Abstract: A computer-implemented method of optimizing charging load on electric vehicle chargers and allocating charging connectors to an electric vehicle fleet at a depot, includes mapping occupancy of charging connectors at each charger, and prior to arrival of an electric vehicle at the depot, checking a priority assigned to unoccupied charging connectors, or sorting unoccupied charging connectors based on their priority. If there is a predetermined priority, mapping the electric vehicle to an unoccupied charging connector depending on priority and if the electric vehicle has a predetermined priority requirement, determining a normalized stress metric, selecting an unoccupied charger with lowest normalized stress metric, calculating an allocation of a connector at the charger based on balancing the normalized stress metric during use, allocating the charging connector to achieve balance, and mapping the electric vehicle to the allocated charging connector.

Abstract: A system and method for managing dispatch of vehicle fleet is provided. The method includes receiving information associated with a plurality of blocks to be served by a fleet of vehicles and determining that information of at least one critical block period is unavailable based on the information associated with the plurality of blocks. Further, the method includes determining the at least one critical block period based on at least one block requiring a number of non-EVs greater than an available number of non-EVs in the fleet of vehicles; scheduling the fleet of vehicles for serving the plurality of blocks by reducing a proportion of a number of non-EVs to serve the at least one critical block period; and dispatching the fleet of vehicles for serving the plurality of blocks including the at least one critical block period.

Abstract: A system, method and computer program product for route planning of electric vehicles (EVs) is provided. The method includes receiving a plurality of parameters associated with an EV fleet. The method includes determining a dttr for each route of a plurality of routes based on the route information associated with each EV of the EV fleet. Further, the method includes determining a set of EVs from the EV fleet based on a validity of each EV of the EV fleet and mapping the set of EVs to a set of the routes of the plurality of routes based on the dttr for each route of the plurality of routes. The method also includes scheduling the set of EVs to the set of the routes based on the mapping.

Abstract: A method, computing system, and computer-program product for robust optimization of charging of a plurality of electric vehicles scheduled for charging at a charging station is provided. The charging station includes a plurality of chargers. In an embodiment, the method includes obtaining vehicle data and station data. Based thereon, it is determined if a set of EVs from amongst the plurality of EVs has failed to achieve a pre-defined target SoC, after a pre-defined charging duration. If the set of EVs has failed to achieve the pre-defined target SoC, a target power is computed. Further, the method includes adjusting at least one of the maximum power of each charger associated with charging of each EV from the set of EVs and the maximum power of the set of EVs as per the target power, to reach the target SoC of the set of EVs.

Abstract: A system and method for block scheduling for an electric vehicle are provided. The method includes obtaining route information including designated routes and their distances in a defined operation period for the EV, and battery information including a consumption factor and a charging rate for a battery of the EV. The method further includes adding dummy routes representative of a predefined charging time period for the battery in the defined operation period. The method further includes defining a dummy factor having a first value for each of the designated routes for the energy being consumed, and a second value for each of the dummy routes for the energy being replenished. The method further includes determining a schedule for the designated routes and the dummy routes for the EV using an optimization model having constraints for the route information, the battery information and the dummy factor.