Batteries MDPI

18/08/2025

🙌 Hot Paper with Excellent Graphical Abstract

“Poly(vinyl benzoate)-b-poly(diallyldimethyl ammonium TFSI)-b-poly(vinyl benzoate) Triblock Copolymer Electrolytes for Sodium Batteries”

by Pierre L. Stigliano, Antonela Gallastegui, Carlos Villacis-Segovia, Marco Amores, Ajit Kumar, Luke A. O’Dell, Jian Fang, David Mecerreyes, Cristina Pozo-Gonzalo and Maria Forsyth

Batteries 2024, 10(4), 125; https://doi.org/10.3390/batteries10040125

👉 Hot Papers with Excellent Graphical Abstracts in Q2 of 2024
https://www.mdpi.com/journal/batteries/announcements/10936

18/08/2025

👏Interview with the Author—Dr. Ashley Willow

The next battery revolution? Dr. Ashley Willow shares cutting-edge research on Prussian white cathodes for anode-free systems. Below is a short interview with the author.
👉https://brnw.ch/21wUZxi

Dr. Ashley Willow’s published paper:

“Design and Validation of Anode-Free Sodium-Ion Pouch Cells Employing Prussian White Cathodes”

by Ashley Willow, Marcin Orzech, Sajad Kiani, Nathan Reynolds, Matthew Houchell, Olutimilehin Omisore, Zari Tehrani and Serena Margadonna
Batteries 2025, 11(3), 97; https://brnw.ch/21wUZxj

Batteries, an international, peer-reviewed Open Access journal.

10/07/2025

🙌 Notable Papers

“Concentrated, Gradient Electrolyte Design for Superior Low-Temperature Li-Metal Batteries”

by Jason S. Packard, Ethan A. Adams and Vilas G. Pol

Batteries 2024, 10(12), 448; https://doi.org/10.3390/batteries10120448

👉 Notable Papers Published in 2024 (Volume 10, Issue 12)
https://www.mdpi.com/journal/batteries/announcements/10937

Improving the low-temperature performance of lithium-ion batteries is critical for their widespread adoption in cold environments. In this study, we designed a novel LHCE featuring a solvent polarity gradient, designed to maximize both room- and low-temperature ion mobility. Extremely polar fluoroethylene carbonate (FEC) and low-freezing-point, −135 °C, non-polar nonaflurobutyl methyl ether (NONA) were supplemented by two intermediate solvents with incremental step-downs in polarity. The intermediate solvents consist of methyl (2,2,2-triflooethyl) carbonate (FEMC) and either diethylene carbonate (DEC), ethyl methyl carbonate (EMC), or dibutyl carbonate (DBC). The four solvents were combined with 1 M lithium bis(fluorosulfonyl)amide (LiFSI) salt and were able to accommodate 37.5% diluent volume, resulting in ultra-low electrolyte freezing points below −120 °C. This contrasts with our previously investigated three-solvent LHCE, which only allowed for a 14% diluent volume and a −85 °C freezing point. Localized high salt concentrations were shown by less than 3% of FSI- anions being free in solution. The gradient LHCEs also showed room-temperature ionic conductivities above 10–3 S/cm and maintained high ion mobility below −40 °C. Lithium metal coin cells with LiFePO4 (LFP) cathodes featuring the gradient LHCEs, a reference three-solvent LHCE, and commercial (1 M LiPF6 in 1:1 EC:DEC) electrolyte were constructed. All gradient LHCEs outperformed both the three-solvent and commercial electrolytes at all temperatures, with the DEC-based gradient LHCE showing the best performance of 159.7 mAh/g at 25 °C and 109.2 mAh/g at −50 °C, corresponding to a 68% capacity retention. These findings highlight the potential of LHCE systems to improve battery performance in low-temperature environments and propose a new gradient design strategy for electrolytes to yield advantages of both polar and weakly polar solvents.

Keywords:

lithium-metal battery; concentrated electrolyte; ionic conductivity; solid electrolyte interface

Improving the low-temperature performance of lithium-ion batteries is critical for their widespread adoption in cold environments. In this study, we designed a novel LHCE featuring a solvent polarity gradient, designed to maximize both room- and low-temperature ion mobility. Extremely polar fluoroet...

