POWER MANAGEMENT SYSTEMS AND METHODS IN A HYBRID VEHICLE

• US 20090259363A1
• Filed: 04/08/2009
• Published: 10/15/2009
• Est. Priority Date: 04/15/2008
• Status: Active Grant

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company), UWM Research Foundation, Inc.
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company), UWM Research Foundation, Inc.
• Inventors: Ishikawa, Yosuke, Li, Yaoyu
• Granted Patent: US 8,190,318 B2
• Time in Patent Office: Days
• US Class Current: 701/36
• CPC Class Codes: B60K 6/445 Differential gearing distribution type
• CPC Class Codes: B60W 2510/244 Charge state
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 2720/103 Speed profile
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: G01C 21/26 specially adapted for navigation in a road network
• CPC Class Codes: Y02T 10/62 Hybrid vehicles

Abstract

A system and method of determining and applying power split ratios to power sources within hybrid vehicles. The power split ratio is determined using a two-scale dynamic programming technique to achieve optimal state of charge depletion over the course of a trip. On the macro-scale level, a global state of charge profile is created for the entire trip. On the micro-scale level, the state of charge profile and accompanying power split ratio is recalculated at the end of each segment as the vehicle proceeds along the trip. Various trip modeling techniques are used to provide constraints for the dynamic programming.

Claims

1. A hybrid vehicle comprising:

a drive train;

an electric power source coupled to the drive train and including an electric energy storage device having a state of charge;

a non-electric power source coupled to the drive-train; and

a control system for controlling the transfer of power from the electric power source and the non-electric power source to the drive train, the control system comprising software stored on a computer readable medium for effecting the steps of;

establishing a power split ratio between the electric power source and the non-electric power source for a defined trip route so that the state of charge reaches a defined threshold at the end of the trip route;

dividing the trip route into a series of trip segments;

receiving data from an information database, the data relating to historic or real-time conditions of each trip segment; and

recalculating the power split ratio for each trip segment based on the data.

2. A hybrid vehicle as set forth in claim 1, wherein the recalculating the power split ratio for each trip segment comprises using neural network modeling to predict traffic flow for at least one trip segment.

3. The hybrid vehicle of claim 1, further comprising software stored on the computer readable medium for effecting the step of modeling the trip route to create a driving cycle that includes a velocity profile of the hybrid vehicle for each of the trip segments.

4. The hybrid vehicle of claim 3, wherein the modeling the trip route comprises selecting a trip model for modeling each of the trip segments, wherein the trip model is one of:

a gas-kinetic trip model,

a Gipps car following model,

a neural network model,

a trip model using historical or real-time traffic data and constant acceleration and deceleration rates, and

a simple trip model using constant acceleration, constant deceleration, and speed limits as velocity rates.

5. The hybrid vehicle of claim 1, wherein the power split ratio is established using dynamic programming and is recalculated using dynamic programming during the trip.

6. The hybrid vehicle of claim 5, wherein the dynamic programming uses a driving cycle for the trip route created by trip modeling, and wherein the driving cycle for the trip route includes a velocity profile of the hybrid vehicle.

7. The hybrid vehicle of claim 5, wherein the dynamic programming is performed in the spatial domain.

8. The hybrid vehicle of claim 1, further comprising software stored on the computer readable medium for effecting the step of recognizing driving patterns at various points along the trip route as the vehicle proceeds along the trip route, and wherein recalculating the power split ratio is performed based on recognized driving patterns.

9. A hybrid vehicle comprising:

a drive train;

an electric power source coupled to the drive train and including an electric energy storage device having a state of charge;

a non-electric power source coupled to the drive-train; and

a control system for controlling the transfer of power from the electric power source and the non-electric power source to the drive train, the control system comprising software stored on a computer readable medium for effecting the steps of;

establishing a power split ratio between the electric power source and the non-electric power source for a defined trip route so that the state of charge reaches a defined threshold at the end of the trip route;

determining the state of charge at various points along the trip route as the vehicle proceeds along the trip route;

recognizing driving patterns at multiple points along the trip route as the vehicle proceeds along the trip route; and

recalculating the power split ratio at the various points along the trip route to ensure that the state of charge approximately reaches the defined threshold when the vehicle reaches the end of the trip route, wherein the recalculating the power split ratio is based on the recognized driving patterns as the vehicle proceeds along the trip route.

10. A hybrid vehicle as set forth in claim 9, wherein the recalculating the power split ratio for each remaining trip segment comprises using neural network modeling to predict traffic flow for at least one segment.

11. The hybrid vehicle of claim 10, further comprising software stored on the computer readable medium for effecting the steps of:

segmenting the trip route into trip segments, and

modeling the trip route to create a driving cycle for the trip route that includes a velocity profile of the hybrid vehicle for each of the trip segments.

12. The hybrid vehicle of claim 10, wherein the modeling the trip route comprises selecting a trip model for modeling each of the trip segments, wherein the trip model is one of:

a gas-kinetic trip model,

a Gipps car following model,

a neural network model,

a trip model using historical or real-time traffic data and constant acceleration and deceleration rates, and

a simple trip model using constant acceleration, constant deceleration, and speed limits as velocity rates.

13. The hybrid vehicle of claim 9, wherein the power split ratio is established using dynamic programming and is recalculated using dynamic programming during the trip.

14. The hybrid vehicle of claim 13, wherein the dynamic programming uses a driving cycle for the trip route created by trip modeling, and wherein the driving cycle for the trip route includes a velocity profile of the hybrid vehicle.

