Vehicle charging control systems and methods

1. A vehicle charging system comprising:

a vehicle propelled by an electric machine that is powered by a chargeable energy storage system; and

at least one controller programmed to,

define a plug-in routine to have a predetermined number of routine plug-in events for a route based on historical charging data, and

set a charging schedule for the vehicle charging system according to how much energy is expected to be consumed between the routine plug-in events and expected occurrence of non-routine plug-in events having a routineness value exceeding a predetermined threshold value such that a target state of charge (SOC) is achieved at a conclusion of each of the routine plug-in events, wherein each target SOC corresponding to a plug-in event is based on minimizing a charging energy cost of the plug-in routine and an expected energy depletion ahead of a next subsequent plug in event.

2. The vehicle charging system of claim 1 wherein the at least one controller is further programmed to, in response to detecting a vehicle usage event outside of the plug-in routine, set a target SOC to a maximum SOC for the next subsequent plug-in event.

3. The vehicle charging system of claim 1 wherein minimizing a charging energy cost comprises receiving third-party data indicative of at least one of an energy cost and a charge rate for a plurality of upcoming plug-in events, and satisfying each target SOC with the lowest possible charging energy cost for each plug-in event.

4. The vehicle charging system of claim 3 wherein the at least one controller is further programmed to set a target SOC for an upcoming plug-in event based on minimizing a recharge time of the plug-in routine.

5. The vehicle charging system of claim 1 wherein the plug-in routine includes a drive event between each of the plurality of upcoming plug-in events, and each target SOC is based on an energy consumption prediction associated with a corresponding drive event.

6. The vehicle charging system of claim 1 wherein the at least one controller is further programmed to adjust the charging schedule based on real-time vehicle data indicative of a deviation from the plug-in routine.

7. The vehicle charging system of claim 1 wherein the historical charging data is grouped by a user-vehicle combined set.

8. A method of managing electrified vehicle charging comprising:

storing data indicative of historical charging and depletion data;

requesting data indicative of third-party energy pricing;

receiving user input data indicative of a user charging preference;

receiving real-time vehicle usage data; and

setting a charging schedule for a plug-in routine including a plurality of expected plug-in events based on the historical charging and depletion data and an expected occurrence of non-routine plug-in events that is not reflected in the historical charging and depletion data, such that a target state of charge (SOC) is achieved at a conclusion of each of the plurality of expected plug-in events, wherein each target SOC corresponding to a plug-in event is based on minimizing a charging energy cost of the plug-in routine and how much energy is expected to be consumed between the routine plug-in events the non-routine plug-in events, and an expected energy depletion ahead of a next subsequent plug-in event.

9. The method of claim 8 further comprising, in response to detecting a vehicle usage event outside of the plug-in routine, setting a target SOC to a maximum SOC for the next subsequent plug-in event.

10. The method of claim 8 wherein minimizing a charging energy cost comprises setting each target SOC with the lowest possible charging energy cost associated with each expected plug-in event based on the data indicative of third-party energy pricing.

11. The method of claim 8 further comprising setting a target SOC for an expected plug-in event based on minimizing a recharge time of the plug-in routine.

12. The method of claim 8 wherein each target SOC of a given plug-in event is based on an energy consumption prediction associated with a corresponding drive event following the given plug-in event.

13. The method of claim 8 wherein the historical charging and depletion data is grouped by a user-vehicle combined set signaled by a user mobile device in a proximity of the vehicle.

14. A vehicle charging system comprising:

a vehicle propelled by an electric machine that is powered by a chargeable energy storage system; and

at least one controller programmed to,

store data indicative of historical energy charging and depletion,

retrieve data indicative of third-party energy pricing over a wireless network,

receive user input data indicative of a user charging preference,

monitor real-time vehicle usage data,

define a plug-in routine including a plurality of expected plug-in events based on the historical energy charging and depletion, each of the expected plug-in events having a location and duration,

generate a charging schedule according to how much energy is expected to be consumed between the routine plug-in events and expected occurrence of non-routine plug-in events having a routineness value exceeding a predetermined threshold value such that a target state of charge (SOC) is achieved at a conclusion of each of the expected plug-in events, wherein each target SOC corresponding to a plug in event is based on minimizing a charging energy cost of the plug in routine and an expected energy depletion ahead of a next subsequent plug in event, and wherein the routineness value is indicative of a likelihood that the plug-in routine is to take place.

15. The vehicle charging system of claim 14 wherein the at least one controller is further programmed to, in response to detecting a vehicle usage event outside of the plug-in routine, set a target SOC to a maximum SOC for the next subsequent plug-in event.

16. The vehicle charging system of claim 14 wherein minimizing a charging energy cost comprises receiving third-party data indicative of at least one of an energy cost and a charge rate for a plurality of expected plug-in events, and satisfying each target SOC with the lowest possible charging energy cost for each plug-in event.

17. The vehicle charging system of claim 16 wherein the at least one controller is further programmed to set a target SOC for an upcoming plug-in event based on minimizing a recharge time of the plug-in routine.

18. The vehicle charging system of claim 14 wherein the plug-in routine includes a drive event between each of the plurality of expected plug-in events, and each target SOC is based on an energy consumption prediction associated with a corresponding drive event.