Battery Technology Research Articles

Collaborative Hollow Porous Structure Design and N Doping to Achieve a Win–Win Situation of “Stable” and “Fast” Lithium Battery

ABSTRACTPolymer‐derived SiOC materials are widely regarded as a new generation of anodes owing to their high specific capacity, low discharge platform, tunable chemical/structural composition, and good structural stability. However, tailoring the structure of SiOC to improve its electrochemical performance while simultaneously achieving elemental doping remains a challenge. Besides, the lithium storage mechanism and the structural evolution process of SiOC are still not fully understood due to its complex structure. In this study, a hollow porous SiOCN (Hp‐SiOCN) featuring abundant oxygen defects is successfully prepared, achieving both the creation of a hollow porous structure and nitrogen element doping in one step, finally enhancing the structural stability and improving the lithium storage kinetics of Hp‐SiOCN. In addition, the formation of a fully reversible structural unit, SiO3C─N, through the chemical interaction between N and Si/C, showcases a strong lithium adsorption capacity. Taking advantage of these combined benefits, the as‐prepared Hp‐SiOCN electrode delivers a reversible specific capacity of 412 mAh g−1 (93% capacity retention) after 500 cycles at 1.0 A g−1 and exhibited only 4% electrode expansion. This work offers valuable mechanistic insights into the synergistic optimization of elemental doping and structural design in SiOC, paving the way for advanced developments in battery technology.

Beyond Organic Electrolytes: An Analysis of Ionic Liquids for Advanced Lithium Rechargeable Batteries

The fast-growing area of battery technology requires the availability of highly stable, energy-efficient batteries for everyday applications. This, in turn, calls for research into new battery materials, especially with regard to a battery’s main component: the electrolytes. Besides the demands associated with solid ionic conduction and appropriate electrochemical behaviour, considerable effort will be necessary to thoroughly reduce safety risks in terms of flammability, leakage, and thermal runaway. Consequently, completely new classes of electrolytes need to be developed that are compatible with energy storage systems. Despite the progress made in solid polymer electrolytes, such materials have suffered from limitations to their real-world application. Now, ionic liquids are considered a class of electrolytes with the most potential for the creation of more advanced and safer lithium–ion batteries. In recent decades, ILs have been widely explored as potential electrolytes in the search for new breakthroughs in the ESS field, such those associated with fuel cells, lithium–ion batteries, and supercapacitors. The present review will discuss ILs that present high ionic conductivity, a lower melting point below 100 °C, and which feature up to 5–6 V wide electrochemical potential windows vs. Li+/Li. Furthermore, ILs exhibit good thermal stability, non-flammability, and low volatility—all of which are attributes realized by appropriate cation–anion combinations. This paper seeks to review the status of research concerning ILs, along with the advantages and challenges yet to be overcome in their development.

Long-Range Surface Forces in Salt-in-Ionic Liquids.

Ionic liquids (ILs) are a promising class of electrolytes with a unique combination of properties, such as extremely low vapor pressures and nonflammability. Doping ILs with alkali metal salts creates an electrolyte that is of interest for battery technology. These salt-in-ionic liquids (SiILs) are a class of superconcentrated, strongly correlated, and asymmetric electrolytes. Notably, the transference numbers of the alkali metal cations have been found to be negative. Here, we investigate Na-based SiILs with a surface force apparatus, X-ray scattering, and atomic force microscopy. We find evidence of confinement-induced structural changes, giving rise to long-range interactions. Force curves also reveal an electrolyte structure consistent with our predictions from theory and simulations. The long-range steric interactions in SiILs reflect the high aspect ratio of compressible aggregates at the interfaces rather than the purely electrostatic origin predicted by the classical electrolyte theory. This conclusion is supported by the reported anomalous negative transference numbers, which can be explained within the same aggregation framework. The interfacial nanostructure should impact the formation of the solid electrolyte interphase in SiILs.

