using optical microscopy indicates structural damage, including separator tears and delamination, contributing to capacity fade. This work highlights the importance of considering intermediate mechanical damage in LIB safety and performance assessments. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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22 pages, 3279 KB

Open AccessArticle

Enabling Holistic Tracking and Tracing in Battery Cell Production: Data Management and Applications

by Lennart Kuhr, Sajedeh Haghi, Matthias Leeb, Alexander Schoo, Mark Mennenga, Arno Kwade, Rüdiger Daub and Christoph Herrmann

Batteries 2026, 12(6), 216; https://doi.org/10.3390/batteries12060216 - 14 Jun 2026

Viewed by 732

Abstract

The battery cell production, a cornerstone of the net-zero vision, is a multifaceted process chain involving diverse processes, spanning from batch to continuous to single-unit steps. The quality of the battery cell as the final product is affected by various product and process [...] Read more.

The battery cell production, a cornerstone of the net-zero vision, is a multifaceted process chain involving diverse processes, spanning from batch to continuous to single-unit steps. The quality of the battery cell as the final product is affected by various product and process parameters along this process chain. In the era of Industry 4.0, data-driven approaches have emerged as a promising solution to navigate these complexities and derive effective quality management practices. A key prerequisite for the successful implementation is the availability of accurate data. A tracking and tracing system in battery cell production provides the foundation to acquire such data. It supports the development of a digital twin of the product, enabling real-time monitoring of key performance indicators, in-line quality control, resource optimization, and compliance fulfillment, among others. This article presents an implementation methodology and discusses the key aspects to consider for upscaling such a system focusing on data management, including relevant parameters, data acquisition, and storage, as well as data structuring and mapping. It highlights the advantages of using ontology-based data descriptions, enabling semantically mapped production environments. Lastly, this article explores potential use cases facilitated by a traceability system, emphasizing its potential to realize intelligent, data-driven production. Full article

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9 pages, 2398 KB

Open AccessCommunication

A Rechargeable Zinc–Copper Voltaic Battery Built from Cost-Effective Electrodes and Electrolytes

by Jose Fernando Florez Gomez, Songyang Chang, Irfan Ullah, Juan C. Velez Reyes, Lisandro Cunci, Gerardo Morell and Xianyong Wu

Batteries 2026, 12(6), 215; https://doi.org/10.3390/batteries12060215 - 13 Jun 2026

Viewed by 1400

Abstract

The zinc–copper (Zn-Cu) voltaic battery is the first battery made in human history, but the Cu²⁺ dissolution issue leads to the reaction’s irreversibility. To tackle this challenge, solid-state electrolytes, ion exchange membranes, and functional electrolytes have been proposed to mitigate the Cu [...] Read more.

The zinc–copper (Zn-Cu) voltaic battery is the first battery made in human history, but the Cu²⁺ dissolution issue leads to the reaction’s irreversibility. To tackle this challenge, solid-state electrolytes, ion exchange membranes, and functional electrolytes have been proposed to mitigate the Cu²⁺ dissolution; however, these approaches incur limitations like cell complexity, high cost, and anode corrosion. Herein, we develop a simple yet effective strategy to mitigate Cu²⁺ dissolution and build a rechargeable voltaic battery from cost-effective materials, including commercially available micro-copper powders and non-corrosive zinc acetate electrolyte. Importantly, the near-neutral Zn(Ac)₂ electrolyte provides some amounts of hydroxide and facilitates the Cu₂O/Cu solid–solid conversion reaction, thereby inhibiting the generation of soluble Cu²⁺ ions. As a result, the Zn-Cu battery exhibits a reversible capacity of ~130 mAh g⁻¹, a feasible voltage of 0.87 V, and a stable cycling life over 100 cycles. Our work provides a feasible strategy for developing rechargeable and cost-effective Zn-Cu batteries. Full article

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18 pages, 3409 KB

Open AccessEditor’s ChoiceArticle

Rescaling Capacity and Power Rating of Spent LIB for Second-Life Application

by Ote Amuta and Julia Kowal

Batteries 2026, 12(6), 214; https://doi.org/10.3390/batteries12060214 - 12 Jun 2026

Viewed by 355

Abstract

The adoption of lithium-ion batteries (LIBs) as secondary rechargeable batteries across many industries, including consumer electronics, electromobility, industrial tools, and electrical energy storage, is on the rise. As lithium-ion batteries approach the end of their life, there is a need to assess them [...] Read more.

The adoption of lithium-ion batteries (LIBs) as secondary rechargeable batteries across many industries, including consumer electronics, electromobility, industrial tools, and electrical energy storage, is on the rise. As lithium-ion batteries approach the end of their life, there is a need to assess them for the possibility of a secondary application or reuse for a less demanding application. The extra connections of individual cells, BMS, temperature sensors, and other components to form a compact battery pack pose a challenge for second-life assessment, which usually prefers to separate individual cells for testing before discarding very bad cells for recycling and grading cells with substantive capacity based on their remaining capacity. This is a high cost for the second-life assessment. This work seeks to investigate an approach that avoids dismantling the battery pack into individual modules, cells, and BMS by including a BMS feature that allows the capacity and power ratings to be rescaled onboard after its first use. A set of cells with different chemistries was used in this work: a nickel–cobalt–aluminium oxide cathode with a silicon-doped graphite anode (NCA-GS), a nickel–cobalt–aluminium oxide cathode and graphite, and a lithium–nickel–manganese–cobalt oxide (NMC) cathode with a graphite anode (NMC-G) with various ageing states and behaviours. Their internal resistance and capacity at the beginning and end of life were compared. The scaling factor was obtained by finding the square root of the ratio of the internal resistance at EOL to that at BOL. With the current obtained by multiplying the cycling current rate by the rescaling factor, the surface temperature profile of the aged cells during cycling became the same as the temperature at the beginning of life. The relaxation voltage after discharge to 0% SOC and charge to 100% SOC was used to set the low and high cut-off voltages, respectively. This contributed significantly to reduced ageing and to a lower temperature rise in the spent cells. This set the stage for rescaling or derating battery systems without separating the individual cells, which is a huge cost for second-life use of lithium-ion batteries. BMS can be designed with configurable voltage and current limits, so that when repurposed for a second life, only a simple configuration or firmware update may be necessary. Full article

