Search Results (367)

Unmanned Aerial Vehicles have started to be used more and more due to the benefits they bring. Failure of Unmanned Aerial Vehicle components may result in loss of control, which may cause property damage or personal injury. In order to increase the operational safety of the Unmanned Aerial Vehicle, the implementation of a Predictive Maintenance system using the Internet of Things is required. In this paper, the authors propose a new architecture of Predictive Maintenance system for Unmanned Aerial Vehicles that is able to identify the fault type of Brushless DC electric motor and determine the Remaining Useful Life of the Li-ion batteries. In order to create the Predictive Maintenance system within the Unmanned Aerial Vehicle, an architecture based on Fog Computing was proposed and Machine Learning was used to extract knowledge from the data. The proposed architecture was practically validated. Full article

Fast charging accelerates lithium-ion battery operation but increases the risk of lithium (Li) plating—a process that undermines efficiency, longevity, and safety. Here, we introduce a predictive modeling framework that captures the onset and severity of Li plating under practical fast-charging conditions. By integrating an empirically parameterized SOC threshold model with time-dependent kinetic simulations and Arrhenius based thermal analysis, we delineate operating regimes prone to irreversible Li accumulation. The framework distinguishes reversible and irreversible plating fractions, quantifies energy losses, and identifies a critical activation energy (0.25 eV) associated with surface-limited deposition. Visualizations in the form of severity maps and voltage-zone risk classifications enable direct application to battery management systems. This approach bridges electrochemical degradation modeling with real-time charge protocol design, offering a practical tool for safe, high-performance battery operation. Full article

The accurate estimation and prediction of internal states in lithium-ion (Li-Ion) batteries, such as State of Charge (SoC) and Remaining Useful Life (RUL), are vital for optimizing battery performance, safety, and longevity in electric vehicles and other applications. This paper presents a unified, modular, and extensible machine learning (ML) framework designed to address the heterogeneity and complexity of battery state prediction tasks. The proposed framework supports flexible configurations across multiple dimensions, including feature engineering, model selection, and training/testing strategies. It integrates standardized data processing pipelines with a diverse set of ML models, such as a long short-term memory neural network (LSTM), a convolutional neural network (CNN), a feedforward neural network (FFNN), automated machine learning (AutoML), and classical regressors, while accommodating heterogeneous datasets. The framework’s applicability is demonstrated through five distinct use cases involving SoC estimation and RUL prediction using real-world and benchmark datasets. Experimental results highlight the framework’s adaptability, methodological transparency, and robust predictive performance across various battery chemistries, usage profiles, and degradation conditions. This work contributes to a standardized approach that facilitates the reproducibility, comparability, and practical deployment of ML-based battery analytics. Full article

Remaining useful life (RUL) serves as a pivotal metric for quantifying lithium-ion batteries’ state of health (SOH) in electric vehicles and plays a crucial role in ensuring their safety and reliability. In order to achieve accurate and reliable RUL prediction, a novel RUL prediction method which employs a back propagation (BP) neural network based on the Harris Hawks optimization (HHO) algorithm is proposed. This method optimizes the BP parameters using the improved HHO algorithm. At first, the circle chaotic mapping method is utilized to solve the problem of the initial value. Considering the problem of local convergence, Gaussian mutation is introduced to improve the search ability of the algorithm. Subsequently, two key health factors are selected as input features for the model, including the constant-current charging isovoltage rise time and constant-current discharging isovoltage drop time. The model is validated using aging data from commercial lithium iron phosphate (LiFePO₄) batteries. Finally, the model is thoroughly verified under an aging test. Experimental validation using training sets comprising 50%, 60%, and 70% of the cycle data demonstrates superior predictive performance, with mean absolute error (MAE) values below 0.012, root mean square error (RMSE) values below 0.017 and mean absolute percentage error (MAPE) within 0.95%. The results indicate that the model significantly improves prediction accuracy, robustness and searchability. Full article

