Residual Bias Compensation Filter for Physics-Based SOC Estimation in Lithium Iron Phosphate Batteries
Feng Guo, Luis D. Couto, Khiem Trad, Guangdi Hu, Mohammadhosein Safari

TL;DR
This paper introduces a dual EKF approach with residual bias compensation for more accurate SOC estimation in LFP batteries, especially in flat OCV regions, validated across various temperatures and operating conditions.
Contribution
The paper proposes a novel dual-filter structure that decouples residual bias estimation from electrochemical state estimation, improving SOC accuracy in flat OCV regions.
Findings
Significantly reduces SOC RMSE from 3.75% to 0.20%.
Decreases voltage RMSE from 32.8 mV to 0.8 mV.
Enhances SOC estimation accuracy across temperature ranges.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
