Vehicle control using stochastic information

• US 8,290,637 B2
• Filed: 06/08/2009
• Issued: 10/16/2012
• Est. Priority Date: 06/16/2008
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: Krupadanam, Ashish S., Chang, Man-Feng, Naik, Sanjeev M.
• Primary Examiner(s): Black, Thomas
• Assistant Examiner(s): MARC-COLEMAN, MARTHE Y
• Application Number:
  US12/479,920
• Publication Number:
  US 20090312889A1
• Time in Patent Office: 1,226 Days
• Field of Search: 701/36, 701/1, 701/104, 701/111, 701/112, 701118-121, 701/70, 165/43, 621/79, 180/65.29
• US Class Current: 701/1
• CPC Class Codes: B60L 2240/36 Temperature of vehicle components or parts
• CPC Class Codes: B60L 2240/423 Torque
• CPC Class Codes: B60L 2240/443 Torque
• CPC Class Codes: B60L 2240/622 by satellite navigation
• CPC Class Codes: B60L 2240/642 Slope of road
• CPC Class Codes: B60L 2240/645 Type of road
• CPC Class Codes: B60L 2240/66 Ambient conditions
• CPC Class Codes: B60L 2240/68 Traffic data
• CPC Class Codes: B60L 2260/50 by future state prediction
• CPC Class Codes: B60L 58/13 Maintaining the SoC within a determined range
• 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 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: Y02T 10/64 Electric machine technologies in electromobility
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/72 Electric energy management in electromobility
• CPC Class Codes: Y02T 90/16 Information or communication technologies improving the operation of electric vehicles

Abstract

A method for operating a vehicle includes estimating expected states and probable ranges for influence factors associated with stochastic parameters over a time horizon. A preferred control state and a candidate control state for an objective function are determined. The objective function is controlled using the preferred control state for the objective function over the time horizon.

Claims

1. A method for controlling a state of an objective function of a vehicle, comprising:

estimating expected states and associated probable ranges for a plurality of influence factors associated with stochastic parameters over a time horizon;

determining a preferred control state for an objective function associated with the probable ranges of the influence factors over the time horizon;

determining a candidate control state for the objective function associated with the expected states for the influence factors over the time horizon;

controlling the objective function using the preferred control state for the objective function over the time horizon when a difference between a preferred objective function state determined using the preferred control state and the candidate objective function state determined using the candidate control state is less than a threshold; and

controlling the objective function using the candidate control state for the objective function over the time horizon only when the difference between the preferred objective function state determined using the preferred control state and the candidate objective function state determined using the candidate control state is not less than the threshold.

2. The method of claim 1, further comprising:

developing historical data related to states of in-vehicle and extra-vehicle stochastic parameters related to the objective function of the vehicle;

determining present and projected states of the in-vehicle and extra-vehicle stochastic parameters related to the objective function of the vehicle; and

wherein estimating the expected states and the associated probable ranges for the plurality of influence factors associated with the stochastic parameters over the time horizon comprises estimating the expected states and the associated probable ranges for the plurality of influence factors associated with the stochastic parameters over the time horizon based upon the historical data and the present and projected states of the in-vehicle and extra-vehicle stochastic parameters related to the objective function of the vehicle.

3. The method of claim 1, further comprising:

determining a plurality of candidate control states for the objective function associated with the expected states for the influence factors over the time horizon; and

wherein controlling the objective function using the preferred control state for the objective function over the time horizon is effected when each respective difference between a preferred objective function state determined using the preferred control state and each of a plurality of candidate objective function states determined using each of the candidate control states is less than a threshold.

4. The method of claim 3, wherein determining the plurality of control states for the objective function comprises iteratively generating the candidate control states from one of a plurality of control states for the objective function associated with the expected states for the influence factors over the time horizon.

5. The method of claim 4, wherein iteratively generating the candidate control states comprises sequentially incrementing between a minimum control state and a maximum control state.

6. The method of claim 4, wherein iteratively generating candidate control states comprises sequentially selecting one of a plurality of discrete control states.

7. The method of claim 1, wherein estimating the expected states and the associated probable ranges for the plurality of influence factors comprises estimating the expected states and the associated probable ranges for the plurality of influence factors associated with states of present and projected in-vehicle and extra-vehicle stochastic parameters determined over the time horizon.

8. The method of claim 7, further comprising determining the states of the present and projected in-vehicle and extra-vehicle stochastic parameters associated with the objective function of the vehicle over the time horizon.

9. The method of claim 1, wherein determining the preferred control state for the objective function associated with the probable ranges of the influence factors over the time horizon comprises:

iteratively generating candidate control states for the objective function;

iteratively generating candidate probable states for the influence factors associated with the probable ranges for the influence factors over the time horizon;

determining candidate objective function states associated with the iteratively generated candidate control states for the objective function and the iteratively generated candidate probable states for the influence factors; and

selecting a minimum of the candidate objective function states as a preferred objective function state and selecting the associated control state for the preferred objective function state as the preferred control state for the objective function.