Electrical engineering

Physics

Electrode

Separator (oil production)

Structural engineering

Anode

Engineering

Artificial intelligence

Composite material

Power (physics)

Materials science

Deformation (meteorology)

Computer science

Cathode

Battery (electricity)

Quantum mechanics

Artificial neural network

Mechanical engineering

Thermodynamics

International Journal of Plasticity

As the automotive industry transitions from combustion to electric motors, there is a growing demand for efficient computational models that can describe the homogenized large deformation response of Li-ion batteries. Here, a detailed three-dimensional unit cell model with periodic boundary conditions is developed to describe the large deformation response of a typical anode-separator-cathode lay-up of a pouch cell. The model makes use of a Deshpande-Fleck foam model for the porous polymer separator and Drucker-Prager cap models of the granular cathode and anode coatings. Using the unit cell model, the stress-strain response of a battery cell is computed for 20'000 random loading paths in the six-dimensional strain space. Based on this data, a recurrent neural network (RNN) model is trained, validated and tested. It is found that an RNN model composed of two gated recurrent units in series with a deep fully connected network is capable to describe the large deformation response with a high level of accuracy. As a byproduct, it is shown that advanced conventional constitutive models such as the anisotropic Deshpande-Fleck model cannot provide any predictions of satisfactory accuracy.

Recurrent neural network modeling of the large deformation of lithium-ion battery cells