Abstract: A method for operating an electric vehicle charging station that comprises a first number of fixed chargers and a second number of mobile devices. Each of the mobile devices moves in the charging station to plug and unplug an electric vehicle. The method includes, at a time step, obtaining, upon receiving a charging request from an electric vehicle arriving at a beginning of the time step, a first charging demand; deriving, upon receiving charging dynamics of an electric vehicle having been staying at the charging station before the time step, a second charging demand; generating, with respect to an optimization horizon including the time step and a plurality of subsequent time steps, a charging demand forecast; and solving, with respect to the optimization horizon, an optimal operation solution, based on the first charging demand, the second charging demand, and the charging demand forecast.

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.