A State of Charge Estimation Method of Lithium-Ion Battery Based on Fused Open Circuit Voltage Curve

Abstract

The open circuit voltage (OCV) and model parameters are critical reference variables for a lithium-ion battery management system estimating the state of charge (SOC) accurately. However, the polarization effect reduces the accuracy of the OCV test, and the model parameters coupled to the polarization voltage increase the non-linearity of the cell model, all challenging SOC estimation. This paper presents an OCV curve fusion method based on the incremental and low-current test. Fusing the incremental test results without polarization effect and the low current test results with non-linear characteristics of electrodes, the fusion method improves the OCV curve’s accuracy. In addition, we design a state observer with model parameters and SOC, and the unscented Kalman filter (UKF) method is employed for co-estimation of SOC and model parameters to eliminate the drift noise effects. The SOC estimation root mean square error (RMSE) of the proposed method achieves 0.99% and 1.67% in the pulse constant current test and dynamic discharge test, respectively. Experimental results and comparisons with other methods highlight the SOC estimation accuracy and robustness of the proposed method.

Practical identifiability of electrochemical P2D models for lithium-ion batteries

“…For ECMs, parameter estimation is frequently conducted [1]. These models commonly focus on estimation of battery states like SOC, state of power, or state of health [16,25,29,42,44]. Dvorak et al introduced a parameter estimation method for an ECM containing 2 RC elements.…”

Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

“…The OCV method estimates the SOC value through the function curve of the OCV and SOC value; however, it is difficult to obtain the OCV value of the lithium‐ion battery in actual working conditions, so it is impossible to directly apply this method to engineering practice. Wang et al 29 proposed a fusion OCV estimation method, which obtains the fused OCV data through the first‐order backward differential integration of the small current test results and fits the data through a neural network to obtain the OCV‐SOC curve. This method reduces the influence of the battery polarization effect and has a high SOC estimation accuracy.…”

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

“…Firstly, the state space representation in discrete form (see Equations ( 1) and ( 2)) is beneficial for KF approaches like the Extended Kalman Filter (EKF) [100]. Parameter estimation is commonly combined with SOC estimation when applying a Dual Kalman Filter (DKF) [101][102][103][104]. In the following equation, ∆T is the discrete simulation step size, τ i is the time constant of the i-th RC element with resistance R i , whereas R 0 expresses the ohmic resistance.…”

“…Sometimes, the model is enhanced by electro-chemical equations based on the Single Particle Model (SPM) [178][179][180]. However, the model accuracy significantly influences the estimation performance [101,103,[181][182][183] as does the measurement accuracy [110,181]. Most recently, SOC estimation is combined with estimation of further states e.g., SOH [173,[184][185][186][187] or State of Function (SOF) [188].…”