Residual Bias Compensation Filter for Physics-Based SOC Estimation in Lithium Iron Phosphate Batteries

TL;DR

This work tackles the challenge of accurate state-of-charge estimation in LFP batteries, where flat OCV-SOC characteristics hinder observability. It introduces a residual bias compensation dual EKF (RBC-DEKF) that decouples residual bias estimation from electrochemical state estimation, enabling real-time correction of voltage deviations without perturbing state dynamics. By applying two coupled EKFs to a CP G-SPMT electrochemical model, the method achieves substantial improvements in SOC and voltage estimation accuracy across a wide temperature range, outperforming a conventional EKF. The results demonstrate that residual bias compensation effectively mitigates model and sensor mismatches, with practical implications for safety and performance in LFP-based energy systems.

Abstract

This paper addresses state of charge (SOC) estimation for lithium iron phosphate (LFP) batteries, where the relatively flat open-circuit voltage (OCV-SOC) characteristic reduces observability. A residual bias compensation dual extended Kalman filter (RBC-DEKF) is developed. Unlike conventional bias compensation methods that treat the bias as an augmented state within a single filter, the proposed dual-filter structure decouples residual bias estimation from electrochemical state estimation. One EKF estimates the system states of a control-oriented parameter-grouped single particle model with thermal effects, while the other EKF estimates a residual bias that continuously corrects the voltage observation equation, thereby refining the model-predicted voltage in real time. Unlike bias-augmented single-filter schemes that enlarge the covariance coupling, the decoupled bias estimator refines the voltage observation without perturbing electrochemical state dynamics. Validation is conducted on an LFP cell from a public dataset under three representative operating conditions: US06 at 0 degC, DST at 25 degC, and FUDS at 50 degC. Compared with a conventional EKF using the same model and identical state filter settings, the proposed method reduces the average SOC RMSE from 3.75% to 0.20% and the voltage RMSE between the filtered model voltage and the measured voltage from 32.8 mV to 0.8 mV. The improvement is most evident in the mid-SOC range where the OCV-SOC curve is flat, confirming that residual bias compensation significantly enhances accuracy for model-based SOC estimation of LFP batteries across a wide temperature range.

Figures (4)

• Figure 1: Experimental validation of the CPG-SPMT battery model under different operating conditions. The figure shows current profiles (left panels) and voltage comparison between measurements and CPG-SPMT model predictions (right panels) for three test conditions: (a,b) US06 at 0°C, (c,d) DST at 25°C, and (e,f) FUDS at 50°C.
• Figure 2: SOC estimation comparison between single EKF and RBC-DEKF under: (a) US06 at 0$^{\circ}$C, (b) DST at 25$^{\circ}$C, and (c) FUDS at 50$^{\circ}$C.
• Figure 3: SOC estimation error comparison under: (a) US06 at 0$^{\circ}$C, (b) DST at 25$^{\circ}$C, and (c) FUDS at 50$^{\circ}$C.
• Figure 4: Voltage estimation error comparison under: (a) US06 at 0$^{\circ}$C, (b) DST at 25$^{\circ}$C, and (c) FUDS at 50$^{\circ}$C.