LF-Net: A Lightweight Architecture for State-of-Charge Estimation of Lithium-Ion Batteries by Decomposing Global Trend and Local Fluctuations
Year: 2025
Type: article
Abstract: Accurate estimation of the State of Charge (SOC) of lithium-ion batteries under complex operating conditions remains challenging, as the SOC signal combines a global linear (quasi-linear) trend with localized dynamic fluctuations driven by polarization, ion diffusion, temperature gradients, and load transients. In practice, open-circuit-voltage (OCV) approaches are affected by hysteresis and parameter drift, while high-fidelity electrochemical models require extensive parameterization and significant computational resources that hinder their real-time deployment in battery management systems (BMS). Purely data-driven methods capture temporal patterns but may under-represent abrupt local fluctuations and blur the distinction between trend and fluctuation, leading to biased SOC tracking when operating conditions change. To address these issues, LF-Net is proposed. The architecture decomposes battery time series into long-term trend and local fluctuation components. A linear branch models the quasi-linear SOC evolution. Multi-scale convolutional and differential branches enhance sensitivity to transient dynamics. An adaptive Fusion Module aggregates the representations, improving interpretability and stability, and keeps the parameter budget small for embedded hardware. Our experimental results demonstrate that the proposed model achieves a mean absolute error (MAE) of 0.0085 and a root-mean-square error (RMSE) of 0.0099 at 40 °C, surpassing mainstream models and confirming the method’s efficacy.
Source: Electronics
Authors: Ruidi Zhou, Xilin Dai, Jinhao Zhang, Keyi He, Fanfan Lin, Hao Ma
Institution: Zhejiang University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4414206991