CONTROL SYSTEM FOR HYBRID VEHICLES WITH HIGH DEGREE OF HYBRIDIZATION

• US 20150298684A1
• Filed: 04/17/2014
• Published: 10/22/2015
• Est. Priority Date: 04/17/2014
• Status: Active Grant

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Inventors: Schwartz, David E., Garner, Sean, Saha, Bhaskar, Barber, Simon
• Granted Patent: US 9,751,521 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60W 10/06 including control of combustion engines
• CPC Class Codes: B60W 10/10 including control of change-speed gearings
• CPC Class Codes: B60W 10/26 for electrical energy, e.g. batteries or capacitors
• CPC Class Codes: B60W 20/11 using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance utilising navigation and traffic information in the control strategy B60W20/12
• 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: B60W 20/15 Control strategies specially adapted for achieving a particular effect
• CPC Class Codes: B60W 2050/146 Display means
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 40/02 related to ambient conditions
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: B60W 50/06 Improving the dynamic response of the control system, e.g. improving the speed of regulation or avoiding hunting or overshoot
• CPC Class Codes: Y02T 10/84 Data processing systems or methods, management, administration
• CPC Class Codes: Y10S 903/93 Conjoint control of different elements

Abstract

Systems and methods for controlling and operating a hybrid vehicle having a high degree of hybridization are disclosed. A power flow control system predicts vehicle power demand to drive the hybrid vehicle based on changing conditions during operation of the hybrid vehicle. The power flow control system controls the power flow so as to provide power to drive the hybrid vehicle based on the predicted vehicle power demand, wherein the predicted vehicle power demand is greater than a maximum

Claims

1. A hybrid vehicle, comprising:

a fuel consuming engine configured to supply power to drive the vehicle;

an energy storage device disposed within the hybrid vehicle, the energy storage device configured to supply power to drive the vehicle;

a prediction processor configured to predict power demand to drive the vehicle based on changing conditions during operation of the vehicle;

a drive train coupled to cause movement of the vehicle; and

a controller configured to automatically control power flow between at least one of;

the engine and the drive train,

the energy storage device and the drive train, and

the engine and the energy storage device, so as to provide the power to drive the vehicle based at least in part on the predicted power demand, wherein the power demand to drive the vehicle is greater than a maximum power available from the engine at at least one point in time during operation of the hybrid vehicle.

2. The hybrid vehicle of claim 1, wherein the energy storage device comprises at least one of:

a flywheel;

a battery; and

a capacitor.

3. The hybrid vehicle of claim 1, wherein the changing conditions include one or more of:

sensed conditions external to the hybrid vehicle;

sensed conditions of the hybrid vehicle;

predicted changes in one or more vehicle components;

predicted conditions external to the vehicle;

driver-specified conditions;

energy usage from the energy storage device;

energy usage by the fuel consuming engine historical data;

predicted destination; and

predicted route.

4. The hybrid vehicle of claim 1, further comprising a sensor system coupled to the prediction processor, wherein the sensor system is configured to sense one or more of the changing conditions and the prediction processor is configured to predict the power demand to drive the vehicle based on the sensed conditions.

5. The hybrid vehicle of claim 1, further comprising a driver interface configured to:

enable a driver to enter destination or route information;

display one or more proposed alternate routes based on real-time conditions;

display at least one drive parameter associated with each of the one or more proposed alternate routes.

6. The hybrid vehicle of claim 1, further comprising a driver interface configured to:

enable a driver to enter a selection between a first route that would cause a time delay in reaching the destination and a second route that would cause at least one of an increase in fuel consumption compared to the first route and an increase in vehicle emissions compared to the first route; and

wherein the prediction processor is configured to use the selection to predict the power demand.

7. A hybrid vehicle control system, comprising:

a prediction processor configured to predict power demand to drive a hybrid vehicle based on changing conditions during operation of the hybrid vehicle, the hybrid vehicle comprising a fuel consuming engine and an energy storage device coupled to a drive train of the hybrid vehicle; and

a controller configured to automatically control power flow between at least one of;

the engine and the drive train,

the energy storage device and the drive train, and

the engine and the energy storage device, so as to provide the power to drive the vehicle based at least in part on the predicted power demand, wherein the power demand to drive the vehicle is greater than a maximum power available from the engine at at least one point in time during operation of the hybrid vehicle.

8. The hybrid vehicle control system of claim 7, wherein the controller is further configured to control regenerative power flow to the energy storage device.

9. The hybrid vehicle control system of claim 7, wherein:

the prediction processor is configured to predict one or more conditions external to the hybrid vehicle, the one or more external conditions including traffic, weather, road conditions and traffic accidents; and

the controller is configured to control power flow from the engine and the energy storage device based on predictions of the one or more external conditions.

