Welcome to Single Particle Model PINN for fast prototyping!
I am a physics-informed neural network trained to provide the evolution of lithium concentration in active material particles in both electrodes during a discharge process as well as the discharge curve of the full battery, based on the Single Particle Model.
In general, I am trained using NMC811 G-Si chemistry and in a wide range of geometrical parameters and C-rates:
- Negative electrode thickness in [5e-5, 2e-4] m
- Positive electrode thickness in [5e-5, 2e-4] m
- Negative electrode porosity in [0.2, 0.6]
- Positive electrode porosity in [0.2, 0.6]
- C-rate from 1C to 3C
But due to my training process I am capable of providing results even further! Just test me and I will let you know when you are requesting simulations out of my limits.
You can request simulations changing the examples bellow or creating a new cell. My inputs are:
- Negative electrode thickness (thickness_n) in meters
- Positive electrode thickness (thickness_n) in meters
- Negative electrode porosity (porosity_n)
- Positive electrode porosity (porosity_p)
- C-rate
And my outputs are:
- Lithium concentration in negative electrode (
$c_{s,NE}$ in mol/m**3) along the radius in the negative particle ($r_{NE}$ in meters) for an instant of time ($t$ in seconds) - Lithium concentration in positive electrode (
$c_{s,PE}$ in mol/m**3) along the radius in the positive particle ($r_{PE}$ in meters) for an instant of time ($t$ in seconds) - Battery voltage (
$V$ in volts) for an instant of time ($t$ in seconds).
This software is copyright (C) 2023 of CIDETEC Energy Storage and is distributed under the terms of the Affero GNU General Public License (GPL) version 3 or later.