(518a) Estimation of Lithium-Ion Battery Condition Using Reduced Electrochemical Cell Modeling

Lithium-ion polymer batteries (LiPB) are extensively used in hybrid electric vehicles (HEV), plug-in hybrid electric vehicle (PHEV) and consumer portable electronics. In order to operate these batteries more efficiently, accurate estimation of battery conditions such as state-of-charge (SOC), state-of-health (SOH) is necessary. Electrochemical modeling of the battery is important for accurate estimation of these battery's conditions. The reduced dynamic equivalent circuit battery model with modified discrete time concept is suggested. This model is based on first order RC circuit model which represents the polarization phenomenon in battery. The model parameters are estimated with adaptive parameter estimation algorithm using least square estimators based on experimental cell data with pulse patterns. The electrochemical model and the parameter estimation algorithm are validated with urban driving pattern data for HEV. Using proposed battery model, estimation of SOC algorithm is developed. From these models, current-based SOC is calculated. Also, voltage-based SOC is estimated by open-circuit voltage calculation from battery model. The current-based SOC and the voltage-based SOC are compensated for each other by PID control system with current-based criteria. The model parameters and the controller parameters are estimated by neural-network and adaptive filters. These models are validated by experimental data with various SOC and temperature environment. The result is that proposed battery model and estimation algorithms are appropriate for estimation of SOC to various use of LiPB.