Physics-Informed Neural Network Modeling of Li-Ion Batteries: Preprint

Malik Hassanaly, Peter Weddle, Kandler Smith, Subhayan De, Alireza Doostan, Ryan King

Research output: Contribution to conferencePaper


Li-ion batteries (LIB) are a promising solution to enable storage of intermittent energy sources due to their high energy density. However, LIBs are known to significantly degrade after about 1000 charge-discharge cycles. LIBs degrade following different degradation modes and at a rate that depends on the operating conditions (external temperature, load). To plan the installation of batteries, appropriate understanding and prediction capabilities of their lifecycle is needed. High-fidelity numerical models of LIBs such as the pseudo-two-dimensional (P2D) model have been shown to accurately represent the charge-discharge-cycle of an LIB given ac- curate choice of the physical parameters. Given the large number of P2D parameters, adjusting them using forward runs is intractable. This work describes the development of a physics-informed neural network (PINN) as a surrogate substitute of the P2D model that captures parameter dependence. The PINN is advantageous as it needs little to no data, and can naturally encode the dependence of every model parameter. Here, a specific training procedure is adopted to efficiently cover parameter space, handle model stiffness and enforce boundary conditions. The trained PINN is validated against numerical solutions of the P2D model, and its applicability to battery degradation modeling is discussed.
Original languageAmerican English
Number of pages5
StatePublished - 2022
Event242nd Electrochemical Society Meeting - Atlanta, Georgia
Duration: 9 Oct 202213 Oct 2022


Conference242nd Electrochemical Society Meeting
CityAtlanta, Georgia

NREL Publication Number

  • NREL/CP-2C00-82015


  • battery degradation
  • lithium-ion battery
  • physics-informed neural network
  • pseudo two-dimensional model


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