Liquid fuel fires pose growing risks to environmental sustainability and industrial safety. Conventional foam extinguishing agents are limited by poor thermal stability, increasing the risk of re-ignition, and often rely on fluorinated surfactants, which persist in the environment. This study proposes a novel thermo-responsive gel foam that integrates heat-induced gelation with high-temperature ceramization. The foam is made from environmentally benign materials, such as konjac glucomannan (KGM) and low-melting glass powders (LGP). By optimizing KGM concentration (0.5 wt%) and NaHCO3/Na2CO3 ratio (0.2–0.4), precise control over the gelation transition was achieved. At room temperature, the foam rapidly spreads over the liquid fuel surface. Upon flame exposure, it undergoes in-situ gelation, forming a stable foam layer that significantly reduces the likelihood of re-ignition. Experimental results show a 366.7–464.7% increase in drainage time on an n-heptane surface. At elevated temperatures, melted LGP infiltrates the carbonaceous residue from the carbonized gel network, forming an in-situ thermal barrier and protective char/silica layer. In small-scale flame suppression tests, the gel foam prolonged the burning delay by 297.0% compared to standard film-forming fluoroprotein (FFFP) foam. In large-scale suppression and anti-reignition tests, although the extinguishing time was similar to that of commercial FFFP foam, the gel foam completely prevented re-ignition, highlighting its potential as a high-performance fire-extinguishing agent for liquid fuel fires.

Community fire resilience is a complex system involving multiple stages and factors, and its enhancement is critical to reducing urban fire risks. This study proposes an intelligent method to assess community fire resilience and identify key factors. Firstly, a community fire resilience concept is proposed as the theoretical foundation. Subsequently, a BERT-BiLSTM-CRF model is trained to automatically extract influencing factors using fire accident reports. A modified technique for order preference by similarity to ideal solution (TOPSIS) method is then employed to screen factors and construct an indicator system. Finally, a Bayesian network (BN) model is constructed to assess resilience and identify key influencing factors. Results indicate that the BERT-BiLSTM-CRF model achieves strong performance with 90.98% F1-score. The indicator system encompasses 23 influencing factors across four resilience stages. BN inference results indicate that Chinese communities exhibit a high-resilience probability of 62%, while recovery and improvement capacities remain relatively weak. Electrical equipment failures, inadequate resident emergency response, underdeveloped emergency medical assistance mechanisms and accident case learning mechanisms are the dominant contributors across the four resilience stages, representing critical vulnerabilities in community fire governance. This study establishes a data-driven assessment framework for community fire resilience, which can be extended to other accident scenarios.

This study employs an integrated experimental and numerical approach to characterize 45 degrees inclined jet fires resulting from high-pressure natural gas pipeline leaks. The numerical model, validated against experimental data, was utilized to systematically investigate the effects of mass flow rate, leak orifice diameter, and ambient wind speed on flame dimensions and temperature distribution. The findings reveal that for upward-inclined natural gas jet flames, the heat release rate is the dominant governing parameter for the flame characteristic length, leading to the development of a predictive model correlating the normalized flame length with this key variable. The influence of following and opposing wind directions on the flame trajectory was analyzed, providing enhanced insight into how crosswinds modulate the characteristic flame length in such scenarios. Furthermore, through detailed analysis of the internal flame structure, a sub-model was established to predict both the temperature distribution along the flame trajectory and its corresponding spatial coordinates. Collectively, these results deliver fundamental insights for formulating preventive strategies against inclined leakage accidents and establish a scientific basis for the quantitative risk assessment of natural gas pipeline operations.

Preventing fire spread between tanks is essential for fuel storage facilities. Therefore, ignition behavior of tank fires with different ullage heights and tank spacings was studied, focusing on the ignition process, ignition mechanism and radiative heat transfer. A series of experiments with n-heptane as fuel was performed with two 0.4 m diameter tanks (Tank I: burning source; Tank II: ignition target) over seven tank spacings (d = 0.2D-0.8D) and four ullage heights (H = 0D-0.3D). The ignition time increased from 2 s to 597 s as the tank spacing increased from 0.2D to 0.7D (H = 0) and from 2 s to 455 s as the ullage height increased from 0D to 0.2D (d = 0.2D). Numerical simulations using fire dynamic simulation confirmed that, near the adjacent tank upper rim, greater tank spacing suppressed flame-driven air entrainment and higher ullage height limited the fuel vapor concentrations. These results are supported by calculations showing that the view factor decreased with increasing tank spacing and ullage height. Moreover, ullage height enhances a “blocking effect” and contracts the effective flame radiation region and the effective radiation reception region, thus lowering radiative heat flux. Based on the dimensionless analysis, the quantitative relationships between the ullage height, tank spacing, tank diameter and ignition time were analyzed, and a prediction model for the ignition time of the tanks was proposed. These findings offer insights into safer design of tank spacings and provide guidance for storage tank protection strategies.

