Adaptive Battery Modeling and State Estimation
Dr. Mo-Yuen Chow, Habiballah Rahimi Eichi

Overview of Project

The recent acceleration of the battery technology, due to emergence of smart grid and electric vehicle (EV) applications, has increased the demand for advanced battery management systems (BMS). State of charge (SOC) and State of health (SOH) estimation are two of the main features of the BMS. Although there have been several attempts to estimate the battery states, the results do not have enough accuracy to satisfy sensitivity analysis criteria for reliability assessment and energy efficiency purposes. The goal of this project is to develop algorithms to estimate the State of Charge (SOC) and State of Health (SOH) of the battery accurately in these applications.

Considering an RC equivalent circuit to model the battery dynamics, we design an adaptive on-line parameters/SOC/SOH co-estimation algorithm that identifies different parameters of the battery model at various temperature, ageing, SOC and charging/discharging rate conditions. This model with updating parameters is used to estimate the SOC with compensating the initial SOC error, accumulative error and noise effect. The estimated SOC and the updating model are also utilized to estimate the full capacity and internal resistance of the battery. This information along with the statistical data regarding the application and the battery performance degradation are deployed to predict the remaining useful life (RUL) and end of life (EOL) as indicators of the battery SOH.