Search Results (73)

In battery energy storage systems, accurately estimating battery capacity and state of health is crucial to ensure satisfactory operation and system efficiency and reliability. However, these tasks present particular challenges under irregular charge–discharge conditions, such as those encountered in renewable energy integration and electric vehicles, where heterogeneous cycling accelerates degradation. This study introduces a hybrid deep learning framework to address these challenges. It combines convolutional layers for localized feature extraction, bidirectional recurrent units for sequential learning and a temporal attention mechanism. The proposed hybrid deep learning model, termed CBATE-Net, uses ensemble averaging to improve stability and emphasizes degradation-critical intervals. The framework was evaluated using voltage, current and temperature signals from four benchmark lithium-ion cells across complete life cycles, as part of the NASA dataset. The results demonstrate that the proposed method can accurately track both smooth and abrupt capacity fade while maintaining stability near the end of the life cycle, an area in which conventional models often struggle. Integrating feature learning, temporal modelling and robustness enhancements in a unified design provides the framework with the ability to make accurate and interpretable predictions, making it suitable for deployment in real-world battery energy storage applications. Full article

Electrochemical processes within a lithium-ion battery cause electrode expansion and gas generation, thus resulting in battery swelling and, in severe cases, reliability and safety issues. This paper presents the mechanisms responsible for swelling, including thermal expansion, lithium intercalation, electrode interphase layer growth, lithium plating, and gas generation, while highlighting their dependence on material properties, design considerations, C-rate, temperature, state of charge (SoC), and voltage. The paper then discusses how swelling correlates with capacity fade, impedance rise, and thermal runaway, and demonstrates the potential of using swelling as a diagnostic and prognostic metric for battery health. Swelling models that connect microscopic mechanisms to macroscopic deformation are then presented. Finally, the paper presents strategies to mitigate swelling, including materials engineering, surface coatings, electrolyte formulation, and mechanical design modifications. Full article

Silicon anodes for lithium-ion batteries (LIBs) offer exceptional theoretical capacity (~4200 mAh/g) but face critical challenges due to significant volume expansion (>300%) during lithiation, leading to mechanical degradation and rapid capacity fading. This review highlights recent advancements in mitigating these issues, including structural designs such as core–shell architectures, porous composites, and multidimensional encapsulation techniques that buffer mechanical stress and stabilize the solid electrolyte interphase (SEI). Binder innovations and hybrid material systems further enhance electrode integrity and cycling stability. While substantial progress has been made, challenges remain in scaling these solutions for commercial applications. This paper provides insights into current strategies and future directions for enabling silicon-based anodes in next-generation LIBs. Full article

Accurate State of Charge (SOC) estimation for retired power batteries remains a critical challenge due to their degraded electrochemical properties and heterogeneous aging mechanisms. Traditional methods relying solely on electrical parameters (e.g., voltage and current) exhibit significant errors, as aged batteries experience altered internal resistance, capacity fade, and uneven heat generation, which distort the relationship between electrical signals and actual SOC. To address these limitations, this study proposes a surface temperature-assisted SOC estimation method, leveraging the distinct thermal characteristics of retired batteries. By employing infrared thermal imaging, key temperature feature regions—the positive/negative tabs and central area—are identified, which exhibit strong correlations with SOC dynamics under varying operational conditions. A Gated Recurrent Unit (GRU) neural network is developed to integrate multi-region temperature data with electrical parameters, capturing spatial–temporal thermal–electrical interactions unique to retired batteries. The model is trained and validated using experimental data collected under constant current discharge conditions, demonstrating superior accuracy compared to conventional methods. Specifically, our method achieves 64.3–68.1% lower RMSE than traditional electrical-parameter-only approaches (V-I inputs) across 0.5 C–2 C discharge rates. Results show that the proposed method reduces SOC estimation errors compared to traditional voltage-based models, achieving RMSE values below 1.04 across all tested rates. This improvement stems from the model’s ability to decode localized heating patterns and their hysteresis effects, which are particularly pronounced in aged batteries. The method’s robustness under high-rate operations highlights its potential for enhancing the reliability of retired battery management systems in secondary applications such as energy storage. Full article

