A Strategy for Estimating State-of-Charge and State-of-Health of Li-Ion Batteries in Electric and Hybrid Electric Vehicles

A task that has to be solved for the application of batteries in vehicles with an electric drive train is the determination of the actual state-of-health (SOH) and state-of-charge (SOC) of the battery cells. In this paper, an on board strategy for estimating SOC and SOH of Li-ion batteries is proposed. The equivalent circuit model is used for both SOC and SOH estimations. In SOH algorithm, the estimated value of battery capacity not only reflects the aging degree of battery pack, but also provides information for SOC estimation. Meanwhile, the extended Kaiman filtering is used in SOC estimation. Because the performance of the equivalent circuit model will be better at small currents than at high currents, extended Kaiman filtering is substituted by Ampere-Hour counting when the absolute value of current is greater than a calibration value. The Digatron battery tester was used to evaluate the proposed estimation method, and results show that the estimation method has high accuracy and efficiency at ordinary temperatures.

Time-Varying Coefficients for Discrete Panel Data / Zeitvariierende Koeffizienten für diskrete Panel Daten

SummaryFor panels which are observed over a long time it has to be assumed that the effects of covariates vary across time. An estimation method based on local likelihood principles is proposed which allows to estimate the variation of coefficients in the case of discrete responses. Approximate confidence intervals are derived for the estimates. The method is applied to business tendency surveys and the results are compared to the extended Kaiman filtering approach.