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Electrospun Si and Si/C Fiber Anodes for Li-Ion Batteries
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Modeling Silicon-Dominant Anodes: Parametrization, Discussion, and Validation of a Newman-Type Model
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Gaining a New Technological Readiness Level for Laser-Structured Electrodes in High-Capacity Lithium-Ion Pouch Cells
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Scale-Up of Lithium Iron Phosphate Cathodes with High Active Materials Contents for Lithium Ion Cells
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An Enhanced Single-Particle Model Using a Physics-Informed Neural Network Considering Electrolyte Dynamics for Lithium-Ion Batteries
Journal Description
Batteries
Batteries
is an international, peer-reviewed, open access journal on battery technology and materials published monthly online by MDPI. International Society for Porous Media (InterPore) is affiliated with Batteries and their members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, Ei Compendex, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Electrochemistry) / CiteScore - Q2 (Electrical and Electronic Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 3.4 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Sections: published in 5 topical sections.
Impact Factor:
4.6 (2023);
5-Year Impact Factor:
5.3 (2023)
Latest Articles
Study on the Preventive Effect of Au/CeO2 on Lithium-Ion Battery Thermal Runaway Caused by Overcharging
Batteries 2024, 10(7), 235; https://doi.org/10.3390/batteries10070235 (registering DOI) - 28 Jun 2024
Abstract
In this study, a flower-like Au/CeO2 supported catalyst composite anode was prepared to explore its impact on thermal runaway triggered by overcharging and flame. Through structural and performance characterization, it was found that the catalyst has a high specific surface area and
[...] Read more.
In this study, a flower-like Au/CeO2 supported catalyst composite anode was prepared to explore its impact on thermal runaway triggered by overcharging and flame. Through structural and performance characterization, it was found that the catalyst has a high specific surface area and good CO catalytic oxidation capability, with a CO removal rate higher than 99.97% at room temperature. Through electrical performance testing, it was discovered that, compared to batteries without the catalyst, batteries using the composite anode did not exhibit significant capacity degradation. In overcharge testing, the catalyst prolonged the voltage rise time and peak voltage occurrence time of the battery. In thermal runaway testing, the addition of the catalyst delayed the detection time of CO and significantly reduced the concentration of thermal runaway products, especially the peak concentration and integrated concentration of CO, demonstrating its effectiveness in reducing thermal runaway products. Therefore, this study provides a new approach for improving the safety of lithium-ion batteries. The catalyst exhibits good performance in reducing toxic gases generated after thermal runaway and delaying the occurrence of thermal runaway, providing strong support for the safe application of lithium-ion batteries.
Full article
(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)
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Open AccessArticle
Estimation Procedure for the Degradation of a Lithium-Ion Battery Pack
by
Natascia Andrenacci, Manlio Pasquali, Francesco Vellucci and Alberto Venanzoni
Batteries 2024, 10(7), 234; https://doi.org/10.3390/batteries10070234 (registering DOI) - 28 Jun 2024
Abstract
This paper proposes a test procedure for evaluating the degradation of cells in a battery pack. The test can be performed using only the charger’s converters and the battery management system (BMS) without requiring sophisticated instrumentation. The method circumvents the difficulties related to
[...] Read more.
This paper proposes a test procedure for evaluating the degradation of cells in a battery pack. The test can be performed using only the charger’s converters and the battery management system (BMS) without requiring sophisticated instrumentation. The method circumvents the difficulties related to the evaluation of derivative quantities for estimating the state of health (SOH) using integral quantities in the evaluation. The method introduces a 'degradation function' that is calculated with respect to the reference performance of pristine cells. The procedure was applied to the JuiceRoll Race Edition system, an innovative electric vehicle (EV) DC charger with internal storage, made in ENEL X and used during the MotoE championship races. Using this procedure, the degradation of performance in individual groups of cells composing the battery pack was quantified in comparison to the reference group. The procedure helps identify modules that have aged too early or show reliability issues. The method is mature for field operational applications.
Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System)
Open AccessArticle
A Practical Methodology for Real-Time Adjustment of Kalman Filter Process Noise for Lithium Battery State-of-Charge Estimation
by
Cynthia Thamires da Silva, Bruno Martin de Alcântara Dias, Rui Esteves Araújo, Eduardo Lorenzetti Pellini and Armando Antônio Maria Laganá
Batteries 2024, 10(7), 233; https://doi.org/10.3390/batteries10070233 - 28 Jun 2024
Abstract
The methodology presented in this work allows for the creation of a real-time adjustment of Kalman Filter process noise for lithium battery state-of-charge estimation. This work innovates by creating a methodology for adjusting the process ( ) and measurement ( )
[...] Read more.