In most commercial lithium-ion batteries, graphite remains the primary anode material. However, its theoretical specific capacity is only 372 mAh∙g⁻¹, which falls short of meeting the demands of high-performance electronic devices. Silicon anodes, despite boasting an ultra-high theoretical specific capacity of 4200 mAh∙g⁻¹, suffer from significant volume expansion (>300%) during cycling, leading to severe capacity fade and limiting their commercial viability. Currently, silicon-based polymer-derived ceramics have emerged as a highly promising next-generation anode material for lithium-ion batteries, thanks to their unique nano-cluster structure, tunable composition, and low volume expansion characteristics. The maximum capacity of the ceramics can exceed 1000 mAh∙g⁻¹, and their unique synthesis routes enable customization to align with diverse electrochemical application requirements. In this paper, we present the progress of silicon oxycarbide (SiOC), silicon carbonitride (SiCN), silicon boron carbonitride (SiBCN) and silicon oxycarbonitride (SiOCN) in the field of LIBs, including their synthesis, structural characteristics and electrochemical properties, etc. The mechanisms of lithium-ion storage in the Si-based anode materials are summarized as well, including the key role of free carbon in these materials. Full article

Manganese oxides (MnO_x) have been confirmed as the most promising candidates for aqueous zinc-ion batteries (AZIBs) due to their cost-effectiveness, high theoretical capacity, high voltage platforms, and environmental friendliness. However, in practical applications, AZIBs are hindered by the Jahn–Teller distortion (JTD) effect, primarily induced by Mn³⁺ (t_2g³e_g¹) in octahedral coordination, which leads to severe structural deformation, rapid capacity fading, and poor cycling stability. This review systematically outlines the fundamental mechanisms of JTD in MnO_x cathodes, including electronic structure changes, lattice distortions, and their side effects on Zn²⁺ storage performance. Furthermore, we critically discuss advanced strategies to suppress JTD, such as cation/anion doping, interlayer engineering, surface/interface modification, and electrolyte optimization, aimed at enhancing both structural stability and electrochemical performance. Finally, we propose future research directions, such as in situ characterization, machine learning-guided material design, and multifunctional interfacial engineering, to guide the design of high-performance MnO_x hosts for next-generation AZIBs. This review may provide a promising guideline for overcoming JTD challenges and advancing MnO_x-based energy storage systems. Full article

Ultrafast-charging (UFC) technology for electric vehicles (EVs) and energy storage devices has brought with it an increase in demand for lithium-ion batteries (LIBs). However, although they pose advantages in driving range and charging time, LIBs face several challenges such as mechanical degradation, lithium dendrite formation, electrolyte decomposition, and concerns about thermal runaway safety. This review evaluates the key challenges and advances in LIB components (anodes, cathodes, electrolytes, separators, and binders), alongside innovations in charging protocols and safety concerns. Material-level solutions such as nanostructuring, doping, and composite architectures are investigated to improve ion diffusion, conductivity, and electrode stability. Electrolyte modifications, separator enhancements, and binder optimizations are discussed in terms of their roles in reducing high-rate degradation. Furthermore, charging protocols are addressed; adjustments can reduce mechanical and electrochemical stress on LIBs, decreasing capacity fade while providing rapid charging. This review highlights the key technological advancements that are enabling ultrafast charging and that are assisting us in overcoming severe limitations, paving the way for the development of next-generation high-performance LIBs. Full article

The capacity of Lithium-ion batteries degrades over the time, making accurate prediction of their Remaining Useful Life (RUL) crucial for maintenance and product lifespan design. However, diverse aging mechanisms, changing working conditions and cell-to-cell variation lead to the inhomogeneous cell lifespan and complicated life prediction. In this work, a data-driven algorithm based on stacked Long Short Term Memory (LSTM) encoder–decoders is proposed for RUL prediction. The encoder and upstream decoder form an autoencoder framework for feature extraction. The encoder and the downstream decoder form the encoder–decoder framework for RUL prediction. To enhance generalization during training, the Maximum Mean Discrepancy (MMD) loss is included in the autoencoder framework. The similarity of aging patterns is analyzed during splitting source and target datasets through k-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The Euclidean metric with accumulated Equivalent Cycle Number (ECN) sequence during aging shows better performance for similarity-based data splitting than the Dynamic Time Wrapping (DTW) distance metric based on capacity fading trajectory. The experimental results indicate that the proposed algorithm can provide accurate RUL prediction using 5% fading data and shows good generalization with Coefficient of Determination (R²) score of 0.98. Full article