With increasing energy density of lithium-ion batteries, the risks of fires and hazardous substance release induced by thermal runaway (TR) have become more prominent. The state of charge (SOC) is considered a key parameter governing TR hazards, yet its role in thermal runaway propagation (TRP) mechanisms and TR emission characteristics at the large-capacity scale remains insufficiently understood. Here, we experimentally investigated on cells and modules under different SOCs, and explained the TR hazards from the perspectives of heat transfer and particle emission toxicity. Results indicate that voltage drop, venting, and TR occur almost simultaneously under high SOC; whereas low-SOC batteries exhibit a delayed TR. When the SOC is below 50%, TRP within modules is effectively suppressed. Increased SOC raises the self-heating ratio from approximately 35% to over 70%, enhancing heat transfer and triggering chain TRP. Debris analysis reveals that high SOC intensifies structural damage, with anode debris primarily composed of graphite, while medium-to-low SOC yields more transition-metal products. Particle investigation demonstrates that with increasing SOC, both the mean size and distribution range of released particle emissions expand significantly. At 100% SOC, particles are enriched in transition metals and lithium species shift from LiF⁻ to more bioactive Li⁺. The deposition fraction of inhalable TR emissions in respiratory tract is 29.3% higher than that of ambient particles, with hazardous ion signal intensity approximately 8 times higher. This study provides guiding insights for SOC management and safe handling of retired batteries.

The increasing complexity of multi-hazard disasters poses significant challenges to sustainable disaster risk governance. However, prevention and control measures are often scattered across heterogeneous and unstructured sources, limiting their systematic reuse and application. To address this issue, this study proposes a structured framework and data-driven analysis approach for organizing prevention and control measures in multi-hazard scenarios. By integrating multi-source information, a four-dimensional framework consisting of human, technical, engineering, and managerial measures was developed, together with a two-dimensional representation model incorporating disaster scenarios. Large language models (LLMs) were employed to automatically extract prevention and control measures from disaster-related documents and construct a multi-hazard prevention dataset. A case study of typhoon-hazardous chemical leakage scenarios yielded 1089 measurement records. Results show that managerial measures had the highest coverage (87.1%), while technical measures mainly focused on critical risk nodes such as leakage monitoring and automatic interlock control. Prevention and preparedness measures accounted for 67.4% of all records, reflecting a proactive risk-governance orientation. Strong associations were observed among the four categories of measures (Jaccard coefficient: 0.624-0.879). The proposed framework supports the structured representation, knowledge organization, and data-driven analysis of prevention and control measures, providing a foundation for sustainable disaster risk governance and resilient emergency management.

Ethylene oxide (EO), a crucial derivative of ethylene, involves high temperature and pressure as well as flammable, explosive, and toxic materials. This complexity predisposes its production to severe industrial accidents. To systematically identify the risk factors that may lead to EO accidents, this study conducted an integrated analysis by leveraging a knowledge graph (KG), a Bayesian network (BN), and computational fluid dynamics (CFD) simulation. First, the key risk factor nodes leading to leakage were identified by using KG. Subsequently, a BN was constructed, which enabled the clarification of typical EO leakage scenarios. Then, for the identified typical leakage scenarios, a CFD geometric model was developed. This model was employed to simulate the dispersion patterns following an EO leak under different conditions. Finally, by integrating the results from the BN and CFD analyses, the working conditions with the highest risk level were identified. The results indicated that leakage caused by chemical corrosion or physical erosion of reactors or pipelines presented the highest quantified risk value. This scenario exerts a significant impact on the safe production of EO, necessitating prioritized and focused inspection. Through the integrated KGu2013BNu2013CFD methodology, this study has effectively elucidated the disaster-causing mechanisms and evolution patterns of EO leakage accidents. It thereby provides a theoretical foundation and practical guidance for the precise identification of risks, the formulation of targeted prevention and control measures, and the enhancement of inherent safety levels in EO production facilities.

