Search Results (177)

The reliability of unmanned aerial vehicle (UAV) energy storage battery systems is critical for ensuring their safe operation and efficient mission execution, and has the potential to significantly advance applications in logistics, monitoring, and emergency response. This paper reviews theoretical and technical advancements in UAV battery reliability, covering definitions and metrics, modeling approaches, state estimation, fault diagnosis, and battery management system (BMS) technologies. Based on international standards, reliability encompasses performance stability, environmental adaptability, and safety redundancy, encompassing metrics such as the capacity retention rate, mean time between failures (MTBF), and thermal runaway warning time. Modeling methods for reliability include mathematical, data-driven, and hybrid models, which are evaluated for accuracy and efficiency under dynamic conditions. State estimation focuses on five key battery parameters and compares neural network, regression, and optimization algorithms in complex flight scenarios. Fault diagnosis involves feature extraction, time-series modeling, and probabilistic inference, with multimodal fusion strategies being proposed for faults like overcharge and thermal runaway. BMS technologies include state monitoring, protection, and optimization, and balancing strategies and the potential of intelligent algorithms are being explored. Challenges in this field include non-unified standards, limited model generalization, and complexity in diagnosing concurrent faults. Future research should prioritize multi-physics-coupled modeling, AI-driven predictive techniques, and cybersecurity to enhance the reliability and intelligence of battery systems in order to support the sustainable development of unmanned systems. Full article

This paper reports on the failure of cells with lithium iron phosphate (LFP) chemistry tested under a range of conditions to understand their effect on the volume and composition of gas generated. Cells of the following formats, 26,650, pouch, and prismatic, and capacities ranging from 3 to 230 Ah, were subjected to external heat, overcharge, and nail penetration tests. Gas volume was calculated, and the following gases analysed: H₂, CO₂, CO, CH₄, C₂H₄, C₂H₆, C₃H₆, and C₃H₈. Cells that failed via external heating under inert conditions (N₂ or Ar atmosphere) at 100% state of charge (SoC) typically generated 0.7 L/Ah of gas; overcharged cells, 0.11–0.68 L/Ah; and nail penetration between 0.3 and 0.5 L/Ah. In general, for all test configurations, regardless of atmosphere, the total gas volume contained a 40% concentration of H₂, 15% of CO₂, and the remaining gas consisted of varying concentrations of CO and flammable hydrocarbons. This demonstrates that despite differences in gas volume, the failure gas composition of LFP cells remains similar. Full article

Thermal safety issues of sodium-ion batteries have become a major challenge, particularly under abusive conditions where the risk of thermal runaway is heightened. This study investigates the effects of overcharging on the thermal safety of sodium-ion batteries. Discharge capacity and time, internal resistance, and electrochemical impedance spectroscopy (EIS) at different states of charge (SOCs) are analyzed. Additionally, heat generation behaviors are evaluated at both normal/elevated temperatures. It is found that the overcharged batteries (OBs) demonstrate a significant increase in internal resistance from 46.72 Ω to 65.99 Ω. The discharge time of OBs at 1 C current (the ratio of the rate at which a battery discharges per unit time to its rated capacity) is reduced by 4.26% compared to normal batteries (NBs). The peak temperature and temperature difference increase by 5.6% and 36.1%, respectively. When discharged at 1 C at 40 °C, OBs have a 5.47% reduction in discharge time compared to NBs. Furthermore, the OBs exhibit an increase in the peak discharge temperature and temperature difference of 0.99 °C and 0.4 °C, respectively. Microscopic analysis of the electrode materials makes clear the irreversible damage to the internal structures of the sodium-ion battery caused by overcharging. This study potentially provides fundamental data support and theoretical insights for sodium-ion battery module thermal safety. Full article

