Control system for hybrid vehicles with high degree of hybridization

• US 9,751,521 B2
• Filed: 04/17/2014
• Issued: 09/05/2017
• 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
• Primary Examiner(s): Jabr, Fadey
• Assistant Examiner(s): Mahne, Kevin P
• Application Number:
  US14/255,091
• Publication Number:
  US 20150298684A1
• Time in Patent Office: 1,237 Days
• Field of Search: None
• 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 hybrid vehicle;

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

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

use a degradation model to predict degradation of one or more hybrid vehicle components of the fuel consuming engine, drive train, movement system, and/or charging system of the hybrid vehicle; and

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

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

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 hybrid vehicle based at least in part on the predicted power demand and on the degradation model, wherein the power demand to drive the hybrid vehicle is greater than a maximum power available from the engine at a point in time during operation of the hybrid vehicle; and

a driver interface configured to;

enable a driver to enter destination or route information;

determine two or more proposed alternate routes based on the destination or route information;

display the two or more or more proposed alternate routes;

receive drive parameter rank information from the driver for two or more of a time delay in reaching the destination, an increase in fuel consumption, an increase in vehicle emissions, and fuel dollars saved from the driver; and

identify one or more routes from the proposed alternate routes based on the driver parameter rank information received from the driver.

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 hybrid vehicle components;

predicted conditions external to the hybrid 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 one or more sensors coupled to the prediction processor, wherein the one or more sensors are configured to sense one or more of the changing conditions and the prediction processor is configured to predict the power demand to drive the hybrid vehicle based on the sensed conditions.

5. The hybrid vehicle of claim 1, wherein the driver interface is 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, wherein the driver interface is 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, the prediction processor configured to;

use a degradation model to predict degradation of one or more hybrid vehicle components of the fuel consuming engine, drive train, movement system, and/or charging system of the hybrid vehicle; and

revise the degradation model based on sensed changes in a condition of the one or more hybrid vehicle components; 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 hybrid vehicle based at least in part on the predicted power demand and on the degradation model, wherein the power demand to drive the hybrid vehicle is greater than a maximum power available from the engine at a point in time during operation of the hybrid vehicle; and

a driver interface configured to;

enable a driver to enter destination or route information;

determine two or more proposed alternate routes based on the destination or route information;

display the two or more or more proposed alternate routes;

receive from the driver drive parameter rank information for two or more of a time delay in reaching the destination, an increase in fuel consumption, an increase in vehicle emissions, and fuel dollars saved from the driver; and

identify one or more routes from the proposed alternate routes based on the driver parameter rank information received from the driver.

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 hybrid 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 hybrid vehicle power demand associated with each of multiple potential routes, wherein the route-specific hybrid vehicle power demand is based on one or more of weather, component degradation, predicted traffic conditions, driver-specified constraints on vehicle emissions, driver-specified hybrid 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 predict the hybrid 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.

13. The hybrid vehicle control system of claim 7, further comprising one or more sensors coupled to the prediction processor, wherein the one or more sensors are configured to sense at least one of a condition of the hybrid vehicle and a condition external to the hybrid vehicle.

14. A computer implemented method, comprising:

predicting, in a prediction processor, hybrid 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, the predicting comprising;

predicting degradation of one or more hybrid vehicle components of the fuel consuming engine, drive train, movement system, and/or charging system of the hybrid vehicle using a degradation model; and

revising the degradation model based on sensed changes in a condition of the one or more hybrid vehicle components;

controlling, in a control processor, the power flow so as to provide power to drive the hybrid vehicle based on the predicted hybrid vehicle power demand and on an output of the degradation model, wherein the predicted hybrid vehicle power demand is greater than a maximum power available from the engine at a point in time during operation of the hybrid vehicle;

enabling a driver to enter destination or route information on a driver interface;

determining two or more proposed alternate routes based on the destination or route information;

displaying the two or more or more proposed alternate routes on the driver interface;

receiving drive parameter rank information from the driver for two or more of a time delay in reaching the destination, an increase in fuel consumption, an increase in vehicle emissions, and fuel dollars saved from the driver; and

identifying one or more routes from the proposed alternate routes based on the driver parameter rank information received from the driver.

15. The method of claim 14, wherein predicting the hybrid 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 hybrid vehicle conditions;

driver-specified conditions; and

information stored in a historical database.

16. The method of claim 14, comprising:

identifying one or more alternate routes to a destination;

predicting a route-specific hybrid 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.

17. The method of claim 14, wherein predicting the hybrid 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.

18. The method of claim 14, wherein predicting the hybrid 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.

19. The method of claim 18, wherein the one or more external conditions include at least one crowd-sourced external condition.