Existing fluorine-containing foams, widely used for suppression of tank fires, often fail to meet the requirements of environmental protection and safety. This study was aimed to develop an environmentally friendly threephase foam fire extinguishing agent using biomass konjac glucomannan, silica aerogel, nanoparticles (Al (OH)3), and calcium hydroxide (Ca(OH)2). It utilized the high temperature environment to form a stable threedimensional structure, exhibiting excellent heat absorption, cooling, and thermal insulation properties. Orthogonal tests were conducted to analyze the foamability and stability, thereby optimizing the foam formulations. The thermal stability, fire extinguishing, and fire resistance performance were examined, whereas the microscale mechanism was elucidated via molecular dynamics (MD) simulations. The experimental results indicated that Al(OH)3 promoted the formation of a carbonaceous layer, enhancing the thermal stability and insulation of the foam. When the mass ratio of silica aerogel to Al(OH)3 was 1:2, the foam demonstrated excellent fire extinguishing and fire resistant performance. MD simulation results showed that Ca2 + shifted the peak of the radial distribution function g(r) to the right by 0.08 & Aring;, thus reducing aggregation of AEG molecules and increasing the stability of the foam. Additionally, Ca2+ increased the number of hydrated water molecules of AEG, leading to better water retention performance. The active free radicals generated by the decomposition of Al(OH)3 exerted an indirect chemical effect, enhancing water dilution efficiency. These results could help to further develop high-performance and environmentally friendly fire extinguishing agents.

Spill fires in confined spaces are not effectively constrained by physical boundaries and are therefore a major contributor to fire scale escalation in typical industrial scenarios. To investigate the effectiveness and underlying mechanisms of water mist in extinguishing spill fires, an experimental platform was established to characterize water mist spray fields and to conduct water mist fire suppression experiments. Taking water mist flow rate and fuel discharge rate as key variables, the evolution of spray field characteristics, flame morphology, temperature, and oxygen concentration during the suppression process was examined, and the stage-dependent suppression mechanisms of water mist on spill fires were elucidated. The results indicate that, at the early stage of suppression, water droplets are unable to effectively penetrate the fire plume, and the flame morphology remains nearly unchanged. At the later stage, combustion is progressively suppressed, accompanied by a reduction in flame height, inward recession of the burning surface toward the center, and eventual extinction of the fire. The extinction time is governed by the coupled effects of water mist spray characteristics and spill fire burning behavior. Based on the analysis of droplet transport dynamics and fuel temperature evolution, the water mist suppression process exhibits a distinct stage-dependent mechanism: an initial synergy between gas-phase cooling and inerting, followed by a gradual transition to a coupled gas-liquid cooling and inerting mechanism.

Flexible photovoltaic (FPV) modules are widely used in building-integrated photovoltaics (BIPV) due to their lightweight and flexibility. However, these modules are commonly encapsulated with polymeric films, which makes them susceptible to fire incidents triggered by DC arc faults. Tilt angle and cavity width are two of the most critical parameters in BIPV system design and exert a substantial influence on fire spread behavior. In the present study, a combined experimental and numerical approach is adopted to quantify how tilt angle and cavity width jointly govern the flame characteristics of PET-laminated FPV modules. Experimental results indicate that the flame length, flame spread rate, and radiative heat flux on the wall all increase with increasing tilt angle and cavity width. Due to the combustible nature of the back side of the FPV module, the presence of a cavity between the module and the wall leads to pronounced changes in flame behavior: the combustion regime transitions from single-to double-sided flaming, and the back-side flame length eventually becomes larger than that on the front side due to the chimney effect. In addition, numerical simulations further demonstrate that the coupled effects of tilt angle and cavity width significantly influence the flow field, temperature field, and oxygen supply within the cavity. Based on dimensionless analysis and experimental data, a correlation coupling the tilt angle and cavity width is established, with an error controlled within +/- 20%. The present study provides a theoretical foundation and experimental evidence for fire risk assessment and safety mitigation of BIPV systems.

