A Systematic Framework for Battery Performance Estimation Considering Model and Parameter Uncertainties

Abstract

Up to date, model and parameter uncertainties are generally overlooked by majority of researchers in the field of battery diagnostics and prognostics. As a consequence, accuracy ofthe battery performance estimation is dominated by the model fidelity and may vary from cell-to-cell. This paper proposes a systematic framework to quantify battery model and parameter uncertainties for more effective battery performance estimation. Such a framework is generally applicable for estimating various battery performances of interest (e.g. state of charge (SOC), capacity, and power capability). Case studies for battery SOC estimation are conducted to demonstrate the effectiveness of the proposed framework.

Battery Modeling for Multi-cell Battery Pack in Electric VehiclesUsing Bias Correction Method

“…Physical uncertainty can be viewed as the cell-to-cell variability due to manufacturing tolerance. Correspondingly, parameter uncertainty is the reflection of the physical uncertainty in the specific battery model [9]. According to the battery model presented in section 3.1, model parameters (e.g.…”

“…In our study, the average model is built on the basic model, and the system identification and parameter uncertainties quantification methods have been presented in Section 3. However, the battery model is built on the basis of many assumptions and simplifications, and there is probably no ideal model which can predict the real physical system without any model bias [9], In other words, model uncertainty may always exist. For one specific battery cell, model uncertainty is the deterministic difference between the predicted terminal voltage and the true terminal voltage, which indicates the model inadequacy for representing the actual functional relationship under various battery operating conditions.…”

“…means the factors that could affect the bias. As mentioned in the references [9], [10], these factors include SOC, charge/discharge current, maximum available capacity, etc. As we studied, the polarization voltages (UD in the n-RC model) may have great influence in the bias.…”

“…A performance estimation method for battery pack based on bias correction was proposed by reference [1], [9]. Dr. Xiong proposed a cell SoC estimation approach based on an online bias correction model, the parameters of the bias correction model were identified in real-time [1].…”

“…Dr. Xiong proposed a cell SoC estimation approach based on an online bias correction model, the parameters of the bias correction model were identified in real-time [1]. Dr. Xi proposed a framework to quantify battery model and parameter uncertainties, and a response surface model was constructed to quantify the model uncertainty (or model bias) [9]. However, the polarization effect of battery has not been taken into consideration when constructing the model bias.…”