Abstract: A method for assessing a state of health of a battery having a plurality of heterogeneous cells includes subjecting the cells of the battery to a plurality of diagnostic current pulse cycles; identifying extreme cells based upon the cycles; estimating model parameters of the extreme cells; and estimating upper and lower bounds for the estimated model parameters. Estimating model parameters includes performing a recursive least squares analysis on the extreme cells. Estimating the upper and lower bounds for the estimated model parameters includes performing a sparse Gaussian process regression using the estimated model parameters.

Abstract: A method of predicting state of power (SOP) for a battery includes estimating first state parameter bounds of the battery using an internal observer process; obtaining a current value based upon the first state parameter bounds, system constraint variables, a scaling factor and a reference current using a modified reference governor process; performing adaptive parameter bounding to obtain second state parameter bounds using the current value; determining a constraint variable based upon the second state parameter bounds and whether the constraint variable is within the system constraint variables the using an interval prediction process; and generating a modified scaling factor based upon whether the constraint variable is within the system constraint variables. Using the modified scaling factor, the obtaining, performing, determining and modifying are repeated to generate a final current value. The SOP is estimated based upon the final current value.