The methodology presented in this work allows for the creation of a real-time adjustment of Kalman Filter process noise for lithium battery state-of-charge estimation. This work innovates by creating a methodology for adjusting the process ( ) and measurement ( ) Kalman Filter noise matrices in real-time. The filter algorithm with this adaptative mechanism achieved an average accuracy of 99.56% in real tests by comparing the estimated battery voltage and measured battery voltage. A cell-balancing strategy was also implemented, capable of guaranteeing the safety and efficiency of the battery pack in all conducted tests. This work presents all the methods, equations, and simulations necessary for the development of a battery management system and applies the system in a practical, real environment. The battery management system hardware and firmware were developed, evaluated, and validated on a battery pack with eight LiFePO4 cells, achieving excellent performance on all conducted tests.
Full article
(This article belongs to the Section Battery Modelling, Simulation, Management and Application)
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Open AccessArticle
The Suppression Effect of Water Mist Released at Different Stages on Lithium-Ion Battery Flame Temperature, Heat Release, and Heat Radiation
by
Bin Miao, Jiangfeng Lv, Qingbiao Wang, Guanzhang Zhu, Changfang Guo, Guodong An and Jianchun Ou
Batteries 2024, 10(7), 232; https://doi.org/10.3390/batteries10070232 - 28 Jun 2024
Abstract
Thermal runaway (TR) is a serious thermal disaster that occurs in lithium-ion batteries (LIBs) under extreme conditions and has long been an obstacle to their further development. Water mist (WM) is considered to have excellent cooling capacity and is widely used in the
[...] Read more.
Thermal runaway (TR) is a serious thermal disaster that occurs in lithium-ion batteries (LIBs) under extreme conditions and has long been an obstacle to their further development. Water mist (WM) is considered to have excellent cooling capacity and is widely used in the field of fire protection. When used in TR suppression, WM also exhibits strong fire-extinguishing and anti-re-ignition abilities. Therefore, it has received widespread attention and research interest among scholars. However, most studies have focused on the cooling rate and suppression effect of TR propagation, and few have mentioned the effect of WM on flame heat transfer, which is a significant index in TR propagation suppression. This study has explored the suppression effect of WM released at different TR stages and has analyzed flame temperature, heat release, and heat radiation under WM conditions. Results show that the flame extinguishing duration for WM under different TR stages was different. WM could directly put out the flame within several seconds of being released when SV opened, 3 min after SV opening and when TR ended, and 3 min for WM when TR was triggered. Moreover, the heat radiation of the flame in relation to the battery QE could be calculated, and the case of WM released 3 min after SV opening exhibited the greatest proportion of heat radiation cooling η (with a value of 88.4%), which was same for the specific cooling capacity of WM Qm with a value of 1.7 × 10−3 kJ/kg. This is expected to provide a novel focus for TR suppression in LIBs.
Full article
(This article belongs to the Special Issue Thermal Safety of Lithium Ion Batteries)
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Open AccessArticle
Stress Analysis of Electrochemical and Force-Coupling Model for Ternary Lithium-Ion Batteries
by
Wei Shi, Ruofan Xu, Changjiang Han, Bingxiang Sun, Jin Chai, Jiachang Liu, Xuewen Jiao, Jiale Xiong and Yinghao Li
Batteries 2024, 10(7), 231; https://doi.org/10.3390/batteries10070231 - 27 Jun 2024
Abstract
The mechanical pressure that arises from the external structure of the automotive lithium battery module and its fixed devices can give rise to the concentration and damage of the internal stress inside the battery and increase the risks of battery degradation and failure.
[...] Read more.
