State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks

Algorithm 1

Input selection using FRA algorithm.

	Require the maximum voltage vector , the minimum voltage , the average voltage , the average temperature of the battery cells, the circuit current , the maximal order of time lags for inputs , the maximal order of time lags for output , the maximal number of selected terms , and the minimal training error .
Ensure the SOC vector of the battery pack .
(1)	Initialization: form the regression matrix for polynomial term selection.
(2)	for to do
(3)	calculate the recursive matrix , and is recursively calculated by
(4)	calculate the net contribution of the terms using equation (10).
(5)	select the significant term.
(6)	end for
(7)	Input selection: find the order of the time lags from the selected model terms.