PINN surrogate of Li-ion battery models for parameter inference, Part II: Regularization and application of the pseudo-2D model

Hassanaly, Malik; Weddle, Peter J.; King, Ryan N.; De, Subhayan; Doostan, Alireza; Randall, Corey R.; Dufek, Eric J.; Colclasure, Andrew M.; Smith, Kandler

doi:10.1016/j.est.2024.113104

PINN surrogate of Li-ion battery models for parameter inference, Part II: Regularization and application of the pseudo-2D model

Journal Article · Tue Jul 30 00:00:00 UTC 2024 · Journal of Energy Storage

DOI:https://doi.org/10.1016/j.est.2024.113104· OSTI ID:2429277

^[1]; ^[1]; King, Ryan N. ^[1]; ^[2]; Doostan, Alireza ^[3]; ^[1]; ^[4]; ^[1]; ^[1]

National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Northern Arizona Univ., Flagstaff, AZ (United States)
Univ. of Colorado, Boulder, CO (United States)
Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help formulate battery aging models. However, it is computationally intensive and cannot be easily repeated for multiple cycles, multiple operating conditions, or multiple replicate cells. To reduce the computational cost of Bayesian calibration, numerical solvers for physics-based models can be replaced with faster surrogates. A physics-informed neural network (PINN) is developed as a surrogate for the pseudo-2D (P2D) battery model calibration. For the P2D surrogate, additional training regularization was needed as compared to the PINN single-particle model (SPM) developed in Part I. Both the PINN SPM and P2D surrogate models are exercised for parameter inference and compared to data obtained from a direct numerical solution of the governing equations. A parameter inference study highlights the ability to use these PINNs to calibrate scaling parameters for the cathode Li diffusion and the anode exchange current density. By realizing computational speed-ups of ~2250x for the P2D model, as compared to using standard integrating methods, the PINN surrogates enable rapid state-of-health diagnostics. Finally, in the low-data availability scenario, the testing error was estimated to ~2 mV for the SPM surrogate and ~10 mV for the P2D surrogate which could be mitigated with additional data.

View Accepted Manuscript (DOE)

Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC07-05ID14517; AC36-08GO28308

OSTI ID:: 2429277

Alternate ID(s):: OSTI ID: 2452746

Report Number(s):: INL/JOU--23-75780-Rev000; NREL/JA--2C00-88180; MainId:88955; UUID:e1c29921-3b31-4dbc-949c-e92c3ed1e201; MainAdminId:73297

Journal Information:: Journal of Energy Storage, Journal Name: Journal of Energy Storage Journal Issue: Part B Vol. 98; ISSN 2352-152X

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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PINN surrogate of Li-ion battery models for parameter inference, Part II: Regularization and application of the pseudo-2D model

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