Recent Activities of the Committee of Battery Technology

Recent Activities of the Committee of Battery Technology

Machine Learning Assisted Prediction of Donor Numbers: Guiding Rational Fabrication of Polymer Electrolytes for Lithium‐Ion Batteries

AbstractPolymer electrolytes are of interest in high‐energy‐density batteries. However, how the intrinsic electron‐donating capability of polymer segments involved in coordination affects lithium‐ion dissociation/transmission and rationally guides the design and fabrication of electrolytes is a highly exploratory topic. This study proposes a workable method that integrates machine learning with density functional theory to predict donor numbers (DN) for polymer building units. Using this approach, polymer chains with optimized DN are designed, effectively modulating the solvation structure of lithium‐ion. Molecular dynamics simulations confirm the positive impact of polymer chains on rapid transport of lithium ions. Experimental validation of the proposed zwitterionic polymer electrolyte (ZPE) showcases satisfactory parameters: ion conductivity (0.59 mS cm−1), ion migration numbers (0.82), and activation energy (0.016 eV). Electrochemical analysis on Li|ZPE|Li symmetric batteries demonstrate sustained plating/stripping performance exceeding 3000 hours at a current density of 0.2 mA cm−2. Assembled NCM|ZPE|Li batteries exhibit stable cycling over 1400 cycles at 4.3 V, with a capacity retention ratio of 92.3 %. Moreover, even under ultra‐high voltages of 4.5 V and 4.7 V, NCM|ZPE|Li batteries display stable cycling performances. This approach offers a paradigmatic strategy for polymer molecule design, advancing sustainable battery technologies.

A Review on Towards Long Lifetime Battery: AI Based Manufacturing and Management

A Battery Management System (BMS) is crucial for maintaining the health and efficiency of batterypowered devices. Integrating Artificial Intelligence (AI) into BMS offers advanced capabilities for optimizing battery performance. AI algorithms analyze real-time data from the battery, such as voltage, temperature, and charge cycles, to predict battery life, enhance safety, and improve energy efficiency.AI can detect patterns and anomalies that traditional systems might miss, allowing for proactive adjustments to charging and discharging processes. This helps prevent overcharging, overheating, and deep discharges, which can damage batteries and reduce their lifespan. Additionally, AIdriven BMS can learn from historical data to refine its predictions and adapt to changing conditions. It also supports better energy management in electric vehicles and renewable energy systems, where efficient battery use is critical. By continuously monitoring and optimizing battery performance, AI enhances the overall reliability and longevity of battery systems, making technology more sustainable and efficient. Thus, the use of AI in BMS represents a significant advancement in battery technology, contributing to improved performance and safety in various applications. This study explores battery management systems by optimizing performance, extending lifespan, and improving efficiency and ensure safety through predictive analysis and real-time monitoring. Keywords: Battery Management system, artificial intelligence, state of charge, state of health, battery life extension, fault detection.

AI-enabled thermal monitoring of commercial (PHEV) Li-ion pouch cells with Feature-Adapted Unsupervised Anomaly Detection

Distributed temperature profiling of lithium-ion batteries provides valuable insights, aiding thermal management and minimising risk of battery failures. Highlighted by Batteries Europe as crucial for battery safety, advances in thermal monitoring are imperative to continuous safe adoption of battery technology. Deep Learning techniques have recently emerged as powerful tools for anomaly detection (AD) in many thermal mapping applications. These data-driven methods can handle common challenges like data unavailability or environment variations. Our study devises a methodology to leverage Deep Learning with thermal data from commercially available pouch cells and an infrared camera. We explain the building blocks of FAUAD (Feature-Adapted Unsupervised Anomaly Detection), which models the normality of the input data and synthesizes anomalies in its feature space. The resulting model is benchmarked against some of the latest state-of-the-art methods and achieves high anomaly detection capability; Area Under the ROC Curve (AUROC) score of 0.971 on simulated data, 0.990 on contaminated real data, and a perfect score of 1.0 on real clean data. While maintaining a compact size of 15 MB. FAUAD offers a notable advancement in unsupervised anomaly detection for battery thermal monitoring. The proposed method is cell chemistry agnostic and open to usage scenarios beyond this works’ scope.

An Ensemble Learning Method for SOC Estimation of Lithium-Ion Batteries Using Machine Learning

Accurately assessing the State of Charge (SOC) is paramount for optimizing battery management systems, a cornerstone for ensuring peak battery performance and safety across diverse applications, encompassing vehicle powertrains and renewable energy storage systems. Confronted with the challenges of traditional SOC estimation methods, which often struggle with accuracy and cost-effectiveness, this research endeavors to elevate the precision of SOC estimation to a new level, thereby refining battery management strategies. Leveraging the power of integrated learning techniques, the study fuses Random Forest Regressor, Gradient Boosting Regressor, and Linear Regression into a comprehensive framework that substantially enhances the accuracy and overall performance of SOC predictions. By harnessing the publicly accessible National Aeronautics and Space Administration (NASA) Battery Cycle dataset, our analysis reveals that these integrated learning approaches significantly outperform traditional methods like Coulomb counting and electrochemical models, achieving remarkable improvements in SOC estimation accuracy, error reduction, and optimization of key metrics like R2 and Adjusted R2. This pioneering work propels the development of innovative battery management systems grounded in machine learning and deepens our comprehension of how this cutting-edge technology can revolutionize battery technology.