(This article belongs to the Special Issue Second-Life Batteries: Challenges and Opportunities)

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25 pages, 8139 KB

Open AccessArticle

Inconsistency Diagnosis of Power Batteries Based on End-Cloud Collaboration

by Bin Ma, Yajin Liu, Dongyang Ma, Guoliang Liu, Changjian Ji and Bosong Zou

Batteries 2026, 12(6), 213; https://doi.org/10.3390/batteries12060213 - 10 Jun 2026

Viewed by 382

Abstract

In electric vehicles, power batteries consist of numerous individual cells connected in series or parallel. Variations in manufacturing, operating conditions, and aging can lead to differences among these cells. Such inconsistencies can compromise the battery pack’s performance, safety, and overall service life. Therefore, [...] Read more.

In electric vehicles, power batteries consist of numerous individual cells connected in series or parallel. Variations in manufacturing, operating conditions, and aging can lead to differences among these cells. Such inconsistencies can compromise the battery pack’s performance, safety, and overall service life. Therefore, accurately diagnosing inconsistencies among battery cells is of great significance for enhancing the reliability of the battery system and ensuring the operational safety of the vehicle. To address the limited computational resources available in vehicles, this paper proposes an end-cloud collaborative fault diagnosis framework and validates its effectiveness using real-world vehicle driving data. On the cloud side, a deep learning-based reconstruction network is developed to enable high-precision reconstruction of cell voltages. On the vehicle side, a second-order equivalent circuit model is used to represent battery dynamics. An adaptive forgetting factor recursive least squares method is introduced for online estimation of the model parameters, enabling accurate local prediction of individual cell voltages. Using the cloud-reconstructed and vehicle-predicted cell voltages, the extreme difference value of voltage for each cell is computed. A comprehensive diagnosis of inconsistency faults is then performed by fusing the extreme difference in voltage results from both the cloud and vehicle sides via the Extended Kalman Filter (EKF); threshold judgment is conducted based on the fused results, and the Cumulative Sum (CUSUM) algorithm is designed to identify cell inconsistency faults. Experimental results show that the proposed method effectively detects battery inconsistency faults and demonstrates strong engineering applicability and practical potential. Full article

(This article belongs to the Special Issue Intelligent Management and Sustainable Development of Lithium-Ion Batteries in Automotive Applications)

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19 pages, 7583 KB

Open AccessEditor’s ChoiceArticle

From Operation to SOH Estimation: Analysis of Lithium-Ion Capacitors Based on Passive EIS for E-Bus Application

by Tarek Ibrahim, Muhammad Usman Tahir, Mohamed Abdel-Monem, Erik Schaltz, Vaclav Knap, Daniel Ioan Stroe and Tamas Kerekes

Batteries 2026, 12(6), 212; https://doi.org/10.3390/batteries12060212 - 10 Jun 2026

Viewed by 770

Abstract

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals [...] Read more.

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article

(This article belongs to the Special Issue Battery Management Systems Based on Electrochemical Impedance Spectroscopy—2nd Edition)

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22 pages, 10891 KB

Open AccessArticle

Experimental Investigation of Multiphysics Responses of Pouch Lithium-Ion Batteries Under Quasi-Static Compression and Dynamic Impact

by Long Ying, Shanglong Xiao, Yulong Zhang, Jianquan Xu, Jieliang Fan and Jiashen Lin

Batteries 2026, 12(6), 211; https://doi.org/10.3390/batteries12060211 - 8 Jun 2026

Viewed by 454

Abstract

Lithium-ion batteries are prone to internal short-circuits and subsequent thermal runaway under compression and impact loads during electric vehicle crashes, posing a critical safety challenge for the industry. However, existing studies lack systematic comparative analysis between quasi-static and dynamic loading conditions. In this [...] Read more.

Lithium-ion batteries are prone to internal short-circuits and subsequent thermal runaway under compression and impact loads during electric vehicle crashes, posing a critical safety challenge for the industry. However, existing studies lack systematic comparative analysis between quasi-static and dynamic loading conditions. In this study, ternary pouch lithium-ion batteries were used as research objects. A test platform for the synchronous acquisition of mechanical load, electrical voltage and thermal temperature was established. Quasi-static compression and drop-weight impact tests were conducted to investigate the effects of indenter diameter, impact velocity and state of charge (SOC) on the multiphysics responses of batteries. The results show significant differences in failure modes between the two loading conditions: quasi-static loading causes progressive plastic deformation and stable short-circuit voltage decay, while dynamic loading is likely to induce brittle shear fracture and soft short-circuit voltage rebound. Dynamic loading reduces peak load by 61.5% and raises peak temperature by up to 46.7%, while also decreasing failure displacement and advancing time-to-peak. Additionally, a high SOC (50% and 100%) alters the heat-release pathway during thermal runaway, leading to deviations in surface temperature measurements. These findings provide critical experimental support for the crash safety design of power batteries and the formulation of thermal runaway prevention and control strategies. Full article

(This article belongs to the Section Energy Storage System Aging, Diagnosis and Safety)

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34 pages, 20678 KB

Open AccessArticle

Lithium-Ion Battery State of Health Prediction Using a Hybrid BiLSTM–Random Forest Framework

by Nur Mohamed Mohamud, Shahrin Md Ayob, Siti Mahfuza Saimon, Ahmed M. Nahhas, Zeeshan Ahmad Arfeen, Muhammad I. Masud and Mohammed Aman

Batteries 2026, 12(6), 210; https://doi.org/10.3390/batteries12060210 - 8 Jun 2026

Cited by 1 | Viewed by 1634

Abstract

The accurate estimation of lithium-ion battery state of health (SOH) is crucial for battery monitoring, safety, and degradation assessment; however, it remains challenging because of the nonlinear nature of battery degradation, measurement noise, and variability in the battery aging trajectory. This study aims [...] Read more.

