Rapid Inverse Parameter Inference Using Physics-Informed Neural Network

Malik Hassanaly, Peter Weddle, Corey Randall, Eric Dufek, Kandler Smith

Research output: NRELPoster


As Li-ion batteries become more essential in today's economy, tools need to be developed to accurately and rapidly diagnose a battery's internal state-of-health. Using a Li-ion battery's (high-rate) voltage response, it is proposed to determine a battery's internal state through Bayesian calibration. However, Bayesian calibration is notoriously slow and requires thousands of model runs. To accelerate parameter inference using Bayesian calibration, a surrogate model is developed to replace the underlying physics-based Li-ion model. Developing a surrogate model for rapid Bayesian calibration analysis is discussed for both the single particle model (SPM) and the pseudo two-dimensional (P2D) model. Surrogate models are constructed using physics-informed neural networks (PINNs) that encode the influence of internal properties on observed voltage responses. In practice, a neural network can be trained by: 1) using simulation results of the physics-based model (i.e., a data-loss approach); 2) using the residuals of the governing equations themselves (i.e., a physics-loss approach); or 3) using a combination of simulation results and governing equation residuals. In the present work, PINNs are developed using a variety of training losses and neural network architectures. In this analysis, it is shown that a PINN surrogate model can be reliably trained with only physics-informed loss. However, using a coupled data-informed and physics-loss approach produced the most accurate PINNs.
Original languageAmerican English
PublisherNational Renewable Energy Laboratory (NREL)
StatePublished - 2024

Publication series

NamePresented at the 245th Electrochemical Society (ECS) Meeting, 26-30 May 2024, San Francisco, California

NREL Publication Number

  • NREL/PO-2C00-89869


  • Bayesian calibration
  • inverse parameter inference
  • lithium-ion batteries
  • physics-informed neural networks


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