Recursive calibration for a lithium iron phosphate battery for electric vehicles using extended Kalman filtering

In this paper, an efficient model structure composed of a second-order resistance-capacitance network and a simply analytical open circuit voltage versus state of charge (SOC) map is applied to characterize the voltage behavior of a lithium iron phosphate battery for electric vehicles (EVs). As a result, the overpotentials of the battery can be depicted using a second-order circuit network and the model parameterization can be realized under any battery loading profile, without a special characterization experiment. In order to ensure good robustness, extended Kalman filtering is adopted to recursively implement the calibration process. The linearization involved in the calibration algorithm is realized through recurrent derivatives in a recursive form. Validation results show that the recursively calibrated battery model can accurately delineate the battery voltage behavior under two different transient power operating conditions. A comparison with a first-order model indicates that the recursively calibrated second-order model has a comparable accuracy in a major part of the battery SOC range and a better performance when the SOC is relatively low.