Optimizing the charging rate is crucial for enhancing lithium iron phosphate (LFP) battery performance. The substantial heat generation during high C-rate charging poses a significant risk of thermal runaway, necessitating advanced thermal management strategies. This study systematically investigates the coupling mechanism between charging rates and ambient temperatures in overcharge-induced thermal runaway, filling the knowledge gaps associated with multi-indicator thermal management approaches. Through experiments on prismatic LFP cells across five operational conditions (1C/35 °C, 1.5C/5 °C, 1.5C/15 °C, 1.5C/25 °C, and 1.5C/35 °C), synchronized infrared thermography and electrochemical monitoring quantitatively characterize the thermal–electric coupling dynamics throughout overcharge-to-runaway transitions. The experimental findings reveal three key observations: (1) Charge rate and temperature have synergistic amplification effects on triggering thermal runaway. (2) Contrary to intuition, while low-current/high-temperature charging enhances safety versus high-current/high-temperature conditions, low-temperature/high-current charging triggers thermal runaway faster than high-temperature/high-current scenarios. (3) Staged multi-indicator lithium battery thermal runaway warning signals would be more accurate (first peaks > 0.5 °C/s temperature rise rate + >10 V/s voltage drop rate). These findings collectively demonstrate the imperative for next-generation battery management systems integrating real-time ambient temperature compensation with adaptive C-rate control, fundamentally advancing beyond conventional single-variable thermal regulation strategies. Intelligent adaptation is critical for mitigating thermal runaway risks in LFP battery operations. Full article

Separators are generally considered inert components in lithium-ion batteries. In the past, some electroactive polymers have been successfully applied in separator modifications for overcharge protection or as acid scavengers. This study highlights the first use of two “electroactive” carbazole polymers (copolymer 9-phenyl-9H-carbazole-phenyl [PCP] and poly(9-vinylcarbazole) [PVC]), which were each applied separately as coatings on the cathode-facing side of commercial Celgard 2325 separators, respectively, to enhance the cycling performance of 0.3Li₂MnO₃·0.7LiMn_0.5Ni_0.5O₂//graphite (LMR-NM//Gr) full cells through interphase engineering. The team observed an irreversible polymer oxidation process of the carbazole-functionalized polymers—occurring only during the first charge—for the modified separator cells, and the results were confirmed by dQ/dV analysis, cyclic voltammetry measurements, and nuclear magnetic resonance characterizations. During this oxidation, carbazole polymers participate in the process of interphase formation, contributing to the improved cycling performance of LMR-NM//Gr batteries. Particularly, oxidation takes place at voltages of ~4.0 and ~3.5 V when PCP and PVC are used as separator coatings, which is highly irreversible. Further postmortem examinations suggest that the improvements using these modified separators arise from the formation of higher-quality and more inorganic SEI, as well as the beneficial CEI enriched in LixPOyFz. These interphases effectively inhibit the crosstalk effect by reducing TM dissolution. Full article

Fault diagnosis accuracy in lithium-ion battery-based energy storage systems is significantly constrained by the limited availability of fault-specific datasets. This study addresses this critical issue by proposing a diffusion-based data augmentation methodology tailored explicitly for battery fault diagnosis scenarios. The proposed conditional diffusion model leverages transfer learning and attention-enhanced fine-tuning strategies to generate high-quality synthetic fault data, ensuring targeted representation of rare fault conditions. By integrating condition-aware sampling strategies, the approach effectively mitigates mode collapse issues frequently encountered in adversarial generative methods, thus substantially enriching the diversity and quality of fault representations. Comprehensive evaluation using statistical similarity measures and downstream classification tasks demonstrates notable improvements. After the integration of attention mechanisms, the Pearson correlation coefficient between the synthetic and real samples increases from 0.29 to 0.91. In downstream diagnostic tasks, models trained on augmented datasets exhibit substantial gains in regards to the recall and F1-score, which increase from near-zero levels to values exceeding 0.91 for subtle overcharge and overdischarge faults. These results confirm the effectiveness and practical utility of the proposed augmentation approach in enhancing diagnostic performance under data-scarce conditions. Full article

Overheating and overcharging are the core triggering conditions for the thermal runaway of lithium-ion batteries. Studying the behavioral differences of thermal runaway of lithium-ion batteries under these two conditions is crucial for the safety design and protection of lithium-ion batteries. In this study, we investigated the temperature, pressure, gas generation, and heat generation characteristics of lithium batteries under these two conditions. Under overheating conditions, the release of lattice oxygen in the cathode and the decomposition of the electrolyte trigger a self-catalytic reaction, generating CO₂ (54.7%) and H₂ (29.7%), with a total heat release of 17.6 kJ and a heat accumulation rate of 24.3 W, forming a local high-temperature core area. Under overcharging conditions, the voltage drop, capacity attenuation of 21.1% (2230→1762 mAh), and internal resistance surge (6→21 mΩ) reflect severe damage to the electrode. Accompanied by the oxygenation of the EC electrolyte (CO₃²⁻ + C₂H₄↑), the gas production rate is faster. The middle pressure was 0.601 MPa, and the proportion of CO₂ was 67.4%. However, the triggering of thermal runaway relies on the synergistic effect of internal electrochemical reactions and ohmic heat accumulation, resulting in a relatively low rate of energy accumulation. Full article

