Vehicle diagnostic tool—running multiple tests

• US 8,239,093 B1
• Filed: 11/23/2009
• Issued: 08/07/2012
• Est. Priority Date: 06/08/2006
• Status: Active Grant

• Current Assignee: Bernie C. Thompson, Neal R. Pederson
• Original Assignee: Bernie C. Thompson, Neal R. Pederson
• Inventors: Thompson, Bernie C., Pederson, Neal R.
• Primary Examiner(s): Black, Thomas G.
• Assistant Examiner(s): Olsen, Lin B
• Application Number:
  US12/624,205
• Time in Patent Office: 988 Days
• Field of Search: None
• US Class Current: 701/32.7
• CPC Class Codes: G01M 15/04 Testing internal-combustion engines
• CPC Class Codes: G01M 15/05 by combined monitoring of two or more different engine parameters
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005

Abstract

An analysis tool which extracts all the available parameter identifications (i.e. PIDS) from a vehicle'"'"'s power train control module for diagnostic decisions. This is done by checking these PIDS and other information (e.g., calculated PIDS, Break Points, charts and algorithms) in three states; key on engine off, key on engine cranking, key on engine running. In all three modes the tool is comparing the live data from PIDS and voltage to the other information (e.g, Break Points). If any of this data are outside the programmed values a flag is assigned to the failure or control problem. The relationship between a particular PID and its associated preprogrammed value(s) may be indicated by a light. The depth of the problem (if any) is conveyed by the color of the light. Also included are tests/charts for fuel trim, engine volumetric efficiency, simulated injector, power, catalyst efficiency, and engine coolant range.

Claims

1. A diagnostic method for an automotive power plant implemented with the aid of instrumentation including a microprocessor;

the power plant including an engine, a powertrain control module and one or more sensors;

the microprocessor being programmed to acquire parameter identification (hereinafter “

PID”

) data from the powertrain control module;

the microprocessor also being programmed with one or more algorithms which permit the analysis of extracted PID data, the transformation of PID data, or both, for the purpose of enabling at least two tests for diagnosing one or more problems associated with the power plant the diagnostic method including the steps of;

a. acquiring PID data from the powertrain control module with the microprocessor, which acquired PID data can be utilized by the one or more algorithms to perform a plurality of diagnostic tests;

b. utilizing data acquired from at least one PID to run a second test for the purpose of diagnosing at least one problem associated with the power plant; and

c. utilizing data acquired from at least a second PID to run a second test for the purpose of diagnosing at least one problem associated with the power plant.

2. The method as set forth in claim 1, wherein the algorithms permit the running of tests selected from the group including (i) determining fuel trim from PID data acquired from the powertrain control module (hereinafter “

Test A”

) and (ii) determining the actual and a theoretical volumetric efficiency of the engine from PID data acquired from the powertrain control module (hereinafter “

Test B”

);

wherein the first test includes running at least one test from the group including Tests A and B; and

wherein the second test includes running a different test also selected from the group including tests A and B.

3. The method as set forth in claim 2, wherein the data used for running Test A includes data acquired from at least one PID selected from the group including Long Term Fuel Trim Bank 1 (“

LTFTB1”

), Short Term Fuel Trim Bank 1 (“

STFTB1”

), Long Term Fuel Trim Bank 2 (“

LTFTB2”

), Short Term Fuel Trim Bank 2 (“

STFTB2”

), Total Fuel Trim Bank 1 (“

TFTB1”

) and Total Fuel Trim Bank 2 (“

TFTB2”

).

4. The method as set forth in claim 3, wherein the group including LTFTB1, also includes Oxygen Sensor Bank 1 Sensor 1 (hereinafter “

O2B1S1”

), Oxygen Sensor Bank 1 Sensor 2 (hereinafter “

O2B1S2”

), Oxygen Sensor Bank 1 Sensor 3 (hereinafter “

O2B1S3”

), Oxygen Sensor Bank 2 Sensor 1 (hereinafter “

O2B2S1”

), Oxygen Sensor Bank 2 Sensor 2 (hereinafter “

O2B2S2”

), Oxygen Sensor Bank 2 Sensor 2 (hereinafter “

O2B2S3”

), Wide Range Air Fuel (hereinafter “

WRAF”

) sensor B1S1, and WRAF sensor B2S1.

5. The method as set forth in claim 2, wherein the data used for running Test B includes data acquired from at least one PID selected from the group including engine load, engine speed, manifold absolute pressure (hereinafter “

MAP”

), and mass air flow (hereinafter “

MAS”

).

6. The method as set forth in claim 2, wherein the data used for running Test A includes data selected from at least one PID selected from the group including LTFTB1, STFTB1, LTFTB2, STFTB2, TFTB1 and TFTB2;

and wherein the data used for running Test B includes data selected from at lease one PID is selected from the group including engine load, engine speed, MAP, and MAS.

7. The method as set forth in claim 2, wherein the algorithms permit the running of tests selected from the group including (i) Test A, (ii) Test B and (iii) determining the difference between actual fuel injector on time and a calculated injector on time at various engine speeds and engine throttle positions from PID data acquired from the powertrain control module (hereinafter “

Test C”

);

wherein the first test includes running at least one test from the group including Tests A, B and C; and

wherein the second test includes running a different test also selected from the group including Tests A, B and C.

