Adaptive real-time driver advisory control for a hybrid electric vehicle to achieve fuel economy

• US 9,361,272 B2
• Filed: 09/11/2013
• Issued: 06/07/2016
• Est. Priority Date: 06/08/2010
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Syed, Fazal Urrahman, Filev, Dimitar Petrov, Tseng, Finn
• Primary Examiner(s): Nguyen, Kim T
• Application Number:
  US14/023,566
• Publication Number:
  US 20140012456A1
• Time in Patent Office: 1,000 Days
• US Class Current: 1/1
• CPC Class Codes: B60K 6/445 Differential gearing distribution type
• CPC Class Codes: B60L 2240/486 Operating parameters
• CPC Class Codes: B60W 10/06 including control of combustion engines
• CPC Class Codes: B60W 10/08 including control of electric propulsion units, e.g. motors or generators
• CPC Class Codes: B60W 20/00 Control systems specially adapted for hybrid vehicles hybrid vehicle design, B60K6/00; electric vehicles B60L
• 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 2050/0095 Automatic control mode change
• CPC Class Codes: B60W 2510/1005 Transmission ratio engaged
• CPC Class Codes: B60W 2540/103 Accelerator thresholds, e.g. kickdown
• CPC Class Codes: B60W 2540/12 Brake pedal position
• CPC Class Codes: B60W 2540/30 Driving style
• CPC Class Codes: B60W 30/1882 characterised by the working point of the engine, e.g. by using engine output chart
• CPC Class Codes: B60W 50/16 Tactile feedback to the driver, e.g. vibration or force feedback to the driver on the steering wheel or the accelerator pedal
• CPC Class Codes: G06F 17/00 Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: Y02T 10/40 Engine management systems
• CPC Class Codes: Y02T 10/62 Hybrid vehicles
• CPC Class Codes: Y02T 10/84 Data processing systems or methods, management, administration
• CPC Class Codes: Y10S 903/903 having energy storing means, e.g. battery, capacitor

Abstract

A vehicle powertrain controller includes a fuzzy logic-based adaptive algorithm with a learning capability that estimates a driver'"'"'s long term driving preferences. An adaptive algorithm arbitrates competing requirements for good fuel economy, avoidance of intrusiveness and vehicle drivability. A driver'"'"'s acceptance or rejection of advisory information may be used to adapt subsequent advisory information to the driving style. Vehicle performance is maintained in accordance with a driver'"'"'s driving style.

Claims

1. A method comprising;

controlling a vehicle having mechanical and electromechanical power sources using a controller having an adaptive algorithm learning a driver'"'"'s driving preference, the controller;

monitoring a driver'"'"'s driving style during a defined term of a given driving event;

providing advisory feedback information for a vehicle operating condition based on the driver'"'"'s driving preference; and

monitoring reaction to the advisory feedback information to determine frequency of acceptance and rejection.

2. The method of claim 1 wherein the advisory feedback information comprises an advisory accelerator pedal position.

3. The method of claim 1 wherein the adaptive algorithm dynamically adapts parameters of fuzzy logic rules to each of multiple sets of vehicle driving conditions and wherein the advisory feedback information includes driver advice for adjusting accelerator pedal position to conform driving style to current driving conditions.

4. The method of claim 1 wherein the adaptive algorithm monitors a reaction of the driver to the advisory feedback information to determine frequency of acceptance and rejection of the advisory feedback information associated with a long term driving style and a short term driving style.

5. The method of claim 1 wherein the controller is configured to calculate a forgetting factor value, the controller adjusting the forgetting factor to learn either a long term driving style or a short term driving style.

6. The method of claim 5 wherein a fast forgetting factor is associated with a short term driving style.

7. The method of claim 1 further comprising:

providing advisory feedback information based on fuzzy rules with inputs based on fuel economy error between actual fuel economy and optimal fuel economy, and rate of change of the fuel economy error.

8. The method of claim 7 wherein the fuzzy rules include inputs based on input power from the mechanical power source.

9. The method of claim 7 wherein the fuzzy rules includes a first set of fuzzy rules governing steady state vehicle operating conditions and a second set of fuzzy rules governing transient operating conditions.

10. An adaptive driver advisory control system for a hybrid vehicle, comprising:

an accelerator pedal;

a controller in communication with the accelerator pedal and having a logic-based algorithm for learning a driver'"'"'s driving style and being calibrated with powertrain operating data corresponding to a desired fuel economy, the controller configured to monitor driver power demands to determine the driving style and provide advisory information based on previous acceptance or rejection of the advisory information.

11. The system of claim 10 wherein the controller is configured to normalize a variable indication of driver accelerator pedal response to changes in vehicle driving conditions and to normalize vehicle operating variables;

and

wherein the controller is configured to develop output signals based on the operating variables indicating an advised accelerator pedal position.