Fixed Point Neural Acceleration and Inverse Surrogate Model for Battery Parameter Identification

TL;DR

This paper introduces a deep learning framework combining a neural surrogate model and a fixed-point iteration method to significantly accelerate and improve the accuracy of lithium-ion battery parameter identification under dynamic conditions.

Contribution

It presents a novel neural surrogate model and a fixed-point iteration approach that drastically reduces computation time and enhances accuracy over traditional methods.

Findings

Accelerates parameter identification by over 2000 times.

Achieves more than 10 times higher accuracy than conventional algorithms.

Demonstrates superior performance under realistic dynamic load profiles.

Abstract

The rapid expansion of electric vehicles has intensified the need for accurate and efficient diagnosis of lithium-ion batteries. Parameter identification of electrochemical battery models is widely recognized as a powerful method for battery health assessment. However, conventional metaheuristic approaches suffer from high computational cost and slow convergence, and recent machine learning methods are limited by their reliance on constant current data, which may not be available in practice. To overcome these challenges, we propose deep learning-based framework for parameter identification of electrochemical battery models. The proposed framework combines a neural surrogate model of the single particle model with electrolyte (NeuralSPMe) and a deep learning-based fixed-point iteration method. NeuralSPMe is trained on realistic EV load profiles to accurately predict lithium concentration dynamics under dynamic operating conditions while a parameter update network (PUNet) performs fixed-point iterative updates to significantly reduce both the evaluation time per sample and the overall number of iterations required for convergence. Experimental evaluations demonstrate that the proposed framework accelerates the parameter identification by more than 2000 times, achieves superior sample efficiency and more than 10 times higher accuracy compared to conventional metaheuristic algorithms, particularly under dynamic load scenarios encountered in practical applications.