The mechanical pressure that arises from the external structure of the automotive lithium battery module and its fixed devices can give rise to the concentration and damage of the internal stress inside the battery and increase the risks of battery degradation and failure. Commercial batteries cannot be disassembled, and the diffusion stress distribution at different times during discharge is notoriously difficult to determine. This paper, therefore, establishes the electrochemical force-coupling model based on the electrochemical and diffusion mechanics principles of batteries and studies the internal stress distribution of the battery under the diffusion stress of the electrode-material level and external pressure. Mainly driven by the electrochemical potential of the electrode particle diffusion stress stemming from the lithium-concentration difference inside and outside the particles, rupture is more likely to occur at the surface of the negative-electrode active particle at the end of discharge or the beginning of charging, as shown in simulation analysis. The variation in the volume of electrode material also leads to different stress and strain inside different areas, with the order of strain and stress being negative active material > negative collector fluid > positive active material > positive fluid. Therefore, huge stress and deformation will first cause the negative active particles to deviate from the fluid gradually and squeeze the diaphragm, resulting in mechanical failure accordingly.
Full article
(This article belongs to the Section Battery Modelling, Simulation, Management and Application)
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Open AccessArticle
Ni3S2@NiMo-LDH Composite for Flexible Hybrid Capacitors
by
Qi He and Xiang Wu
Batteries 2024, 10(7), 230; https://doi.org/10.3390/batteries10070230 - 26 Jun 2024
Abstract
Ni3S2 is a kind of transition metal sulfide (TMD) with excellent electrical conductivity and electrochemical activity. To further enhance the specific capacity of Ni3S2-based supercapacitors, we synthesize several nanosheet-decorated Ni3S2@NiMo-LDH nanostructures by
[...] Read more.
Ni3S2 is a kind of transition metal sulfide (TMD) with excellent electrical conductivity and electrochemical activity. To further enhance the specific capacity of Ni3S2-based supercapacitors, we synthesize several nanosheet-decorated Ni3S2@NiMo-LDH nanostructures by a combination of hydrothermal and electrodeposition processes. The mesoporous structure provides a large number of electroactive sites, which shortens the charge transfer distance and increases the specific surface area of electrode materials. The assembled asymmetric supercapacitor shows an energy density of 62.8 W h kg−1 at 2701.6 W kg−1 and long-term cycling stability.
Full article
(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)
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Open AccessArticle
Attention Mechanism-Based Neural Network for Prediction of Battery Cycle Life in the Presence of Missing Data
by
Yixing Wang and Benben Jiang
Batteries 2024, 10(7), 229; https://doi.org/10.3390/batteries10070229 - 26 Jun 2024
Abstract
As batteries become widespread applications across various domains, the prediction of battery cycle life has attracted increasing attention. However, the intricate internal mechanisms of batteries pose challenges to achieving accurate battery lifetime prediction, and the inherent patterns within temporal data from battery experiments
[...] Read more.
As batteries become widespread applications across various domains, the prediction of battery cycle life has attracted increasing attention. However, the intricate internal mechanisms of batteries pose challenges to achieving accurate battery lifetime prediction, and the inherent patterns within temporal data from battery experiments are often elusive. Meanwhile, the commonality of missing data in real-world battery usage further complicates accurate lifetime prediction. To address these issues, this article develops a self-attention-based neural network (NN) to precisely forecast battery cycle life, leveraging an attention mechanism that proficiently manages time-series data without the need for recurrent frameworks and adeptly handles the data-missing scenarios. Furthermore, a two-stage training approach is adopted, where certain network hyperparameters are fine-tuned in a sequential manner to enhance training efficacy. The results show that the proposed self-attention-based NN approach not only achieves superior predictive precision compared with the benchmarks including Elastic Net and CNN-LSTM but also maintains resilience against missing-data scenarios, ensuring reliable battery lifetime predictions. This work highlights the superior performance of utilizing attention mechanism for battery cycle life prognostics.
Full article
(This article belongs to the Special Issue Machine Learning for Advanced Battery Systems)
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Open AccessArticle
Optimal Battery Energy Storage Dispatch for the Day-Ahead Electricity Market
by
Julio Gonzalez-Saenz and Victor Becerra
Batteries 2024, 10(7), 228; https://doi.org/10.3390/batteries10070228 - 25 Jun 2024
Abstract
This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, focusing on enhancing profitability while factoring in battery degradation. This study incorporates the effects of battery degradation on the dynamics in
[...] Read more.
