State of charge estimation using an unscented filter for high power lithium ion cells

Abstract

SUMMARYHigh power lithium ion batteries are increasingly used in power tools, hybrid electric vehicles and military applications, as a transient power source capable of delivering instant energy, around a relatively fixed state of charge (SOC). Maintaining the battery within pre-specified limits for SOC is important, since lithium ion batteries are prone to safety and/or performance issues during overcharge or rapid discharge below the cut-off voltages. With an increase in the number of cells used in the battery, SOC has a crucial role in cell balancing and optimization of the pack performance. Several techniques have been proposed for the SOC estimation. Most of the existing literature supports an empirical model based on either an electric circuit, arbitrary pole placement or an analytical expression with an arbitrary set of parameters. In spite of their simplicity, the empirical battery models do not provide information on the physical cell limitations. Alternatively, a rigorous electrochemical cell model, aimed at incorporating transport, kinetic and thermodynamic limitations, can be used to estimate parameters that hold a physical significance and hence provide an accurate measure of the cell performance. However, the demand for onboard estimation devices requires estimation techniques that are computationally efficient. In this work, estimation of the SOC of a lithium ion cell using an unscented filtering algorithm is illustrated. The relative advantages and disadvantages of the proposed methodology are briefly discussed.

Stability Analysis for Li-Ion Battery Model Parameters and State of Charge Estimation by Measurement Uncertainty Consideration

“…Afterward, more extensive studies were conducted to assess the effect of the initial SoC creation, process covariance Rk, and measurement covariance Qk [16,17]. To overcome the uncertainty of measurement noise covariance and initial SoC creation, the unscented Kalman filter [2,18,19], the particle filter [9,20,21], the AEKF [1,7,12], and the adaptive observer [22,23] techniques are proposed for SoC estimation. In Class IV, the measurement uncertainty remains an emerging research field according to our knowledge.…”

A Review of Approaches for the Design of Li-Ion BMS Estimation Functions

“…Consisting in integrating the battery current, this open-loop and non-model based method is easy to implement online but it is affected by the uncertainty on the initial condition, by the measurement error accumulated during the battery life and by the battery capacity degradation due to usage [3,5,6]. To overcome these issues and improve the BMS functions, several approaches based on dynamic battery models have been investigated in [4,5,[7][8][9][10][11][12][13][14][15][16][17][18][19]. Regarding the SOC estimation, the main advantage of the model-based methods is that the initialization error can be recovered by means of output (voltage and temperature) feedback, the closed-loop estimation concept consisting in comparing the measurement of the cell voltage and temperature with model predictions.…”

State-of-Charge Estimation and Active Cell Pack Balancing Design of Lithium Battery Power System for Smart Electric Vehicle

“…The unscented KF was utilized [39] to avoid such linearization of the nonlinear equation in EKF. A nonlinear SOC estimator [40] was then employed on the electrochemical model of the battery instead of the ECM.…”