scholarly journals A Review of Experimental and Numerical Studies of Lithium Ion Battery Fires

2021 ◽  
Vol 11 (3) ◽  
pp. 1247
Author(s):  
Matt Ghiji ◽  
Shane Edmonds ◽  
Khalid Moinuddin
Keyword(s):  
Heat Transfer ◽  
Source Term ◽  
Input Parameter ◽  
Lithium Ion ◽  
Fire Risk ◽  
State Of Charge ◽  
Numerical Studies ◽  
Varied Range ◽  
Fire Properties ◽  
Model Input Parameter

Lithium-ion batteries (LIBs) are used extensively worldwide in a varied range of applications. However, LIBs present a considerable fire risk due to their flammable and frequently unstable components. This paper reviews experimental and numerical studies to understand parametric factors that have the greatest influence on the fire risks associated with LIBs. The LIB chemistry and the state of charge (SOC) are shown to have the greatest influence on the likelihood of a LIB transitioning into thermal runaway (TR) and releasing heats which can be cascaded to cause TR in adjacent cells. The magnitude of the heat release rate (HRR) is quantified to be used as a numerical model input parameter (source term). LIB chemistry, the SOC, and incident heat flux are proven to influence the magnitude of the HRR in all studies reviewed. Therefore, it may be conjectured that the most critical variables in addressing the overall fire safety and mitigating the probability of TR of LIBs are the chemistry and the SOC. The review of numerical modeling shows that it is quite challenging to reproduce experimental results with numerical simulations. Appropriate boundary conditions and fire properties as input parameters are required to model the onset of TR and heat transfer from thereon.

The fire risk of portable batteries in their end-of-life: Investigation of the state of charge of waste lithium-ion batteries in Austria

2021 ◽  
pp. 0734242X2110106
Author(s):  
Thomas Nigl ◽  
Tanja Bäck ◽  
Stefan Stuhlpfarrer ◽  
Roland Pomberger
Keyword(s):  
Lithium Ion Batteries ◽  
End Of Life ◽  
Influence Factors ◽  
Lithium Ion ◽  
Fire Risk ◽  
Cell Voltage ◽  
Thermal Runaway ◽  
State Of Charge ◽  
Degradation Reactions ◽  
Current State

The increased utilisation of lithium-ion batteries in the last years does not come without cost. Due to thermal runaway and exothermic degradation reactions, portable batteries pose enormous risks to waste management systems and infrastructure in their end-of-life phase. All over Europe, the number of waste fires caused by lithium-ion batteries are rising. The risk of a battery fire is mainly influenced by the probability and severity of a thermal runaway or exothermic degradation, which depends on the current state of charge (SOC) of the respective battery. In order to determine the distribution of the SOC which is one of the main influence factors to waste fires caused by lithium-ion batteries, 980 waste battery cells were representatively sampled, manually dismantled and analysed using a prototypic laboratory test stand. Approximately 24% of the analysed cells and batteries had a residual SOC of at least 25%, and approximately 12% had a residual SOC of at least 50%. Hence, approximately every fourth to eighth portable battery threatens to cause a waste fire when critically damaged. Furthermore, a distinct relationship between the actual cell voltage and the residual SOC was found for end-of-life portable batteries.

State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

2021 ◽  
pp. 095440702110180
Author(s):  
Meng Wei ◽  
Min Ye ◽  
Jia Bo Li ◽  
Qiao Wang ◽  
Xin Xin Xu
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector ◽  
Battery Management System ◽  
Dynamic Neural Network ◽  
Soc Estimation ◽  
Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

scholarly journals Performance Analysis of Indentation Punch on High Energy Lithium Pouch Cells and Simulated Model Improvement

Polymers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1971
Author(s):  
Lihua Ye ◽  
Muhammad Muzamal Ashfaq ◽  
Aiping Shi ◽  
Syyed Adnan Raheel Shah ◽  
Yefan Shi
Keyword(s):  
Short Circuit ◽  
Lithium Ion ◽  
Mechanical Deformation ◽  
High Energy ◽  
State Of Charge ◽  
Punch Test ◽  
Short Course ◽  
Model Cell ◽  
The Impact

In this research, the aim relates to the material characterization of high-energy lithium-ion pouch cells. The development of appropriate model cell behavior is intended to simulate two scenarios: the first is mechanical deformation during a crash and the second is an internal short circuit in lithium-ion cells during the actual effect scenarios. The punch test has been used as a benchmark to analyze the effects of different state of charge conditions on high-energy lithium-ion battery cells. This article explores the impact of three separate factors on the outcomes of mechanical punch indentation experiments. The first parameter analyzed was the degree of prediction brought about by experiments on high-energy cells with two different states of charge (greater and lesser), with four different sizes of indentation punch, from the cell’s reaction during the indentation effects on electrolyte. Second, the results of the loading position, middle versus side, are measured at quasi-static speeds. The third parameter was the effect on an electrolyte with a different state of charge. The repeatability of the experiments on punch loading was the last test function analyzed. The test results of a greater than 10% state of charge and less than 10% state of charge were compared to further refine and validate this modeling method. The different loading scenarios analyzed in this study also showed great predictability in the load-displacement reaction and the onset short circuit. A theoretical model of the cell was modified for use in comprehensive mechanical deformation. The overall conclusion found that the loading initiating the cell’s electrical short circuit is not instantaneously instigated and it is subsequently used to process the development of a precise and practical computational model that will reduce the chances of the internal short course during the crash.

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