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.