09/07/2025

🙌 Notable Papers

“A Comprehensive Review of Multiple Physical and Data-Driven Model Fusion Methods for Accurate Lithium-Ion Battery Inner State Factor Estimation”

by Junjie Tao, Shunli Wang, Wen Cao, Carlos Fernandez and Frede Blaabjerg

Batteries 2024, 10(12), 442; https://doi.org/10.3390/batteries10120442

👉 Notable Papers Published in 2024 (Volume 10, Issue 12)
https://www.mdpi.com/journal/batteries/announcements/10937

With the rapid global growth in demand for renewable energy, the traditional energy structure is accelerating its transition to low-carbon, clean energy. Lithium-ion batteries, due to their high energy density, long cycle life, and high efficiency, have become a core technology driving this transformation. In lithium-ion battery energy storage systems, precise state estimation, such as state of charge, state of health, and state of power, is crucial for ensuring system safety, extending battery lifespan, and improving energy efficiency. Although physics-based state estimation techniques have matured, challenges remain regarding accuracy and robustness in complex environments. With the advancement of hardware computational capabilities, data-driven algorithms are increasingly applied in battery management, and multi-model fusion approaches have emerged as a research hotspot. This paper reviews the fusion application between physics-based and data-driven models in lithium-ion battery management, critically analyzes the advantages, limitations, and applicability of fusion models, and evaluates their effectiveness in improving state estimation accuracy and robustness. Furthermore, the paper discusses future directions for improvement in computational efficiency, model adaptability, and performance under complex operating conditions, aiming to provide theoretical support and practical guidance for developing lithium-ion battery management technologies.

Keywords:

lithium-ion battery; state of charge estimation; physical modeling approach; data-driven approach; multi-model fusion approach

With the rapid global growth in demand for renewable energy, the traditional energy structure is accelerating its transition to low-carbon, clean energy. Lithium-ion batteries, due to their high energy density, long cycle life, and high efficiency, have become a core technology driving this transfor...

08/07/2025

🙌 Notable Papers

“Li Chemical and Tracer Diffusivities in LiCoO2 Sintered Pellets”

by Erwin Hüger and Harald Schmidt

Batteries 2024, 10(12), 446; https://doi.org/10.3390/batteries10120446

👉 Notable Papers Published in 2024 (Volume 10, Issue 12)
https://www.mdpi.com/journal/batteries/announcements/10937

LiCoO2 (LCO) is a crucial active material for positive electrodes of commercial lithium-ion batteries. It is typically present in the form of micrometer-sized LCO particles, which are surrounded by binders and conductive agents with a thickness of tens of microns. In order to determine the intrinsic Li transport parameters of pure crystalline LCO, it is necessary to measure the Li diffusivity at room temperature in sintered LCO pellets free of additives. The LCO sintered bulk material consists of interconnected, about 3 µm clusters, composed of grains of about 70 nanometers in size. The Li chemical and tracer diffusivities are determined using electrochemical impedance spectroscopy (EIS) and potentiostatic intermittent titration technique (PITT), while the latter ones are in the range between 10−9 and 10−28 m2s−1, depending on the application of different relevant formulas and characteristic parameters. Consequently, it is essential to apply a classical non-electrochemical and Li selective method of tracer diffusion determination like 6Li depth profiling and secondary ion mass spectrometry (SIMS) for comparison. Li tracer diffusivities of about 10−22 m2s−1 at room temperature are obtained by the extrapolation of the SIMS results from higher temperatures. This significantly narrows the range of reliable electrochemically determined Li tracer diffusivities to a more limited range, between 10−21 and 10−22 m2s−1.

Keywords:

LiCoO2; diffusion; thermodynamic factor; EIS; PITT; SIMS; LIB; cathode materials

LiCoO2 (LCO) is a crucial active material for positive electrodes of commercial lithium-ion batteries. It is typically present in the form of micrometer-sized LCO particles, which are surrounded by binders and conductive agents with a thickness of tens of microns. In order to determine the intrinsic...

07/07/2025

🙌 Notable Papers

“Recent Advancements in Artificial Intelligence in Battery Recycling”

by Subin Antony Jose, Connor Andrew Dennis Cook, Joseph Palacios, Hyundeok Seo, Christian Eduardo Torres Ramirez, Jinhong Wu and Pradeep L. Menezes

Batteries 2024, 10(12), 440; https://doi.org/10.3390/batteries10120440

👉 Notable Papers Published in 2024 (Volume 10, Issue 12)
https://www.mdpi.com/journal/batteries/announcements/10937

