ADVANCED BATTERY EARLY WARNING AND MONITORING SYSTEM

• US 20130135110A1
• Filed: 12/31/2012
• Published: 05/30/2013
• Est. Priority Date: 01/20/2011
• Status: Active Grant

• Current Assignee: Indiana University Research & Technology Corporation (Indiana University)
• Original Assignee: Indiana University Research & Technology Corporation (Indiana University)
• Inventors: Xie, Jian, Chen, Jie, Chen, Yaobin, Li, Lingxi
• Granted Patent: US 9,177,466 B2
• Time in Patent Office: Days
• US Class Current: 340/636.11
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G01R 31/374 with means for correcting the measurement for temperature or ageing
• CPC Class Codes: G01R 31/382 Arrangements for monitoring battery or accumulator variables, e.g. SoC
• CPC Class Codes: G01R 31/396 Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
• CPC Class Codes: G08B 21/185 Electrical failure alarms
• CPC Class Codes: G08B 29/181 due to failing power supply

Abstract

Systems, apparatuses and methods for detecting internal cell faults using online and real-time sensing techniques, and providing an accurate and reliable early warning for the incoming failure of battery cells hours or days prior to failure. A system is configured to perform real-time and direct measurement of battery cell parameters of temperature, voltage, and AC impedance through online sensing. These parameters may be simultaneously processed using advanced probabilistic and/or fuzzy logic-based algorithms to provide an early warning for the incoming failure of battery cells in a battery pack. This technology may safeguard the LIB battery packs while allowing them to operate at or near 100% capacity in both transportation and stationary applications.

Claims

1. A processor-based method for monitoring and predicting battery faults, comprising the steps of:

receiving a signal from a battery apparatus comprising AC impedance data;

processing the signal to form signature data, said signature data comprising deconvoluted aspects of the AC impedance data;

processing the signature data to determine a predictive value representing a likelihood of a physical condition existing in the battery apparatus; and

transmitting a warning signal if the predictive value is at or above a predetermined value.

2. The processor-based method of claim 1, wherein the signature data comprises data relating to at least one of contact resistance (R₀), solid electrolyte interface resistance (R_SEI), solid electrolyte interface capacitance (C_SEI), double layer capacitance (C_dl), and charge transfer resistance (R_ct) of the battery apparatus.

3. The processor-based method of claim 1, wherein the signature data comprises data relating to diffusion coefficients from electrode materials of the battery apparatus, the coefficients being based on the AC impedance data and data relating to in situ high energy synchrotron XRD.

4. The processor-based method of claim 1, wherein the physical condition comprises at least one of a battery fault and battery failure.

5. The processor-based method of claim 4, wherein the warning signal comprises information relating to the at least one of the battery fault and battery failure.

6. The processor-based method of claim 5, wherein the warning signal comprises a time value indicating an estimated time in which the at least one of the battery fault and battery failure is about to occur.

7. The method of claim 6, wherein the approaching battery failure condition comprises an overdischarge condition.

8. The processor-based method of claim 1, wherein the signature data comprises a data histogram, and wherein the predictive value is based on a probability density function.

9. The processor-based method of claim 1, wherein the predictive value is based on a fuzzy rule set relating to a fuzzy membership function for the aspects of the AC impedance data.

10. The processor-based method of claim 9, wherein the fuzzy membership function comprises current, voltage and temperature.

11. A system for monitoring and predicting battery faults, comprising:

an input for receiving a signal from a battery apparatus, said signal comprising AC impedance data;

a processing apparatus operatively coupled to the input and configured to process the signal to form signature data, said signature data comprising deconvoluted aspects of the AC impedance data, wherein the processing apparatus is configured to process the signature data to determine a predictive value representing a likelihood of a physical condition existing in the battery apparatus; and

a communication operatively coupled to the processing apparatus for transmitting a warning signal if the predictive value is at or above a predetermined value.

12. The system of claim 11, wherein the signature data comprises data relating to at least one of contact resistance (R₀), solid electrolyte interface resistance (R_SEI), solid electrolyte interface capacitance (C_SEI), double layer capacitance (C_dl), and charge transfer resistance (R_ct) of the battery apparatus.

13. The system of claim 11, wherein the signature data comprises data relating to diffusion coefficients from electrode materials of the battery apparatus, the coefficients being based on the AC impedance data and data relating to in situ high energy synchrotron XRD.

14. The system of claim 11, wherein the physical condition comprises at least one of a battery fault and battery failure.

15. The system of claim 14, wherein the warning signal comprises information relating to the at least one of the battery fault and battery failure.

16. The system of claim 15, wherein the warning signal comprises a time value indicating an estimated time in which the at least one of the battery fault and battery failure is about to occur.

17. The method of claim 16, wherein the approaching battery failure condition comprises an overdischarge condition.

18. The system of claim 11, wherein the signature data comprises a data histogram, and wherein the predictive value is based on a probability density function.

19. The system of claim 11, wherein the predictive value is based on a fuzzy rule set relating to a fuzzy membership function for the aspects of the AC impedance data.

20. The system of claim 19, wherein the fuzzy membership function comprises current, voltage and temperature.