A Variable Effective Capacity Model for \LiFePO4 Traction Batteries Using Computational Intelligence Techniques.

Computational intelligence techniques are used to approximate the nonlinear operation of \LiFePO4 batteries using rule-based systems. In this paper, rule-based systems are not directly fitted to data, but comprise constructive blocks in a differential-equation-based dynamical model that is numerically integrated to infer battery voltage, charge, and temperature. The design methodology has been validated with three different \ \hspace*-1.8ptLiFePO4 batteries, and the results were found to be more accurate than those of a selection of statistical models and state-of-the-art artificial intelligence techniques. [ABSTRACT FROM PUBLISHER]