mlsanityData to 7 Oct 2026

PINN surrogate of Li-ion battery models for parameter inference. Part I: Implementation and multi-fidelity hierarchies for the single-particle model

arXiv 2312.17329 ↗First posted 28 Dec 2023cs.LG physics.app-phcited by 6

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Abstract (arXiv)

To plan and optimize energy storage demands that account for Li-ion battery aging dynamics, techniques need to be developed to diagnose battery internal states accurately and rapidly. This study seeks to reduce the computational resources needed to determine a battery's internal states by replacing physics-based Li-ion battery models -- such as the single-particle model (SPM) and the pseudo-2D (P2D) model -- with a physics-informed neural network (PINN) surrogate. The surrogate model makes high-throughput techniques, such as Bayesian calibration, tractable to determine battery internal parameters from voltage responses. This manuscript is the first of a two-part series that introduces PINN surrogates of Li-ion battery models for parameter inference (i.e., state-of-health diagnostics). In this first part, a method is presented for constructing a PINN surrogate of the SPM. A multi-fidelity hierarchical training, where several neural nets are trained with multiple physics-loss fidelities is shown to significantly improve the surrogate accuracy when only training on the governing equation residuals. The implementation is made available in a companion repository (https://github.com/NREL/pinnstripes). The techniques used to develop a PINN surrogate of the SPM are extended in Part II for the PINN surrogate for the P2D battery model, and explore the Bayesian calibration capabilities of both surrogates.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 20 of its 78 references that are in the map. First posted on arXiv 28 Dec 2023.

Builds on

20 of 78 references.

References · 20

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8 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 3.

6 papers.

  1. 2024Forward and Inverse Simulation of Pseudo-Two-Dimensional Model of Lithium-Ion Batteries Using Neural Networkscited by 2
  2. 2025Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networkscited by 0
  3. 2025Fixed Point Neural Acceleration and Inverse Surrogate Model for Battery Parameter Identificationcited by 0
  4. 2026A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Lifecited by 0
  5. 2026Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteriescited by 0
  6. 2026Large Models for Battery Prognostics and Health Management: A Review and Future Roadmapcited by 0