With the widespread application of lithium-ion batteries (LIBs), safety performance has become a critical factor limiting the commercialization of large-scale, high-capacity LIBs. The main reason for the safety problem is that the electrolytes of LIBs are extremely flammable. Adding flame retardants to conventional electrolytes is an effective method to improve battery safety. In this paper, trimethyl phosphate (TMP) and trimethyl phosphite (TMPi) were used as research objects, and the flame-retardant test and differential scanning calorimetry (DSC) of the electrolytes configured by them were first carried out. The self-extinguishing time of the electrolyte with 5% TMP and TMPi is significantly reduced, achieving a flame-retardant effect. Secondly, the electrochemical performance of LiFePO₄|Li half-cells after adding different volume ratios of TMP and TMPi was studied. Compared with TMPi5, the peak potential difference between the oxidation peak and the reduction peak of the LiFePO₄|Li half-cell with TMP5 added is reduced, the battery polarization is reduced, the discharge specific capacity after 300 cycles is large, the capacity retention rate is as high as 99.6%, the discharge specific capacity is larger at different current rates, and the electrode resistance is smaller. TMPi5 causes the discharge-specific capacity to attenuate, which is more obvious at high current rates. LiFePO₄|Li half-cells with 5% volume ratio of flame retardant have the best electrochemical performance. Finally, the influence mechanism of the phosphorus valence state on battery safety and electrochemical performance was compared and studied. After 300 cycles, the surface of the LiFePO₄ electrode with 5% TMP added had a smoother and more uniform CEI film and higher phosphorus (P) and fluorine (F) content, which was beneficial to the improvement of electrochemical performance. The cross-section of the LiFePO₄ electrode showed slight collapse and cracks, which slowed down the attenuation of battery capacity. Full article

Solid-state electrolytes (SSEs) have emerged as a promising solution for next-generation lithium-ion batteries due to their excellent safety and high energy density. However, their practical application is still hindered by critical challenges such as their low ionic conductivity and high interfacial resistance at room temperature. The innovative application of 3D printing in the field of electrochemistry, particularly in solid-state electrolytes, endows energy storage devices with attractive characteristics. In this study, ceramic-in-gel polymer electrolytes (GPEs) based on PVDF-HFP/PAN@LLZTO were fabricated using a direct ink writing (DIW) 3D printing technique. Under the optimal printing conditions (printing speed of 40 mm/s and fill density of 70%), the printed electrolyte exhibited a uniform and dense sponge-like porous structure, achieving a high ionic conductivity of 5.77 × 10⁻⁴ S·cm⁻¹, which effectively facilitated lithium-ion transport. A structural analysis indicated that the LLZTO fillers were uniformly dispersed within the polymer matrix, significantly enhancing the electrochemical stability of the electrolyte. When applied in a LiFePO₄|GPEs|Li cell configuration, the electrolyte delivered excellent electrochemical performance, with high initial discharge capacities of 168 mAh·g⁻¹ at 0.1 C and 166 mAh·g⁻¹ at 0.2 C, and retained 92.8% of its capacity after 100 cycles at 0.2 C. This work demonstrates the great potential of 3D printing technology in fabricating high-performance GPEs. It provides a novel strategy for the structural design and industrial scalability of lithium-ion batteries. Full article

Lithium–ion (Li–ion) batteries are fundamental for advancing intelligent and sustainable transportation, particularly in electric vehicles, due to their long lifespan, high energy density, and strong power efficiency. Ensuring the safety and reliability of EV batteries remains a critical challenge, as undetected faults can lead to hazardous failures or gradual performance degradation. While numerous studies have addressed battery fault detection, most existing reviews adopt isolated perspectives, often overlooking interdisciplinary and intelligent approaches. This paper presents a comprehensive review of advanced battery fault detection using modern machine learning, deep learning, and hybrid methods. It also discusses the pressing challenges in the field, including limited fault data, real-time processing constraints, model adaptability across battery types, and the need for explainable AI. Furthermore, emerging AI approaches such as transformers, graph neural networks, physics-informed models, edge computing, and large language models present new opportunities for intelligent and scalable battery fault detection. Looking ahead, these frameworks, combined with AI-driven strategies, can enhance diagnostic precision, extend battery life, and strengthen safety while enabling proactive fault prevention and building trust in EV systems. Full article

