Thermal Runaway Of Lithium-ion Batteries Research Articles

Toxicity, Emissions and Structural Damage from Lithium-Ion Battery Thermal Runaway

Toxicity, emissions and structural damage results on lithium-ion battery (LIB) thermal runaway triggered by the electrothermal method were performed in this work. The electrothermal triggering method was determined to study the thermal runaway behaviors of three types of commercial LIBs. The structural damage of the cathode material of the batteries after thermal runaway was observed by scanning electron microscope (SEM), transmission electron microscope (TEM) and X-ray diffraction (XRD). It was found that as the state of charge (SOC) of the battery increases, the lower the temperature at which thermal runaway occurs, and the more badly the structural damage of the electrode material after thermal runaway. Qualitative analysis of products from LIBs thermal runaway emissions was conducted by GC-MS, and the toxicity and formation mechanism of the emissions were analyzed in detail. Dozens of toxic substances were detected from the emissions after thermal runaway of batteries using LixNi1/3Co1/3Mn1/3O2 and LiCoO2 as the cathode material, the types of toxic substances increase gradually with the increase in the SOC, while as for batteries using LiFePO4 as the cathode material, most types of toxic substances were detected from 30% SOC.

Gas Characterization-based Detection of Thermal Runaway Fusion in Lithium-ion Batteries

A novel approach for real-time detection of lithium-ion battery thermal runaway has been proposed to enable the monitoring of thermal runaway states during storage, transportation, and use, and to prevent safety hazards such as fire and explosion. This approach uses the fusion of high-precision, high-sensitivity photoelectric and electrochemical detection techniques based on the dual-wavelength principle. To analyze the thermal runaway mechanism of lithium-ion batteries, four important gas parameters — CO, EX, H2, and CO2 — were obtained to indicate the thermal runaway state, and the characterization of these parameters under different thermal runaway states of lithium-ion batteries was studied. A real-time detection system is designed and validated through experiments involving overcharging, over-discharging, and puncturing of lithium-ion batteries. This method is suitable for the real-time detection of thermal runaway in lithium-ion battery products and can also provide a basis for evaluating the life and reliability of lithium-ion batteries.

Monitoring and diagnostic approaches for thermal runaway in lithium-ion batteries

Monitoring and diagnostic approaches for thermal runaway in lithium-ion batteries

Hydrogen gas diffusion behavior under fault conditions and detector installation optimization of electric vehicles

The thermal runaway of lithium-ion batteries presents a significant threat to electric vehicles by elevating the risk of fires or explosions. Safety warnings based on special gases such as H2 and CO are crucial to avoid thermal runaway. However, few studies or applications regarding gas warnings in electric vehicles have been reported. In this study, H2 detection experiments were performed in a real electric vehicle battery pack, and the H2 diffusion behavior was studied. The results showed that H2 can effectively warn about battery faults. In damaged batteries, H2 may be released from the micro-cracks in the battery or the vent. While H2 was detected and the power supply was cut off, the cell surface temperature tended to decrease and thermal runaway did not occur. The installation of the detector affects the detection time. Thus, H2 diffusion simulations starting from different locations were performed, and the installation location was optimized. The results indicated that setting two detectors was optimal, and the optimized detection time (from release to detection of H2) was 60 s shorter than that before optimization. The experimental and simulation results provide an effective msethod for the early warning of thermal runaway and the installation of gas detectors in electric vehicles.

Multi-Objective Optimization Design and Experimental Investigation for a Prismatic Lithium-Ion Battery Integrated with a Multi-Stage Tesla Valve-Based Cold Plate

High current rate charging causes inevitable severe heat generation, thermal inconsistency, and even thermal runaway of lithium-ion batteries. Concerning this, a liquid cooling plate comprising a multi-stage Tesla valve (MSTV) configuration with high recognition in microfluidic applications was proposed to provide a safer temperature range for a prismatic-type lithium-ion battery. Meanwhile, a surrogate model with the objectives of the cooling performance and energy cost was constructed, and the impact of some influential design parameters was explored through the robustness analysis of the model. On this basis, the multi-objective optimization design of the neighborhood cultivation genetic algorithm (NCGA) was carried out. The obtained results demonstrated that if the MSTV channel was four channels, the valve-to-valve distance was 14.79 mm, and the thickness was 0.94 mm, the cold plate had the most effective cooling performance and a lower pumping power consumption. Finally, the optimization results were verified by a numerical simulation and an experiment, and the performance evaluation was compared with the traditional serpentine channel. The results reported that the optimized design reduced the maximum temperature and standard surface standard deviation of the cold plate by 26% and 35%, respectively. The additional pump power consumption was 17.3%. This research guides the design of battery thermal management systems to improve efficiency and energy costs, especially under the high current rate charging conditions of lithium-ion batteries.

