An adaptive estimation approach based on fisher information to overcome the flat voltage plateau challenges of SOC estimation in LFP batteries

Journal article, 2026

Accurate and robust state-of-charge (SOC) estimation remains a critical challenge for lithium iron phosphate (LFP) batteries due to their flat SOC-open-circuit-voltage (OCV) characteristics, pronounced hysteresis, and non-ideal operating conditions such as current sensor bias, voltage quantization, temperature variation, and insufficient excitation. This paper proposes an adaptive SOC estimation framework that addresses these challenges through an information-aware fusion strategy. The method adaptively fuses Coulomb counting and voltage-based SOC estimation using Fisher information, allowing the estimator to automatically adjust its reliance on each source according to excitation conditions and SOC-OCV observability. To explicitly capture hysteresis effects, a three-dimensional OCV-hysteresis-SOC (OCV-H-SOC) mapping is introduced for reliable SOC inversion within extended flat voltage plateaus. The proposed method is experimentally validated on an LFP cell using four realistic driving cycles under a wide range of challenging scenarios, including large initial SOC errors, prolonged operation in SOC-OCV flat zones, current bias, voltage quantization noise, low-temperature operation (10 degrees C), and insufficient current excitation. Compared with state-of-the-art benchmarks, including the unscented Kalman filter (UKF), LSTM, and Transformer-based estimators, the proposed approach consistently achieves superior accuracy and robustness across all tested conditions. In particular, the proposed method reduces SOC estimation error by up to approximately 80% relative to the UKF, while maintaining real-time computational efficiency.