Lithium-ion batteries risk failing when subjected to different abuse tests, resulting in gas and flames. In this study, 5 Ah nickel manganese cobalt oxide (NMC) pouch cells were subjected to external heating; overcharge at rates of 2.5, 5 and 10 A; and nail penetration. Tests were conducted in air and N₂ atmospheres. Additional external heat tests were performed on cells at 5, 25, 50, and 75% SoC and on two, three, and four cell blocks. Gas volumes were calculated, and the gas composition was given for H₂, CO, CO₂, C₂H₄, C₂H₆, CH₄, C₃H₆, and C₃H₈. For tests under an air atmosphere at 100% SoC, the volume of gas varied between abuse methods: 3.9 L (external heat), 6.4 L (overcharge), and 8.9 L (nail penetration). The gas composition was found to predominantly contain H₂, CO₂, and CO for all abuse methods; however, higher concentrations of H₂ and CO were present in tests performed under N₂. External heat tests at different SoCs showed that the gas volume decreased with SoC. Overall, the type of abuse method can have a large effect on the gas volume and composition produced by cell failure. Full article

Today, power electronic-based converters are at the core of many modern systems, such as smart grids and electric vehicles. In this context, the Dual Two-Level Inverter (DTLI) supplying an open-end winding machine offers an innovative and promising solution for marine propulsion, aeronautics, and electric vehicles. This configuration provides several advantages, including a reduced DC bus voltage, enhanced fault tolerance, and improved overall system performance. However, ensuring optimal energy efficiency and high-power quality remains a major challenge given the increasing demands for performance and sustainability. This paper presents a state-of-the-art review of the main DTLI configurations and their impact on system performance. Three architectures are analyzed, highlighting their benefits and limitations. This study aims to demonstrate the influence of the DC bus voltage ratio and pulse width modulation strategies on power quality and energy efficiency. The objective is to enhance the understanding of the DTLI’s potential and to guide its integration into other electrical systems. Full article

Sodium-ion batteries (SIBs) share similar working principles with lithium-ion batteries while demonstrating cost advantages. However, the current understanding of their safety characteristics remains insufficient, and the thermal runaway mechanisms of different SIB systems have not been fully elucidated. This study investigated the following two mainstream sodium-ion battery systems: polyanion-type compound (PAC) and layered transition metal oxide (TMO) cathodes. Differential scanning calorimetry (DSC) was employed to evaluate the thermal stability of cathodes and anodes, examining the effects of state of charge (SOC), cycling, and overcharging on electrode thermal stability. The thermal stability of electrolytes with different compositions was also characterized and analyzed. Additionally, adiabatic thermal runaway tests were conducted using an accelerating rate calorimeter (ARC) to explore temperature–voltage evolution patterns and temperature rise rates. The study systematically investigated heat-generating reactions during various thermal runaway stages and conducted a comparative analysis of the thermal runaway characteristics between these two battery systems. Full article

Efficient and safe charging of lithium-ion batteries is essential for maximizing their lifespan and performance. This paper presents the design and implementation of a microcontroller-based Li-ion battery charger that employs real-time monitoring, adaptive charging strategies, and built-in safety mechanisms. The system integrates a CC/CV charging approach with automatic current regulation, overcharge protection, and reverse polarity detection. A current sensor module ensures continuous monitoring, while an LCD interface provides real-time feedback on charging parameters. Experimental validation was conducted using multiple Li-ion cells in various conditions, like new, aged, and deeply discharged, to evaluate charging behavior and safety under different scenarios. The system successfully regulated current and voltage, managed preconditioning for low-voltage cells, and transitioned smoothly between charging phases. A key contribution of this work is the development of a low-cost, microcontroller-based platform that enables flexible implementation and testing of diverse charging strategies. Its open-source architecture and modular design make it highly suitable for research, educational use, and experimental development in battery management systems. Future enhancements may include the integration of adaptive algorithms based on internal resistance and temperature, enabling smarter and more efficient charging. Full article