The accurate estimation of lithium-ion battery state of health (SOH) is crucial for battery monitoring, safety, and degradation assessment; however, it remains challenging because of the nonlinear nature of battery degradation, measurement noise, and variability in the battery aging trajectory. This study aims to solve these problems by proposing a hybrid attention-based BiLSTM–RF model, which combines wavelet-based signal denoising, incremental capacity analysis (ICA)-based feature extraction, stacked Bidirectional Long Short-Term Memory (BiLSTM) networks, multi-head self-attention, principal component analysis (PCA)-based feature compression, and ensemble regression using a Random Forest (RF) model with adaptive weighted fusion. The proposed framework was tested on the NASA battery datasets (B0005, B0006, B0007 and B0018) and was further validated on the Oxford Battery Degradation Dataset using leave-one-battery-out cross validation conditions. Experimental results indicated that, in general, the proposed framework outperformed the evaluated benchmark models (CNN-LSTM, BiLSTM, and RF models) in terms of the prediction error, with a minimum RMSE value of 0.0229 for NASA battery B0007 and 0.0024 for Oxford Cell3. Ablation analysis also showed that the combination of wavelet denoising, PCA compression, temporal sequence learning and ensemble regression played a role in the overall SOH estimation performance. These results show that the proposed hybrid approach is effective and stable for SOH estimation in different battery degradation trajectories under the tested experimental conditions. Full article

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13 pages, 8292 KB

Open AccessArticle

Battery Systems Using Adhesively Bonded Cells for Scalable and Serviceable Applications

by Felix Mannerhagen, Elena Simona Udrescu, Erik Hultman and Mats Leijon

Batteries 2026, 12(6), 209; https://doi.org/10.3390/batteries12060209 - 7 Jun 2026

Viewed by 570

Abstract

This paper presents a battery cell joining solution leveraging adhesively bonded lithium-ion cells as a foundation for scalable, serviceable, and recyclable energy storage platforms. The proposed design methodology enables mechanically and electrically functional connections and supports a design concept intended for compatibility with [...] Read more.

This paper presents a battery cell joining solution leveraging adhesively bonded lithium-ion cells as a foundation for scalable, serviceable, and recyclable energy storage platforms. The proposed design methodology enables mechanically and electrically functional connections and supports a design concept intended for compatibility with automated manufacturing and future robotic disassembly. A123 26650-format cells were tested using Epo-Tek 430 conductive adhesive, with performance evaluated through ESR and G-force measurement experiments. The results indicate that no measurable change in electrical performance was observed within the resolution of the measurement system, while supporting a design concept intended to improve modularity and serviceability. The proposed system shows potential for further investigation in electric vehicle and industrial energy system applications, although further validation under realistic operating conditions is required. Full article

(This article belongs to the Special Issue Energy Storage and Sustainable Circularity: Materials, Systems, and Lifecycle Approaches)

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40 pages, 2259 KB

Open AccessReview

Recent Progress in Non-Precious and Carbon-Based Electrocatalysts for the Oxygen Reduction Reaction in Alkaline Media

by Aleksandar Mijajlović, Dušan Mladenović, Kristina Radinović, David Tomić, Ana Nastasić, Dalibor Stanković and Jadranka Milikić

Batteries 2026, 12(6), 208; https://doi.org/10.3390/batteries12060208 - 7 Jun 2026

Viewed by 861

Abstract

The oxygen reduction reaction (ORR) is a key process in electrochemical energy conversion technologies such as fuel cells and metal–air batteries; however, its sluggish kinetics and reliance on precious metal catalysts limit large-scale application. This review provides a comprehensive overview of recent advances [...] Read more.

The oxygen reduction reaction (ORR) is a key process in electrochemical energy conversion technologies such as fuel cells and metal–air batteries; however, its sluggish kinetics and reliance on precious metal catalysts limit large-scale application. This review provides a comprehensive overview of recent advances in non-precious nanoscale electrocatalysts for ORR in alkaline media. Particular emphasis is placed on reaction mechanisms, including dominant pathways, kinetics, and key intermediates, as well as the advantages of alkaline electrolytes over acidic systems. The performance of various catalyst classes is systematically discussed, including transition metal-based materials (Fe, Co, Zn, Cu, and bimetallic systems) and metal-free carbon-based electrocatalysts. Special attention is given to heteroatom-doped carbon materials, carbon nanostructures, and emerging hybrid systems such as MXene-based composites. Comparative analysis highlights the relationship between catalyst composition, structure, and electrochemical performance metrics, including half-wave potential, onset potential, Tafel slope, number of electron transfer, and operational stability. Overall, non-precious catalysts demonstrate promising activity and durability, approaching that of noble metals under alkaline conditions. The insights summarized in this review guide the rational design of efficient, cost-effective ORR electrocatalysts and support the development of sustainable energy technologies. Full article

(This article belongs to the Section Aqueous Energy Storage Devices and Systems)

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36 pages, 5613 KB

Open AccessArticle

Analysis of Liquid Cooling Performance of Honeycomb-Structured Automotive Power Batteries and Research on Machine Learning Algorithm Predictions

by Han Tian, Mingfei Yang and Shanhua Zhang

Batteries 2026, 12(6), 207; https://doi.org/10.3390/batteries12060207 - 6 Jun 2026

Viewed by 356

Abstract

To address the thermal management challenges of electric vehicle power batteries under complex operating conditions, this study proposes a biomimetic honeycomb-shaped liquid cooling plate and conducts a systematic analysis of its cooling performance along with machine learning-based prediction for CTP lithium iron phosphate [...] Read more.

