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Even though electric vehicles (EV) were invented over a century ago, their popularity has grown significantly within the last 10 years due to the development of Li-ion battery technology. This evolution created an increase in the fire risk and hazards associated with this type of high-energy battery. This review focuses on lessons learned from elec...
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Electromobility has grown rapidly, and especially in China, Europe, and the United States. Within Europe, Germany is the largest market. Our goal in this paper is to provide a data-driven overview of the key data, including the number of vehicles sold, place of registration, battery capacity, and charging power, in Germany. The results were generat...
Citations
... It can be concluded that VLES model has better predictions of the swirling flow field for both the mean and the root mean results than the LES models [28]. Therefore, maximum heat release rate is 7000 kW per EV [29]. The fire in its growth stage and during its decay period can be described by a t2 curve and total simulation time is taken as 1000s. ...
This study brings a unique perspective of electric vehicles (EVs) transportation via maritime with using performance-based design of fire safety management. In the recent years, due to the increase of production and transportation of EVs, this study is highlighted the fire safety management. The integration of electric vehicles (EVs) into maritime transport, particularly via ships and ferries, brings about unique challenges, notably concerning fire safety management. Given the increasing prevalence of EVs and their potential fire hazards, it's crucial to address these risks comprehensively. The fire safety management of electric vehicles in ferry transport is dealt with, as this form of maritime transport is becoming increasingly important due to the increased production of this type of vehicle, which develops a complex chemical reaction mechanism and dangerous properties such as initial exother-mal temperature, self-heating speed, pressure increase speed, etc. Therefore, relevant rules and regulations should be considered to ensure a safe journey. This study brings novelty to the fire safety analysis of EVs transportation onboard ships with using performance-based design and fire dynamics simulation tools to predict temperature level of the case incident. The Fire Dynamic Simulator was used for the simulations for the prediction of temperature distributions during an electric vehicle fire inside a ferry. The presented case study demonstrates how fire simulations could predict conditions for performance-based design of ferries that transport electric vehicles. Depending on simulations, temperature at initial times approximately 40s of fire incidents caused by EVs is around 1200°C and this cause severe results in terms of life and asset safety. In conclusion, this paper presents a brief insight to find an effective method for simulating and mitigating EV fires on ships to ensure crew safety and minimize fire damage. Cite this article as: Ayci T, Barlas B, Olcer AI. Evaluation on fire incident of electric vehicle spaces onboard ferries with using fire dynamics simulations.
... AI-based fire detection models offer a solution to these challenges by leveraging CCTV systems and advanced algorithms to detect flames and smoke in real time [26]. In contrast to conventional methods, AI-based systems are able to analyze visual data and provide immediate alerts, rendering them indispensable for environments such as EVs, where fires can propagate rapidly due to thermal runaway. ...
The rapid expansion of the electric vehicle (EV) market has raised significant safety concerns, particularly regarding fires caused by the thermal runaway of lithium-ion batteries. To address this issue, this study investigates the real-time fire detection performance of segmentation-based object detection models for EVs. The evaluated models include YOLOv5-Seg, YOLOv8-Seg, YOLOv11-Seg, Mask R-CNN, and Cascade Mask R-CNN. Performance is analyzed using metrics such as precision, recall, F1-score, mAP50, and FPS. The experimental results reveal that the YOLO-based models outperform Mask R-CNN and Cascade Mask R-CNN across all evaluation metrics. In particular, YOLOv11-Seg demonstrates superior accuracy in delineating fire and smoke boundaries, achieving minimal false positives and high reliability under diverse fire scenarios. Additionally, its real-time processing speed of 136.99 FPS validates its capability for rapid detection and response, even in complex fire environments. Conversely, Mask R-CNN and Cascade Mask R-CNN exhibit suboptimal performance in terms of precision, recall, and FPS, limiting their applicability to real-time fire detection systems. This study establishes YOLO-based segmentation models, particularly the advanced YOLOv11-Seg, as highly effective EV fire detection and response systems.
... The battery can re-fire multiple days or weeks after the accident due to slow heat transfer to adjacent modules or cells [17]. • LIB fires produce a range of additional toxic compounds as a result of electrolyte decomposition (e.g., lithium hexafluorophosphate), and these compounds may be released in lethal doses [18], especially in enclosed structures [19,20]. • Battery fires require enormous amounts of water to cool-down and extinguish as the heat capacity of the cells is substantial and their accessibility is heavily impaired [21,22]. ...
The widespread adoption of electric vehicles (EVs) has elevated the importance of rigorous safety standards, particularly for fire resistance in post-crash scenarios. Existing testing protocols, such as Regulation No. 100, utilize petrol pool fires to simulate real-world fire hazards but lack comprehensive analysis regarding their repeatability and reliability. This study addresses this critical gap by evaluating the variability and consistency of fire-resistance tests performed on multiple battery energy storage systems (BESSs) under standardized conditions. A custom-built measurement system incorporating thermocouples, anemometers, and hygrometers provided high-resolution data on flame dynamics, ambient conditions, and pool fire efficiency. Statistical evaluations following ISO 5725 series guidelines revealed substantial inconsistencies, including unstable exposure temperatures and sensitivity to local turbulence. These findings call into question the robustness of current testing methods, and we propose an alternative approach employing LPG burners for improved precision and repeatability. By identifying significant flaws in existing standards and offering scientifically grounded enhancements, this work contributes a novel perspective to the field of EV safety, advancing global fire-resistance testing protocols.