An efficient Battery Management System (BMS) specifically for Electric Vehicles is crucial for improving battery run time performance. A primary function of an effective BMS is accurately determining the State of Charge (SOC) and State of Health (SOH) of lithium-ion batteries in Electric Vehicles (EVs). However, many existing studies have concentrated on examining sensor malfunctions in batteries to avert problems such as overcharging and overheating and are lacking in terms of effective handling of non-linear behaviors. To overcome these limitations, the proposed work introduces a hybrid approach for estimating the state of lithium-ion batteries. It employs an Extended Kalman Filter (EKF) for SOC estimation and modified Multi-Layer Perceptron (MLP) for SOH estimation in batteries. It can handle the non-linear characteristics often exhibited by sensor readings and fault behaviors. The EKF algorithm involves initialization, prediction, and correction phases, allowing for accurate state estimation based on measurements. For SOH estimation, the NASA battery dataset, which includes various battery conditions across different temperatures, is analyzed using a modified MLP regression process. This modified MLP employs a gradient shift bias adjustment technique to minimize error rates by refining the gradients and biases introduced during the training process. It also effectively adjusts the model’s weights for better SOH estimation. The results demonstrate improved accuracy in battery performance, as indicated by lower RMSE, MSE, MAE and R² values. Furthermore, the study highlights the effectiveness of this hybrid method for significant battery management at different temperatures, which emphasizes the potential of this model, with enhanced state estimation for EV applications. Full article

The increasing adoption of electric vehicles (EVs) needs efficient and cost-effective charging solutions. This study presents a smart on-board charging system using low-cost materials while ensuring safe and optimized battery management. The proposed system is controlled by an Arduino MEGA 2560 microcontroller, integrating Pulse-Width Modulation (PWM) for precise voltage regulation and real-time monitoring of charging parameters, including voltage, current, and state of charge (SoC). The charging process is structured into three states (connection, standby, and charging) and follows a multi-stage strategy to prevent overcharging and prolong battery lifespan. A relay system and safety mechanisms detect disconnections and voltage mismatches, automatically halting charging when unsafe conditions arise. Experimental validation with a 12 V lead-acid battery verifies that the system follows standard charging profiles, ensuring optimal energy management and charging efficiency. The proposed charger demonstrates significant cost savings (~94.82 €) compared to commercial alternatives (1200 €–2000 €), making it a viable low-power solution for EV charging research and a valuable learning tool in academic environments. Future improvements include a printed circuit board (PCB) redesign to enhance system reliability and expand compatibility with higher voltage batteries. This work proves that affordable smart charging solutions can be effectively implemented using embedded control and modulation techniques. Full article

Flooded lead–acid batteries start producing oxygen and hydrogen during the final stages of charge and subsequent overcharge. The collection of the hydrogen produced allows for an increase in overall energy efficiency and transforms the system into a hybrid device typically referred to as a “Battolyzer” (battery electrolyzer). The present work explores the feasibility of the above approach through a detailed study of the long-term ageing process of flooded tubular lead–acid cells subjected to various rates of discharge and overcharge, emulating four different scenarios of Battolyzer use, starting from 70% depth of discharge cycling to nearly continuous water electrolysis. The combined results from the electrochemical and corrosion studies showed that the Battolyzer cells’ degradation was driven by the corrosion of the positive current collectors. The progress of the corrosion process was strongly correlated with the amount of hydrogen produced. The increase in the depth of discharge resulted in minor decreases in the corrosion current, indicating that the battery functionality of the Battolyzer was more advantageous than the continuous water electrolysis. Full article

The transition to environmentally sustainable maritime operations has gained urgency with the International Maritime Organization’s (IMO) 2023 GHG reduction strategy, aiming for net-zero emissions by 2050. While alternative fuels like LNG and methanol serve as transitional solutions, lithium-ion battery energy storage systems (ESSs) offer a viable low-emission alternative. However, safety concerns such as thermal runaway, overcharging, and internal faults pose significant risks to marine battery systems. This study presents an AI-based fault prediction algorithm to enhance the safety and reliability of lithium-ion battery systems used in electric propulsion ships. The research employs a Long Short-Term Memory (LSTM)-based predictive model, integrating electrochemical impedance spectroscopy (EIS) data and voltage deviation analyses to identify failure patterns. Bayesian optimization is applied to fine-tune hyperparameters, ensuring high predictive accuracy. Additionally, a recursive multi-step prediction model is developed to anticipate long-term battery performance trends. The proposed algorithm effectively detects voltage deviations and pre-emptively predicts battery failures, mitigating fire hazards and ensuring operational stability. The findings support the development of safer and more reliable energy storage solutions, contributing to the broader adoption of electric propulsion in maritime applications. Full article