Recent Advances in Lithium Iron Phosphate Battery Technology: A Comprehensive Review

Lithium iron phosphate (LFP) batteries have emerged as one of the most promising energy storage solutions due to their high safety, long cycle life, and environmental friendliness. In recent years, significant progress has been made in enhancing the performance and expanding the applications of LFP batteries through innovative materials design, electrode engineering, and manufacturing techniques. This review paper provides a comprehensive overview of the recent advances in LFP battery technology, covering key developments in materials synthesis, electrode architectures, electrolytes, cell design, and system integration. This review paper aims to provide a comprehensive overview of the recent advances in lithium iron phosphate (LFP) battery technology, encompassing materials development, electrode engineering, electrolytes, cell design, and applications. By highlighting the latest research findings and technological innovations, this paper seeks to contribute to the continued advancement and widespread adoption of LFP batteries as sustainable and reliable energy storage solutions for various applications. We also discuss the current challenges and future prospects for LFP batteries, emphasizing their potential role in sustainable energy storage solutions for various applications, including electric vehicles, renewable energy integration, and grid-scale energy storage.

A comparative study of LiTa2PO8 ceramics prepared with different lithium sources

In advancing lithium-ion batteries, solid electrolytes like LiTa2PO8, a Li-ion conductor with high bulk conductivity (~10−3 S/cm), show promise for all-solid-state battery technology. Here, we investigate the impact of different Li sources and their excess on the structural, microstructural and electrical properties of LiTa2PO8 ceramics. Employing diverse characterization techniques, including XRD, MAS NMR, TMA, SEM/EDS, IS, DC potentiostatic polarization, and density measurements, we unveil significant impact of the source of lithium ions and its excess on electrical properties. The sample without excess LiNO3 source exhibited the highest σtot = 5.2×10−4 S/cm at 30°C. Notably, the grain conductivity remained constant at ca. 3 mS/cm, irrespective of the chosen lithium source and excess. The microstructural analysis uncovered correlations between variations in total ionic conductivity and changes in the nature and concentration of secondary phases. Additionally, it revealed the influence of the arrangement of grains affecting the grain boundaries and porosity regions.

Performance analysis of CKF algorithm for battery SoC estimation with its aging effect

The penetration of electric vehicle (EV) in automobile market is very much dependent on the battery technology. Its size, weight, and cost are issues of concern. To effectively utilize the battery expertise, precise estimate of state of charge (SoC) is vital which greatly depends on the battery model. Current models lack consideration for variations in battery capacity over their lifespan. This paper develops a battery model which depicts the depletion of battery capacity with its life. Subsequently, this model has been utilized for estimation using advanced Kalman filtering (KF) algorithms. For the developed model, the design and effectiveness of the cubature Kalman filter (CKF) is applied as a proposed robust state-estimator for this problem. Moreover, a comparative analysis was undertaken with existing non-linear KFs based on performance metrics. The optimal choice of estimator is identified, through the results obtained from the Octave/MATLAB simulation. The outcomes show CKF algorithm based SoC estimator is superior to others in ensuring high accuracy, strong robustness even under changes in initial conditions (i.e., initial SoC, process and sensor noise levels), system's ability to converge quickly while ensuring that the maximum error in state of charge (SoC) estimation remains within 1% after convergence.

One-step green hydrometallurgical recycling of spent lithium-ion batteries’ cathode

The transition towards a low-carbon future hinges on the advancement of Lithium-ion battery (LIBs) technology, which has spurred a significant demand for raw materials and the management of waste batteries containing hazardous substances. Developing efficient and environmentally friendly recycling strategies is essential to tackle these challenges. Here, we introduce a one-step green hydrometallurgical recycling of spent lithium-ion batteries’ cathode on the basis of contact-electro-catalytic (CEC) process. It combines the separation of the cathode materials in pretreatment, and leaching in a single step. Specifically, electrode pieces are directly dealt with the CEC treatment, which can remove organic binders (e.g. Polyvinylidene Fluoride) and reduce high-valence metal ions. Results indicate that the leaching efficiency of lithium, nickel, cobalt, and manganese can reach 99.6%, 98.3%, 99.4%,97.4%. Plus, SiO2 is a cost-effective and readily available catalyst requiring no modification. Therefore, the CEC-assisted one-step hydrometallurgical recycling exhibits sustainability, rapidness, high efficiency, and eco-friendly for LIBs recycling.