A Polyacrylonitrile Shutdown Film for Prevention of Thermal Runaway in Lithium-Ion Cells

The electrodeposition of a polymer (polyacrylonitrile, PAN) is used to reduce the risk of thermal runaway in lithium-ion batteries, which is the most important cause of battery accidents and fires. PAN was electrodeposited on a graphite battery electrode, using cyclic voltammetry or chronoamperometry, in a solution with acrylonitrile as the solvent. The electrodeposited PAN film was characterised by Raman spectroscopy, microscopy, energy dispersive X-ray analysis, and thermogravimetric analysis, and it was found that the film thickness could be controlled by the amount of charge passed in the electrochemical experiments. The PAN-coated graphite battery electrode was then tested in lithium half-cells, obtaining capacities close to the uncoated graphite sample (ca. 360 mA h g−1) for thin (<10 µm) polymer coatings at 25 °C. Interestingly, for thicker polymer coatings (>20 µm) it was found that the capacity decreased drastically as the temperature increased beyond 80 °C. Such suppression in capacity has applications for thermal runaway protection since the electrochemical reactions of degradation of the electrolyte in contact with the electrode are the root cause of the thermal runaway process. Further work should look into alternative polymer and liquid electrolyte formulations to achieve the desired suppression of electrochemical capacity at high temperatures while retaining high capacities at the operational temperature range.

Detailed characterization of particle emissions due to thermal failure of batteries with different cathodes

Sufficient levels of thermal, electrical, mechanical, or electrochemical abuse can cause thermal runaway in lithium-ion batteries, leading to the release of electrolyte vapor, combustible gas mixtures, and high-temperature particles. Particle emissions due to thermal failure of batteries may cause serious pollution of the atmosphere, water sources, and soil as well as enter the human biological chain through crops, posing a potential threat to human health. Furthermore, high-temperature particle emissions may ignite the flammable gas mixtures produced during the thermal runaway process, resulting in combustion and explosions. This research focused on determining the particle size distribution, elemental composition, morphology, and crystal structure of particles released from different cathode batteries after thermal runaway. Accelerated adiabatic calorimetry tests were performed on a fully charged Li(Ni0.3Co0.3Mn0.3)O2 battery (NCM111), Li(Ni0.5Co0.2Mn0.3)O2 battery (NCM523), and Li(Ni0.6Co0.2Mn0.2)O2 battery (NCM622). Results of all three batteries indicate that particles with a diameter less than or equal to 0.85 mm exhibit an increase in volume distribution followed by a decrease in volume distribution as the diameter increases. F, S, P, Cr, Ge, and Ge were detected in particle emissions with mass percentages ranging from 6.5% to 43.3%, 0.76–1.20%, 2.41–4.83%,1.8–3.7%, and 0–0.14%, respectively. When present in high concentrations, these may have negative impacts on human health and the environment. In addition, the diffraction patterns of the particle emissions were approximately the same for NC111, NCM523, and NCM622, with emissions primarily composed of Ni/Co elemental, graphite, Li2CO3, NiO, LiF, MnO, and LiNiO2. This study can provide important insights into the potential environmental and health risks associated with particle emissions from thermal runaway in lithium-ion batteries

Explosion characteristic of CH4–H2-Air mixtures vented by encapsulated large-scale Li-ion battery under thermal runaway

Explosion-proof diesel-powered vehicles are the main auxiliary transport vehicles in underground coal mines, although many of these have been replaced by battery electric vehicles (BEVs) nowadays. The explosion-proof structure and performance of the power supply of the BEVs should meet the IEC 60079 standard, however, and the explosion-proof cavity where the batteries are placed should be able to withstand a static pressure of not less than 1.5 MPa. In this paper, a nail penetration experiment is carried out on an encapsulated lithium-ion battery (LIB) pack under an atmosphere consisting of air, 9.5% methane, and 12.5% mixed combustible gas, and the temperature and the pressure data of the thermal runaway LIBs in the explosion-proof tank are comprehensively analysed. Moreover, the ignition explosion test of the gas released by thermal runaway of LIBs in different environments is conducted. Finally, the explosion process is simulated by the chemical reaction kinetics model in Ansys Fluent. The experimental and simulation results can guide the structural design of the explosion-proof tank of the power supply in extreme working environments, and provide an important reference for explosion-proof protection design and overall integrated safety design of LIB power supplies for explosive special environments.