Battery recycling has become increasingly crucial in mitigating environmental pollution and conserving valuable resources. As demand for battery-powered devices rises across industries like automotive, electronics, and renewable energy, efficient recycling is essential. Traditional recycling methods, often reliant on manual labor, suffer from inefficiencies and environmental harm. However, recent artificial intelligence (AI) advancements offer promising solutions to these challenges. This paper reviews the latest developments in AI applications for battery recycling, focusing on methodologies, challenges, and future directions. AI technologies, particularly machine learning and deep learning models, are revolutionizing battery sorting, classification, and disassembly processes. AI-powered systems enhance efficiency by automating tasks such as battery identification, material characterization, and robotic disassembly, reducing human error and occupational hazards. Additionally, integrating AI with advanced sensing technologies like computer vision, spectroscopy, and X-ray imaging allows for precise material characterization and real-time monitoring, optimizing recycling strategies and material recovery rates. Despite these advancements, data quality, scalability, and regulatory compliance must be addressed to realize AI’s full potential in battery recycling. Collaborative efforts across interdisciplinary domains are essential to develop robust, scalable AI-driven recycling solutions, paving the way for a sustainable, circular economy in battery materials.

Keywords:

battery recycling; artificial intelligence; computer vision; lithium ion battery

Battery recycling has become increasingly crucial in mitigating environmental pollution and conserving valuable resources. As demand for battery-powered devices rises across industries like automotive, electronics, and renewable energy, efficient recycling is essential. Traditional recycling methods...

04/07/2025

🙌 Notable Papers

“Comparative Analysis of Computational Times of Lithium-Ion Battery Management Solvers and Battery Models Under Different Programming Languages and Computing Architectures”

by Moin Ahmed, Zhiyu Mao, Yunpeng Liu, Aiping Yu, Michael Fowler and Zhongwei Chen

Batteries 2024, 10(12), 439; https://doi.org/10.3390/batteries10120439

👉 Notable Papers Published in 2024 (Volume 10, Issue 12)
https://www.mdpi.com/journal/batteries/announcements/10937

With the global rise in consumer electronics, electric vehicles, and renewable energy, the demand for lithium-ion batteries (LIBs) is expected to grow. LIBs present a significant challenge for state estimations due to their complex non-linear electrochemical behavior. Currently, commercial battery management systems (BMSs) commonly use easier-to-implement and faster equivalent circuit models (ECMs) than their counterpart continuum-scale physics-based models (PBMs). However, despite processing more mathematical and computational complexity, PBMs are attractive due to their higher accuracy, higher fidelity, and ease of integration with thermal and degradation models. Various reduced-order PBM battery models and their computationally efficient numerical schemes have been proposed in the literature. However, there is limited data on the performance and feasibility of these models in practical embedded and cloud systems using standard programming languages. This study compares the computational performance of a single particle model (SPM), an enhanced single particle model (ESPM), and a reduced-order pseudo-two-dimensional (ROM-P2D) model under various battery cycles on embedded and cloud systems using Python and C++. The results show that reduced-order solvers can achieve a 100-fold reduction in solution times compared to full-order models, while ESPM with electrolyte dynamics is about 1.5 times slower than SPM. Adding thermal models and Kalman filters increases solution times by approximately 20% and 100%, respectively. C++ provides at least a 10-fold speed increase over Python, varying by cycle steps. Although embedded systems take longer than cloud and personal computers, they can still run reduced-order models effectively in Python, making them suitable for embedded applications.

Keywords:

battery management systems; lithium-ion battery continuum-scale models; battery packs

With the global rise in consumer electronics, electric vehicles, and renewable energy, the demand for lithium-ion batteries (LIBs) is expected to grow. LIBs present a significant challenge for state estimations due to their complex non-linear electrochemical behavior. Currently, commercial battery m...

03/07/2025

🙌 Notable Papers

“Application of Defect Engineering via ALD in Supercapacitors”

by Tiange Gao, Xiaoyang Xiao, Zhenliang D**g, Xilong Lu, Liwen Mao, Jinzheng Wang, Yiming Liu, Qingmin Hu and Jiaqiang Xu

Batteries 2024, 10(12), 438; https://doi.org/10.3390/batteries10120438

👉 Notable Papers Published in 2024 (Volume 10, Issue 12)
https://www.mdpi.com/journal/batteries/announcements/10937

Supercapacitors are a kind of energy storage device that lie between traditional capacitors and batteries, characterized by high power density, long cycle life, and rapid charging and discharging capabilities. The energy storage mechanism of supercapacitors mainly includes electrical double-layer capacitance and pseudocapacitance. In addition to constructing multi-level pore structures to increase the specific surface area of electrode materials, defect engineering is essential for enhancing electrochemical active sites and achieving additional extrinsic pseudocapacitance. Therefore, developing a simple and efficient method for defect engineering is essential. Atomic layer deposition (ALD) technology enables precise control over thin film thickness at the atomic level through layer-by-layer deposition. This capability allows the intentional introduction of defects, such as vacancies, heteroatom doping, or misalignment, at specific sites within the material. The ALD process can regulate the defects in materials without altering the overall structure, thereby optimizing both the electrochemical and physical properties of the materials. Its self-limiting surface reaction mechanism also ensures that defects and doping sites are introduced uniformly across the material surface. This uniform defect distribution is particularly profitable for high surface area electrodes in supercapacitor applications, as it promotes consistent performance across the entire electrode. This review systematically summarizes the latest advancements in defect engineering via ALD technology in supercapacitors, including the enhancement of conductivity and the increase of active sites in supercapacitor electrode materials through ALD, thereby improving specific capacitance and energy density of the supercapacitor device. Furthermore, we discuss the underlying mechanisms, advantages, and future directions for ALD in this field.

