Data processing: artificial intelligence – Fuzzy logic hardware – Fuzzy neural network
Reexamination Certificate
2002-04-15
2004-02-10
Davis, George B. (Department: 2121)
Data processing: artificial intelligence
Fuzzy logic hardware
Fuzzy neural network
C706S900000, C706S902000, C702S063000, C320S130000, C320S134000
Reexamination Certificate
active
06691095
ABSTRACT:
BACKGROUND
The present invention relates to determining the state-of-health (SOH) of an electrochemical device. More particularly, the present invention relates to determining the SOH of a proton exchange membrane fuel cell using an intelligent system, e.g. a fuzzy logic system.
The SOH of a battery, fuel cell, or other electrochemical device has been interpreted in different ways by scientists/engineers in the field. Most research in this area has focused on electrochemical batteries. In the case of valve regulated lead acid (VRLA) batteries used by utility companies for providing emergency backup power, SOH is interpreted to mean that a battery is close to the end of its cycle life and needs replacement. Several papers including Feder and Hlavac 1994 INTELEC Conf. Proc. pp. 282-291 (1994) and Hawkins and Hand 1996 INTELEC Conf. Proc. pp. 640-645 (1996) demonstrate that the increase in impedance of aging VRLA batteries can be used to indicate the SOH of the battery.
Another interpretation of battery SOH is the capability of a battery to meet its load demand. This is also referred to as “battery condition” by others in the field. To obtain the SOH of a battery in the terms defined, both the available charge capacity of the battery and the maximum power available from the battery are required. Several approaches have been used to determine the condition of a battery. In U.S. Pat. No. 5,365,453 is described a method in which a ratio of a change in battery voltage to a change in load is used to predict impending battery failure in battery powered electronic devices. Similar methods in which the battery response to and recovery from the application of a load is used to determine the SOH of batteries are reported in U.S. Pat. Nos. 4,080,560 and 5,159,272. While these load profiling approaches work reasonably well for batteries integrated into a system, they are not necessarily accurate or reliable ways of determining the SOH of batteries outside a system.
SUMMARY
The above-discussed and other drawbacks and deficiencies of the prior art are overcome or alleviated by a method of and system for determining the state-of-health of a proton exchange membrane (PEM) fuel cell stack connected to a load, comprising: detecting the real part of the impedance Z
1
of the fuel cell stack at a selected frequency; detecting the voltage V
1
of the PEM fuel cell stack at open circuit; detecting the voltage V
2
of the PEM fuel cell stack when the maximum load current is being drawn from said PEM fuel cell stack; and determining the state of health of said PEM fuel cell stack from a fuzzy system trained in a relationship between said real part of the impedance of the fuel cell stack at the selected frequency, the voltage of the PEM fuel cell stack at open circuit, and the voltage of the PEM fuel cell stack when the maximum load current is being drawn from said PEM fuel cell stack.
The above-discussed and other features and advantages of the present invention will be appreciated and understood by those skilled in the art from the following detailed description and drawings.
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Fennie, Jr. Craig
Reisner David E.
Singh Pritpal
Davis George B.
U.S. Nanocorp
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