Abstract: Systems and methods for including overstay and human behavior in the pricing structure of a plug-in electric vehicle (PEV) charging station, in order to maximize operating revenue, and manage overstay duration. A mathematical framework with discrete choice models (DCM) is incorporated to operate a PEV charging station with an optimal pricing policy and charge control. The framework determines the probability of a PEV driver selecting various charging options, including overstay price. The pricing options are charging-flexibility, which allows a controller to manage charging costs over a parking duration, charging-asap, which charges the electric vehicle immediately and continuously until the vehicle departs, the battery is fully charged, or a desired level of charge has been reached, and leaving, which is an opportunity cost to the charging station.

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.

Abstract: A system for configuring an Electric Vehicle (EV) charging infrastructure, the apparatus comprising. The system receives user direction and inputs regarding a custom EV charging infrastructure, submits the data an optimal planning module which is configured for performing optimization based on objectives in view of a set of constraints; and generates a recommendation from the optimal planning module for a custom EV charging infrastructure based on charging needs and behavior of the consumers toward deferring upgrades in electric infrastructure without compromising energy required for electrical transportation.

Abstract: A station-level framework to operate one or multiple plug-in electric vehicle (PEV) charging stations with optimal pricing policy and charge scheduling, which incorporates human behavior to capture the driver charging decision process. The user is presented with menu of price-differentiated charging services, which differ in per-unit price and the energy delivery schedule. Involving human in the loop dynamics, the operation model results in the alleviation of the overstay issue may occur when a charging session has completed. A multi-block convex transformation is used to reformulate the resulting non-convex problem via the Fenchel-Young Inequality and a Block Coordinate Descent algorithm is applied to solve the overall problem with an efficiency which enables real-time implementation. The pricing control policy realizes benefits in three aspects: (i) net profits gain, (ii) overstay reduction, and (iii) increased quality-of-service.

Abstract: Systems and methods for including overstay and human behavior in the pricing structure of a plug-in electric vehicle (PEV) charging station, in order to maximize operating revenue, and manage overstay duration. A mathematical framework with discrete choice models (DCM) is incorporated to operate a PEV charging station with an optimal pricing policy and charge control. The framework determines the probability of a PEV driver selecting various charging options, including overstay price. The pricing options are charging-flexibility, which allows a controller to manage charging costs over a parking duration, charging-asap, which charges the electric vehicle immediately and continuously until the vehicle departs, the battery is fully charged, or a desired level of charge has been reached, and leaving, which is an opportunity cost to the charging station.

Abstract: An interval observer based on an equivalent circuit-thermal model for lithium-ion batteries is presented. State of charge-temperature-dependent parameters are considered as unknown but bounded uncertainties in a single cell model. A parallel and a series arrangement of five cells are used for observer design, where cell heterogeneity is accounted for through the uncertainty bounding functions.

Abstract: An interval observer based on an equivalent circuit-thermal model for lithium-ion batteries is presented. State of charge-temperature-dependent parameters are considered as unknown but bounded uncertainties in a single cell model. A parallel and a series arrangement of five cells are used for observer design, where cell heterogeneity is accounted for through the uncertainty bounding functions.

Abstract: A system for configuring an Electric Vehicle (EV) charging infrastructure, the apparatus comprising. The system receives user direction and inputs regarding a custom EV charging infrastructure, submits the data an optimal planning module which is configured for performing optimization based on objectives in view of a set of constraints; and generates a recommendation from the optimal planning module for a custom EV charging infrastructure based on charging needs and behavior of the consumers toward deferring upgrades in electric infrastructure without compromising energy required for electrical transportation.