10. The hybrid vehicle control system of claim 7, wherein the prediction processor is configured to:

collect at least one of vehicle-specific and driver-specific historical data;

predict a route based on the historical data; and

determine a drive parameter based on the predicted route, wherein the at least one drive parameter is predicted based on the predicted power demand and predicted available power associated with the predicted route, and the drive parameter includes at least one of time to destination, emissions to destination, and fuel consumption to destination associated with the predicted route.

11. The hybrid vehicle control system of claim 7, wherein the prediction processor is configured to predict a route-specific vehicle power demand associated with each of multiple potential routes, wherein the route-specific vehicle power demand is based on one or more of weather, component degradation, predicted traffic conditions, driver-specified constraints on vehicle emissions, driver-specified vehicle behavior, driver-specified constraints on arrival time at the destination, driver-specified constraints on fuel consumption.

12. The hybrid vehicle control system of claim 7, wherein the prediction processor is configured to:

use a model to predict degradation of one or more vehicle components of the hybrid vehicle;

revise the model based on sensed changes in a condition of the one or more vehicle components; and

control power flow from the engine and the energy storage device based on the model.

13. The hybrid vehicle control system of claim 7, wherein the prediction processor is configured to predict the vehicle power demand using one or more of:

a Monte Carlo algorithm in a model-predictive control framework;

stochastic programming;

an adaptive optimization control algorithm, one or more parameters of the adaptive optimization control algorithm revised based on real-time data; and

an autoregressive model configured to account for differences in predicted and actual time evolution of traffic.

14. The hybrid vehicle control system of claim 7, further comprising a sensor system coupled to the prediction processor, wherein the sensor system is configured to sense at least one of a condition of the hybrid vehicle and a condition external to the hybrid vehicle.

15. The hybrid vehicle control system of claim 7, further comprising a driver interface configured to:

enable a driver to enter destination or route information; and

display one or more proposed alternate routes.

16. The hybrid vehicle control system of claim 15, wherein the driver interface is further configured to:

prompt the driver to provide rank information for one or more of time delay in reaching the destination, an increase in fuel consumption, an increase in vehicle emissions, and fuel dollars saved; and

the controller is configured to use the rank information to propose a route.

17. The hybrid vehicle control system of claim 15, wherein the driver interface is configured to:

prompt the driver to enter rank information for a number a number of drive parameters, the driver parameters including one or more of time delay in reaching the destination, an increase in fuel consumption, an increase in vehicle emissions, and fuel dollars saved;

receive the drive parameter rank information from the driver; and

use the drive parameter rank information to identify a route from the proposed alternate routes.

18. A method, comprising:

predicting vehicle power demand to drive a hybrid vehicle by a fuel consuming engine and an energy storage device based on changing conditions during operation of the hybrid vehicle; and

controlling the power flow so as to provide power to drive the hybrid vehicle based on the predicted vehicle power demand, wherein the predicted vehicle power demand is greater than a maximum power available from the engine at at least one point in time during operation of the vehicle.

19. The method of claim 18, wherein predicting the vehicle power demand based on changing conditions comprises predicting based on one or more of:

sensed conditions of the hybrid vehicle;

sensed conditions external to the hybrid vehicle;

a degradation model for vehicle conditions;

driver-specified conditions; and

information stored in a historical database.

20. The method of claim 18, comprising:

identifying one or more alternate routes to a destination;

predicting a route-specific vehicle power demand associated with each of the one or more alternate routes;

determining at least one drive parameter associated with each of the alternate routes, the at least one drive parameter comprising one or more of time to destination, emissions to destination, and fuel consumption to destination.

21. The method of claim 18, further comprising:

displaying one more alternate routes to a destination on a driver interface;

displaying the route-specific vehicle power demand associated with each of the alternate routes;

displaying the at least one drive parameter associated with each of the alternate routes; and

prompting the driver to enter at least one of;

a selection between two or more drive parameters; and

rank information for a number of drive parameters, the drive parameters including at least one of time delay in reaching the destination, an increase in fuel consumption, an increase in vehicle emissions, and fuel dollars saved.

22. The method of claim 18, wherein predicting the vehicle power demand comprises predicting based on the one or more sensed conditions of the hybrid vehicle and one or more sensed conditions external to the hybrid vehicle.

23. The method of claim 22, wherein the predicting based on the one or more sensed conditions of the hybrid vehicle includes predicting based on a degradation model configured to be adapted based on the sensed conditions.

24. The method of claim 18, wherein predicting the vehicle power demand comprises predicting based on the one or more external conditions transmitted from a location physically separate and remote from the hybrid vehicle.

25. The method of claim 24, wherein the one or more external conditions include at least one crowd-sourced external condition.