... It can be concluded that VLES model has better predictions of the swirling flow field for both the mean and the root mean results than the LES models [28]. Therefore, maximum heat release rate is 7000 kW per EV [29]. The fire in its growth stage and during its decay period can be described by a t2 curve and total simulation time is taken as 1000s. ...
... When temperatures exceed 300 °C, steel structures begin to lose their ability to support static loads effectively [51]. Beyond structural deformation, intensified fires can compromise human safety and hinder evacuation efforts [52]. ...
Lithium-ion batteries are fundamental to modern electric vehicles, offering high energy density, long cycle life, and low self-discharge rates. However, thermal runaway—a critical safety issue involving uncontrolled temperature increases—can lead to fire or explosion. Ensuring flame retardancy is crucial in accidents where battery packs are exposed to external fires. Additionally, battery packs are susceptible to mechanical stresses and potential damage from ground impacts like debris or uneven road surfaces. Effective thermal management significantly impacts capacity and longevity. This review emphasizes the importance of researching flame retardancy, ground impact resistance, and thermal management, especially in composite battery enclosures. Composites serve as a lightweight alternative to metals and help overcome one of the main constraints of EVs, which is weight. Ground impact refers to the physical force battery packs endure during collisions, hitting potholes, debris, or accidents. Therefore, understanding the effects of ground impact on battery enclosures is crucial for design considerations. Effective thermal management is also essential, as it directly affects the performance and safety of Lithium-ion battery packs in EVs.
... Firefighters may be overly exposed to strong toxins despite safety procedures and respiratory protection equipment (RPE). 4 During firefighting, all factors influencing firefighters' exposure must be considered. A fundamental principle in firefighting tactics is for the firefighting team to act from the upwind side relative to the burning vehicle. ...
Background
Firefighters while extinguishing fires, are exposed to air pollution resulting from combustion processes, especially dangerous when originating from the combustion of plastics. Air pollution during a fire results from the release of gases, vapors, smokes, and dusts of varying composition and toxicity levels into the atmosphere.
Objective
To analyse and estimate the health hazards to firefighters resulting from exposure to harmful chemical compounds generated during vehicle fires in Poland between 2017 and 2022.
Methods
Data from the Decision Support System of the State Fire Service (DSS-SFS), provided by the Operational Planning Office, were used. Events from the period 1 st January 2017—31 st December 2022 were analysed. Quantitative data were described using the mean (Mean) and standard deviation (SD). Correlations and differences at the significance level of p < 0.05 were considered statistically significant. The analysis is anonymous for both the victims and the officers involved in the described fire and rescue operations.
Results
According to the DSS-SFS system records, firefighters carried out n = 55,422 (min 8441, max 9847, mean 9237, SD 494.2) fire interventions (F), in the category of object: motor vehicles, during 2017–2022. The maximum firefighting operation times were observed for trucks, which may result from the purpose of these vehicles (carrying loads), the necessity of firefighting operations in the cargo space and the transported materials.
Conclusion
Attention should be paid to the various possibilities of exposure to fire substances, not only directly at the source of combustion but also in the fire engine, on protective clothing, firefighting equipment, which can cause secondary exposures long after the firefighting operation has ended, and the correct decontamination process..
... The research contributes valuable insights for designing ventilation systems in tunnels, focusing on optimizing oxygen concentration to manage fire risks effectively while ensuring safe environments during emergencies [84]. Other significant perspectives on using Computational Fluid Dynamics (CFD) for fire safety evaluations, especially regarding smoke evacuation in parking areas, including those with electric fires, was studied by Brzezińska and Bryant [85]. Technical solutions were analyzed for mitigating fire hazards from electric vehicles and methods were presented of performance-based analysis for assessing fire safety in car parks. ...
... The focus was on understanding and mitigating these risks through performance-based analysis and using Computational Fluid Dynamics (CFD) simulations to model fire scenarios. The use of the Fire Dynamic Simulator (FDS) software for predicting smoke dispersion and temperature distribution during an EV fire is highlighted, illustrating its utility in planning for safe evacuation and firefighting operations [85]. ...
The proper design and installation of systems that enable the efficient control and removal of smoke and hot gases in underground parking facilities are necessary for protecting the public and property in the event of a fire. This paper discusses how studies using Computational Fluid Dynamics (CFD) related to smoke venting have contributed to improving fire safety in underground parking facilities. As vehicle fire incidents continue to rise globally, particularly in regions with a high density of underground parking, the need for comprehensive measures to mitigate these incidents has become increasingly urgent. This paper examines the applicability of CFD as a tool to address the challenges of smoke control in underground car parks, including those caused by fires involving electric vehicles. CFD application under various fire scenarios and ventilation strategies allows for identifying more effective smoke removal solutions, improving the protection of occupants and property. However, despite the potential of CFD simulations to enhance fire safety and smoke exhaust efficiency in underground parking, it is important to recognize the limitations of these simulations, particularly in dealing with the complex challenges posed by electric vehicle fires.
