13. Making Battery Quick-Test Feasible 4

GUIDE: Batteries in a portable world. 13. Making Battery Quick-Test Feasible 4

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13.5 How does the Cadex Quicktest work?

The first stage of the Cadex Quicktest™analysis uses a waveform to gather battery information under certain stresses, establishing probability levels for the given battery. Since there are many battery types with several interacting variables, a set of rules is applied to further evaluate the data. The results are averaged and an estimated battery capacity is predicted. The initial inference to categorize the batteries is computed from a set of specialized shapes called membership functions. These membership functions are unique to every battery model and are developed using a specialized trend-learning algorithm.

The raw data, consisting of three or more items, flows through the input layer. Vectors leading from the input layer are weighted and the derived values are passed through a function in the hidden layer. Another vector set channels the information to the output.

Figure 13-3:  Flowchart of a neuro-network based on fuzzy logic.
The first three circles on the left are the inputs. The data entering is ‘fuzzified’ according to a set of curves called membership functions. A set of rules that depend on fixed knowledge is evaluated. The results of the rules are combined and distilled, or ‘defuzzified’. The result is a crisp, non-fuzzy number.

The weights are highly significant and function as the learning facility of the network. A run would proceed with a certain set of weights. If the result is off by a certain range, the weights are changed and the process is repeated until a certain number of iterations have passed or the algorithm produces the correct output.

The Cadex Quicktest™ requires less time than most other methods. While current quick test systems, such as those used in defense applications, need hundreds of learning cycles and run on large computers, the Cadex method requires minimal experience and can be performed on relatively simple hardware. Typically less than five learning cycles are necessary to achieve robust, model-specific solution sets, also known as matrices. This massive reduction in time is the result of a new self-learning algorithm that acquires numerous measures of the battery’s characteristics. The algorithm uses a unique decision-making formula that determines the best solution set for each battery model.

Of course, artificial intelligence is a complicated subject, and is beyond the scope of this book. With respect to complexity, Dr. Lofti Zadeh spoke these famous words: “As complexity rises, precise statements lose meaning and meaningful statements lose precision.”

Battery quick testing has raised the interest of manufacturers and users alike. The race is on to provide a product that is accurate, easy to use and cost effective. The true winner may not be an individual or organization that amasses the largest number of patents, but a company that can offer a product that is cost effective and truly works.

13.6 Battery Testing and the Internet

Increasingly, the Internet plays a pivotal role in battery testing. The ability to send all battery test results to a central global database is an exciting prospect. With this information on hand, battery manufacturers would be able to perform battery analysis based on battery type, geographic area and user pattern. Field failures could be identified quickly and appropriate corrections implemented.

Another application for the Internet is establishing a global database for all major battery types, complete with matrix settings. With compatible systems, users would be able to select and download battery information from a central database. Batteryshop™, a software product offered by Cadex, provides such a service. The database lists all common batteries, complete with battery specifications and matrix information. Point and click technology programs the battery analyzer to the correct battery parameters.

Collaborating with battery manufacturers enables Cadex to create the most accurate vector settings. Manufacturers welcome such a system because it reduces beta testing and puts the manufacturer in closer contact with the battery user. The aim is to reduce warranty returns and increase customer satisfaction.

Another powerful feature of the Internet is downloading new software for hardware upgrades. Since battery quick testing is still in its infancy, improved software will be made available in the future that allows upgrading existing equipment with the latest developments.

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