PHEV Energy Management Control with Trip-Oriented Energy Consumption Preplanning

• US 20150120107A1
• Filed: 10/31/2013
• Published: 04/30/2015
• Est. Priority Date: 10/31/2013
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Yu, Hai, Wang, Qing, McGee, Ryan Abraham
• Granted Patent: US 9,409,563 B2
• Time in Patent Office: Days
• US Class Current: 701/22
• CPC Class Codes: B60L 2240/622 by satellite navigation
• CPC Class Codes: B60L 2240/645 Type of road
• CPC Class Codes: B60L 2260/52 drive range estimation, e.g. of estimation of available travel distance
• CPC Class Codes: B60L 2260/54 Energy consumption estimation
• CPC Class Codes: B60L 3/12 Recording operating variables ; Monitoring of operating variables
• CPC Class Codes: B60L 53/14 Conductive energy transfer
• CPC Class Codes: B60W 20/00 Control systems specially adapted for hybrid vehicles hybrid vehicle design, B60K6/00; electric vehicles B60L
• CPC Class Codes: B60W 20/12 using control strategies taking into account route information estimation or calculation of non-directly measurable driving parameters B60W40/00
• CPC Class Codes: B60Y 2400/214 by power from domestic supply, e.g. plug in supplies
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 10/72 Electric energy management in electromobility
• CPC Class Codes: Y02T 90/14 Plug-in electric vehicles
• CPC Class Codes: Y02T 90/16 Information or communication technologies improving the operation of electric vehicles
• CPC Class Codes: Y10S 903/903 having energy storing means, e.g. battery, capacitor

Abstract

A driving pattern based plug-in hybrid electric vehicle (PHEV) energy consumption preplanning process enables a PHEV trip-oriented energy management control (TEMC) to utilize scalable levels of available trip foreknowledge in order to optimize the onboard energy (fuel and electricity) usage. The preplanning process generates an optimal battery state-of-charge (SOC) depletion profile for a given trip to be traveled by a PHEV. The preplanning process may generate the battery SOC profile using a driving pattern based dynamic programming (DP) algorithm. The TEMC controls the onboard energy usage in accordance with the battery SOC profile, which is optimized for the trip. The preplanning process makes use of spatial domain normalized drive power demand (SNDP) (or S-NDP) distributions in which each set of distributions is indicative of a respective driving pattern. The trip foreknowledge is used to select the driving pattern best representative of the driving process for the trip.

Claims

1. A method for operating a vehicle comprising:

controlling battery usage of the vehicle according to a battery state-of-charge (SOC) profile based on a spatial domain normalized drive power (S-NDP) distribution of a driving pattern.

2. The method of claim 1 further comprising:

generating the SOC profile using dynamic programming with an energy consumption characteristic based on the S-NDP distribution.

3. The method of claim 1 further comprising:

selecting the driving pattern based on information associated with a trip of the vehicle.

4. The method of claim 3 wherein:

the driving pattern is representative of each of a plurality of driving processes having similar S-NDP profiles.

5. The method of claim 4 wherein:

the driving pattern is selected as being the selected driving pattern when the information associated with the trip is indicative of a driving process that is one of the plurality of driving processes.

6. The method of claim 3 further comprising:

generating a library of driving patterns, each driving pattern having a corresponding S-NDP distribution and each driving pattern associated with a driving process parameter.

7. The method of claim 6 wherein:

the selected driving pattern is a driving pattern of the library of driving patterns having the driving process parameter which best corresponds to the information associated with the trip.

8. The method of claim 6 wherein:

each driving pattern of the library of driving patterns is representative of a set of driving processes having similar S-NDP profiles.

9. The method of claim 8 wherein:

the selected driving pattern is selected as being the selected driving pattern when the information associated with the trip is indicative of a driving process that is one of the set of driving processes of the selected driving pattern.

10. The method of claim 1 further comprising:

selecting a driving pattern for each portion of a trip of the vehicle based on information associated with each portion of the trip, wherein each driving pattern has a corresponding S-NDP distribution; and

generating the battery SOC profile using dynamic programming with an energy consumption characteristic based on the S-NDP distribution of each selected driving pattern and each portion of the trip associated with the selected driving pattern.

11. A system for operating a vehicle comprising:

a controller configured to control battery usage of the vehicle according to a battery state-of-charge (SOC) profile based on a spatial domain normalized drive power (S-NDP) distribution of a driving pattern.

12. The system of claim 11 wherein:

the controller is further configured to generate the SOC profile using dynamic programming with an energy consumption characteristic based on the S-NDP distribution.

13. The system of claim 11 wherein:

the controller is further configured to select the driving pattern based on information associated with a trip of the vehicle.

14. The system of claim 13 wherein:

the driving pattern is representative of each of a plurality of driving processes having similar S-NDP profiles.

15. The system of claim 14 wherein:

the controller is further configured to select the driving pattern as being the selected driving pattern when the information associated with the trip is indicative of a driving process that is one of the plurality of driving processes.

16. The system of claim 13 wherein:

the controller is further configured to generate a library of driving patterns each having a corresponding S-NDP distribution and each driving pattern associated with a driving process parameter.

17. The system of claim 16 wherein:

the controller is further configured to select the selected driving pattern as being a driving pattern of the library of driving patterns having the driving process parameter which best corresponds to the information associated with the trip.

18. The system of claim 16 wherein:

each driving pattern of the library of driving patterns is representative of a set of driving processes having similar S-NDP profiles.

19. The system of claim 11 wherein:

the controller is further configured to select a driving pattern for each portion of a trip of the vehicle based on information associated with each portion of the trip, wherein each driving pattern has a corresponding S-NDP distribution, and generate the battery SOC profile using dynamic programming with an energy consumption characteristic based on the S-NDP distribution of each selected driving pattern and each portion of the trip associated with the selected driving pattern.

20. A method comprising:

classifying a first set of driving processes having similar spatial domain normalized drive power (S-NDP) profiles as a first driving pattern, the first driving pattern having a corresponding S-NDP distribution based on the S-NDP profiles of the first set of driving processes;

classifying a second set of driving processes having similar S-NDP profiles different than the S-NDP profiles of the first set of driving processes as a second driving pattern, the second driving pattern having a corresponding S-NDP distribution based on the S-NDP profiles of the second set of driving processes;

selecting from the first and second sets of driving patterns for a trip of a vehicle the driving pattern in which information associated with the trip is most indicative of a driving process that is one of the set of driving processes of the selected driving pattern;

generating a battery state-of-charge (SOC) profile using dynamic programming with an energy consumption characteristic based on the S-NDP distribution of the selected driving pattern; and

controlling battery usage of the vehicle during the trip according to the SOC profile.