SOC Estimation Method, Application and Prospect of LFP Battery: A Review

Xiaonan Liu

doi:10.62051/ijepes.v5n1.03

Authors

Xiaonan Liu

DOI:

https://doi.org/10.62051/ijepes.v5n1.03

Keywords:

Lithium Iron Phosphate Batteries, State of Charge, Battery Management System

Abstract

The precise determination of the State of Charge is crucial for Lithium Iron Phosphate batteries, yet it continues to present significant challenges. These difficulties primarily stem from the batteries' notably flat voltage profile, considerable hysteresis effects, and their high sensitivity to thermal fluctuations and aging processes. This review systematically analyzes two dominant SOC estimation methodologies: purely data-driven models and physical-data-driven hybrid models. Data-driven approaches (e.g., DNN, LSTM) excel at capturing complex nonlinearities but lack interpretability and require substantial data. Hybrid models, combining equivalent circuit models with state estimators like Kalman filters, offer a balance of physical insight and computational efficiency, yet their accuracy often depends on offline calibration. The analysis concludes that future advancements hinge on developing online adaptive algorithms and deeply integrated hybrid strategies to enhance robustness and generalization across diverse real-world operating conditions.

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