Nickel-rich layered oxides such as LiNi_xMn_yCo_zO₂ (NMC), LiNi_xCo_yAl_zO₂ (NCA), and LiNi_xMn_yCo_zAl_{(1–x–y–z)}O₂ (NMCA), where x ≥ 0.6, have emerged as key cathode materials in lithium-ion batteries due to their high operating voltage and superior energy density. These materials, characterized by low cobalt content, offer a promising path toward sustainable and cost-effective energy storage solutions. However, their electrochemical performance remains below theoretical expectations, primarily due to challenges related to structural instability, limited thermal safety, and suboptimal cycle life. Intensive research efforts have been devoted to addressing these issues, resulting in substantial performance improvements and enabling the development of next-generation lithium-ion batteries with higher nickel content and reduced cobalt dependency. In this review, we present recent advances in material design and engineering strategies to overcome the problems limiting their electrochemical performance (cation mixing, phase stability, oxygen release, microcracks during cycling). These strategies include synthesis methods to optimize the morphology (size of the particles, core–shell and gradient structures), surface modifications of the Ni-rich particles, and doping. A detailed comparison between these strategies and the synergetic effects of their combination is presented. We also highlight the synergistic role of compatible lithium salts and electrolytes in achieving state-of-the-art nickel-rich lithium-ion batteries. Full article

Silicon oxide–graphite is a promising high-capacity anode material for next-generation lithium-ion batteries (LIBs). However, despite using a small fraction (≤5%) of Si, it suffers from a short cycle life owing to intrinsic swelling and particle pulverization during cycling, making practical application challenging. High-nickel (Ni ≥ 80%) oxide cathodes for high-energy-density LIBs and their operation beyond 4.2 V have been pursued, which requires the anodic stability of the electrolyte. Herein, we report a nonflammable multi-functional fluorinated ester–based liquid electrolyte that stabilizes the interfaces and suppresses the swelling of highly loaded 5 wt% SiO–graphite anode and LiNi_0.88Co_0.08Mn_0.04O₂ cathode simultaneously in a 3.5 mAh cm⁻² full cell, and improves cycle life and battery safety. Surface characterization results reveal that the interfacial stabilization of both the anode and cathode by a robust and uniform solid electrolyte interphase (SEI) layer, enriched with fluorinated ester-derived inorganics, enables 80% capacity retention of the full cell after 250 cycles, even under aggressive conditions of 4.35 V, 1 C and 45 °C. This new electrolyte formulation presents a new opportunity to advance SiO-based high-energy density LIBs for their long operation and safety. Full article

Accurate estimation of the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries (LiBs) is critical for ensuring battery reliability and safety in applications such as electric vehicles and energy storage systems. However, existing methods developed for estimating the SOH and RUL of LiBs often rely on full-cycle charging data, which are difficult to obtain in engineering practice. To bridge this gap, this paper proposes a novel data-driven method to estimate the SOH and RUL of LiBs only using partial charging curve features. Key health features are extracted from the constant voltage (CV) charging process and voltage relaxation, validated through Pearson correlation analysis and SHapley Additive exPlanations (SHAP) interpretability. A hybrid framework combining CatBoost for SOH estimation and particle swarm optimization-support vector regression (PSO-SVR) for RUL estimation is developed. Experimental validation on public datasets demonstrates superior performance of the methodology described above, with an SOH estimation root mean square error (RMSE) and mean absolute error (MAE) below 1.42% and 0.52% and RUL estimation relative error (RE) under 1.87%. The proposed methodology also exhibits robustness and computational efficiency, making it suitable for battery management systems (BMSs) of LiBs. Full article

Sodium-ion batteries are renowned for their abundant reserves, cost-efficiency, safety, and eco-friendliness and are prime candidates for large-scale energy storage applications. The development of cathode materials plays a crucial role in shaping both the commercialization path and the ultimate performance capabilities of SIBs. To overcome the intricate synthesis challenges associated with pure-phase sodium-ion cathode materials, this study introduces an innovative and streamlined electrochemical Li⁺/Na⁺ exchange process, successfully fabricating a high-capacity Ni-rich cathode material. This cathode material boasts a remarkable reversible capacity of 180 mAh g⁻¹ at 0.1 C and retains a high-rate capacity of 115 mAh g⁻¹ even at 5 C. Additionally, it exhibits exceptional cycling stability, retaining about 85% of its capacity at 1 C after 50 cycles and still maintaining a capacity greater than 60% after 100 cycles. The Na-NMA85 full cell preserves a discharge capacity of 110 mAh g⁻¹ after 100 cycles, with a capacity retention rate of 80%. This research underscores innovative strategies for designing ion-intercalation-based cathode materials that enhance battery performance, providing fresh perspectives for advancing high-performance battery technologies. Full article

