Abstract: The disclosure includes embodiments for optimizing a route selection for a highly autonomous vehicle (“HAV”) which provides an automated driving service. In some embodiments, a method includes: generating a set of candidate routes for a journey; aggregating external data describing one or more external factors which limit an availability of the automated driving service while traveling along the candidate routes; determining a first value for an optimal degree of automation for traveling the journey based on a condition of the driver and the preferences of the driver; determining a plurality of second values for a plurality of possible degrees of automation for each candidate route based on the external data; and selecting an optimum route from the set of candidate routes by comparing the first value to the plurality of second values to determine which of the candidate routes has a second value which is closest to the first value.

Abstract: The disclosure includes a system and method for providing charging services to mobile client devices. The system includes a processor and a memory storing instructions that, when executed, cause the system to: estimate a departure time of a future journey associated with a mobile client device; verify one or more electrical characteristics of a battery associated with the mobile client device; determine a target state of charge for the battery at the departure time; and determine a charge scheme to improve an operational life of the battery. The charge scheme has a combining charge cost that satisfies a charge objective of the mobile client device. The combining charge cost includes a weighted combination of a power cost and a battery degradation cost. The charge scheme is configured to charge the battery to achieve the target state of charge at the departure time.

Abstract: The disclosure includes embodiments for optimizing a route selection for a highly autonomous vehicle (“HAV”) which provides an automated driving service. In some embodiments, a method includes: generating a set of candidate routes for a journey; aggregating external data describing one or more external factors which limit an availability of the automated driving service while traveling along the candidate routes; determining a first value for an optimal degree of automation for traveling the journey based on a condition of the driver and the preferences of the driver; determining a plurality of second values for a plurality of possible degrees of automation for each candidate route based on the external data; and selecting an optimum route from the set of candidate routes by comparing the first value to the plurality of second values to determine which of the candidate routes has a second value which is closest to the first value.

Abstract: The disclosure includes a system and method for charging and discharging a Plug-in Electric Vehicle (“PEV”). The method may include determining that first event data describes a Renewable Energy Output Control event (“REOC event”) including instructions directing a Renewable Energy Power Generation facility (“REPG facility”) to not output power to a power grid during a time period specified by the REOC event. The method may include the REPG facility charging a battery system included in a PEV during the REOC event. The battery system may be coupled to the REPG facility via a coupling that does not including the power grid. The battery system may be charged by the REPG facility via the coupling with power generated by the REPG facility during the first duration of the REOC event so that the REPG facility continues to generate power during the REOC event.

Abstract: The disclosure includes a system and method for determining a departure time for a Plug-in Electric Vehicle (“PEV”). The method may include analyzing power meter data to determine that a connector of a control device is coupled to an inlet of the PEV. The method may include charging a battery set of the PEV with electricity. The method may include determining a next day of a next journey for the PEV based on clock data provided by a clock. The clock data may describe time information describing a present day and a category of the next day. The method may include analyzing history data associated with the category of the next day to determine a habitual time associated with the category of the next day. The method may include estimating that a departure time for the next journey is substantially equal to the habitual time.

Abstract: The disclosure includes a system and method for providing charging services to mobile client devices. The system includes a processor and a memory storing instructions that, when executed, cause the system to: receive demand response event data associated with a geographic region; determine a last-mile distribution network that includes a first endpoint in the geographic region, the first endpoint associated with a mobile client device; estimate one or more last-mile power usage factors describing power usage of a set of endpoints in the last-mile distribution network, the set of endpoints including the first endpoint associated with the mobile client device; and determine a charge schedule for the mobile client device based on the demand response event data and the one or more last-mile power usage factors.

Abstract: The disclosure includes a system and method for determining a departure time for a Plug-in Electric Vehicle (“PEV”). The method may include analyzing power meter data to determine that a connector of a control device is coupled to an inlet of the PEV. The method may include charging a battery set of the PEV with electricity. The method may include determining a next day of a next journey for the PEV based on clock data provided by a clock. The clock data may describe time information describing a present day and a category of the next day. The method may include analyzing history data associated with the category of the next day to determine a habitual time associated with the category of the next day. The method may include estimating that a departure time for the next journey is substantially equal to the habitual time.