... In addition to conventional crash-worthiness, battery thermal runaway and fire have emerged as a new concern in BEV safety, as shown in figure 4. BEV fires have been reported in various scenarios, including charging [52], driving [53], parking [54], crash and post-crash fire [55]. The lithium-ion battery may catch fire if it is mechanically, thermally, or electrically abused. ...
... For individual cell-level experiments, the spherical punch, external short circuit, needle piercing, and thermal chamber tests are commonly employed in cell safety studies. These tests can potentially induce thermal runaway [53,[59][60][61][62][63][64][65][66][67]. ...
Driven by the rising number of fire incidents involving Battery Electric Vehicles (BEVs), this work reviews the current state of knowledge in electric vehicle battery safety, focusing on simulation and experiment methodologies. The critical importance of battery safety is emphasized by the potential for thermal runaway and fires due to various factors. These factors include design and manufacturing flaws, excessive current loads, mechanical damage, improper charging practices (overcharging/overdischarging), extreme temperature exposure, and even as-yet unidentified causes. This study provides a comprehensive review of methodologies employed in lithium-ion battery safety modeling and experiment for BEVs. The review includes various aspects. It includes the high voltage battery system in BEVs, battery safety considerations in BEVs, geometry modeling of battery cells, material modeling of battery cells, simulation framework for batteries, cell-level experiment, testing of materials for cell components, and the application of machine learning. Physics-based simulations that accurately predict battery thermal runaway are crucial for guaranteeing the safety and optimizing the performance of BEVs. While Finite Element Analysis (FEA) is a well-established technique for evaluating the crashworthiness of conventional vehicles, its application to BEVs presents several significant challenges. However, limited literature exists on cell-level experiments involving spray and dropping scenarios. Furthermore, additional data on melting points, thermal properties, and porosity is necessary for component-level testing. This work also highlights the need for robust friction and fatigue models, which remain a critical knowledge gap in this field. Finally, the integration of machine learning approaches for constitutive laws and the development of more complex frameworks are essential advancements for future research. This review is expected to provide a guide in simulation and experiment in EV battery safety engineering.
... Most of the behavioral measures are determined by accident reports and assessments of subjective experiences. They are mainly related to the extent of driving avoidance in different situations [3][4][5][6][7][8]. ...
While driving, stress and frustration can affect safe driving and pose the risk of causing traffic accidents. Therefore, it is important to control the driver’s anxiety level in order to improve the driving experience. In this paper, we propose and implement an intelligent system based on fuzzy logic (FL) for deciding the driver’s anxiety level (DAL). In order to investigate the effects of the considered parameters and compare the evaluation results, we implement two models: DAL Model 1 (DALM1) and DAL Model 2 (DALM2). The input parameters of DALM1 include driving experience (DE), in-car environment conditions (IECs), and driver age (DA), while for DALM2, we add a new parameter called the accident anxiety state (AAS). For both models, the output parameter is DAL. We carried out many simulations and compared the results of DALM1 and DALM2. The evaluation results show that the DAL is very good for drivers’ ages between 30 to 50 years old. However, when the driver’s age is below 30 or above 50, DAL tends to decline. With an increase in DE and IECs, the DAL value is decreased. But when the AAS is increased, the DAL is increased. DALM2 is more complex because the rule base is larger than DALM1, but it makes a better decision of DAL value.
... Cylindrical cells have benefits relative to other cell formats, namely pouch and prismatic cells, in terms of their mechanical stability due to even distribution of internal pressure build-up and slower failure propagation in multi-cell battery modules 12 thanks to air-filled gaps between individual cells which reduce thermal contact. 13 These advantages, paired with the suitability of cylindrical cells for automated manufacturing, mean that they are widely deployed in devices such as laptops, power tools and electric vehicles. ...
Acoustic emission (AE) is a low-cost, non-invasive, and accessible diagnostic technique that uses a piezoelectric sensor to detect ultrasonic elastic waves generated by the rapid release of energy from a localised source. Despite the ubiquity of the cylindrical cell format, AE techniques applied to this cell type are rare in literature due to the complexity of acoustic wave propagation in cylindrical architectures alongside the challenges associated with sensor coupling. Here, we correlate the electrochemical performance of cells with their AE response, examining the differences during pristine and aged cell cycling. AE data was obtained and used to train various supervised binary classifiers in a supervised setting, differentiating pristine from aged cells. The highest accuracy was achieved by a deep neural network model. Unsupervised machine learning (ML) models, combining dimensionality reduction techniques with clustering, were also developed to group AE signals according to their form. The groups were then related to battery degradation phenomena such as electrode cracking, gas formation, and electrode expansion. There is the potential to integrate this novel ML-driven approach for widespread cylindrical cell testing in both academic and commercial settings to help improve the safety and performance of lithium-ion batteries.