To address the thermal management challenges of electric vehicle power batteries under complex operating conditions, this study proposes a biomimetic honeycomb-shaped liquid cooling plate and conducts a systematic analysis of its cooling performance along with machine learning-based prediction for CTP lithium iron phosphate battery packs. A fluid–solid coupling numerical model was developed using ANSYS Fluent, employing the control variable method to investigate the effects of coolant flow rate (0.2–4.2 m/s), coolant inlet temperature (5–32 °C), ambient temperature (15–39 °C), and battery heating power (1000–5500 W/m³) on the maximum battery temperature. Simulation results demonstrate that the honeycomb structure leverages its hexagonal channel geometry and large specific surface area to achieve rapid and uniform heat transfer, with no localized hot spots observed across all operating conditions. The maximum battery temperature exhibits a marginal decreasing trend as coolant flow rate increases, with 1.4 m/s approaching the optimal flow rate; it rises approximately linearly with elevated inlet temperature, ambient temperature, and heating power—each 3 °C increase in inlet or ambient temperature raises the maximum temperature by approximately 1.98 °C and 3 °C, respectively, while a 500 W/m³ increase in heating power corresponds to an approximately 2.8 °C rise. Under standard conditions (heating power: 3000 W/m³; inlet temperature ≤23 °C; ambient temperature ≤27 °C), the maximum battery temperature remains below 45 °C; high-heating (≥3500 W/m³) or high-temperature (≥30 °C) scenarios require coordinated control strategies. Furthermore, based on simulation data, seven machine learning models—BPNN, GA-BP, PSO-BP, SVM, RBFNN, RF, and LSTM—were constructed and evaluated for their performance in predicting the maximum temperature of battery packs. The results showed that the LSTM model achieved the highest prediction accuracy on the validation set, with RMSE, MAE, MAPE, and R² values of 0.8068, 0.6891, 1.5653%, and 0.9865, respectively, while models such as SVM and RBFNN exhibited severe overfitting. This study validated the engineering effectiveness of the honeycomb structure liquid cooling plate and identified LSTM as the optimal model for predicting battery pack maximum temperature, providing a theoretical foundation and data support for the structural design and intelligent control of power battery thermal management systems. Full article

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34 pages, 2483 KB

Open AccessArticle

Ant Colony Optimization for the Optimal Placement of Lithium-Ion Battery Energy Storage Systems in Electrical Distribution Networks

by Hector Daniel Lema Chicaiza and Alexander Aguila Téllez

Batteries 2026, 12(6), 206; https://doi.org/10.3390/batteries12060206 - 5 Jun 2026

Viewed by 286

Abstract

This study presents an Ant Colony Optimization (ACO)-based methodology for the optimal placement of lithium-ion battery energy storage systems (BESSs) in radial electrical distribution networks. The proposed framework integrates base-case power-flow assessment, critical-bus identification, discrete BESS siting, technical–economic objective evaluation, and post-optimization validation. [...] Read more.

This study presents an Ant Colony Optimization (ACO)-based methodology for the optimal placement of lithium-ion battery energy storage systems (BESSs) in radial electrical distribution networks. The proposed framework integrates base-case power-flow assessment, critical-bus identification, discrete BESS siting, technical–economic objective evaluation, and post-optimization validation. The methodology is applied to the IEEE 33-bus radial distribution test system, where the initial operating condition is characterized in terms of nodal voltage profile, voltage deviation, voltage-stability index, active-power losses, and annual loss cost. The optimization process identifies buses 13 and 31 as the most suitable locations for two identical BESS units, with the reported validation case evaluating each unit at upper admissible capacity limits of

1000 kW

and

4000 kWh

. The obtained results show that the optimized BESS allocation increases the minimum voltage profile to values above

0.94 p . u .

, raises the voltage-stability index to more than 0.88, reduces active-power losses to approximately

0.0166 p . u .

, and decreases the annual cost associated with active-power losses by more than

66 %

relative to the base case. Additional validation through sensitivity analysis, repeated stochastic runs, operating-mode evaluation, and comparison against a genetic algorithm confirms the consistency and robustness of the proposed ACO-based methodology. The results demonstrate that the proposed framework provides a technically consistent and computationally accessible solution for improving voltage regulation, reducing feeder losses, and lowering loss-related operating costs in radial distribution systems. Full article

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19 pages, 6923 KB

Open AccessArticle

Post-Leaching Water, Ultrasonic and Mild-Acid Washing for Purifying Graphite Recovered from Spent NMC111 Lithium-Ion Batteries

by José E. Arevalo-Fester, Magnus Larsson, Sofia Öiseth, Jonas Löfvendahl, Mykhailo Zhybak, Erik Khranovskyy and Martina Petranikova

Batteries 2026, 12(6), 205; https://doi.org/10.3390/batteries12060205 - 5 Jun 2026

Viewed by 688

Abstract

Recovered graphite from spent lithium-ion batteries is an important secondary resource that can reduce reliance on primary graphite and lower the environmental footprint of battery production. In this work, graphite obtained as a carbon-rich residue after industrial hydrometallurgical leaching of NMC111 black mass [...] Read more.