Effects of environmental temperature on the thermal runaway of lithium-ion batteries during charging process

Lithium-ion batteries with relatively narrow operating temperature range have provoked concerns regarding the safety of LIBs. In this work, a series of experiments were conducted to explore the thermal runaway (TR) behaviors of charging batteries in a high/low temperature test chamber. The effects of charging rates (0.5 C, 1 C, 2 C, and 3 C), and ambient temperature (2 °C, 32 °C and 56 °C) are comprehensively investigated.The results indicate that the cell exhibited greater thermal hazard at the high charging rate and ambient temperature conditions. As the charging rate increased from 0.5 C to 3 C, more lithium intercalated in the anode prompt the TR triggered in advance, the TR onset temperature decreased from 297.5 °C to 264.7 °C. In addition, the charging time decreased with the elevated ambient temperature, resulting in a relatively higher TR onset temperature and lower maximum temperature, and the average TR critical time declined by 115–143 s. Finally, the TR required less heat accumulation with increasing of charging rate and ambient temperature, and the heat generation of side reaction played a substantial role that accounted for approximately 54%∼63%. These results provide an insight into the charging cell thermal runaway behaviors in complex operation environments and deliver valuable guidance for improving the safety of cell operation.

The influence of tab overheating on thermal runaway propagation of pouch-type lithium-ion battery module with different tab connections

Thermal runaway (TR) of lithium-ion batteries is the main safety obstacle to the further development of electric vehicles (EVs) and electric energy storage systems (EESSs). Understanding the mechanism of TR propagation is one of the primary ways to improve the thermal safety of the lithium-ion battery. In this paper, an electro-thermal model coupled with the lumped thermal runaway model is applied to investigate the TR behaviors induced by tab overheating and the TR propagation in a battery module with different tab connection methods. The electro-thermal model is used to calculate the heat generation at tabs and the cell body during the normal charge/discharge process, and the lumped thermal runaway model is applied to calculate the TR behavior and TR propagation in the battery module. The results exhibit that the TR behavior and TR propagation are influenced by different aspects (C-rates, insulator, and tab connection modes) in the battery module. The largest quantity of heat generation is at 3C-rate, about 0.98 kJ, because of the heat accumulation in the battery. Besides, the quantity of heat generation that induce TR in lithium-ion batteries in this paper is lower than that from the reference, which means that tab overheating is more dangerous than other heating strategies for triggering the thermal runaway of batteries. The trigger time of TR decreases with the increasing C-rate and the tab connection modes mitigate the TR propagation in the battery module at a low C-rate because of the increasing heat dissipation rate, while the TR propagation is accelerated in the battery module at a high C-rate. At low C-rate conditions, the parallel tab connection exhibits a more significant effect on mitigating the TR behavior, and the effect of mitigation decreases with the increasing C-rate. At high C-rate conditions, the series tab connection accelerates the TR propagation to the adjacent battery, while the parallel tab connection speeds up the TR propagation of all the connected batteries. This work is important for understanding the TR propagation mechanism and improving the thermal safety of pouch-type lithium-ion batteries in EVs.

Investigation of cooling control effect of fine water mist on lithium-ion battery thermal runaway

ABSTRACT The lithium-ion battery (LIB) thermal runaway (TR) has been a major safety problem, and effective countermeasure to suppress the TR becomes indispensable. Owing to the outstanding cooling capacity, the water mist presents efficient suppression effect on TR, but the comprehensive understanding on the cooling mechanisms remains unclear. In this work, the fine water mist (FWM) that is driven by the air flow has been applied to suppress the TR, and the cooling control processes under various thermal hazard conditions have been comprehensively analyzed. Results show that TR can be efficiently suppressed with the FWM applied before the critical temperature, which is identified as 252.5 ± 2.5°C. The average surface cooling rate increases from 1.04 to 1.22°Cs−1 with the increasing flow rate, and the total heat dissipation maintains approximately at 2.10 kJ, once the flow rate exceeds 0.2 mLs−1. It is identified that the most efficient cooling process can be obtained under the duration of 80 s. The influence of the flow rate and duration of the FWM on the cooling effect has been discussed to comprehensive reveal the control mechanism, and the excellent cooling control effect of the FWM flow has been verified.