15. The hybrid vehicle of claim 13, wherein the dynamic programming is performed in the spatial domain.

16. A method of controlling a hybrid vehicle comprising the steps of:

retrieving trip data;

determining a trip route based on the trip data;

dividing the trip route into (n) segments;

modeling each of the (n) segments of the trip route to determine a driving cycle along the trip route for the hybrid vehicle, wherein at least one segment is modeled using a neural network model;

determining a global state of charge profile estimating the state of charge at the end of each of the (n) segments such that the state of charge approximately reaches the defined threshold when the vehicle reaches the end of the trip route;

determining a power split ratio for each of the (n) segments based on the actual state of charge at the beginning of a segment about to be traversed and the estimated state of charge at the end of the segment about to be traversed, such that the determined power split ratio causes the state of charge to approximately reach the estimated state of charge at the end of the segment about to be traversed; and

applying the determined power split ratio for each of the (n) segments.

17. The method of controlling a hybrid vehicle of claim 16, wherein modeling each of the (n) segments of the trip route comprises selecting a trip model for modeling each of the (n) segments, wherein the trip model is one of:

a gas-kinetic trip model,

a Gipps car following model,

a neural network model,

a trip model using historical or real-time traffic data and constant acceleration and deceleration rates, and

a simple trip model using constant acceleration, deceleration, and speed limits as velocity rates.

18. The method of controlling a hybrid vehicle of claim 16, wherein determining a power split ratio for each of the (n) segments is performed using dynamic programming.

19. The method of controlling a hybrid vehicle of claim 18, wherein the dynamic programming uses the driving cycle, wherein the driving cycle includes a velocity profile of the hybrid vehicle.

20. The method of controlling a hybrid vehicle of claim 18, wherein the dynamic programming is performed in the spatial domain.

21. The method of controlling a hybrid vehicle of claim 16, further comprising recognizing driving patterns at various points along the trip route as the vehicle proceeds along the trip route, wherein determining a power split ratio for each of the (n) segments is based on recognized driving patterns.

22. The method of controlling a hybrid vehicle of claim 16, wherein determining a power split ratio for each of the (n) segments is based on real-time traffic data received from an information database.

23. A method of controlling a hybrid vehicle comprising the steps of:

retrieving trip data;

determining a trip route based on the trip data;

dividing the trip route into (n) segments;

determining a global state-of-charge profile estimating the state of charge at the end of each of the (n) segments such that the state of charge approximately reaches the defined threshold when the vehicle reaches the end of the trip route;

establishing a power split ratio for each of the (n) segments based on the global state-of-charge profile;

receiving data from an information database, the data relating to historic or real-time conditions of each trip segment;

recalculating the power split ratio for each of the (n) segments based on the data; and

applying the recalculated power split ratio for each of the (n) segments.

24. The method of controlling a hybrid vehicle of claim 23, and further comprising modeling each of the (n) segments of the trip route to create a velocity profile of the hybrid vehicle for each of the trip segments.

25. The method of controlling a hybrid vehicle of claim 24, wherein modeling each of the (n) segments of the trip route comprises selecting a trip model for modeling each of the (n) segments, wherein the trip model is one of:

a gas-kinetic trip model,

a Gipps car following model,

a neural network model,

a trip model using historical or real-time traffic data and constant acceleration and deceleration rates, and

a simple trip model using constant acceleration, deceleration, and speed limits as velocity rates.

26. The method of controlling a hybrid vehicle of claim 23, wherein establishing a power split ratio for each of the (n) segments is performed using dynamic programming.

27. The method of controlling a hybrid vehicle of claim 26, wherein the dynamic programming uses the driving cycle, and wherein the driving cycle includes a velocity profile of the hybrid vehicle.

28. The method of controlling a hybrid vehicle of claim 26, wherein the dynamic programming is performed in the spatial domain.

29. The method of controlling a hybrid vehicle of claim 23, further comprising recognizing driving patterns at various points along the trip route as the vehicle proceeds along the trip route, and wherein the recalculating the power split ratio for each of the (n) segments considers the recognized driving patterns.

36. The method of controlling a hybrid vehicle of claim 23, further comprising receiving data from an information database, the data relating to historic or real-time conditions of each trip, and wherein the recalculating the power split ration considers the data.

30. A method of controlling a hybrid vehicle comprising the steps of:

retrieving trip data;

determining a trip route based on the trip data;

dividing the trip route into (n) segments;

determining a global state-of-charge profile estimating the state of charge at the end of each of the (n) segments such that the state of charge approximately reaches the defined threshold when the vehicle reaches the end of the trip route;

establishing a power split ratio for each of the (n) segments based on the global state-of-charge profile;

recognizing driving patterns at multiple points along the trip route as the vehicle proceeds along the trip route;

recalculating the power split ratio for each of the (n) segments based on the recognized driving patterns; and

applying the recalculated power split ratio for each of the (n) segments.

31. The method of controlling a hybrid vehicle of claim 30, and further comprising modeling each of the (n) segments of the trip route to create a velocity profile of the hybrid vehicle for each of the trip segments.

32. The method of controlling a hybrid vehicle of claim 31, wherein modeling each of the (n) segments of the trip route comprises selecting a trip model for modeling each of the (n) segments, wherein the trip model is one of:

a gas-kinetic trip model,

a Gipps car following model,

a neural network model,

a trip model using historical or real-time traffic data and constant acceleration and deceleration rates, and

a simple trip model using constant acceleration, deceleration, and speed limits as velocity rates.

33. The method of controlling a hybrid vehicle of claim 30, wherein establishing a power split ratio for each of the (n) segments is performed using dynamic programming.

34. The method of controlling a hybrid vehicle of claim 33, wherein the dynamic programming uses the driving cycle, and wherein the driving cycle includes a velocity profile of the hybrid vehicle.

35. The method of controlling a hybrid vehicle of claim 33, wherein the dynamic programming is performed in the spatial domain.