Pattern based charge scheduling

Patent metadata

Field	Value
Publication number	US-2016159240-A1
Application number	US-201414560323-A
Country	US
Kind code	A1
Filing date	Dec 4, 2014
Priority date	Dec 4, 2014
Publication date	Jun 9, 2016
Grant date	—

Abstract

A method according to an exemplary aspect of the present disclosure includes, among other things, scheduling charging of an energy storage device of an electrified vehicle based on a learned key-on pattern. The learned key-on pattern is derived by recursively updating the probability that a subsequent key-on event is likely to occur at any given time and day.

First claim

What is claimed is:

1 . A method, comprising:
scheduling charging of an energy storage device of an electrified vehicle based on a learned key-on pattern, the learned key-on pattern derived by recursively updating the probability that a subsequent key-on event is likely to occur at any given time and day.

2 . The method as recited in claim 1 , wherein the scheduling step includes scheduling a charging start time and a charging end time for charging the energy storage device.

3 . The method as recited in claim 1 , wherein the scheduling step includes:
estimating an energy requirement for an upcoming trip; and charging the energy storage device to a level sufficient to meet the energy requirement prior to a predicted upcoming key-on event.

4 . The method as recited in claim 3 , wherein the energy requirement is learned based on a learned driving pattern of the electrified vehicle.

5 . The method as recited in claim 1 , wherein the learned key-on pattern is learned at a predefined learning rate.

6 . The method as recited in claim 5 , wherein the predefined learning rate is at least 100 key-on events.

7 . The method as recited in claim 1 , wherein the updating step includes applying a low pass filter to each of a plurality of key-on signals.

8 . The method as recited in claim 1 , wherein, upon receiving a key-on signal, the updating step includes:
increasing the probability that the subsequent key-on event will occur on the same day and the same time as the key-on signal; and decreasing the probability that the subsequent key-on event will occur at a different day and a different time from the key-on signal.

9 . The method as recited in claim 1 , wherein the updating step includes:
partitioning each day of a week into a plurality of predefined segments; and each time another key-on event occurs, updating the probability that the subsequent key-on event is likely to occur during the same predefined segment.

10 . The method as recited in claim 9 , wherein updating step includes:
increasing the probability that the subsequent key-on event will occur on the same day and the same time as each key-on signal occurs; and decreasing the probability that the subsequent key-on event will occur at a different day and a different time than each key-on signal occurred.

11 . The method as recited in claim 1 , comprising performing a learning process for learning the key-on pattern, wherein the learning process includes:
identifying a driver operator associated with the electrified vehicle.

12 . The method as recited in claim 11 , wherein the learning process includes:
confirming a first key-on event; and communicating a first key-on signal indicative of the first key-on event.

13 . The method as recited in claim 12 , wherein the learning process includes:
accessing key-on probability information associated with the driver operator.

14 . The method as recited in claim 13 , wherein the learning process includes:
modifying the key-on probability information by either increasing or decreasing the probability that the subsequent key-on event will occur at the same time and same day as the first key-on event.

15 . The method as recited in claim 1 , comprising:
charging the energy storage device base on a charging schedule derived from the learned key-on pattern.

16 . A vehicle system, comprising:
an electrical storage device; and a control module configured to schedule charging of said electrical storage device based on a learned key-on pattern that is derived by recursively updating the probability that a subsequent key-on event is likely to occur at any given time and day.

17 . The vehicle system as recited in claim 16 , comprising a power electronics module configured to control charging of said electrical storage device.

18 . The vehicle system as recited in claim 17 , comprising a charger configured to supply electrical power to said power electronics module.

19 . The vehicle system as recited in claim 16 , wherein said control module includes a processing unit and non-transitory memory, and a key-on probability plot is stored in said non-transitory memory.

20 . The vehicle system as recited in claim 16 , wherein said control module is configured to estimate an energy requirement for an upcoming trip.

Assignees

• Ford Global Tech Llc

Inventors

• Tseng Fling
• Yang Hsin-Hsiang
• Prakah-Asante Kwaku O
• Michelini John Ottavio
• Filev Dimitar Petrov

Classifications

• B60L53/66

  Data transfer between charging stations and vehicles · CPC title

• Y02T10/7072

  Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors · CPC title

• B60L58/10

  for monitoring or controlling batteries · CPC title

• H02J7/1446

  in response to parameters of a vehicle · CPC title

• Y02T90/14

  Plug-in electric vehicles · CPC title