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

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.

1999 ◽  
Vol 575 ◽  
Author(s):  
E. Peter Roth ◽  
G. Nagasubramanian

ABSTRACTThermal instabilities were identified in SONY-type lithium-ion cells and correlated with interactions of cell constituents and reaction products. Three temperature regions of interaction were identified and associated with the state of charge (degree of Li intercalation) of the cell. Anodes were shown to undergo exothermic reactions as low as 100°C involving the solid electrolyte interface (SEI) layer and the LiPF6 salt in the electrolyte (EC:PC:DEC/LiPF6). These reactions could account for the thermal runaway observed in these cells beginning at 100°C. Exothermic reactions were also observed in the 200°C-300°C region between the intercalated lithium anodes, the LiPF6 salt, and the PVDF. These reactions were followed by a hightemperature reaction region, 300°C-400°C, also involving the PVDF binder and the intercalated lithium anodes. The solvent was not directly involved in these reactions but served as a moderator and transport medium. Cathode exothermic reactions with the PVDF binder were observed above 200°C and increased with the state of charge (decreasing Li content). This offers an explanation for the observed lower thermal runaway temperatures for charged cells.


2019 ◽  
Vol 21 (41) ◽  
pp. 22740-22755 ◽  
Author(s):  
Mei-Chin Pang ◽  
Yucang Hao ◽  
Monica Marinescu ◽  
Huizhi Wang ◽  
Mu Chen ◽  
...  

Solid-state lithium batteries could reduce the safety concern due to thermal runaway while improving the gravimetric and volumetric energy density beyond the existing practical limits of lithium-ion batteries.


Author(s):  
Meng Wei ◽  
Min Ye ◽  
Jia Bo Li ◽  
Qiao Wang ◽  
Xin Xin Xu

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.


RSC Advances ◽  
2021 ◽  
Vol 11 (39) ◽  
pp. 24132-24136
Author(s):  
Liurui Li ◽  
Tairan Yang ◽  
Zheng Li

The pre-treatment efficiency of the direct recycling strategy in recovering end-of-life Li-ion batteries is predicted with levels of control factors.


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