Characterization on the thermal runaway of commercial 18650 lithium-ion batteries used in electric vehicle

2016 ◽  
Vol 127 (1) ◽  
pp. 983-993 ◽  
Author(s):  
Yih-Shing Duh ◽  
Meng-Ting Tsai ◽  
Chen-Shan Kao
Keyword(s):  
Lithium Ion Batteries ◽  
Electric Vehicle ◽  
Lithium Ion ◽  
Thermal Runaway

A Novel Approach for Predicting Thermal Runaway in Electric Vehicle Batteries When Involved in a Collision

2015 ◽  
Author(s):  
Muhammad Sheikh ◽  
David Baglee ◽  
Michael Knowles ◽  
Ahmed Elmarakbi ◽  
Mohammad Al-Hariri
Keyword(s):  
Lithium Ion Batteries ◽  
Electric Vehicle ◽  
Heat Generation ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Thermal Runaway ◽  
Electrical Behaviour ◽  
Physical Chemical ◽  
Novel Approach ◽  
Charge Discharge

Electric Vehicle (EV) battery manufactures are under pressure to ensure their products are safe and not prone to undetectable heat after an impact, which could lead to thermal runaway. Constant monitoring of the battery’s behaviour and, in particular, heat generation is therefore important for the safety of the vehicle and the occupant. An aim of this research is to use a series of battery models to study the charge/discharge and thermal behaviour of EV lithium ion batteries under normal and damaged conditions through modelling and physical/electrical testing. An equivalent circuit model is identified and tested to determine the electrical behaviour of the batteries and a 2 degree of freedom (DOF) model is discussed for the plastic deformational behaviour of the battery compartment as the result of an impact. The ultimate goal of this work is to develop a new model integrating physical, chemical, thermal and electrical behaviour to improve safety.

Experimental and numerical analysis to identify the performance limiting mechanisms in solid-state lithium cells under pulse operating conditions

2019 ◽  
Vol 21 (41) ◽  
pp. 22740-22755 ◽  
Author(s):  
Mei-Chin Pang ◽  
Yucang Hao ◽  
Monica Marinescu ◽  
Huizhi Wang ◽  
Mu Chen ◽  
...  
Keyword(s):  
Solid State ◽  
Numerical Analysis ◽  
Energy Density ◽  
Lithium Ion Batteries ◽  
Lithium Batteries ◽  
Safety Concern ◽  
Lithium Ion ◽  
Thermal Runaway ◽  
Operating Conditions ◽  
Lithium Cells

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.

A multi-factor evaluation method for the thermal runaway risk of lithium-ion batteries

2022 ◽  
Vol 45 ◽  
pp. 103767
Author(s):  
Zhirong Wang ◽  
Shichen Chen ◽  
Xinrui He ◽  
Chao Wang ◽  
Dan Zhao
Keyword(s):  
Lithium Ion Batteries ◽  
Evaluation Method ◽  
Lithium Ion ◽  
Thermal Runaway ◽  
Factor Evaluation

scholarly journals Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7521
Author(s):  
Shaheer Ansari ◽  
Afida Ayob ◽  
Molla Shahadat Hossain Lipu ◽  
Aini Hussain ◽  
Mohamad Hanif Md Saad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lithium Ion Batteries ◽  
Electric Vehicle ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Systematic Sampling ◽  
Channel Input ◽  
Useful Life ◽  
Artificial Neural

Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets.

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