Keywords:

supercapacitors; defect engineering; atomic layer deposition; oxygen vacancy; electrode materials; surface engineering

Supercapacitors are a kind of energy storage device that lie between traditional capacitors and batteries, characterized by high power density, long cycle life, and rapid charging and discharging capabilities. The energy storage mechanism of supercapacitors mainly includes electrical double-layer ca...

02/07/2025

🙌 Notable Papers

“Lithium-Ion Battery Life Prediction Using Deep Transfer Learning”
by Wen Zhang, R. S. B. Pranav, Rui Wang, Cheonghwan Lee, Jie Zeng, Migyung Cho and Jaesool Shim
Batteries 2024, 10(12), 434; https://doi.org/10.3390/batteries10120434

👉 Notable Papers Published in 2024 (Volume 10, Issue 12)
https://www.mdpi.com/journal/batteries/announcements/10937

Lithium-ion batteries are critical components of various advanced devices, including electric vehicles, drones, and medical equipment. However, their performance degrades over time, and unexpected failures or discharges can lead to abrupt operational interruptions. Therefore, accurate prediction of the remaining useful life is essential to ensure device safety and reliability. Conventional RUL prediction methods typically rely on regression analysis, signal processing, and machine learning techniques to assess battery conditions such as charge/discharge cycles, voltage, temperature, and durability. Although effective, these approaches are constrained by their dependence on large amounts of labeled data and the necessity for complex feature engineering to capture battery physical characteristics. In this study, we propose an approach that employs deep transfer learning to address these limitations. By leveraging pretrained model weights, the proposed method significantly improves the efficiency and accuracy of RUL prediction even under limited training data conditions. Furthermore, we investigate the impact of external environmental factors and physical battery characteristics on RUL prediction precision, thereby contributing to a more robust and reliable prediction framework.

Keywords:

deep transfer learning; vgg16; lithium-ion battery; remaining useful life; prediction model

Lithium-ion batteries are critical components of various advanced devices, including electric vehicles, drones, and medical equipment. However, their performance degrades over time, and unexpected failures or discharges can lead to abrupt operational interruptions. Therefore, accurate prediction of....

01/07/2025

🙌 Notable Papers

“Synthesis of Three Ternary NiPP@PDA@DTA by Bridging Polydopamine and Its Flame Retardancy in Epoxy Resin”

by Wenxin Zhu, Huiyu Ch, Yue Lu, Wang Zhan and Qinghong Kong

Batteries 2024, 10(12), 428; https://doi.org/10.3390/batteries10120428

👉 Notable Papers Published in 2024 (Volume 10, Issue 12)
https://www.mdpi.com/journal/batteries/announcements/10937

Epoxy resin (EP) is an indispensable packaging material for batteries. Excellent thermal and flame-retardant properties of EP can ensure the safety performance of batteries. To solve the low-efficiency flame retardant of EP, nickel phenyl phosphate (NiPP) was synthesized and its surface was modified by polymerization of dopamine (PDA). [3-(hydroxy-phenyl-methylidene) imimine] triazole (DTA) was synthesized using 9,10-dihydro-9-oxygen-10-phosphophene-10-oxide (DOPO), 3-amino-1,2,4-triazole and p-hydroxybenzaldehyde. The hybrid flame retardance NiPP@PDA@DTA was further synthesized by self-assembly between the negative charge on the surface of DTA and the positive charge on the surface of modified NiPP@PDA. Then, NiPP@PDA@DTA was added to EP to prepare EP/NiPP@PDA@DTA composites. The results showed that the incorporation of NiPP@PDA@DTA promoted the residual yield at high temperatures. Furthermore, EP composites showed excellent flame retardancy when NiPP@PDA@DTA was added. The EP/4 wt% NiPP@PDA@DTA composites can reach UL-94 V0 grade with a limit oxygen index (LOI) of 33.7%. While the heat release rate (HRR), total release rate (THR), CO2 production (CO2P) and total smoke release (TSR) of EP/4 wt% NiPP@PDA@DTA composites decreased by 16.9%, 30.8%, 16.9% and 27.7% compared with those of EP. These improvements are mainly due to the excellent catalytic carbonization performance of Ni metal and P compounds. The azazole and phosphaphenanthrene groups have the effects of dilution quenching in the gas phase and cross-linking network blocking, as well as enhanced blowing-out effects.

