Abstract: The autonomous charging management system for electric vehicles utilizes a voltage feedback control structure that compares the system voltage at the point of charging against a reference, while also taking into account the EV battery state of charge (SOC). A proportional control structure reduces the EV charging as the system voltage approaches the reference. Moreover, the control structure does not require any communication between the EV and the utility.

Abstract: The autonomous charging management system for electric vehicles utilizes a voltage feedback control structure that compares the system voltage at the point of charging against a reference, while also taking into account the EV battery state of charge (SOC). A proportional control structure reduces the EV charging as the system voltage approaches the reference. Moreover, the control structure does not require any communication between the EV and the utility.

Abstract: The fuzzy linear programming method for optimizing charging schedules in unidirectional vehicle-to-grid systems is a computerized fuzzy linear programming method for an electric vehicle (EV) aggregator that coordinates the provision of ancillary services, such as regulation and spinning reserves, to electricity markets using unidirectional vehicle-to-grid (V2G). The fuzzy optimization incorporates uncertainties while maintaining the tractability of the problem size since, in fuzzy optimization, there is no need to represent each stochastic parameter by a number of scenarios. This allows for optimizing the charging of all EVs simultaneously, as well as taking market aspects into account, guaranteeing maximization of aggregator profits, and further considering electricity market uncertainties, such as ancillary service prices and ancillary service deployment signals.

Abstract: The memory polynomial based digital predistorter utilizes a cluster-based particle swarm optimization (PSO) technique and has an embedded model-size estimator. The PSO technique and model-size estimator efficiently find the dimensions and accurately estimate the coefficients of the memory polynomial based digital predistorter for improved linearity of a power amplifier.