Recovered graphite from spent lithium-ion batteries is an important secondary resource that can reduce reliance on primary graphite and lower the environmental footprint of battery production. In this work, graphite obtained as a carbon-rich residue after industrial hydrometallurgical leaching of NMC111 black mass (2 M H₂SO₄ + 3% H₂O₂) is subjected to three post-leaching washing treatments to assess how far simple, low-intensity steps can further clean the leach residue while preserving the carbon structure. The washing routes are water washing (GW), water washing with ultrasonication (GU) and mild sulfuric-acid washing with 0.1 M H₂SO₄ (GA). ICP-OES and SEM–EDX show that, relative to the leached black mass, all washing treatments reduce residual transition-metal contents by two to three orders of magnitude, and that the mild acid wash provides the lowest bulk metal levels, with several elements at or below detection limits. X-ray diffraction and Raman spectroscopy indicate graphite-dominated patterns and improved structural order, with the ID/IG ratio decreasing from 0.62 (GW) to 0.11 (GA) and the corresponding in-plane crystallite size increasing from 30.6 nm to 168 nm. Overall, the mild acid washing step is the most effective low-impact post-leaching purification route, yielding a thoroughly cleaned low-metal graphite fraction that preserves the graphite framework and constitutes a suitable intermediate for further upgrading or reuse in secondary applications. Full article

(This article belongs to the Section Lithium-Ion and Solid-State Batteries)

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35 pages, 9780 KB

Open AccessReview

Data-Driven Thermal Runaway Warning for Batteries: Research Progress and Prospects of Machine Learning Approaches

by Jie Hu, Haowen Zu, Yaran Zhao, Siyu Zhao, Te Ma, Libo Zhang, Yulong Zhang, Hongwentao Yu and Yalun Li

Batteries 2026, 12(6), 204; https://doi.org/10.3390/batteries12060204 - 4 Jun 2026

Viewed by 1105

Abstract

As lithium-ion batteries are widely deployed, thermal runaway (TR) poses severe safety risks, making early and accurate warning systems critical. While machine learning (ML) has advanced data-driven TR prediction, challenges remain regarding model interpretability, generalization under unseen conditions, and real-time deployment. This review [...] Read more.

As lithium-ion batteries are widely deployed, thermal runaway (TR) poses severe safety risks, making early and accurate warning systems critical. While machine learning (ML) has advanced data-driven TR prediction, challenges remain regarding model interpretability, generalization under unseen conditions, and real-time deployment. This review evaluates recent progress in ML-driven TR warning technologies, moving beyond a mere compilation of algorithms to provide an organized synthesis of the field. As a key contribution, we critically analyze the paradigm shift toward physics-informed ML, demonstrating how embedding electrochemical and thermodynamic principles into neural networks reduces prediction errors by 40–60% while enhancing robustness. Furthermore, we synthesize a Battery Digital Twin (BDT) framework integrating Internet of Things (IoT), cloud computing, and on-board master BMS for closed-loop collaboration, effectively balancing low-latency control with high-precision health assessment. Finally, we outline strategic pathways for future breakthroughs: advancing physics-informed cross-scale modeling, optimizing cloud-edge architectures, and establishing open access benchmark databases. By calling for standardized evaluation protocols to break down data silos, this review provides a comprehensive roadmap and actionable insights to accelerate the industrial implementation of next-generation intelligent battery safety management. Full article

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11 pages, 739 KB

Open AccessEditor’s ChoicePerspective

Sustainable Working Life Within the Production and Recycling of Lithium-Ion Batteries for Electric Vehicles (GreenWorkLiB)

by Klara Midander, Anneli Julander, Erik Rosengren, Sandra Johannesson and Florencia Harari

Batteries 2026, 12(6), 203; https://doi.org/10.3390/batteries12060203 - 3 Jun 2026

Cited by 1 | Viewed by 573

Abstract

Achieving the EU’s climate goals by 2050 requires a rapid transition to a resource-efficient and circular economy. The electrification of transport increases the demand for rechargeable lithium-ion batteries (LiBs), where lithium–nickel–cobalt–manganese (Li-NMC) is the predominant cathode technology in the European automotive sector. Large-scale [...] Read more.

Achieving the EU’s climate goals by 2050 requires a rapid transition to a resource-efficient and circular economy. The electrification of transport increases the demand for rechargeable lithium-ion batteries (LiBs), where lithium–nickel–cobalt–manganese (Li-NMC) is the predominant cathode technology in the European automotive sector. Large-scale facilities for LiB production and recycling are emerging worldwide, bringing not only technical challenges but also challenges regarding healthy and safe working environments. Current knowledge on occupational exposure and health risks in the LiB industry is limited and largely based on evidence from other occupational settings. However, the LiB industry involves legacy and new combinations of metals and chemicals in novel contexts. Some of these substances have well-known adverse health effects, and combined exposure may increase their absorption and toxicity. Although processes are often highly specialised and automated, manual handling tasks remain, which put workers at risk of exposure. Important knowledge gaps remain regarding exposure levels, exposure pathways, dermal and systemic uptake, combined exposures, and potential health effects among workers. This perspective paper discusses current exposure scenarios and health risks in LiB production and recycling, identifies key knowledge gaps, and highlights future research needs to support evidence-based occupational risk management. To address several of these challenges, the GreenWorkLiB initiative applies a multidisciplinary approach combining exposure assessment, biomonitoring, and occupational medicine. The initiative investigates exposure pathways via air and skin, internal dose through biomonitoring, and potential health effects among workers in LiB production and recycling. The results can support the assessment of human health and safety within the EU’s Safe and Sustainable by Design (SSbD) framework and contribute to safe and sustainable working environments in the LiB industry. Full article

(This article belongs to the Special Issue Selected Papers from Circular Materials Conference 2025)

