PINEAPPLE: Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter Inference in Lithium-Ion Battery Electrodes

A research team from Singapore University of Technology and Design has introduced PINEAPPLE (Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter Inference in Lithium-ion battery Electrodes), a novel AI framework that could revolutionize battery health monitoring. The system combines physics-informed neural networks (PINNs) with evolutionary search algorithms to analyze battery discharge patterns and predict internal degradation with unprecedented accuracy.

Technically, PINEAPPLE achieves test errors below 0.1% while operating an order-of-magnitude faster than conventional battery simulation solvers. The model demonstrates robust parameter inference solely from voltage-time discharge curves across multiple batteries from the open-source CALCE repository, successfully recovering the evolution of critical internal state parameters like Li-ion diffusion coefficients across usage cycles. Notably, it achieves this without requiring customized degradation physics-embedded heuristics, highlighting the regularizing effect of incorporating fundamental physics principles.

This research matters because current battery health monitoring often requires destructive testing or complex laboratory analysis. PINEAPPLE's non-destructive approach using readily available discharge data could enable real-time, physics-based characterization of inter-cell and intra-cell variability in battery modules and packs. The framework shows consistent trend identification across different battery types, suggesting strong generalization capabilities that could accelerate development of next-generation battery management systems.

For practical applications, PINEAPPLE opens doors to individual cell-scale state-of-health diagnostics that could be deployed in electric vehicles, grid storage systems, and consumer electronics. By enabling computationally efficient, real-time parameter estimation, the technology could help optimize battery usage strategies, extend operational lifespans, and improve safety through early degradation detection.