Tank fires are typically characterized by intense combustion, high temperatures, and strong thermal radiation. The coupled effect of crosswind and tank ullage height leads to complex evolution of internal burning parameters. Existing monitoring methods are mainly limited to external measurements, making it difficult to capture internal temperature dynamics. In this study, large-scale tank fire experiments were conducted under the coupled influence of crosswind and ullage height to examine the internal temperature distributions. A dataset was generated from the experimental data, which was then used to predict the flow and temperature distributions using two deep learning methods, namely residual-enhanced multi-layer perceptron (RMLP) and point transformer (PT). Experimental results show that the internal temperature exhibits non-uniform distribution, with high-temperature zones aggregating on the leeward side as wind speed increases. Two distinct flame behaviors were observed at different ullage heights: bottom-contacting and non-bottom-contacting. Both models are capable of predicting the internal temperature field. The RMLP model achieves slightly better pointwise prediction accuracy, while the PT model shows superior capability in reconstructing the overall spatial temperature distribution and demonstrates stronger robustness under bottom-contacting and extreme conditions. Sensitivity analysis and SHAP results indicate that the internal temperature is strongly correlated with radial position r, ullage height-to-diameter ratio h/D, and the coupled effect of wind speed and ullage height (h/D) & times; u, while the correlation with u and D alone is low, suggesting that wind influences internal temperature primarily through coupling with h/D. This study validates the feasibility of using deep learning for tank internal temperature predictions and provides insights for tank fire research and fire suppression strategy optimization.

In incidents involving liquid fuel spills, the application of porous media such as sand is widely regarded as an efficient and safe mitigation measure. However, the effectiveness of this approach in preventing ignition has not been thoroughly investigated. This study investigated the evaporation and piloted ignition behavior of quartz sand soaked in heptane (representative flammable liquid) under varying external heat fluxes (10-50 kW/m2) and fuel levels, using a cone calorimeter with electric spark igniter. A numerical model, complemented by theoretical ignition models, was developed to simulate the underlying processes. Results showed that the time required for flash ignition and sustained ignition decreased nonlinearly with increasing external heat flux and fuel level, with sustained ignition times ranging from 28 s to 3400 s. The critical mass flux (the minimum mass flux of fuel gases for which the flame is sustained for at least 5 s) required for sustained ignition remained consistent under different conditions and was accurately predicted by a gas-phase ignition model. By calibrating key parameters and boundary conditions, the numerical model effectively predicted both evaporation rates and temperature evolution. The integration of numerical and ignition models enabled the prediction of sustained ignition times with errors within 20%. The findings of this study provide data and a valuable tool for assessing the ignition behavior of fuel-soaked porous media.

Sand is subject to various national standards for storage in chemical parks and energy storage companies, and it is widely recognized as an effective measure for preventing and controlling accidental spill fires. This study investigates the propagation process of spill fires on sand barriers through experimental tests and evaporation analysis. The findings from these experiments reveal two distinct phenomena. In scenarios involving thin, fine-grained sand layers, both the flame and the fuelseep through the sand layer simultaneously (Type I). In cases with thicker, coarser sand layers, the flame is blocked by the sand while un-ignited fuel gradually flows out (Type II). The difference in blocking effects is primarily governed by the fuel preheating duration. When the preheating duration is shorter than the seepage duration (time for fuel to penetrate the sand layer), Type I occurs. Conversely, Type II emerges when the preheating duration exceeds the seepage duration. Temperaturedatain the sand layer confirms that the preheating process is mainly driven by flame radiation. To further investigate the characteristics of the preheating duration with various sand layer configurations, a numerical model was developed to predict the evaporation rate during the preheating process. It was found that the distance between the fuel level and the sand layer surface, as well as the thickness of the sand layer, are positively correlated with the preheating duration. In contrast, the grain size is negatively correlated with the preheating duration. Among these factors, the distance between the fuel level and the top of the sand layer plays the predominant role in controlling the preheating process. By comparing the experimental seepage duration with the simulated preheating duration, the model was validated for predicting transitions between fire-blocking modes. These findings provide critical insights for optimizing sand barrier designs, mitigating thermal hazards, and refining industrial safety standards.