Roll-to-roll solvent-free manufactured electrodes for fast-charging batteries

Roll-to-roll solvent-free manufactured electrodes for fast-charging batteries

High-Sensitivity Lithium-Ion Battery Thermal Runaway Gas Detection Based on Fiber-Enhanced Raman Spectroscopy

Thermal runaway gas analysis is a powerful technique for lithium-ion battery (LIB) safety management and risk assessment. Here, we propose a novel hollow-core antiresonant fiber (HC-ARF)-based Raman gas sensing device for simultaneously sensitive detection of thermal runaway gas products (CH4, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{C}_{{2}}\text{H}_{{6}}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{C}_{{2}}\text{H}_{{4}}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{C}_{{2}}\text{H}_{{2}}$ </tex-math></inline-formula> , CO, CO2, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{H}_{{2}}{)}$ </tex-math></inline-formula> . A micromirror is attached to the rear of fiber, which provides 3.1 times Raman signal enhancement compared to bare fiber, and two filtering methods are adopted to effectively filter out the background noise signal of fiber. With 200-mW excitation power and 60-s integration time, a low limit of detection (LOD) is achieved: 0.8, 3.0, 1.4, 1.5, 9.2, 4.2, and 4.1 ppm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula> bar for CH4, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{C}_{{2}}\text{H}_{{6}}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{C}_{{2}}\text{H}_{{4}}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{C}_{{2}}\text{H}_{{2}}$ </tex-math></inline-formula> , CO, CO2, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{H}_{{2}}$ </tex-math></inline-formula> , respectively, which is verified through the detection of low-concentration gases. The proposed device has a short response time and does not require gas separation from the battery, and we believe that this provides a new idea for nondestructive detection of battery status and in situ assessment of battery health.

Early Warning Method and Fire Extinguishing Technology of Lithium-Ion Battery Thermal Runaway: A Review

Lithium-ion batteries (LIBs) are widely used in electrochemical energy storage and in other fields. However, LIBs are prone to thermal runaway (TR) under abusive conditions, which may lead to fires and even explosion accidents. Given the severity of TR hazards for LIBs, early warning and fire extinguishing technologies for battery TR are comprehensively reviewed in this paper. First, the TR reaction mechanism and hazards of LIBs are discussed. Second, the TR early warning and monitoring methods of LIBs are summarized in five aspects consisting of acoustic, heat, force, electricity, and gas. In addition, to reduce the fire and explosion hazards caused by the TR of LIBs, the highly efficient extinguishing agents for LIBs are summarized. Finally, the early warning technology and fire extinguishing agent are proposed, which provides a reference for the hazard prevention and control of energy storage systems.

In Situ Detecting Thermal Stability of Solid Electrolyte Interphase (SEI).

Solid electrolyte interphase (SEI) plays an important role in regulating the interfacial ion transfer and safety of Lithium-ion batteries (LIBs). It is unstable and readily decomposed releasing much heat and gases and thus triggering thermal runaway. Herein, in situ heating X-ray photoelectron spectroscopy is applied to uncover the inherent thermal decomposition process of the SEI. The evolution of the composition, nanostructure, and the released gases are further probed by cryogenic transmission electron microscopy, and gas chromatography. The results show that the organic components of SEI are readily decomposed even at room temperature, releasing some flammable gases (e.g., H2 , CO, C2 H4 , etc.). The residual SEI after heat treatment is rich in inorganic components (e.g., Li2 O, LiF, and Li2 CO3 ), provides a nanostructure model for a beneficial SEI with enhanced stability. This work deepens the understanding of SEI intrinsic thermal stability, reveals its underlying relationship with the thermal runaway of LIBs, and enlightens to enhance the safety of LIBs by achieving inorganics-rich SEI.

Identification of Internal Short-Circuit Faults in Lithium-Ion Batteries Based on a Multi-Machine Learning Fusion

Internal short-circuit (ISC) faults are a common cause of thermal runaway in lithium-ion batteries (LIBs), which greatly endangers the safety of LIBs. Different LIBs have common features related to ISC faults. Due to the insufficient volume of acquired ISC fault data, conventional machine learning models could not effectively identify ISC faults. To compensate for the above deficiencies, this paper proposes a multi-machine learning fusion method to predict ISC faults and to perform faults warning classification under multiple operating conditions using the input of voltage normalization. Firstly, learning data acquisition is captured by experiments and simulation. Secondly, the simulation data are inputted into the ResNet-convolutional neural network (CNN) for pretraining, followed by the transfer learning method to freeze parts of the model layers in the CNN, and part of the experimental data are also inputted into the CNN model for parameter fine-tuning to build a multi-machine learning model. Finally, the degree of ISC faults within the laboratory battery is predicted based on the multi-machine learning model. The results show that the CNN model had a 99.9% prediction accuracy on the simulated dataset, and the multi-machine learning fusion model after transfer learning had a 96.67% prediction accuracy on the laboratory battery dataset, which can accurately identify different levels of ISC faults in batteries and realize the graded warning of ISC faults.