8. The method as set forth in claim 7, wherein the data used for running Test C includes data acquired from at least one PID selected from the group including engine load, actual injector on time, fuel trim, and engine speed.

9. The method as set forth in claim 2, wherein the algorithms permit the running of tests selected from the group including (i) Test A, (ii) Test B and (iii) calculating the engine'"'"'s horsepower from PID data acquired from the powertrain control module (hereinafter “

Test D”

);

wherein the first test includes running at least one test from the group including Tests A, B and D; and

wherein the second test includes running a different test also selected from the group including Tests A, B and D.

10. The method as set forth in claim 9, wherein the data used for running Test D includes data acquired from at least one PID selected from the group including fuel trim and a sensor used to determine air flow through the engine.

11. The method as set forth in claim 10, wherein the sensor data is PID data selected from the group mass air flow (hereinafter “

MAF”

), MAP and Calculated Load.

12. The method as set forth in claim 2, wherein the power plant includes a catalytic converter;

wherein the algorithms permit the running of tests selected from the group including (i) Test A, (ii) Test B and (iii) determining the efficiency of the catalytic converter from PID data acquired from the powertrain control module (hereinafter “

Test E”

);

wherein the first test includes running at least one test from the group including Tests A, B and E; and

wherein the second test includes running a different test also selected from the group including Tests A, B and E.

13. The method as set forth in claim 12, wherein the data used for running Test E is selected from at least one included in the group PID data from the O2 sensor before the catalytic converter, PID data from the WRAF sensor, and PID data from the O2 sensor after the catalytic converter.

14. The method as set forth in claim 2, wherein the power plant includes a cooling system;

wherein the algorithms permit the running of tests selected from the group including (i) Test A, (ii) Test B and (iii) diagnosing the cooling system of the power plant with PID data acquired from the powertrain control module (hereinafter “

Test F”

);

wherein the first test includes running at least one test from the group including Tests A, B and F; and

wherein the second test includes running a different test also selected from the group including Tests A, B and F.

15. The method as set forth in claim 14, wherein the data used for running Test F includes data acquired from at least one PID selected from the group including engine coolant temperature (hereinafter “

ECT”

), vehicle speed, revolutions per minute (hereinafter “

RPM”

), and throttle position sensor (hereinafter “

TPS”

).

16. The method as set forth in claim 2, wherein the power plant includes a catalytic converter;

wherein the algorithms permit the running of tests selected from the group including;

(i) Test A;

(ii) Test B;

(iii) determining the difference between actual fuel injector on time and a calculated injector on time at various engine speeds and engine throttle positions from PID data acquired from the powertrain control module (hereinafter “

Test C”

);

(iv) calculating the engine'"'"'s horsepower from PID data acquired from the powertrain control module (hereinafter “

Test D”

);

(v) determining the efficiency of the catalytic converter from PID data acquired from the powertrain control module (hereinafter “

Test E”

); and

(vi) diagnosing the cooling system of the power plant with PID data acquired from the powertrain control module (hereinafter “

Test F”

);

wherein the first test includes running at least one test from the group consisting of Tests A, B, C, D, E and F; and

wherein the second test includes running at least one different test also selected from the group including Tests A, B, C, D, E and F.

17. The method as set forth in claim 16, wherein the data used for running Test A includes data acquired from at least one PID selected from the group including LTFTB1, STFTB1, LTFTB2, STFTB2, TFTB1 and TFTB2;

wherein the data used for running Test B includes data acquired from at least one PID selected from the group including engine load, engine speed MAP, and MAS;

wherein the data used for running Test C includes data acquired from at least one PID selected from the group including engine load, actual injector on time, fuel trim, and engine speed;

wherein the data used for running Test D includes data acquired from at least one PID selected from the group including fuel trim, and a sensor used to determine air flow through the engine;

wherein the PID data used for running Test E is selected from at least one included in the group PID data from the O2 sensor before the catalytic converter, WRAF PID data, and PID data from the O2 sensor after the catalytic converter; and

wherein the data used for running Test F includes data acquired from at least one PID selected from the group including ECT, vehicle speed, RPM, and TPS.

18. An automotive power plant diagnostic method implemented with the aid of instrumentation including a microprocessor and apparatus for obtaining exhaust gas data the power plant including an engine, a powertrain control module, one or more sensors, and an exhaust system;

the microprocessor being programmed to acquire parameter identification (hereinafter “

PID”

) data from the powertrain control module;

the microprocessor also being programmed with one or more algorithms which permit at least one of the analysis or transformation of data from both the powertrain control module and the apparatus for obtaining exhaust gas data for the purpose of enabling at least one test for diagnosing one or more problems associated with the power plant the method including the steps of;

a. acquiring PID data from the powertrain control module with the microprocessor;

b.

c. obtaining exhaust gas data from the engine with the apparatus for obtaining exhaust gas data; and

d. with at least one of the one or more algorithms comparing at least some of the acquired PID data with the exhaust gas data.

19. The method as set forth in claim 18, wherein the PID data is acquired from at least one PID selected from the group including LTFTB1, STFTB1, LTFTB2, STFTB2, TFTB1, TFTB2, O2B1S1, O2B1S2, O2B1S3, O2B2S1, O2B2S2, O2B2S3, WRAF sensor B1S1, and WRAF sensor B2S1.