Abstract: The disclosure includes a system and method for charging and discharging a Plug-in Electric Vehicle (“PEV”). The method may include determining that first event data describes a Renewable Energy Output Control event (“REOC event”) including instructions directing a Renewable Energy Power Generation facility (“REPG facility”) to not output power to a power grid during a time period specified by the REOC event. The method may include the REPG facility charging a battery system included in a PEV during the REOC event. The battery system may be coupled to the REPG facility via a coupling that does not including the power grid. The battery system may be charged by the REPG facility via the coupling with power generated by the REPG facility during the first duration of the REOC event so that the REPG facility continues to generate power during the REOC event.

Abstract: The disclosure includes a system and method for performing one or more vehicle functions associated with a vehicle. The system includes a processor and a memory storing instructions that, when executed, cause the system to: receive sensor data indicating that a vehicle has arrived at a destination location; determine a synchronized arrival time describing when the vehicle arrived at the destination location, the synchronized arrival time determined based in part on the sensor data; and determine one or more vehicle functions associated with the vehicle based in part on the synchronized arrival time.

Abstract: A system and method for controlling charging for a vehicle is disclosed. The system comprises a statistical analyzer, an estimation module and a determination module. The statistical analyzer analyzes a set of data describing utility rate information and one or more behaviors of the vehicle. The estimation module estimates one or more tendency parameters based at least in part on the analyzing. The determination module determines a charging setting based at least in part on the one or more tendency parameters. The charging setting comprises one or more of a target charging battery state of charge (SoC), a charging start time and a charging end time.

Abstract: A system and method for managing smart grid communication is disclosed. The system includes a communication module, a discovery module, a translation module and a smart grid module. The communication module receives data initiating smart grid communication between a utility server and a communication node. The discovery module identifies a server protocol used by the utility server to communicate with the communication node. The discovery module identifies a node protocol used by the communication node to communicate with the utility server. The node protocol is incompatible with the server protocol. The translation module performs a protocol translation between the server protocol and the node protocol. The smart grid module handles the smart grid communication between the utility server and the communication node using the protocol translation.

Abstract: The disclosure includes a system and method for providing charging services to mobile client devices. The system includes a processor and a memory storing instructions that, when executed, cause the system to: receive demand response event data associated with a geographic region; determine a last-mile distribution network that includes a first endpoint in the geographic region, the first endpoint associated with a mobile client device; estimate one or more last-mile power usage factors describing power usage of a set of endpoints in the last-mile distribution network, the set of endpoints including the first endpoint associated with the mobile client device; and determine a charge schedule for the mobile client device based on the demand response event data and the one or more last-mile power usage factors.

Abstract: The disclosure includes a system and method for performing one or more vehicle functions associated with a vehicle. The system includes a processor and a memory storing instructions that, when executed, cause the system to: receive sensor data indicating that a vehicle has arrived at a destination location; determine a synchronized arrival time describing when the vehicle arrived at the destination location, the synchronized arrival time determined based in part on the sensor data; and determine one or more vehicle functions associated with the vehicle based in part on the synchronized arrival time.

Abstract: A system and method for managing event control schedules is disclosed. The system includes a retrieval module, an estimation module, a plan module and a scheduling module. The retrieval module retrieves mobile system data associated with a mobile computer system and user profile data associated with a user of the mobile computer system. The estimation module estimates a synchronized start time of a future trip for the user based at least in part on one or more of the mobile system data and the user profile data. The plan module generates one or more provisioning plans for the mobile computer system based at least in part on the synchronized start time of the future trip and determines a preferred provisioning plan from the one or more provisioning plans. The scheduling module schedules to provision the mobile computer system based at least in part on the preferred provisioning plan.

Abstract: A system and method for controlling charging for a vehicle is disclosed. The system comprises a statistical analyzer, an estimation module and a determination module. The statistical analyzer analyzes a set of data describing utility rate information and one or more behaviors of the vehicle. The estimation module estimates one or more tendency parameters based at least in part on the analyzing. The determination module determines a charging setting based at least in part on the one or more tendency parameters. The charging setting comprises one or more of a target charging battery state of charge (SoC), a charging start time and a charging end time.