ELECTRIC VEHICLE PERSONAL BENEFITS ANALYZER

• US 20130073267A1
• Filed: 09/21/2011
• Published: 03/21/2013
• Est. Priority Date: 09/21/2011
• Status: Abandoned Application

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Tamor, Michael A., Gearhart, Chris C., Soto, Ciro A.
• Application Number:
  US13/238,005
• Publication Number:
  US 20130073267A1
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: B60L 3/12 Recording operating variables ; Monitoring of operating variables

Abstract

A benefit analysis system allows a user to compare energy consumption between a first electrified vehicle and a second vehicle. A data collector receives user driving characteristics. A parameter calculation module determines a peak parameter, a width parameter, a weigh factor, a scale factor, and a frequency parameter in response to the user driving characteristics. An analyzer is responsive to the parameters from the parameter calculation module to generate respective energy consumption results for the first and second vehicles. The analyzer represents an individual trip chain distribution as a composite function including a habitual component defined by the peak parameter and the width parameter and a non-habitual component defined by the scale factor. The composite function combines the habitual component and the non-habitual component according to the weight factor. The analyzer determines the energy consumption results in response to the individual trip chain distributions.

Claims

1. A benefit analysis system in which a user compares energy consumption between a first electrified vehicle and a second vehicle, comprising:

a data collector receiving user driving characteristics comprised of a commute distance, a commute repetition, a long-term aggregate driving distance, and a daily usage rate;

a parameter calculation module receiving the user driving characteristics, wherein the parameter calculation module determines a peak parameter, a width parameter, a weigh factor, a scale factor, and a frequency parameter in response to the user driving characteristics; and

an analyzer responsive to the parameters from the parameter calculation module to generate respective energy consumption results for the first and second vehicles, wherein the analyzer represents an individual trip chain distribution as a composite function including a habitual component defined by the peak parameter and the width parameter and a non-habitual component defined by the scale factor, wherein the composite function combines the habitual component and the non-habitual component according to the weight factor, and wherein the analyzer determines the energy consumption results in response to the individual trip chain distributions.

2. The system of claim 1 wherein the peak parameter is proportional to the commute distance, wherein the width parameter is proportional to the commute distance, wherein the frequency parameter is proportional to the daily usage rate, wherein the weight factor is determined in response to the commute distance, the commute repetition, and the long-term aggregate driving distance, and wherein the scale factor is determined in response to the commute distance, the commute repetition, the long-term aggregate driving distance, and the daily usage rate.

3. The system of claim 2 wherein the commute distance is collected as a round trip distance, wherein the commute repetition is collected in days per week, wherein the long-term aggregate driving distance is collected in distance per year, and wherein the daily usage rate is collected in days per year.

4. The system of claim 2 wherein the parameters are determined as follows:

μ

=X₂

σ

=min(X₂/5,7.5)

λ

=X₄/365

w=(X₃−

52X₂X₁)/X₃

k=X₃/(365λ

w)−

(1−

w)μ

/w

where μ

is the peak parameter, σ

is the width parameter, λ

is the frequency parameter, w is the weight factor, k is the scale factor, X₁ is the commute distance, X₂ is the commute repetition, X₃ is the long-term aggregate driving distance, and X₄ is the daily usage rate.

5. The system of claim 4 wherein the individual trip chain distribution p(x) is represented by:

6. The system of claim 1 wherein the habitual component is comprised of a normal distribution and the non-habitual component is comprised of an exponential distribution.

7. The system of claim 1 wherein the second vehicle is powered by a combustion engine.

8. The system of claim 1 wherein the first and second vehicles are electric vehicles powered by a respective battery.

9. The system of claim 1 wherein the first vehicle is a hybrid electric vehicle powered by both a combustion engine and a battery.

10. The system of claim 9 wherein the second vehicle is a hybrid electric vehicle powered by both a combustion engine and a battery.

11. The system of claim 1 wherein the energy consumption results are comprised of an annual fuel savings of one of the first or second vehicles over the other.

12. The system of claim 1 wherein the energy consumption results include a number of days for which an individual trip chain distribution exceeds an electric range of one of the first or second vehicles.

13. A method of comparing energy consumption between a first electrified vehicles and a second vehicle in response to characteristics of a driver, comprising the steps of:

the driver specifying a commute distance, a commute repetition, a long-term aggregate driving distance, and a daily usage rate;

determining a peak parameter, a width parameter, a weigh factor, a scale factor, and a frequency parameter in response to the user driving characteristics;

represents an individual trip chain distribution for the driver as a composite function including a habitual component defined by the peak parameter and the width parameter and a non-habitual component defined by the scale factor, wherein the composite function combines the habitual component and the non-habitual component according to the weight factor;

determining an energy consumption for each of the first and second vehicles in response to the individual trip chain distributions; and

presenting the energy consumptions to the driver for evaluating the relative benefits of driving the first and second vehicles.

14. The method of claim 13 wherein the peak parameter is proportional to the commute distance, wherein the width parameter is proportional to the commute distance, wherein the frequency parameter is proportional to the daily usage rate, wherein the weight factor is determined in response to the commute distance, the commute repetition, and the long-term aggregate driving distance, and wherein the scale factor is determined in response to the commute distance, the commute repetition, the long-term aggregate driving distance, and the daily usage rate.

15. The method of claim 14 wherein the commute distance is collected as a round trip distance, wherein the commute repetition is collected in days per week, wherein the long-term aggregate driving distance is collected in distance per year, and wherein the daily usage rate is collected in days per year.

16. The method of claim 14 wherein the parameters are determined as follows:

μ

=X₂

σ

min(X₂/5,7.5)

λ

=X₄/365

w=(X₃−

52X₂X₁)/X₃

k=X₃/(365λ

w)−

(1−

w)μ

/w

where μ

is the peak parameter, σ

is the width parameter, λ

is the frequency parameter, w is the weight factor, k is the scale factor, X₁ is the commute distance, X₂ is the commute repetition, X₃ is the long-term aggregate driving distance, and X₄ is the daily usage rate.

17. The method of claim 16 wherein the individual trip chain distribution p(x) is represented by:

18. The method of claim 13 wherein the habitual component is comprised of a normal distribution and the non-habitual component is comprised of an exponential distribution.

19. The method of claim 13 wherein the second vehicle is powered by a combustion engine.

20. The method of claim 13 wherein the first and second vehicles are electric vehicles powered by a respective battery.

21. The method of claim 13 wherein the first vehicle is a hybrid electric vehicle powered by both a combustion engine and a battery.

22. The method of claim 21 wherein the second vehicle is a hybrid electric vehicle powered by both a combustion engine and a battery.

23. The method of claim 13 wherein the energy consumption results are comprised of an annual fuel savings of one of the first or second vehicles over the other.

24. The method of claim 13 wherein the energy consumption results include a number of days for which an individual trip chain distribution exceeds an electric range of one of the first or second vehicles.