Spill fires caused by leaking vehicles are a typical fire scenario in road tunnels. The behaviour of a spill fire is primarily influenced by longitudinal ventilation, followed by the complex evolution of the flame geometry and unpredictable fire risks. In this paper, spill fire experiments were conducted in a 1:8 reduced-scale road tunnel model at different wind speeds (0-2 m/s) and fuel discharge rates (0.80-2.75 ml/s). The dynamic behaviour of spill fires was studied by combining physical analysis and a machine learning method. Results showed that the flame tilt angle decreases with the discharge time, while the flame base length showed the opposite trend. By dimensionless analysis, correlations for the steady stage flame tilt angle and flame base length were proposed and validated against the experimental data. Subsequently, dynamic prediction models for the spill fire flame shape (flame tilt angle and flame base length) were established by incorporating the dimensionless correlations into a PHAST (fuel layer spreading) model. Using the machine learning method, a real-time prediction algorithm for the discharge rate was established and the prediction error was found to be less than 10 %. Finally, the dynamic evolution of the flame shape was forecasted by integrating the predicted discharge rate into the physical model. Compared to traditional approaches, the combination of physical modelling and machine learning effectively improves the interpretability of fire prediction models, showcasing the potential for enhancing intelligent firefighting systems through physical theoretical models and limited experimental data.

Confined spill fires are governed by the coupled evolution of liquid fuel spreading, buoyant plume development, ceiling smoke layer formation, and thermal feedback redistribution. This study experimentally investigated n-heptane spill fires in a 3 × 3 × 4 m3 confined space at fuel discharge rates of 5.8–12.6 g/s, focusing on the scaling of the maximum centerline temperature rise and the interaction between the buoyant plume and the ceiling smoke layer. The maximum flame temperature rise followed a piecewise relationship with hHQ̇*. When hHQ̇*<1.2, the temperature rise decreases with height following a negative power law, corresponding to a flame and plume-controlled region. When hHQ̇*≥1.2, the temperature rise approaches a plateau, indicating the transition to a ceiling smoke layer-controlled region. This transition arises from confinement-modified plume entrainment, vertical flame stretching, hot gas accumulation, and thermal stratification beneath the ceiling. Heat transfer analysis further shows that the dominant feedback mode shifts from convection-influenced feedback in the early stage to radiation-dominated feedback once the flame, smoke layer, and heated walls all contribute to radiative exchange. At peak burning, radiative feedback accounts for approximately 72% of the total feedback, while the fuel absorbs only about 18%. These findings provide a fluid physics interpretation of the regime transition and thermal feedback redistribution in confined spill fires.

Flexible photovoltaic panels are widely used in the field of building-integrated photovoltaics due to their lightweight nature, but their materials also present fire safety challenges. This study investigates the combined influence of crosswind and tilt angles on the flame morphology and flame spread behavior of polyethylene terephthalate (PET)-laminated photovoltaic panels. Key parameters, including pyrolysis length, flame length, and flame spread rate on both the front and back sides of the panel, were systematically analyzed under varying wind velocities (0–2 m/s) and tilt angles (0°–90°). Experimental results reveal that, under small tilt angles, increasing wind velocity promotes flame elongation and accelerates flame spread. In contrast, at large tilt angles, wind inhibits flame propagation, especially on the front side. Complementary numerical simulations were used to interpret the airflow-field variations around the inclined panels, showing that wind speed and tilt angle modify local recirculation, vortex formation, and flame deflection, especially near the back side of the panel. Based on dimensional analysis and experimental observations, a semi-empirical correlation was developed to describe the variation of flame spread rate within the tested range of wind velocity and tilt angle. These findings provide essential insights for fire risk assessment and the development of fire safety strategies for flexible photovoltaic panels in BIPV applications.

Traditional fluorinated foams (AFFF/FFFP), widely used for liquid fire suppression, suffer from poor thermal stability, re-ignition propensity, and often contain environmentally harmful fluorocarbon surfactants. This study developed an interpenetrating double-network gel foam (IDNGF) using non-fluorinated surfactants (AEG and AOS), gelling agent (CMC-Na and Na₂SiO₃), and crosslinking agent (AlCit). Orthogonal experiments optimized the foam expansion ratio and water retention. The optimal formulation-0.6 wt% AOS/AEG (1:9), 2.2 wt% Na₂SiO₃, 0.14 wt% CMCNa, and 1.0 wt% AlCit-produced homogeneous foams with enhanced thermal stability due to the formation of a CMC-Al3+/silicate double-network structure. Thermogravimetric analysis revealed a significantly higher complete evaporation temperature than conventional FFFP. Reactive forcefield molecular dynamics (ReaxFF-MD) simulations identified orthosilicic acid and aluminum-containing oxides as key pyrolysis products. Fire extinguishing and re-ignition existence experiments demonstrated that IDNGF reduced extinguishing time by 3.6% relative to FFFP while significantly extending the burnback time to 603 s-a 76.3% improvement. These results highlight the potentil of using IDNGF as a high-performance, re-ignition-resistant, and sustainable fire suppression agent.

