Predictive monitoring for vehicle efficiency and maintenance

• US 7,899,591 B2
• Filed: 07/13/2006
• Issued: 03/01/2011
• Est. Priority Date: 07/14/2005
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Shemyakin, Mikhail, Richmond, James, Shah, Baiju
• Primary Examiner(s): Tran; Dalena
• Application Number:
  US11/486,476
• Publication Number:
  US 20070027593A1
• Time in Patent Office: 1,692 Days
• Field of Search: 701/30, 701/36, 701/104, 701/29, 123/406.03, 123/525, 123/406.13, 607/80, 607/93, 702/33, 702/50, 702/55, 702/81, 180/54.1, 180/60, 180/65.1, 180/69.5, 180/301
• US Class Current: 701/30
• CPC Class Codes: B60W 30/188 Controlling power parameters of the driveline, e.g. determining the required power
• CPC Class Codes: B60W 50/00 Details of control systems for road vehicle drive control not related to the control of a particular sub-unit , e.g. process diagnostic or vehicle driver interfaces

Abstract

A system and method for monitoring fuel efficiency of a vehicle is provided. Fuel efficiency of a vehicle may be monitored in real-time using telemetry transmitted from the vehicle. The telemetry may be analyzed in combination with a fuel efficiency model in order to determine whether the real-time fuel efficiency determination deviates from a baseline fuel efficiency. The real-time fuel efficiency monitoring system and method may be used for feedback for the driver, such as sending real-time messages to the driver to modify operation of the vehicle to increase fuel efficiency. The fuel efficiency model may further be used to assign vehicles in a fleet to particular routes, vehicles to particular drivers, or vehicles to particular routes with particular drivers.

Claims

1. An energy efficiency determination system comprising:

a receiver that receives operational data about an asset from a sensor indicative of at least one operational characteristic;

a memory that stores the operational data;

an asset efficiency model indicative of energy efficiency of the asset;

a processor in communication with the asset efficiency model and the memory, the processor analyzing real-time energy efficiency of the asset based on the operational data and the asset efficiency model, assessing whether to perform maintenance on the asset based at least on improved efficiency of the asset due to the performed maintenance, and scheduling the maintenance based on the assessing whether to perform the maintenance,

wherein the asset comprises a vehicle;

wherein the asset efficiency model comprises data indicative of optimal efficiency of the vehicle,

further wherein assessing whether to perform maintenance on the asset based at least on improved efficiency of the asset due to the performed maintenance comprises;

determining a first cost of an increased fuel consumption until a next scheduled vehicle maintenance event;

determining a second cost of performing maintenance on the asset; and

comparing the first cost impact with the second cost impact.

2. The energy efficiency determination system of claim 1, wherein analyzing real-time energy efficiency of the asset based on the operational data and the asset efficiency model comprises analyzing fuel efficiency variance.

3. The energy efficiency determination system of claim 2, wherein the asset efficiency model comprises a baseline measure of fuel economy;

wherein the operational data comprises average fuel economy; and

wherein analyzing fuel efficiency variance is based on the baseline measure of fuel economy and the average fuel economy.

4. The energy efficiency determination system of claim 1, wherein determining the second cost comprises determining a cost of removing the vehicle from service, a labor cost of performing maintenance on the vehicle outside an existing maintenance schedule, a cost of parts for the maintenance, and a cost of use of a spare vehicle to replace the vehicle that is receiving maintenance.

5. The energy efficiency determination system of claim 1, further comprising an operational model for the vehicle to predict potential failures;

and

wherein the processor predicts failure of the vehicle based on the operational data and the operational model and assesses whether to perform maintenance on the vehicle based on the predicted failure.

6. The energy efficiency determination system of claim 5, wherein assessing whether to perform maintenance on the vehicle based on the predicted failure comprises:

determining an urgency of performing maintenance;

determining a cost allowing the predicted failure to occur;

determining a cost of performing maintenance; and

comparing the cost of allowing the predicted failure to occur versus the cost of performing maintenance.

7. The energy efficiency determination system of claim 6, wherein the processor determines who to alert based on at least one of the urgency of performing maintenance, the cost allowing the predicted failure to occur, or the cost of performing maintenance.

8. A method for determining energy efficiency, the method comprising:

receiving operational data about an asset from a sensor indicative of at least one operational characteristic;

storing the operational data;

analyzing real-time energy efficiency of the asset based on the operational data and an asset efficiency model indicative of energy efficiency of the asset;

assessing whether to perform maintenance on the asset based at least on improved efficiency of the asset due to the performed maintenance;

scheduling the maintenance based on the assessing whether to perform the maintenance,

wherein the asset comprises a vehicle; and

wherein the asset efficiency model comprises data indicative of optimal efficiency of the vehicle,

further wherein assessing whether to perform maintenance on the asset based at least on improved efficiency of the asset due to the performed maintenance comprises;

determining a first cost of an increased fuel consumption until a next scheduled vehicle maintenance event;

determining a second cost of performing maintenance on the asset; and

comparing the first cost impact with the second cost impact.

9. The method of claim 8, wherein analyzing real-time energy efficiency of the asset based on the operational data and the asset efficiency model comprises analyzing fuel efficiency variance.

10. The method of claim 9, wherein the asset efficiency model comprises a baseline measure of fuel economy;

wherein the operational data comprises average fuel economy; and

wherein analyzing fuel efficiency variance is based on the baseline measure of fuel economy and the average fuel economy.

11. The method of claim 8, wherein determining the second cost comprises

determining a cost of removing the vehicle from service, a labor cost of performing maintenance on the vehicle outside an existing maintenance schedule, a cost of parts for the maintenance, and

a cost of use of a spare vehicle to replace the vehicle that is receiving maintenance.

12. The method of claim 8, further comprising:

accessing an operational model for the vehicle to predict potential failures;

predicting failure of the vehicle based on the operational data and the operational model; and

assessing whether to perform maintenance on the vehicle based on the predicted failure.

13. The method of claim 12, wherein assessing whether to perform maintenance on the vehicle based on the predicted failure comprises determining an urgency of performing maintenance, determining a cost allowing the predicted failure to occur, determining a cost of performing maintenance, and comparing the cost of allowing the predicted failure to occur versus the cost of performing maintenance.

14. The method of claim 13, further comprising determining who to alert based on at least one of the urgency of performing maintenance, the cost allowing the predicted failure to occur, or the cost of performing maintenance.