Hybrid electric vehicle control system and method of use

• US 7,832,511 B2
• Filed: 10/20/2006
• Issued: 11/16/2010
• Est. Priority Date: 10/20/2006
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Smith, Matt, Kuang, Ming, Okubo, Shunsuke, Syed, Fazal, Czubay, John
• Primary Examiner(s): Dickson; Paul N
• Assistant Examiner(s): FOLLMAN, BRODIE J
• Application Number:
  US11/584,921
• Publication Number:
  US 20080097684A1
• Time in Patent Office: 1,488 Days
• Field of Search: 180/65.265, 180/65.275, 180/65.28, 180/65.285, 903/930, 701/48
• US Class Current: 180/65.275
• CPC Class Codes: B60K 1/02 comprising more than one electric motor
• CPC Class Codes: B60K 6/365 with the gears having orbital motion
• CPC Class Codes: B60K 6/445 Differential gearing distribution type
• CPC Class Codes: B60T 1/10 by utilising wheel movement for accumulating energy, e.g. driving air compressors
• CPC Class Codes: B60W 10/06 including control of combustion engines
• 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 2050/0057 Frequency analysis, spectral techniques or transforms
• CPC Class Codes: B60W 2710/0644 Engine speed
• CPC Class Codes: Y02T 10/62 Hybrid vehicles

Abstract

A rule-based fuzzy gain-scheduling proportional integral (PI) controller is provided to control desired engine power and speed behavior in a power-split HEV. The controller includes a fuzzy logic gain-scheduler and a modified PI controller that operates to improve on the control of engine power and speed in a power-split HEV versus using conventional PI control methods. The controller improves the engine power and speed behavior of a power-split HEV by eliminating overshoots, and by providing enhanced and uncompromised rise-time and settling-time.

Claims

1. A control system for a hybrid electric vehicle (HEV) comprising:

a fuzzy gain-scheduling proportional integral (PI) controller that operates to control an engine power, engine speed, and vehicle powertrain in response to predefined operating conditions, said fuzzy gain-scheduling proportional integral (PI) controller comprising a fuzzy logic gain-scheduler having an associated output in communication with a PI controller, said output comprising an input to said PI controller, said input modified within said PI controller to produce a gain output from said (PI) controller, said gain output comprising an output of said fuzzy gain-scheduling proportional integral (PI) controller.

2. The control system of claim 1, wherein the fuzzy gain-scheduling PI controller controls the associated fuzzy logic gain-scheduler output, β

_f, to a first multiplier associated with a proportional portion of the PI controller and to a second multiplier associated with an integral portion of the PI controller.

3. The control system of claim 1, wherein the fuzzy gain-scheduling PI controller comprises:

said fuzzy logic gain-scheduler comprising a multiple-input single output (MISO) fuzzy logic gain-scheduler; and

said PI controller having a proportional gain of β

_fK_p and an integral gain of β

_fK_i that are each dynamically modified by said fuzzy gain-scheduler output β

_f from the MISO fuzzy logic gain-scheduler.

4. The control system of claim 1, further comprising:

an associated first input variable X₁ defined as a magnitude of an error e(n) between a desired hybrid vehicle battery power (P<sub>batt—

des</sub>(n)) and an actual hybrid vehicle battery power (P<sub>bat—

act</sub>(n)), said actual battery power dependent on engine power, torque, and power losses attributable to said HEV system;

an associated second input variable X₂ defined as a magnitude of a rate of change of the error e(n); and

an associated third input variable X₃(n) defined as an absolute difference between a commanded engine speed and a target engine speed.

5. The control system of claim 4, further comprises:

a final output u(n) of the fuzzy gain-scheduling PI controller, the output having a sum of a proportional term P(n) defined by the proportional gain multiplied by the error e(n) and an integral term I(n) defined by a sum of the integral gain multiplied by an error e(i) and a sampling time T_s, wherein i is calculated over the sampling time T_s from i equals 1 to n.

6. The control system of claim 1, wherein a plurality of fuzzy rules associated with the fuzzy gain-scheduling PI controller are provided to distinguish between various HEV powertrain conditions and to make decisions regarding current and future states of said powertrain associated with the HEV.

7. The control system of claim 6, wherein at least one of the plurality of associated fuzzy logic rules comprises:

a rule to detect a steady state condition.

8. The control system of claim 6, wherein at least one of the plurality of associated fuzzy logic rules comprises:

a rule to determine that the powertrain is not generating enough power.

9. The control system of claim 6, wherein at least one of the plurality of associated fuzzy logic rules comprises:

a rule to detect a close to steady state condition.

10. The control system of claim 6, wherein at least one of the plurality of associated fuzzy logic rules comprises:

a rule to predict a condition resulting in a possible PI controller windup.

11. The control system of claim 6, wherein at least one of the plurality of associated fuzzy logic rules comprises:

a rule to detect a quick system response resulting in no PI controller windup.

12. The control system of claim 6, wherein at least one of the plurality of associated fuzzy logic rules comprises:

a rule to detect a slower system response resulting in a possible PI controller windup.

13. The control system of claim 6, wherein at least one of the plurality of associated fuzzy logic rules comprises:

a rule to detect a relatively slow transient event, wherein the relatively slow transient event predicts when a PI controller may windup.

14. The control system of claim 6, wherein at least one of the plurality of associated fuzzy logic rules comprises:

a rule to detect a slower transient event that may transition to a fast event, thus resulting in a PI controller possible windup.

15. The control system of claim 6, wherein at least one of the plurality of associated fuzzy logic rules comprises:

a rule to detect a fast transient event, wherein a PI controller may windup but will not have a large windup associated therewith.

16. The control system of claim 6, wherein at least one of the plurality of associated fuzzy logic rules comprises:

a rule to detect a fast transient event resulting in a PI controller windup.

17. The control system of claim 6, wherein at least one of the plurality of associated fuzzy logic rules comprises:

a rule to detect that the powertrain is producing power resulting in a possible PI controller windup.

18. The control system of claim 6, wherein at least one of the plurality of associated fuzzy logic rules comprises:

a rule to detect a fast transient event resulting in a PI controller windup.

19. A control method for controlling a hybrid electric vehicle comprising:

providing a fuzzy gain-scheduling proportional integral (PI) controller that operates to control an engine power, engine speed, and vehicle powertrain in response to predefined operating conditions, said fuzzy gain-scheduling proportional integral (PI) controller comprising a fuzzy logic gain-scheduler having an associated output in communication with a PI controller, said output comprising an input to said PI controller, said input modified within said PI controller to produce a gain output from said (PI) controller, said gain output comprising an output of said fuzzy gain-scheduling proportional integral (PI) controller;

using said fuzzy gain-scheduling proportional integral controller to control said engine power, engine speed, and vehicle powertrain.

20. The method for controlling a hybrid electric vehicle of claim 19, further comprises:

inputting a first variable into the controller to control engine behavior during steady-state events, said first variable defining a magnitude of an error e(n) between a desired hybrid vehicle battery power (P<sub>batt—

des</sub>(n)) and an actual hybrid vehicle battery power (P<sub>bat—

act</sub>(n)), said actual battery power dependent on engine power, torque, and power losses attributable to said HEV system;

inputting a second input variable into the controller to control engine behavior during transient events, said second input variable defining a magnitude of a rate of change of the error e(n); and

inputting a third variable into the controller to predict and control undesired engine behavior, said third variable defining an absolute difference between a commanded engine speed and a target engine speed.