Model-based determination of power source replacement in wireless and other devices

• US 8,046,180 B2
• Filed: 07/13/2007
• Issued: 10/25/2011
• Est. Priority Date: 07/13/2007
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Berezowski, Andrew G., Huseth, Steve D.
• Primary Examiner(s): Nghiem; Michael
• Assistant Examiner(s): SUAREZ, FELIX E
• Application Number:
  US11/827,795
• Publication Number:
  US 20090018785A1
• Time in Patent Office: 1,565 Days
• Field of Search: 702/42, 702/63, 702/64, 702/73, 702/178, 702/182, 702/183, 702/188, 320/132, 324/426, 607/29
• US Class Current: 702/60
• CPC Class Codes: G01R 31/3648 comprising digital calculation means, e.g. for performing an algorithm
• CPC Class Codes: G01R 31/392 Determining battery ageing or deterioration, e.g. state of health

Abstract

A model-based system and method for analyzing power source performance and optimizing operational costs are provided. Data from the power source (such as a battery) and/or a device associated with the power source is analyzed and processed to predict an operating life of the power source. This could allow, for example, a power source replacement schedule to be generated for the device. If the analysis indicates that abnormal conditions exist or that any user-defined alerts are warranted, a message could also be sent to an operator terminal. The system and method may continue to monitor the device and thus provide real-time data. The data may also be stored in memory, collected over time, and analyzed or used in various ways. The system and method thus provide a cost effective and reliable analysis of power source performance and any associated operational and replacement costs.

Claims

1. A system comprising:

a processing module configured to process data associated with a device and a power source associated with the device to predict an operating life of the power source by modeling real-time power consumption of the device using at least one of;

a logarithmic curve fitting method and an exponential curve fitting method;

wherein the data associated with the device and the power source comprises historical data; and

wherein the processing module is configured to model the real-time power consumption of the device by applying at least one of the logarithmic curve fitting method and the exponential curve fitting method to the historical data to predict operating life of the power source.

2. The system of claim 1, wherein the device comprises a wireless sensor, and the power source comprises a battery.

3. The system of claim 1, wherein the processing module is configured to predict the operating life of the power source by estimating the power consumed by the device in each of a plurality of different modes of operation during each of multiple repeating sequences of the different modes.

4. The system of claim 1, wherein the data comprises at least one of:

an operating condition of the power source, an operating condition of the device, a safety rating of the power source, a safety rating of the device, an industry standard, and a user-defined threshold.

5. The system of claim 1, wherein:

the device comprises a sensor; and

the data comprises at least one of;

an operational cost of the power source, an operational cost of the sensor, and an operational cost of the device.

6. The system of claim 1, wherein the processing module is further configured to predict a replacement schedule for the power source.

7. The system of claim 6, wherein the replacement schedule includes a preferred time frame for replacing the power source.

8. The system of claim 1, wherein the processing module is further configured to output a warning message to an operator terminal when the data indicates that an abnormal condition exists or that a user-defined level is exceeded.

9. The system of claim 1, wherein the data comprises real-time data.

10. The system of claim 1 further comprising:

a memory configured to store data associated with performance of the power source.

11. The system of claim 3, wherein:

each of the multiple repeating sequences of the different modes comprises a sleep mode, a data processing mode, a ready mode, a transmitting mode, and a receiving mode;

the power consumed by the device in each mode is different; and

the processing module is configured to predict the operating life of the power source by estimating the power consumed by the device in each of the five modes of operation during an estimated number of sequences that occur at a specified periodic interval.

12. A method comprising:

receiving data associated with a power source and with a device associated with the power source; and

predicting an operating life of the power source from the received data by modeling real-time power consumption of the device using at least one of;

a logarithmic curve fitting method and an exponential curve fitting method;

wherein the data associated with the power source and the device comprises historical data and

wherein modeling the real-time power consumption of the device comprises applying at least one of the logarithmic curve fitting method and the exponential curve fitting method to the historical data to predict the operating life of the power source.

13. The method of claim 12, wherein predicting the operating life of the power source comprises estimating the power consumed by the device in each of a plurality of different modes of operation during each of multiple repeating sequences of the different modes.

14. The method of claim 12, wherein predicting the operating life of the power source comprises accounting for at least one of:

an operating condition of the power source, an operating condition of the device, a safety rating of the power source, a safety rating of the device, an industry standard, and a user-defined threshold.

15. The method of claim 12, wherein:

the device comprises a sensor; and

predicting the operating life of the power source comprises accounting for at least one of;

an operational cost of the power source, an operational cost of the wireless sensor, and an operational cost of the device.

16. The method of claim 12, further comprising:

providing a replacement schedule for the power source.

17. The method of claim 16, wherein the replacement schedule includes a preferred time frame for replacing the power source.

18. The method of claim 12, further comprising:

providing a warning message to an operator terminal when the data indicates that an abnormal condition exists or that a user-defined level is exceeded.

19. The method of claim 12, wherein the data comprises real-time data.

20. A non-transitory computer readable medium embodying a computer program, the computer program comprising computer readable program code for:

correlating data associated with a power source and with a device associated with the power source; and

predicting an operating life of the power source using the correlated data by modeling real-time power consumption of the device using at least one of;

a logarithmic curve fitting method and an exponential curve fitting method;

wherein the data associated with the power source and the device comprises historical data; and

wherein the computer readable program code for modeling the real-time power consumption of the device comprises computer readable program code for applying at least one of the logarithmic curve fitting method and the exponential curve fitting method to the historical data to predict the operating life of the power source.

21. The computer readable medium of claim 20, further comprising computer readable program code for:

providing a replacement schedule for the power source.