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16 pages, 3047 KB

Open AccessArticle

Simulation of Thermal Runaway in Ternary Lithium-Ion Batteries Based on an Electrochemical–Thermal Coupling Model

by Yao Li, Rong Wang, Yi Jin, Zhenxin Sun, Hui Liu, Yu Liu, Yanhui Liu, Jiahuan Xu, Ye Tao, Zhaoyu Jiang, Yue Ma and Jiuchun Jiang

Batteries 2026, 12(6), 202; https://doi.org/10.3390/batteries12060202 - 2 Jun 2026

Viewed by 1211

Abstract

To address the issue of thermal runaway in ternary lithium-ion batteries under overcharging conditions, this paper establishes a multi-physics simulation model based on electrochemical–thermal coupling theory to systematically investigate the thermal behavior and runaway mechanisms of the battery. A P2D electrochemical model and [...] Read more.

To address the issue of thermal runaway in ternary lithium-ion batteries under overcharging conditions, this paper establishes a multi-physics simulation model based on electrochemical–thermal coupling theory to systematically investigate the thermal behavior and runaway mechanisms of the battery. A P2D electrochemical model and the Bernardi heat generation model were combined to construct an electrochemical–thermal coupling model suitable for overcharging conditions. Simulation results indicate that under normal charging conditions, the battery temperature rise is small and uniformly distributed; however, under overcharging conditions, side reactions significantly intensify, leading to a rapid increase in heat generation. The battery temperature exhibits a distinct inflection point and rises rapidly, displaying typical thermal runaway characteristics. Charging rate and ambient temperature have a significant impact on the thermal runaway process; both high charging rates and high ambient temperatures accelerate heat accumulation and reduce battery thermal safety. The study demonstrates that the established model effectively reveals the evolution of thermal runaway in overcharged ternary lithium-ion batteries, providing a theoretical basis for battery thermal management design and safety early warning systems. Full article

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18 pages, 1709 KB

Open AccessArticle

Thermal Modeling of a Cylindrical Lithium-Ion Battery in 3D with the Taguchi Optimization Method

by Elif Kaya and Alessandro d’Adamo

Batteries 2026, 12(6), 201; https://doi.org/10.3390/batteries12060201 - 1 Jun 2026

Viewed by 646

Abstract

Thermal management is critical for the safety, performance, and life cycle of lithium-ion (Li-ion) batteries. This study aims to determine the optimum settings and contribution levels of key parameters affecting the operating temperature of a three-dimensional (3D) thermal model of a cylindrical Li-ion [...] Read more.

Thermal management is critical for the safety, performance, and life cycle of lithium-ion (Li-ion) batteries. This study aims to determine the optimum settings and contribution levels of key parameters affecting the operating temperature of a three-dimensional (3D) thermal model of a cylindrical Li-ion battery. A Taguchi L9 orthogonal array was designed with four: (A) base fluid and (B) Al₂O₃volume fraction (Φ-Al₂O₃) of the nanofluid coolant, (C) battery–battery distance, and (D) inlet temperature (T_inlet), each varied on 3-level control factors. To minimize the maximum battery temperature (T_max), the “smaller-is-better” signal-to-noise (S/N) ratio approach and Analysis of Variance (ANOVA) were applied. The S/N analysis and ANOVA revealed that the base fluid (A: 44.96%) and T_inlet (D: 36.00%) were the most dominant factors influencing the T_max. The optimal design identified by the Taguchi method (A3-B3-C3-D1) successfully reduced the T_max to 33.5 °C, a 29.0 °C reduction compared with the initial air-cooled reference model (62.5 °C). Furthermore, the maximum temperature rise during the 2100 s operation was reduced by approximately 62%. This optimal T_max of 33.5 °C was even lower than the best result in the L9 array (35.5 °C), validating the strong predictive capability of the method. Full article

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

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16 pages, 4149 KB

Open AccessEditor’s ChoiceArticle

Binder-Free Self-Assembled Zn Nanowire Networks as Enhanced Electrochemical Performance Anodes for Aqueous Rechargeable Zinc-Based Batteries

by Rouz Barjoud, Veronika Moiseja, Davis Gavars, Margarita Volkova, Artis Kons and Jana Andzane

Batteries 2026, 12(6), 200; https://doi.org/10.3390/batteries12060200 - 1 Jun 2026

Viewed by 728

Abstract

This work presents advanced binder-free self-assembling Zn nanowire anodes synthesized by an easy-to-handle one-step low-pressure physical vapor deposition method. The morphology and structure of zinc nanowire networks are controlled and altered by the substrate temperature during deposition. Electrochemical performance of two types of [...] Read more.

This work presents advanced binder-free self-assembling Zn nanowire anodes synthesized by an easy-to-handle one-step low-pressure physical vapor deposition method. The morphology and structure of zinc nanowire networks are controlled and altered by the substrate temperature during deposition. Electrochemical performance of two types of Zn nanowire network samples of different morphology is studied in alkaline and mildly acidic aqueous electrolytes using cyclic voltammetry and electrochemical impedance spectroscopy techniques and compared to that of Zn foil electrodes. It is found that the morphology and structure of the Zn nanowire electrodes are directly related to their electrochemical performance and can be tuned for the type and concentration of the electrolyte to reach optimal electrochemical performance. The resulting binder-free self-assembled Zn nanowire anodes significantly outperform traditional Zn-based electrodes in both mild acidic and alkaline electrolytes, showing an areal capacitance of ~3.3 F/cm² and 3.5 F/cm² for acidic and alkaline electrolytes, respectively, and stability up to 1000 h of cycling in mild acidic electrolytes. These findings provide a pathway to fabricate and optimize binder-free zinc anodes for a variety of efficient and long-lasting aqueous zinc-based batteries and supercapacitors. Full article

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30 pages, 16529 KB

Open AccessEditor’s ChoiceArticle

Data-Driven Analysis and Machine Learning-Based Estimation of SOC and RUL in Lithium-Ion Batteries Using Heterogeneous Operational Data

by Pierpaolo Dini and Davide Paolini

Batteries 2026, 12(6), 199; https://doi.org/10.3390/batteries12060199 - 30 May 2026

Viewed by 766

Abstract

The accurate estimation of State of Charge (SOC) and Remaining Useful Life (RUL) is a key challenge in lithium-ion battery management systems, due to the nonlinear, time-varying, and multi-physics nature of battery dynamics. This work presents a systematic comparative study for SOC and [...] Read more.

