Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics

TL;DR

This paper introduces a hybrid data-driven approach combining genetic algorithms and regression techniques to enhance low-fidelity lithium-ion battery models, significantly improving voltage prediction accuracy across various conditions.

Contribution

It develops a novel hybrid modeling method that integrates physics-based models with data-driven corrections using GA-STRidge, improving fidelity of battery models.

Findings

Significant reduction in voltage prediction error.

High correlation with actual terminal voltage.

Robust performance across different operating conditions.

Abstract

Accurate modeling of lithium ion (li-ion) batteries is essential for enhancing the safety, and efficiency of electric vehicles and renewable energy systems. This paper presents a data-inspired approach for improving the fidelity of reduced-order li-ion battery models. The proposed method combines a Genetic Algorithm with Sequentially Thresholded Ridge Regression (GA-STRidge) to identify and compensate for discrepancies between a low-fidelity model (LFM) and data generated either from testing or a high-fidelity model (HFM). The hybrid model, combining physics-based and data-driven methods, is tested across different driving cycles to demonstrate the ability to significantly reduce the voltage prediction error compared to the baseline LFM, while preserving computational efficiency. The model robustness is also evaluated under various operating conditions, showing low prediction errors and high Pearson correlation coefficients for terminal voltage in unseen environments.