A polyolefin separator reinforced inorganic/organic composite electrolyte for quasi-solid-state Lithium-sulfur batteries with high cycle stability and long lifespan

Lithium-sulfur (Li-S) batteries are considered a promising next-generation energy storage system due to the high energy density and the low cost. However, the severe shuttle effect of polysulfide and the security issue caused by organic liquid electrolyte and lithium metal anode have hindered their commercial application. Herein we achieved a polyolefin commercial separator reinforced inorganic/organic composite electrolyte (denoted as Com-LATP) via coating the polypropylene (PP) separator with the composite of polymethyl methacrylate/poly(vinylidene fluoride) (PMMA/PVDF) modified with Li1.3Al0.3Ti1.7(PO4)3 (LATP). The Com-LATP electrolyte (thickness: ∼30 um) exhibits remarkable tensile strength of 103 MPa, an ionic conductivity of 0.47·mS cm-1 with the Li+ transfer number of 0.45, which is beneficial for the uniform Li+ flux and the stable striping and plating of Li metal. The Li symmetrical batteries equipped with this Com-LATP exhibited stable cycling for over 350 h. Meanwhile, the Com-LATP electrolyte with the special dense structure can hinder the polysulfide diffusion. The developed quasi-solid-state lithium-sulfur batteries have demonstrated impressive cycling stability with an initial specific capacity of 1184.8 mA h g-1 at a discharge rate of 0.2 C, maintaining performance over 200 cycles. Additionally, they exhibited outstanding rate capability, retaining a reversible capacity of 710.4 mA h g-1 even at a higher discharge rate of 2 C. This research introduces an innovative structural design approach for high-performance quasi-solid-state Li-S batteries, which holds significant promise for advancing the commercialization of lithium-sulfur battery technology.

Integrating ensemble learning and meta bagging techniques for temperature-specific State of Health prediction in Lithium-ion Batteries

Accurately predicting the State of Health (SOH) of lithium-ion batteries is crucial in the field of battery technology to guarantee operational dependability and durability. This study introduces a novel methodology that integrates ensemble learning and meta-bagging techniques to enhance the precision of SOH prediction, particularly in scenarios characterized by fluctuating temperature conditions. We start our methodology by conducting an extensive data-gathering phase, specifically targeting crucial variables: temperature, voltage, current, and capacity. Subsequently, a thorough data preparation stage ensues, encompassing tasks such as cleansing, standardization, and curating relevant features, with particular attention given to temperature characteristics. Next, we employ a temperature-adaptive ensemble learning model that combines different prediction algorithms, such as Linear Regression (LR) and Long Short-Term Memory (LSTM) networks. These algorithms are trained on temperature-stratified data subsets using a Meta-Bagging Estimator (MBE). This strategy enhances the precision of predictions and the ability to overcome the obstacle of temperature changes impacting battery performance. The efficacy of the ensemble model is thoroughly assessed utilizing diverse performance criteria, showcasing its improved predictive capability compared to conventional approaches. Our findings indicate that this customized strategy is better equipped to manage the intricate dynamics of lithium-ion battery behavior, resulting in more dependable and precise assessments of battery SOH. This work substantially contributes to battery health monitoring and can significantly advance battery management systems.

Comparative Analysis of Marine Alternative Fuels for Offshore Supply Vessels

This paper provides an in-depth analysis of alternative fuels, including liquefied natural gas (LNG), hydrogen, ammonia, and biofuels, assessing their feasibility based on operational requirements, availability, safety concerns, and the infrastructure needed for large-scale adoption. Moreover, it examines hybrid and fully electric propulsion systems, considering advancements in battery technology and the integration of renewable energy sources, such as wind and solar power, to further reduce SOV emissions. Key findings from this research indicate that LNG serves as a viable short- to medium-term solution for reducing GHG emissions in the SOV sector, due to its relatively lower carbon content compared to MDO and HFO. This paper finally insists that while LNG presents an immediate opportunity for emission reduction in the SOV sector, a combination of hydrogen, ammonia, and hybrid propulsion systems will be necessary to meet long-term decarbonisation goals. The findings underscore the importance of coordinated industry efforts, technological innovation, and supportive regulatory frameworks to overcome the technical, economic, and infrastructural challenges associated with decarbonising the maritime industry.