The accurate estimation of State of Charge (SOC) and Remaining Useful Life (RUL) is a key challenge in lithium-ion battery management systems, due to the nonlinear, time-varying, and multi-physics nature of battery dynamics. This work presents a systematic comparative study for SOC and RUL estimation based on the analysis of the NASA battery dataset, characterized by significant heterogeneity in operating conditions, temperature regimes, and cycle durations. The study combines a physically informed feature engineering process with machine learning models, including tree-based ensembles, kernel methods, and neural networks. The dataset is analyzed from an electrochemical, thermal, and impedance perspective, highlighting the role of internal resistance evolution, SOC–voltage characteristics, and temperature dynamics as indicators of battery degradation. Based on these observations, two regression problems are formulated: a local window-based representation for SOC estimation and a cycle-level representation for RUL prediction. Particular attention is devoted to the impact of dataset heterogeneity, feature construction, and target representation on the predictive behavior of the considered models. In addition, the work investigates the effect of normalized RUL representations and provides an interpretability-oriented comparison of the learned regressors through feature-importance analysis and parity plots. Experimental results show that SOC estimation is a comparatively well-conditioned problem, achieving high accuracy across nonlinear models, although the dominant role of temporal and current-derived features highlights the strong dependence of the prediction task on the structure of the experimental protocol. In contrast, RUL prediction exhibits significantly higher complexity due to long-term degradation uncertainty and inter-battery variability. The introduction of a normalized RUL representation substantially improves prediction accuracy and stability, particularly for ensemble-based approaches. Feature importance analysis confirms that capacity-related variables dominate RUL estimation, while voltage, temporal, and current-derived features play a central role in SOC prediction. Overall, the results show that physically interpretable feature construction combined with ensemble learning methods provides an effective framework for battery state estimation and degradation analysis under heterogeneous operating conditions. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries: 3rd Edition)

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32 pages, 3174 KB

Open AccessArticle

Electrochemical-Informed Equivalent-Circuit Thermal Framework for Smartphone Battery Drain: Mechanism Analysis, TTE Prediction, and Power-Saving Strategies

by Chuhan Yang, Boyang Gu, Xudong Li, Xinke Zhang and Xuejun Zhang

Batteries 2026, 12(6), 198; https://doi.org/10.3390/batteries12060198 - 29 May 2026

Viewed by 468

Abstract

Smartphone battery drain is governed by coupled effects of workload, electrochemical aging, and thermal feedback. Nonlinear behaviors such as voltage collapse remain challenging for traditional models. An electrochemical-informed equivalent-circuit and lumped-thermal continuous-time framework is developed by integrating an equivalent-circuit voltage model with lumped [...] Read more.

Smartphone battery drain is governed by coupled effects of workload, electrochemical aging, and thermal feedback. Nonlinear behaviors such as voltage collapse remain challenging for traditional models. An electrochemical-informed equivalent-circuit and lumped-thermal continuous-time framework is developed by integrating an equivalent-circuit voltage model with lumped thermal dynamics, aging-aware resistance and capacity evolution, driven by a modular decomposition of smartphone power into CPU load, screen power, network power and base power. Time-to-empty (TTE) is defined using the practical voltage collapse rather than the SOC to zero assumption. The model is assessed via local and global sensitivity analysis, and power-saving strategies are derived using an AHP multi-criteria decision-making framework. The SOC fitting quality reaches

R^{2} = 0.979

, and rank-correlation-based importance analysis identifies CPU-related workload factors as the dominant contributor to endurance variation, with a normalized importance score of approximately 40%. The model is evaluated using a train/test-separated validation protocol rather than relying only on fitting quality. Prediction errors are reported separately for SOC, terminal voltage, temperature, and voltage-cutoff-defined TTE. On the unseen test segments, the proposed model achieves SOC RMSE of 0.0402, terminal-voltage RMSE of 0.162 V, temperature RMSE of 1.954 K, and TTE RMSE of 0.34 h under the controlled simulation-based validation setting. These findings support strategies that prioritize CPU-load reduction and usage-aware control, and motivate voltage-collapse-aware power management for heavy workloads and aged batteries. Overall, the main message of this work is that reliable smartphone TTE prediction requires voltage-collapse-aware modeling rather than SOC-only extrapolation. Full article

(This article belongs to the Section Energy Storage System Aging, Diagnosis and Safety)

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25 pages, 5230 KB

Open AccessArticle

Adaptive, Demand-Driven Thermal Management of Battery Packs via Branch-Level Flow Allocation

by Nasim Saber, Runar Unnthorsson and Christiaan Petrus Richter

Batteries 2026, 12(6), 197; https://doi.org/10.3390/batteries12060197 - 29 May 2026

Viewed by 609

Abstract

Second-life lithium-ion batteries offer strong potential for sustainable stationary energy storage, but their practical reuse is limited by cell-to-cell heterogeneity, non-uniform heat-generation, and the resulting thermal safety risks. Conventional battery thermal management systems (BTMSs), which rely on fixed and uniformly distributed coolant flow, [...] Read more.