Confined space operation involves working in (semi-)enclosed spaces. While confined space is an important workspace in chemical industry and urban development, there is also an increased risk of injury or even death due to hazardous factors, such as limited entry and exit area or a lack of adequate ventilation. In this paper, an intelligent method was proposed combining knowledge graph (KG), association rules mining (ARM) and Bayesian network (BN) to assess the risk and determine the key factors for confined space operation. First, a causative indicator system was established using 601 previous accidents, for which KG was also constructed to allow automatic extraction of accident causes. Based on the association rules determined by ARM, a risk assessment method was developed using BN. The key factors were analyzed and countermeasures were proposed. The results show that the association between failure to conduct ventilation detection on the site and failure to wear safety protective equipment demonstrates significant correlation strength, while association rule between inadequate safety education and training and failure to wear safety protective equipment also high. By the analysis in BN, it can be seen that the probability of confined space operation accidents is significantly higher (59%) with the baseline probability of nodes in BN. The other important factors include failure to wear safety protective equipment, blindly rescue, insufficient provision of protective equipment and operation without a license. This study can evaluate the risk and determine key factors in a data-driven manner to reduce the subjectivity, which provides a reference for the targeted safety management of confined space operation.

As a core component of power systems, fire safety for underground transformers is of paramount importance. The high temperatures generated by fires pose a threat to equipment operation, personnel safety, and structural stability. In this study, confined-space fire tests were conducted on 110 kV transformers. Based on a Long Short-Term Memory (LSTM) neural network combined with Fire Dynamics Simulator (FDS) software, a fire temperature field prediction model was developed, and the temperature distribution within the confined space was analyzed. The results indicate that the LSTM model can accurately predict the characteristics of the temperature field. In scenarios involving only a top fire and a top-bottom compound fire, the error relative to experimental and FDS simulation results was <= 20%. The temperature rise rate increases with the burning area. The temperature field exhibits a three-zone pattern: Zone III (ceiling) > Zone II (sidewall boundary) > Zone I (lower zone), with sequentially decreasing temperature rise rates. The model also enables fire risk assessment by introducing a reduction factor (K-c,K-theta) and a Safe Escape Assessment Coefficient (SEAC) to evaluate ceiling structural strength degradation and evacuation limits. For a composite fire with a 9.5 m(2) burning area, ceiling structural strength loss was <= 25%, and the critical safe evacuation time was 73 s. This study offers an efficient method for predicting temperature and assessing dynamic fire risk in underground transformer fires.

Large-capacity lithium nickel cobalt manganese oxide (NCM) batteries are widely used in electric vehicles (EVs) due to their high energy density. With its widespread applications and continuously developing charging technology, overcharge-induced fire incidents have been frequently reported globally. In this work, the thermal runaway (TR) characteristics and gas generation behaviors of 95 Ah NCM523 battery cells and modules induced by overcharge were investigated under different charging rates. Experimental measurements included video recording, voltage, surface temperature, heat release rate, mass loss, and evolved gases. Violent flame ejection was observed during TR with increasing overcharging rates, accompanied by a reduction in the angle between the ejected flames from the safety valve and ruptured sites. With an increase in the overcharging rate, a decrease in the proportion of heat generated by side reactions was identified. Much higher heat generation ratios from side reactions were recorded for battery modules compared to single cells. For single cells, higher concentrations of HQ, CO, and COQ were observed at high overcharging rates. In all tests, HQwas released consistently earlier than CO and CO2. Based on experimental results and observations, a safety assessment scoring system was established for evaluating TR risk and hazard. It was found that both increased with the overcharging rate, with battery modules consistently having higher TR risk than single cells. The present findings provide not only the mechanisms governing TR and gas generation of large capacity NCM lithium-ion batteries but critical insights into charging optimization, which are valuable for the EV industry to develop and assess the safety of charging systems for these types of batteries.