NARX modelling of a lithium iron phosphate battery used for electrified vehicle simulation

Non-linear autoregressive exogenous (NARX) black-box modelling methodology is presented to model a lithium iron phosphate battery for system-level electrified vehicle simulation. The NARX model regressor vector is carefully chosen for dynamically representing the battery voltage and its dependence on state of charge (SOC) and charging/discharging current. Three types of non-linearity estimators, i.e., wavelet network, one-layer sigmoid network, and binary tree partition, are investigated and compared. The prediction error minimisation by means of the advanced adaptive Gaussian-Newton search algorithm is applied to implement the model parameterisation. The impact of the number of basis function units on the model accuracy and complexity is also studied. A preferred NARX model is determined, according to a comprehensive evaluation of model accuracies in two different datasets and complexity. A comparison between the preferred NARX model and a conventionally statically non-linear black-box battery model is made.