Power management systems and methods in a hybrid vehicle

• US 8,190,318 B2
• Filed: 04/08/2009
• Issued: 05/29/2012
• 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: Li, Yaoyu, Ishikawa, Yosuke
• Primary Examiner(s): Nguyen, John
• Assistant Examiner(s): NG, MICHAEL L
• Application Number:
  US12/420,689
• Publication Number:
  US 20090259363A1
• Time in Patent Office: 1,147 Days
• Field of Search: 701/22, 701/25, 701/27, 701/36, 701/117, 701200-202, 701/207, 701/213, 701/408, 701/409, 701/400, 180/65.1, 180/65.21, 180 6527- 6529, 180/65.31
• US Class Current: 701/22
• 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 over a defined trip route, the control system comprising software stored on a computer readable medium for effecting the steps of;

generating a macro-scale state-of-charge profile for the state-of-charge over the defined trip route by;

dividing the trip route into a series of trip segments,

dividing each trip segment into a series of sub-segments,

identifying as electric-only sub-segments that include an amount of acceleration or deceleration above a first threshold and hybrid sub-segments including sub-segments not identified as electric-only sub-segments,

selecting a plurality of potential power split ratios for the hybrid sub-segments

estimating a change in the state-of-charge

for each said electric-only sub-segment and

for each of the plurality of potential power split ratios for each said hybrid sub-segment, and

performing a dynamic programming optimization to determine a macro-scale estimated change in the state-of-charge for each said hybrid sub-segment; and

controlling a power split ratio between the electric power source and the non-electric power source for the defined trip route based on the macro-scale state-of-charge profile.

2. The hybrid vehicle of claim 1, wherein estimating the change in the state-of-charge for each of the plurality of potential power split ratios for each said hybrid sub-segment includes estimating a first change in the state-of-charge for a first potential power split ratio for a first hybrid sub-segment by

determining a total power demand over the first hybrid sub-segment for the hybrid vehicle based on a trip model of the first hybrid sub-segment;

calculating a power demand of the electric energy storage device for the first hybrid sub-segment based on the first potential power split ratio and the total power demand;

estimating the first change in the state-of-charge for the first potential power split ratio based on the power demand of the electric energy storage device.

3. The hybrid vehicle of claim 2, wherein the trip model is one of:

a gas-kinetic trip model,

a Gipps car following model,

a neural network model,

a traffic data 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.

4. A hybrid vehicle as set forth in claim 1, further comprising software stored on the computer readable medium for effecting the step of identifying one of the series of sub-segments as a ramp sub-segment, wherein the ramp sub-segment includes an entrance or exit ramp for a highway, wherein estimating a change in the state-of-charge for the ramp sub-segment includes using neural network modeling.

5. The hybrid vehicle of claim 1, wherein the control system is further operable to, during each trip segment as the hybrid vehicle traverses the trip route, generate a micro-scale state-of-charge profile for a next trip segment, wherein the micro-scale state-of-charge profile re-estimates a change in the state-of-charge over the next trip segment, which was previously estimated in the macro-scale state-of-charge profile.

6. 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 hybrid vehicle proceeds along the trip route, and wherein identifying sub-segments as electric-only sub-segments and hybrid sub-segments is based on recognized driving patterns.

7. The hybrid vehicle of claim 1, wherein the macro-scale state-of-charge profile includes the estimated change in the state-of-charge for each said electric-only sub-segment and the macro-scale estimated change in the state-of-charge for each said hybrid sub-segment such that each trip segment of the defined trip route has an associated change in the state-of-charge estimate.

8. The hybrid vehicle of claim 1, wherein the estimated change in the state-of-charge for each said electric-only sub-segment and the macro-scale estimated change in the state-of-charge for each said hybrid sub-segment is negative to indicate a reduction in the state-of-charge of the electric energy storage device over each trip segment.

9. A method of controlling a hybrid vehicle that includes a drive train, an electric power source coupled to the drive train, and a non-electric power source coupled to the drive train, the method comprising the steps of:

retrieving trip data;

determining a trip route based on the trip data;

dividing, by a controller of the hybrid vehicle, the trip route into (n) segments;

modeling, by the controller, each of the (n) segments of the trip route to determine a driving cycle along the trip route for the hybrid vehicle;

dividing, by the controller, each of the (n) trip segment into a series of sub-segments;

identifying, by the controller, as electric-only sub-segments that include an amount of acceleration or deceleration above a first threshold and hybrid sub-segments including sub-segments not identified as electric-only sub-segments;

selecting a plurality of potential power split ratios for the hybrid sub-segments;

estimating, by the controller, a change in the state-of-charge

for each said electric-only sub-segment and

for each of the plurality of potential power split ratios for each said hybrid sub-segment;

performing, by the controller, a dynamic programming optimization to determine a macro-scale estimated change in the state-of-charge for each said hybrid sub-segment;

generating a macro-scale state-of-charge profile based on the estimated change in the state-of-charge for each said electric-only sub-segment and the macro-scale estimated change in the state-of-charge for each said hybrid sub-segment; and

controlling a power split ratio between the electric power source and the non-electric power source of the hybrid vehicle based on the macro-scale state-of-charge profile.

