Computer-Aided Optimization for Predictive Battery State-of-Charge Determination

Abstract

Optimizing the battery management of today’s portable electronic applications goes hand in hand with the reliable and accurate knowledge of the battery’s state-of-charge (SoC). During periods of low load, usually the SoC is determined based on the measurement of the corresponding open-circuit voltage (OCV). This requires a battery to be in a well-relaxed state, which can take more than 3 hours depending on influence factors like the SoC itself and the temperature. Unfortunately, a well-relaxed state is rarely reached in real world scenarios. As an alternative, predicted OCV values can be used to estimate the SoC. In this work, we use a polynomial-enhanced model description for the OCV prediction process. After identifying the critical model parameters, a computer-aided parameter optimization methodology is applied to optimize the OCV prediction process. As a major result, the proposed methodology enables the possibility to optimize the OCV prediction process with respect to a specified SoC estimation accuracy.