This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, focusing on enhancing profitability while factoring in battery degradation. This study incorporates the effects of battery degradation on the dynamics in the optimisation framework. Considering this cost in economic analysis and operational strategies is essential to optimise long-term performance and economic viability. Neglecting degradation costs can lead to suboptimal operation and dispatch strategies. We employ a continuous-time representation of the dynamics, in contrast with many other studies that use a discrete-time approximation with rather coarse intervals. We adopt an equivalent circuit model coupled with empirical degradation parameters to simulate a battery cell’s behaviour and degradation mechanisms with good support from experimental data. Utilising direct collocation methods with mesh refinement allows for precise numerical solutions to the complex, nonlinear dynamics involved. Through a detailed case study of Belgium’s day-ahead electricity market, we determine the optimal charging and discharging schedules under varying objectives: maximising net revenues, maximising profits considering capacity degradation, and maximising profits considering both capacity degradation and internal resistance increase due to degradation. The results demonstrate the viability of our approach and underscore the significance of integrating degradation costs into the market strategy for battery operators, alongside its effects on the battery’s dynamic behaviour. Our methodology extends previous work by offering a more comprehensive model that empirically captures the intricacies of battery degradation, including a fine and adaptive time domain representation, focusing on the day-ahead market, and utilising accurate direct methods for optimal control. This paper concludes with insights into the potential of optimal control applications in energy markets and suggestions for future research avenues.
Full article
(This article belongs to the Special Issue Advanced Control and Optimization of Battery Energy Storage Systems)
Open AccessArticle
Utilizing Electronic Resistance Measurement for Tailoring Lithium-Ion Battery Cathode Formulations
by
Christoph Seidl, Sören Thieme, Martin Frey, Kristian Nikolowski and Alexander Michaelis
Batteries 2024, 10(7), 227; https://doi.org/10.3390/batteries10070227 - 25 Jun 2024
Abstract
Cathode formulation, which describes the amount of cathode active material (CAM), conductive additives (CAs), and binder within a cathode compound, is decisive for the performance metrics of lithium-ion battery (LIB) cells. The direct measurement of electronic resistance can be an enabler for more
[...] Read more.
Cathode formulation, which describes the amount of cathode active material (CAM), conductive additives (CAs), and binder within a cathode compound, is decisive for the performance metrics of lithium-ion battery (LIB) cells. The direct measurement of electronic resistance can be an enabler for more time- and cost-efficient cathode formulation improvements. Within this work, we correlate the electronic resistance with the electrochemical performance of cathodes. Two different high Nickel NCM cathode materials and numerous CAs are used to validate the findings. A detailed look into the resistance reduction potential of carbon black (CB) and single-walled carbon nanotubes (SWCNT) and their mixtures is made. Finally, an impact estimation of cathode formulation changes on LIB key performance factors, such as energy density and cost, is shown.
Full article
(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)
Open AccessArticle
Joint Concern over Battery Health and Thermal Degradation in the Cruise Control of Intelligently Connected Electric Vehicles Using a Model-Assisted DRL Approach
by
Xiangheng Cheng and Xin Chen
Batteries 2024, 10(7), 226; https://doi.org/10.3390/batteries10070226 - 25 Jun 2024
Abstract
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Eco-driving aims to enhance vehicle efficiency by optimizing speed profiles and driving patterns. However, ensuring safe following distances during eco-driving can lead to excessive use of lithium-ion batteries (LIBs), causing accelerated battery wear and potential safety concerns. This study addresses this issue by
[...] Read more.
Eco-driving aims to enhance vehicle efficiency by optimizing speed profiles and driving patterns. However, ensuring safe following distances during eco-driving can lead to excessive use of lithium-ion batteries (LIBs), causing accelerated battery wear and potential safety concerns. This study addresses this issue by proposing a novel, multi-physics-constrained cruise control strategy for intelligently connected electric vehicles (EVs) using deep reinforcement learning (DRL). Integrating a DRL framework with an electrothermal model to estimate unmeasurable states, this strategy simultaneously manages battery degradation and thermal safety while maintaining safe following distances. Results from hardware-in-the-loop simulation testing demonstrated that this approach reduced overall driving costs by 18.72%, decreased battery temperatures by 4 °C to 8 °C in high-temperature environments, and reduced state-of-health (SOH) degradation by up to 46.43%. These findings highlight the strategy’s superiority in convergence efficiency, battery thermal safety, and cost reduction compared to existing methods. This research contributes to the advancement of eco-driving practices, ensuring both vehicle efficiency and battery longevity.
Full article
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