Polymer electrolytes (PEs) provide enhanced safety for high–energy–density lithium metal batteries (LMBs), yet their practical application is hampered by intrinsically low ionic conductivity and insufficient electrochemical stability, primarily stemming from suboptimal Li⁺ solvation environments and transport pathways coupled with slow polymer dynamics. Herein, we demonstrate a molecular design strategy to overcome these limitations by regulating the Li⁺ solvation structure through the synergistic interplay of conventional Lewis acid–base coordination and engineered hydrogen bond (H–bond) networks, achieved by incorporating specific H–bond donor functionalities (N,N′–methylenebis(acrylamide), MBA) into the polymer architecture. Computational modeling confirms that the introduced H–bonds effectively modulate the Li⁺ coordination environment, promote salt dissociation, and create favorable pathways for faster ion transport decoupled from polymer chain motion. Experimentally, the resultant polymer electrolyte (MFE, based on MBA) enables exceptionally stable Li metal cycling in symmetric cells (>4000 h at 0.1 mA cm⁻²), endows LFP|MFE|Li cells with long–term stability, achieving 81.0% capacity retention after 1400 cycles, and confers NCM622|MFE|Li cells with cycling endurance, maintaining 81.0% capacity retention after 800 cycles under a high voltage of 4.3 V at room temperature. This study underscores a potent molecular engineering strategy, leveraging synergistic hydrogen bonding and Lewis acid–base interactions to rationally tailor the Li⁺ solvation structure and unlock efficient ion transport in polymer electrolytes, paving a promising path towards high–performance solid–state lithium metal batteries. Full article

Internal short-circuit (ISC) is a critical failure mode in lithium-ion (Li-ion) batteries that can trigger thermal runaway and pose serious risks to both environmental and human safety. Early-stage ISC faults are particularly challenging to detect due to their subtle characteristics and the masking effects of voltage fluctuations. To address these challenges, this study proposes a rapid and accurate ISC diagnosis method based on the connectivity-based outlier factor (COF) algorithm. The key innovation lies in the preprocessing of terminal voltage to amplify fault signatures and suppress natural fluctuations, thereby enhancing sensitivity to early anomalies. The COF algorithm is then applied to identify ISC faults in real time. Validation under urban-dynamometer driving schedule (UDDS) conditions demonstrates the method’s effectiveness: it successfully detects early ISC faults with an equivalent resistance as high as 100 Ω within 207 s of onset. This unsupervised, data-driven approach improves fault detection speed and accuracy, contributing to the advancement of safe, reliable, and sustainable LIB deployment in clean energy and transportation systems. Full article

Lithium-ion batteries are restricted in development due to safety issues such as poor chemical stability and flammability of organic liquid electrolytes. Replacing liquid electrolytes with solid ones is crucial for improving battery safety and performance. This study aims to enhance the performance of polyethylene oxide (PEO)-based polymer via blending with poly(vinylidene fluoride-hexafluoropropylene) (P(VDF-HFP)). The experimental results showed that the addition of P(VDF-HFP) disrupted the crystalline regions of PEO by increasing the amorphous domains, thus improving lithium-ion migration capability. The electrolyte membrane with 30 wt% P(VDF-HFP) and 70 wt% PEO exhibited the highest ionic conductivity, widest electrochemical window, and enhanced thermal stability, as well as a high lithium-ion transference number (0.45). The cells assembled with this membrane electrolyte demonstrated an excellent rate of performance and cycling stability, retaining specific capacities of 122.39 mAh g⁻¹ after 200 cycles at 0.5C, and 112.77 mAh g⁻¹ after 200 cycles at 1C and 25 °C. The full cell assembled with LiFePO₄ as the positive electrode exhibits excellent rate performance and good cycling stability, indicating that prepared solid electrolytes have great potential applications in lithium batteries. Full article

This study addresses the challenge of gaining a deeper understanding of charging and discharging mechanisms in lithium-ion batteries to enhance their reliability and safety, necessitating the development of novel modeling techniques. A comprehensive analytical model is introduced, capable of accurately reconstructing the voltage rise during constant-current charging. The novelty of this approach lies in its use of spectral analysis (similar to that employed in linear viscoelasticity) to describe the physical processes occurring during battery charging. The model’s effectiveness was validated using experimental data from a rechargeable lithium-ion battery with a nominal capacity of 25 Ah and a nominal voltage of 3.2 V. The results demonstrate that spectral characterization is a reliable tool for modeling battery response to constant-current charging, with the potential for application in battery lifespan prediction. Full article