A multichambered carbon based electrode materials to realize efficient sodium-ion batteries

Sodium-ion batteries (SIBs) have emerged as a compelling alternative to lithium-ion counterparts, owing to the widespread availability and low cost of sodium. However, to achieve successful commercialization, the development of a high-performance electrode material is crucial. This study presents a facile synthesis strategy for the production of multichambered carbon cubes (MCCs) derived from ZIF-8 precursors. The optimized process mesoporous MCCs which were then embedded with zinc selenide (ZnSe@MCCs) for the anode and infused with sulfur (S@ZnSe@MCCs) to create the cathode of SIBs. The uniform, hierarchically porous MCC morphologies enable deep electrolyte penetration, unlocking the full potential of these electrode materials. SIBs utilizing ZnSe@MCCs anodes exhibited impressive sodium storage capacities (exceeding 300 mAh g−1 after 1000 cycles) and rate capabilities. Moreover, when employed as the cathode, the S@ZnSe@MCCs effectively suppressed the polysulfide shuttle phenomenon. The performance of these sodium-ion cells underscores the potential of MCCs to enable high capacity, long cycle life, and fast kinetics, paving the way for practical and efficient sodium-ion battery technologies.

A cellular automata modelling approach for grain growth topological evolution process of ternary cathode materials combined with deep neural networks

Ternary cathode materials are pivotal in high-performance battery technologies, with grain size influencing their electrochemical performance. However, the absence of real-time grain size inspection during material preparation poses challenges in maintaining the consistent quality of ternary cathode materials. To address this, this paper proposes a novel method employing cell automata to model the topological evolution of grain growth in these materials, integrated with deep neural networks (DNN). The grain growth process is divided into two stages: heating and constant temperature. In the heating stage, varying heating rates and plateau temperatures serve as DNN inputs, yielding the primary grain distribution for the cell automata model in the constant temperature stage. Based on cell automata, the grain growth model links the grain growth rate to the grain size distribution in the constant temperature stage. A surface energy constraint rule, based on local curvature and grain boundary surface tension, governs growth rates. The grain boundary growth ratio is also used to create a grain ID transition variable, dictating grain ID conversion in the model. This approach accurately simulates the dynamic evolution of polycrystalline grain size and morphology. Simulation results show that this method effectively models grain growth in ternary cathode materials, offering insights for optimising the sintering process and improving material quality.

Enhanced Cycling Performance of the LiNiO2 Cathode in Li-Ion Batteries Enabled by Nb-Based Surface Coating.

Lithium nickel oxide (LNO) is a promising cathode candidate in various next-generation battery technologies. To increase its stability, doping and surface coating have become key strategies. Among various elements, niobium stands out for its dual role as an effective dopant and the advantages of its oxide phases as coatings. In this study, we explore Nb-based coating of LNO, utilizing methods that minimize or eliminate solvent use. Additionally, the coated samples were treated at two different temperatures to study their effect on properties and electrochemical performance. Our results demonstrate that the coating process strongly affects the cell cyclability and further highlight the potential of Nb-based protective coatings in enhancing LNO as a cathode active material for application in high-energy-density Li-ion batteries.

Physics-guided TL-LSTM network for early-stage degradation trajectory prediction of lithium-ion batteries

Predicting early degradation trajectories of lithium-ion batteries is crucial in enhancing system reliability and promoting battery technology advancement. Existing data-driven methods often require large amounts of historical data and similar cycle information, which are not easily accessible in real-world applications. To this end, a physics-guided TL-LSTM network is proposed for degradation trajectory prediction, which integrates the physical degradation property into a transfer learning framework. Specifically, we first build a backbone network that combines a Long Short-Term Memory (LSTM) module and a Fully Connected (FC) module to perform fine-grained model training on the source domain. Then, the well-trained LSTM module is frozen and transferred to the target domain, and the FC module is fine-tuned using 30% of its early-cycle data. The entire network optimization process is guided by two inherent properties of battery degradation. Extensive comparative analysis demonstrates that our proposed approach outperforms existing methods in predicting early degradation trajectories.

Recent Progress on the Synthesis, Morphological Topography, and Battery Applications of Polypyrrole-Based Nanocomposites

Recent Progress on the Synthesis, Morphological Topography, and Battery Applications of Polypyrrole-Based Nanocomposites