用遗传算法+稀疏回归提升低精度电池模型精度，误差大幅下降。
Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics
- 结合遗传算法与稀疏回归，自动识别并修正模型偏差。
- 在多种工况下电压预测误差显著降低，相关系数超0.99。
- 适合电池仿真、电车控制等需高效高精度建模的场景。
锂离子电池的精准建模对提升电动汽车和可再生能源系统的安全性与效率至关重要。本文提出一种数据驱动方法，用于改进降阶锂离子电池模型的精度。该方法结合遗传算法与逐次阈值岭回归（GA-STRidge），识别并补偿低精度模型（LFM）与实测数据或高精度模型（HFM）之间的差异。所提出的混合模型融合物理机制与数据驱动，已在多种驾驶循环下验证，显著降低电压预测误差，同时保持计算高效。模型在不同工况下的鲁棒性评估显示，在未见环境中终端电压预测误差小，皮尔逊相关系数高于0.99。
原文摘要 · Abstract (English)
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.
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