The increasing demand for energy storage solutions, particularly in electric vehicles and renewable energy systems, has intensified research on lithium-ion (Li-ion) battery safety and performance. A critical challenge is thermal runaway (TR), a highly exothermic sequence of reactions triggered by mechanical, electrical, or thermal abuse, which can lead to catastrophic failures. While most TR models focus on fresh cells, aging significantly impacts battery behavior and safety. This study develops an electrochemical–thermal coupled model that incorporates aging effects to better predict thermal behavior and TR initiation in cylindrical Li-ion batteries. The model is validated against experimental data for fresh NMC and aged NCA cells, and statistical analysis is conducted to identify key factors influencing TR (p < 0.05). A full factorial design evaluates the effects of internal resistance (10, 20, 30, and 40 mΩ), capacity (1, 2, 3, and 5 Ah), and current rate (1C, 3C, 6C, and 8C) on temperature evolution. Additionally, a machine learning algorithm (logistic regression) is employed to identify an internal resistance threshold, beyond which thermal runaway (TR) becomes highly probable, and to predict TR probability based on key battery parameters. The model achieved a high prediction accuracy of 95% on the test dataset. Results indicate that aging affects thermal stability in complex ways. The increased internal resistance exacerbates heating rates, while capacity fade reduces stored energy, mitigating TR risk. These findings provide a validated framework for enhancing battery thermal management and predictive safety mechanisms, which contributed to the development of safer, more reliable Li-ion energy storage systems. Full article

Lithium–sulfur batteries (LSBs), with a high energy density (2600 $\mathrm{W}$$\mathrm{h}$ kg⁻¹) and theoretical specific capacity (1672 mA h g⁻¹), are considered the most promising next-generation rechargeable energy storage devices. However, polysulfide dissolution and the shuttle effect cause severe capacity fading and the rapid loss of the active material; hence, these must be addressed first. This review provides an overview of various strategies employed to immobilise polysulfides via polysulfide trapping and physical and chemical adsorption using porous cathode designs, heterostructures, functionalised separators, and polymer binders. The working mechanism of each strategy is reviewed and discussed, highlighting their advantages and disadvantages, and they are analysed through comparisons of the battery performance and limitations in terms of practical applications. Finally, the future prospects for the design and synthesis of LSBs to limit polysulfide dissolution are discussed. Full article

Lithium nickel manganese cobalt oxides (NMCs) are widely used as cathode materials in commercial batteries. Efforts have been made to enhance battery energy density and stability by adjusting the element ratio. Nickel-rich NMC shows promise due to its high capacity; however, its commercial viability is hindered by severe capacity fade, primarily caused by poor mechanical stability. To address this, understanding the chemo-mechanical behavior of Ni-rich NMC is crucial. The mechanical failure of Ni-rich NMC materials during battery operation has been widely studied through theoretical approaches to identify possible solutions. The elastic properties are key parameters for structural analysis. However, experimental data on NMC materials are scarce due to the inherent difficulty of measuring the properties of electrode active particles at such a small scale. In this study, we employ molecular dynamics (MDs) simulations to investigate the elastic properties of NMC materials with varying compositions (NMC111, NMC532, NMC622, NMC721, and NMC811). Our results reveal that elasticity increases with nickel content, ranging from 200 GPa for NMC111 to 290 GPa for NMC811. We further analyze the contributing factors to this trend by examining the individual components of the elastic properties. The simulation results provide valuable input parameters for theoretical models and continuum simulations, offering insights into strategies for reducing the mechanical instability of Ni-rich NMC materials. Full article

