Safety modeling and protection for lithium-ion batteries based on artificial neural networks method under mechanical abuse

2021 ◽  
Author(s):  
YiDing Li ◽  
WenWei Wang ◽  
Cheng Lin ◽  
FengHao Zuo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Artificial Neural

Damaged Cell Location on Lithium-Ion Batteries Using Artificial Neural Networks

2020 ◽  
pp. 477-486
Author(s):  
Mateus Moro Lumertz ◽  
Felipe Gozzi da Cruz ◽  
Rubisson Duarte Lamperti ◽  
Leandro Antonio Pasa ◽  
Diogo Marujo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Artificial Neural

scholarly journals Application of artificial neural networks in design of lithium-ion batteries

2018 ◽  
Vol 395 ◽  
pp. 128-136 ◽  
Author(s):  
Bin Wu ◽  
Sangwoo Han ◽  
Kang G. Shin ◽  
Wei Lu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Artificial Neural

scholarly journals Impedance Based Temperature Estimation of Lithium Ion Cells Using Artificial Neural Networks

Batteries ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 85
Author(s):  
Marco Ströbel ◽  
Julia Pross-Brakhage ◽  
Mike Kopp ◽  
Kai Peter Birke
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Electrochemical Impedance ◽  
Estimation Method ◽  
Lithium Ion ◽  
Computational Effort ◽  
Estimation Accuracy ◽  
Temperature Estimation ◽  
Lithium Ion Cells ◽  
Artificial Neural

Tracking the cell temperature is critical for battery safety and cell durability. It is not feasible to equip every cell with a temperature sensor in large battery systems such as those in electric vehicles. Apart from this, temperature sensors are usually mounted on the cell surface and do not detect the core temperature, which can mean detecting an offset due to the temperature gradient. Many sensorless methods require great computational effort for solving partial differential equations or require error-prone parameterization. This paper presents a sensorless temperature estimation method for lithium ion cells using data from electrochemical impedance spectroscopy in combination with artificial neural networks (ANNs). By training an ANN with data of 28 cells and estimating the cell temperatures of eight more cells of the same cell type, the neural network (a simple feed forward ANN with only one hidden layer) was able to achieve an estimation accuracy of ΔT= 1 K (10 ∘C <T< 60 ∘C) with low computational effort. The temperature estimations were investigated for different cell types at various states of charge (SoCs) with different superimposed direct currents. Our method is easy to use and can be completely automated, since there is no significant offset in monitoring temperature. In addition, the prospect of using the above mentioned approach to estimate additional battery states such as SoC and state of health (SoH) is discussed.

scholarly journals Accelerated Modeling of Lithium Diffusion in Solid State Electrolytes Using Artificial Neural Networks

2020 ◽  
Author(s):  
Karun Kumar Rao ◽  
Yan Yao ◽  
Lars Grabow
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Artificial Neural Networks ◽  
Solid State ◽  
Ab Initio ◽  
Density Functional ◽  
Lithium Ion ◽  
Md Simulations ◽  
High Accuracy ◽  
Artificial Neural

There is great interest in solid state lithium electrolytes to replace the flammable organic electrolyte for an all solid state battery. Previous efforts trying to understand the structure-function relationships resulting in high ionic conductivity materials have mainly relied on <i>ab initio</i> molecular dynamics. Such simulations, however, are computationally demanding and cannot be reasonably applied to large systems containing more than a few hundred atoms. Herein, we investigate using artificial neural networks (ANN) to accelerate the calculation of high accuracy atomic forces and energies used during molecular dynamics (MD) simulations, to eliminate the need for costly <i>ab initio </i>force and energy evaluation methods, such as density functional theory (DFT). After carefully training a robust ANN for four and five element systems, we obtain nearly identical lithium ion diffusivities for Li<sub>10</sub>GeP<sub>2</sub>S<sub>12</sub> (LGPS) when benchmarking the ANN-MD results with DFT-MD. To demonstrate the power of the outlined ANN-MD approach we apply it to a doped LGPS system to calculate the effect of concentrations of chlorine on the lithium diffusivity at a resolution that would be unrealistic to model with DFT-MD. We find that ANN-MD simulations can provide the framework to study systems that require a large number of atoms more efficiently while maintaining high accuracy.

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