Second-life lithium-ion batteries offer strong potential for sustainable stationary energy storage, but their practical reuse is limited by cell-to-cell heterogeneity, non-uniform heat-generation, and the resulting thermal safety risks. Conventional battery thermal management systems (BTMSs), which rely on fixed and uniformly distributed coolant flow, are not well-suited to the asymmetric thermal behaviour of aged battery packs. In this study, an adaptive liquid-cooling framework with locally regulated branch-level flow allocation is proposed for second-life prismatic LiFePO₄ battery modules. A three-dimensional transient conjugate heat transfer model was developed in COMSOL Multiphysics. The analysis was conducted on a 3 × 3 battery module under nine thermal heterogeneity scenarios, followed by a larger 5 × 4 module to evaluate scalability. The results show that thermal severity depends not only on heat-generation magnitude but also on the spatial arrangement of degraded cells. Under the most critical 3 × 3 configuration, the adaptive BTMS reduced the maximum temperature from 37.16 °C to 28.77 °C, corresponding to a reduction of about 8.38 °C, while limiting the cell-to-cell temperature difference to approximately 1.16 °C. A comparison with a conventional constant-flow cooling configuration in the larger 5 × 4 module further showed that adaptive branch-level coolant redistribution improves thermal uniformity under heterogeneous thermal loading by selectively directing cooling capacity toward thermally stressed regions. The results demonstrate the potential of demand-driven flow allocation as a distributed thermal-management strategy for heterogeneous second-life battery systems. Full article

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

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24 pages, 940 KB

Open AccessArticle

Multimodal State of Health Prediction for Lithium-Ion Batteries via Mamba-Based Fusion of Discharge Curves and Impedance Spectra

by Yawei Meng, Qiang Sun, Jianping Xu, Antai Bian, Qizheng Yang, Zhi Wang, Zijian Yang and Maoyong Zhi

Batteries 2026, 12(6), 196; https://doi.org/10.3390/batteries12060196 - 29 May 2026

Viewed by 479

Abstract

Existing deep learning methods for lithium-ion battery State of Health (SOH) prediction rely almost exclusively on discharge voltage–current curves, ignoring electrochemical impedance spectroscopy (EIS) data that directly reflects internal degradation mechanisms. Fusing these two modalities is non-trivial: discharge curves are high-dimensional temporal sequences [...] Read more.

Existing deep learning methods for lithium-ion battery State of Health (SOH) prediction rely almost exclusively on discharge voltage–current curves, ignoring electrochemical impedance spectroscopy (EIS) data that directly reflects internal degradation mechanisms. Fusing these two modalities is non-trivial: discharge curves are high-dimensional temporal sequences residing on a continuous dynamical manifold, while impedance features are low-dimensional static snapshots with fundamentally different statistical distributions. However, naive concatenation introduces modal conflicts rather than complementary gains. We propose the Hybrid Sensing Synergy Architecture (HSSA), which combines a Mamba backbone (

O (L)

complexity) for discharge curve modeling with a Q-former module that aligns impedance features into the temporal representation space via learnable query tokens and cross-attention. A prepend fusion strategy injects the aligned queries as prefix tokens, enabling the backbone to condition on internal electrochemical context from the first time step. On the NASA battery dataset, HSSA achieves MAE of 0.887 (large-scale, 11 batteries, a 9.8% improvement over unimodal Mamba), 1.457 (medium-scale, five batteries, a 28.0% improvement), and 2.705 (small-scale, four batteries, an 8.7% improvement), demonstrating consistent improvements across all data regimes. On out-of-sample battery B28, HSSA achieves 65.3% improvement. Ablation studies confirm that Q-former alignment is essential and prepend fusion significantly outperforms concatenation-based alternatives. Full article

(This article belongs to the Special Issue Recent Advances in Numerical Modeling and Experimental Validation of Batteries)

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22 pages, 2981 KB

Open AccessArticle

Investigation of Thermal Runaway Propagation Behavior of 280 Ah LiFePO₄ Battery and Pack Under Overheating Conditions

by Kai Cao, Hao Zheng, Xu Wu, Yuqi Ding and Ye Lu

Batteries 2026, 12(6), 195; https://doi.org/10.3390/batteries12060195 - 29 May 2026

Viewed by 610

Abstract

The extensive utilization of LiFePO₄ (LFP) batteries in energy storage facilities has been impeded by the inherent property of thermal runaway (TR). This study examines the TR propagation characteristics of 280 Ah LFP batteries and their module through the application of dual-side [...] Read more.

The extensive utilization of LiFePO₄ (LFP) batteries in energy storage facilities has been impeded by the inherent property of thermal runaway (TR). This study examines the TR propagation characteristics of 280 Ah LFP batteries and their module through the application of dual-side heating to trigger TR. Experimental investigations on single battery TR reveal that the timing and temperature at which the battery safety valve opens exhibit stochastic behavior. Moreover, a correlation is observed between the time required for the safety valve to open and the average surface temperature of the battery, with longer durations corresponding to higher temperatures. Surface temperature variations in batteries manifest in three primary phenomena: temperature decline, abrupt temperature spikes, and peak temperatures. In TR experiments involving packs, it is depicted that temperature signals can detect internal development processes earlier than smoke signals when TR initiates within the module. Heat transfer within batteries of the same sub-module primarily occurs through conduction, exhibiting an average heat transfer fraction of 25.8%. These findings hold significant implications for enhancing early detection systems for TR in both batteries and modules. Full article

(This article belongs to the Special Issue Lithium-Ion Batteries and Li-Ion Capacitors: From Fundamentals to Practical Applications: 2nd Edition)

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