Organic materials have emerged as promising candidates for cathode materials in lithium-ion batteries and supercapacitors, offering unique properties and advantages over traditional inorganic counterparts. This review investigates the use of organic compounds as cathode materials in energy storage devices, focusing on their application in lithium-ion batteries and supercapacitors. The review covers various types of organic materials, organosulfur compounds, organic free radical compounds, organic carbonyl compounds, conducting polymers, and imine compounds. The advantages, challenges, and ongoing developments in this area are examined and the potential of organic cathode materials to achieve higher energy density, improved cycling stability, and environmental sustainability is highlighted. The comprehensive analysis of organic cathode materials provides insights into their electrochemical performance, electrode reaction mechanisms, and design strategies such as molecular structure modification, hybridization with inorganic components, porous architectures, conductive additives, electrolyte optimization, binder selection, and electrode architecture to improve their efficiency and performance. In addition, future research in the field of organic cathode materials should focus on addressing current limitations such as low energy density, cycling stability, poor discharge capability, potential safety concerns and improving their performance. To do this, it will be necessary to improve structural stability, conductivity, cycle life, and capacity fading, explore new redox-active organic compounds, and pave the way for the next generation of high-performance energy storage devices. For organic cathode materials to be commercially viable, it is also essential to develop scalable and economical manufacturing processes. Full article

Lithium–ion battery development necessitates predicting capacity fading using early cycle data to minimize testing time and costs. This study introduces a hybrid physics–guided data–driven approach to address this challenge by accurately determining the dominant fading mechanism and predicting the average capacity fading rate. Physics–guided features, derived from the electrochemical properties and behaviors within the battery, are extracted from the first five cycles to provide meaningful, interpretable, and predictive data. Unlike previous models that rely on a single regression approach, our method utilizes two separate regression models tailored to the identified dominant fading mechanisms. Our model achieves 95.6% accuracy in determining the dominant fading mechanism using data from the second cycle and a mean absolute percentage error of 17.09% in predicting lifetime capacity fade from the first five cycles. This represents a substantial improvement over state–of–the–art models, which have an error rate approximately three times higher. This study underscores the significance of physics–guided data characterization and the necessity of identifying the primary fading mechanism prior to predicting the capacity fading rate in lithium–ion batteries. Full article

Commercialized lithium iron phosphate (LiFePO₄) batteries have become mainstream energy storage batteries due to their incomparable advantages in safety, stability, and low cost. However, LiFePO₄ (LFP) batteries still have the problems of capacity decline, poor low-temperature performance, etc. The problems are mainly caused by the following reasons: (1) the irreversible phase transition of LiFePO₄; (2) the formation of the cathode–electrolyte interface (CEI) layer; (3) the dissolution of the iron elements; (4) the oxidative decomposition of the electrolyte; (5) the repeated growth and thickening of the solid–electrolyte interface (SEI) film on the anode electrode; (6) the structural deterioration of graphite anodes; (7) the growth of lithium dendrites. In order to eliminate the problems, methods such as the modification, doping, and coating of cathode materials, electrolyte design, and anode coating have been studied to effectively improve the electrochemical performance of LFP batteries. This review briefly describes the working principle of the LFP battery, the crystal structure of the LFP cathode material, and its electrochemical performance as a cathode. The performance degradation mechanism of LFP batteries is summarized in three aspects—cathode material, anode material, and electrolyte—and the research status of LFP material modification and electrolyte design is emphatically discussed. Finally, the challenges and future development of LFP batteries are prospected. Full article

In this paper, a novel 2D Nolen beamforming phased array with 3D scanning capability to achieve high channel capacity is presented for multiple-input multiple-output (MIMO) Internet-of-Things (IoT) applications. The proposed 2D beamforming phased array is designed by stacking a fundamental building block consisting of a 3 × 3 tunable Nolen matrix, which applies a small number of phase shifters with a small tunning range and reduces the complexity of the beam-steering control mechanism. Each 3 × 3 tunable Nolen matrix can achieve a full 360° range of progressive phase delay by exciting all three input ports, and nine individual radiation beams can be generated and continuously steered on azimuth and elevation planes by stacking up three tunable Nolen matrix in horizontal and three in vertical to maximize signal-to-noise ratio (SNR) in the corresponding spatial directions. To validate the proposed design, the simulations have been conducted on the circuit network and assessed in a fading channel environment. The simulation results agree well with the theoretical analysis, which demonstrates the capability of the proposed 2D Nolen beamforming phased array to realize high channel capacity in MIMO-enabled IoT communications. Full article