10. The method of claim 9, wherein estimating the change in the state-of-charge for each of the plurality of potential power split ratios for each said hybrid sub-segment includes estimating a first change in the state-of-charge for a first potential power split ratio for a first hybrid sub-segment by

determining a total power demand for the hybrid vehicle based on a trip model of the first hybrid sub-segment;

calculating a power demand of the electric energy storage device for the first hybrid sub-segment based on the first potential power split ratio and the total power demand;

calculating the first change in the state-of-charge for the first potential power split ratio based on the power demand of the electric energy storage device.

11. The method of claim 10, wherein the trip model is one of:

a gas-kinetic trip model,

a Gipps car following model,

a neural network model,

a traffic data 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.

12. The method of claim 9, further comprising identifying one of the series of sub-segments as a ramp sub-segment, wherein the ramp sub-segment includes an entrance or exit ramp for a highway, wherein estimating a change in the state-of-charge for the ramp sub-segment includes using neural network modeling.

13. The method of claim 9, wherein the control system is further operable to, during each trip segment as the hybrid vehicle traverses the trip route, generate a micro-scale state-of-charge profile for a next trip segment, wherein the micro-scale state-of-charge profile re-estimates a change in the state-of-charge over the next trip segment, which was previously estimated in the macro-scale state-of-charge profile.

14. The method of claim 9, further comprising recognizing driving patterns at various points along the trip route as the hybrid vehicle proceeds along the trip route, and wherein identifying sub-segments as electric-only sub-segments and hybrid sub-segments is based on recognized driving patterns.

15. The method of claim 9, combining the estimated change in the state-of-charge for each said electric-only sub-segment and the macro-scale estimated change in the state-of-charge for each said hybrid sub-segment to generate the macro-scale state-of-charge profile with each trip segment of the defined trip route having an associated change in the state-of-charge estimate.

16. The method of claim 9, wherein the estimated change in the state-of-charge for each said electric-only sub-segment and the estimated change in the state-of-charge for each said hybrid sub-segment is negative to indicate a reduction in the state-of-charge of the electric energy storage device over each trip segment.

17. The method of claim 9, wherein the step of identifying, by the controller, as electric-only sub-segments that include an amount of acceleration or deceleration above a first threshold is based on an analysis of the driving cycle.

18. 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 over a defined trip route, the control system operable to;

generate a macro-scale state-of-charge profile for the state-of-charge over the defined trip route by;

dividing the trip route into a series of trip segments,

dividing each trip segment into a series of sub-segments,

identifying as electric-only sub-segments that include an amount of acceleration or deceleration above a first threshold and hybrid sub-segments including sub-segments not identified as electric-only sub-segments,

estimating a change in the state-of-charge

for each said electric-only sub-segment and

for each of the plurality of potential power split ratios for each said hybrid sub-segment, and

performing an optimization to determine a macro-scale estimated change in the state-of-charge for each said hybrid sub-segment; and

control a power split ratio between the electric power source and the non-electric power source over the defined trip route according to the macro-scale state-of-charge profile.

19. The hybrid vehicle of claim 18, wherein estimating the change in the state-of-charge for each of the plurality of potential power split ratios for each said hybrid sub-segment includes estimating a first change in the state-of-charge for a first potential power split ratio for a first hybrid sub-segment by

determining a total power demand over the first hybrid sub-segment for the hybrid vehicle based on a trip model of the first hybrid sub-segment;

calculating a power demand of the electric energy storage device for the first hybrid sub-segment based on the first potential power split ratio and the total power demand;

estimating the first change in the state-of-charge for the first potential power split ratio based on the power demand of the electric energy storage device.

20. The hybrid vehicle of claim 19, wherein the trip model is one of:

a gas-kinetic trip model,

a Gipps car following model,

a neural network model,

a traffic data 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.

21. A hybrid vehicle as set forth in claim 18, wherein the control system is further operable to identify one of the series of sub-segments as a ramp sub-segment, wherein the ramp sub-segment includes an entrance or exit ramp for a highway, wherein estimating a change in the state-of-charge for the ramp sub-segment includes using neural network modeling.

22. The hybrid vehicle of claim 18, wherein the control system is further operable to, during each trip segment as the hybrid vehicle traverses the trip route, generate a micro-scale state-of-charge profile for a next trip segment, wherein the micro-scale state-of-charge profile re-estimates a change in the state-of-charge over the next trip segment, which was previously estimated in the macro-scale state-of-charge profile.

23. The hybrid vehicle of claim 18, wherein the control system is further operable to recognize driving patterns at various points along the trip route as the hybrid vehicle proceeds along the trip route, and wherein identifying sub-segments as electric-only sub-segments and hybrid sub-segments is based on recognized driving patterns.

24. The hybrid vehicle of claim 18, wherein the macro-scale state-of-charge profile includes the estimated change in the state-of-charge for each said electric-only sub-segment and the macro-scale estimated change in the state-of-charge for each said hybrid sub-segment such that each trip segment of the defined trip route has an associated change in the state-of-charge estimate.

25. The hybrid vehicle of claim 18, wherein the estimated change in the state-of-charge for each said electric-only sub-segment and the macro-scale estimated change in the state-of-charge for each said hybrid sub-segment is negative to indicate a reduction in the state-of-charge of the electric energy storage device over each trip segment.