Discharging/Charging Voltage-Temperature Pattern Recognition for Improved SOC/Capacity Estimation and SOH Prediction at Various Temperatures

Abstract

This study investigates an application of the Hamming network-dual extended Kalman filter (DEKF) based on pattern recognition for high accuracy state-of-charge (SOC)/capacity estimation and state-of-health (SOH) prediction at various temperatures. The averaged nine discharging/charging voltage-temperature (DCVT) patterns for ten fresh Li-Ion cells at experimental temperatures are measured as representative patterns, together with cell model parameters. Through statistical analysis, the Hamming network is applied to identify the representative pattern that matches most closely with the pattern of an arbitrary cell measured at any temperature. Based on temperature-checking process, model parameters for a representative DCVT pattern can then be applied to estimate SOC/capacity and to predict SOH of an arbitrary cell using the DEKF. This avoids the need for repeated parameter measuremet.

State Estimation Technique for VRLA Batteries for Automotive Applications

“…In this paper, the DEFK approach is used to estimate the SOC and SOH of a battery. The DEFK process essentially combines two EKFs that run in parallel, where one is the state filter for estimating the SOC, and the other is the weighting filter for estimating the capacity [16,30]. At each time interval, the state filter uses the priori value of the weight filter and vice versa.…”