Keywords:
phenyl metal phosphate; azazole; DOPO; packaging safety; flame retardant

Epoxy resin (EP) is an indispensable packaging material for batteries. Excellent thermal and flame-retardant properties of EP can ensure the safety performance of batteries. To solve the low-efficiency flame retardant of EP, nickel phenyl phosphate (NiPP) was synthesized and its surface was modified...

30/06/2025

🙌 Notable Papers

“Optimising Grid-Connected PV-Battery Systems for Energy Arbitrage and Frequency Containment Reserve”

by Rodolfo Dufo-López, Juan M. Lujano-Rojas, Jesús S. Artal-Sevil and José L. Bernal-Agustín

Batteries 2024, 10(12), 427; https://doi.org/10.3390/batteries10120427

👉 Notable Papers Published in 2024 (Volume 10, Issue 12)
https://www.mdpi.com/journal/batteries/announcements/10937

This study introduces a novel method for optimising the size and control strategy of grid-connected, utility-scale photovoltaic (PV) systems with battery storage aimed at energy arbitrage and frequency containment reserve (FCR) services. By applying genetic algorithms (GA), the optimal configurations of PV generators, inverters/chargers, and batteries were determined, focusing on maximising the net present value (NPV). Both DC- and AC-coupled systems were explored. The performance of each configuration was simulated over a 25-year lifespan, considering varying pricing, solar resources, battery ageing, and PV degradation. Constraints included investment costs, capacity factors, and land use. A case study conducted in Wiesenthal, Germany, was followed by sensitivity analyses, revealing that a 75% reduction in battery costs is needed to make AC-coupled PV-plus-battery systems as profitable as PV-only systems. Further analysis shows that changes in electricity and FCR pricing as well as limits on FCR charging can significantly impact NPV. The study confirms that integrating arbitrage and FCR services can optimize system profitability.

Keywords:

photovoltaic; battery; PV-plus-battery; utility-scale; battery degradation; simulation; optimisation

This study introduces a novel method for optimising the size and control strategy of grid-connected, utility-scale photovoltaic (PV) systems with battery storage aimed at energy arbitrage and frequency containment reserve (FCR) services. By applying genetic algorithms (GA), the optimal configuration...

27/06/2025

🙌 Notable Papers

“Sequential Multi-Scale Modeling Using an Artificial Neural Network-Based Surrogate Material Model for Predicting the Mechanical Behavior of a Li-Ion Pouch Cell Under Abuse Conditions”

by Alexander Schmid, Christian Ellersdorfer, Eduard Ewert and Florian Feist

Batteries 2024, 10(12), 425; https://doi.org/10.3390/batteries10120425

👉 Notable Papers Published in 2024 (Volume 10, Issue 12)
https://www.mdpi.com/journal/batteries/announcements/10937

To analyze the safety behavior of electric vehicles, mechanical simulation models of their battery cells are essential. To ensure computational efficiency, the heterogeneous cell structure is represented by homogenized material models. The required parameters are calibrated against several characteristic cell experiments. As a result, it is hardly possible to describe the behavior of the individual battery components, which reduces the level of detail. In this work, a new data-driven material model is presented, which not only provides the homogenized behavior but also information about the components. For this purpose, a representative volume element (RVE) of the cell structure is created. To determine the constitutive material models of the individual components, different characterization tests are performed. A novel method for carrying out single-layer compression tests is presented for the characterization in the thickness direction. The parameterized RVE is subjected to a large number of load cases using first-order homogenization theory. This data basis is used to train an artificial neural network (ANN), which is then implemented in commercial FEA software LS-DYNA R9.3.1 and is thus available as a material model. This novel data-driven material model not only provides the stress–strain relationship, but also outputs information about the condition of the components, such as the thinning of the separator. The material model is validated against two characteristic cell experiments. A three-point-bending test and an indentation test of the cell is used for this purpose. Finally, the influence of the architecture of the neural network on the computational effort is discussed.

Keywords:

lithium-ion battery; mechanical behavior; data-driven material model; neural networks; scale bridging

To analyze the safety behavior of electric vehicles, mechanical simulation models of their battery cells are essential. To ensure computational efficiency, the heterogeneous cell structure is represented by homogenized material models. The required parameters are calibrated against several character...