Abstract
An improved nonlinear model for the electrode voltage-current relationship for online battery system identification is proposed. In contrast to the traditional linear-circuit model, the new approach employs a more accurate model of the battery electrode nonlinear steady-state voltage drop based on the Butler-Volmer (BV) equation. The new form uses an inverse hyperbolic sine approximation for the BV equation. Kalman-filter-based system identification is proposed for determining the model parameters based on the measured voltage and current. Both models have been implemented for lead-acid batteries and exercised using test data from a Corbin Sparrow electric vehicle. A comparison of predictions for the two models demonstrates the improvements that can be achieved using the new nonlinear model. The results include improved battery voltage predictions that provide the basis for more accurate state-of-function readings.
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