Robustness of SOC Estimation Algorithms for EV Lithium-Ion Batteries against Modeling Errors and Measurement Noise

Abstract

State of charge (SOC) is one of the most important parameters in battery management system (BMS). There are numerous algorithms for SOC estimation, mostly of model-based observer/filter types such as Kalman filters, closed-loop observers, and robust observers. Modeling errors and measurement noises have critical impact on accuracy of SOC estimation in these algorithms. This paper is a comparative study of robustness of SOC estimation algorithms against modeling errors and measurement noises. By using a typical battery platform for vehicle applications with sensor noise and battery aging characterization, three popular and representative SOC estimation methods (extended Kalman filter, PI-controlled observer, andH∞observer) are compared on such robustness. The simulation and experimental results demonstrate that deterioration of SOC estimation accuracy under modeling errors resulted from aging and larger measurement noise, which is quantitatively characterized. The findings of this paper provide useful information on the following aspects: (1) how SOC estimation accuracy depends on modeling reliability and voltage measurement accuracy; (2) pros and cons of typical SOC estimators in their robustness and reliability; (3) guidelines for requirements on battery system identification and sensor selections.

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

“…Then the SOC stays with the reference, overlapping with very small errors. The error band of the SOC estimation method has to be between the ±2% [21,38]. The obtained SOC estimation error, when the initial SOC for the EKF has been set 50%, 70% and 100% are showed in Figure 12b.…”

State of charge estimation of a Li‐ion battery based on extended Kalman filtering and sensor bias

“…Kalman filtering-based methods were specifically used and proposed in many papers. [15][16][17][18][19][20][21][22][23][24] It was proposed to estimate the states using the statistical estimation theory. 15 First, a state prediction model is proposed to obtain-over time-the state constancy.…”

“…A comparative study of algorithms robustness for estimating SOC against measurement noises and modeling errors has been presented. 21 Using a typical battery platform for vehicle applications with sensor noise and battery aging characterization, three popular and representative SOC estimation methods ofEKF, proportional-integral (PI)-controlled observer, and H ∞ observer were compared. The simulated results showed that the deterioration of the estimation accuracy under modeling errors caused by aging and larger measurement noise, which was characterized quantitatively.…”

“…Kalman filtering‐based methods were specifically used and proposed in many papers 15‐24 . It was proposed to estimate the states using the statistical estimation theory 15 .…”

A capacity model based on charging process for state of health estimation of lithium ion batteries

“…In recent years, many researchers focused on SOC and SOH estimations. Many robust and accurate estimation techniques to estimate SOC have been studied [17][18][19][20]. In contrast, the development of the SOH estimation methods is more challenging due to the complicated electrochemical mechanisms involved in the battery fading [21].…”