Low-cost and highly thermally conductive lauric acid–paraffin–expanded​ graphite multifunctional composite phase change materials for quenching thermal runaway of lithium-ion battery

The cooling role of phase change materials (PCMs) has emerged as a promising solution for battery thermal management systems (BTMS). However, issues such as high phase change temperature, low thermal conductivity, and easy leakage have been the long-term bottlenecks in the application of phase change materials. Herein, a composite phase change materials (CPCMs) is prepared by incorporating a mixture of lauric acid (LA) and paraffin (PA) with an expanded graphite (EG) thermally conductive unit. The mixture of lauric acid and paraffin provides latent heat, whereas expanded graphite enhances the thermal conductivity while preventing the leakage of lauric acid and paraffin. As expected, the lauric acid–paraffin–expanded graphite-based composite phase change materials demonstrate the high thermal conductivity of 1.22 W/(m k), the elevated melting enthalpy of 146.9 J/g, as well as the relatively low melting temperature of 36.1 °C. When used as a cooling system for battery thermal management systems, composite phase change materials can keep the maximum temperature of the battery below 43 °C with a temperature difference of 1.96 °C only, while ensuring the long-term cycling stability of the battery. Therefore, composite phase change materials with such a remarkable performance provide a promising and effective solution for battery thermal management systems.

Modeling and prediction of lithium-ion battery thermal runaway via multiphysics-informed neural network

In this study, a multiphysics-informed neural network (MPINN) is proposed for the estimation and prediction of thermal runaway (TR) in lithium-ion batteries (LIBs). MPINNs are encoded with the governing laws of physics, including the energy balance equation and Arrhenius law, ensuring accurate estimation of time and space-dependent temperature and dimensionless concentration in comparison to a purely data-driven approach. Specifically, the network is trained using data from a high-fidelity model of an LIB, which describes TR by addressing several coupled partial differential equations. Quantitative analysis reveals that the mean absolute error (MAE) and root mean squared error (RMSE) of the MPINN for TR estimation are less than an artificial neural network (ANN) by 0.71 and 1.57, respectively, when using fully labeled data for training. It outperforms the ANN in terms of MAE and RMSE by 90.56 and 118.64, when only a small portion of labeled data (semi-supervision) are used for TR prediction. Importantly, it predicts TR without any labeled data when the decomposition of reactive species is modeled in the positive electrode. The MPINN exhibits promising results in surrogate modeling, implying it can be successfully implemented in practical scenarios and stimulate further research related to TR modeling using physics-informed deep learning.

An integrated methodology for dynamic risk prediction of thermal runaway in lithium-ion batteries

The risk of thermal runaway in lithium-ion battery (LIB) attracts significant attention from domains of society, industry, and academia. However, the thermal runaway prediction in the framework of system safety requires further efforts. In this paper, we propose a methodology for dynamic risk prediction by integrating fault tree (FT), dynamic Bayesian network (DBN) and support vector regression (SVR). FT graphically describes the logic of mechanism of thermal runaway. DBN allows considering multiple states and uncertain inference for providing quantitative results of the risk evolution. SVR is subsequently utilized for predicting the risk from the DBN estimation. The proposed methodology can be applied for risk early warning of LIB thermal runaway.

A Critical Review of Thermal Runaway Prediction and Early-Warning Methods for Lithium-Ion Batteries

Lithium-ion batteries are widely used in electric vehicles because of their high energy density and long cycle life. However, the spontaneous combustion accident of electric vehicles caused by thermal runaway of lithium-ion batteries seriously threatens passengers' personal and property safety. This paper expounds on the internal mechanism of lithium-ion battery thermal runaway through many previous studies and summarizes the proposed lithium-ion battery thermal runaway prediction and early warning methods. These methods can be classified into battery electrochemistry-based, battery big data analysis, and artificial intelligence methods. In this paper, various lithium-ion thermal runaway prediction and early warning methods are analyzed in detail, including the advantages and disadvantages of each method, and the challenges and future development directions of the intelligent lithium-ion battery thermal runaway prediction and early warning methods are discussed.