Control system for hybrid vehicles with high degree of hybridization

Patent metadata

Field	Value
Publication number	US-2017361832-A1
Application number	US-201715690967-A
Country	US
Kind code	A1
Filing date	Aug 30, 2017
Priority date	Apr 17, 2014
Publication date	Dec 21, 2017
Grant date	—

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.

First claim

1 - 25 . (canceled)

26 . 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.

27 . The hybrid vehicle of claim 26 , wherein the energy storage device comprises at least one of:
a flywheel; a battery; and a capacitor.

28 . The hybrid vehicle of claim 26 , 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.

29 . The hybrid vehicle of claim 26 , 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.

30 . The hybrid vehicle of claim 26 , wherein the degradation model comprises a degradation equation.

31 . The hybrid vehicle of claim 26 , wherein the degradation model comprises a look up table.

32 . The hybrid vehicle of claim 26 , wherein the degradation model includes multiple models, each of the multiple models associated with a component of the hybrid vehicle.

33 . The hybrid vehicle of claim 26 , wherein the prediction processor is configured to revise the degradation model based on predicted changes to the one or more hybrid vehicle components.

34 . The hybrid vehicle of claim 26 , 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.

35 . 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.

36 . The hybrid vehicle control system of claim 35 , wherein the controller is further configured to control regenerative power flow to the energy storage device.

37 . The hybrid vehicle control system of claim 35 , 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.

38 . The hybrid vehicle control system of claim 35 , 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.

39 . The hybrid vehicle control system of claim 35 , 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.

40 . The hybrid vehicle control system of claim 35 , 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.

41 . The hybrid vehicle control system of claim 35 , 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.

42 . 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

Assignees

• Palo Alto Res Ct Inc

Inventors

• Schwartz David E
• Garner Sean
• Saha Bhaskar
• Barber Simon

Classifications

• B60W2050/146

  Display means · CPC title

• B60W10/06

  including control of combustion engines · CPC title

• B60W50/0097

  Predicting future conditions · CPC title

• B60W10/10

  including control of change-speed gearings · CPC title

• B60W10/24

  including control of energy storage means · CPC title