State of Charge (SoC) Estimation Based on an Extended Kalman Filter Model

The lithium iron phosphate (LFP) cell chemistry is finding wide acceptance for energy storage in hybrid electric vehicles (HEVs) and electric vehicles (EVs) due to its high intrinsic safety, fast charging, and long cycle life. However, three main challenges need to be addressed for the accurate estimation of the LFP cell’s state of charge (SOC) at run time:

• Long voltage relaxation time to reach its open circuit voltage (OCV) after a current pulse
• Time-, temperature-, and SOC-dependent hysteresis
• Very flat OCV-SOC curve for most of the SOC range

In view of these problems, traditional state of charge (SOC) estimation techniques such as coulomb counting with error correction using the state-of-charge open-circuit voltage (SOC-OCV) correlation curve are not suitable for this chemistry.

This paper addresses these challenges with a novel combination of the extended Kalman filter (EKF) algorithm, a two-RC-block equivalent circuit, and the traditional coulomb counting method. The simplified implementation of the EKF algorithm offers a computationally efficient option for runtime SOC evaluation on vehicles. The SOC estimation was validated with experimental data of a current profile contaminated with pseudo-random noise and with an offset in the initial condition. The model rapidly converged to within 4% of the true SOC even with imposed errors of 40% to initial SOC and 25% to current measurement.

This paper, Simplified Extended Kalman Filter Model for SOC Estimation of Commercial Power-Oriented LFP Lithium Battery Cells, was presented at SAE World Congress.

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