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

2024/11/20 by Samuel Filgueira da Silva, Mehmet Fatih Ozkan, da Silva, Samuel Filgueira +7 · 2 citations
Computer Science · Engineering · #Advanced Battery Technologies Research #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Systems and Control (eess.SY) #VLSI and Analog Circuit Testing #Wireless Power Transfer Systems #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2411.12935

openalex publication_date 2024/11/20 · openalex created_date 2024/11/24 · openalex updated_date 2026/07/28

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.

Cited by

• Augmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction
• Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model

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