Computer Methods For The Reduction - NASA · Computer Methods For ... Sheer bulk of raw test data without adequate analysis does not help the power ... Inclusion of a computer in

Computer Methods For The Reduction

Correlation And Analysis Of

Space Battery Test Data i

By

Dr. J. W. Mauchly

J. H. Waite

Final Report

May i, 1866 - - Dec. 31, 1966

Prepared For

Goddard Space Flight Center

Contract NAS 5-10203

Mauchly Systems, Inc.

Montgomeryville Industrial Center

Montgomeryville, Pa.

https://ntrs.nasa.gov/search.jsp?R=19670029800 2018-07-15T01:00:00+00:00Z

TABLEOFCONTENTS

ABSTRACT

INTRODUCTION

SECTIONIData Collection

SECTIONIIData Analysis and Reduction

SECTION III

Cell Failure Prediction

SECTION IV

Conclusions and Recommendations

APPENDIX A

Data Collection Charts

APPENDIX B

Data Analysis and Reduction Charts

APPENDIX C

Cell Failure Prediction Charts

APPENDIX D

5 Cycle-Delta-Volt-Histogram Program

APPENDIX E

Failure Charts

PAGE

i

1

9

17

33

39

44

49

72

98

ii0

GLOSSARY 122

ACKNOWLEDGEMENT

The authors of this report would like to give recognition to the following

people whose effort and assistance were appreciated in its preparation.

Dr. Arthur Fleischer -- Consultant

Mr. EugeneStroup -- NASA- Project Manager

Mr. Ed Colston -- NASA

Mr. HughM. Schultz -- NADCrane

Mr. DonMains -- NADCrane

Mr. David Rausch-- NADCrane

Hr. Sheldon D. Epstein -- Mauchly Systems

ABSTRACT

This report describes the computer techniques developed by Mauchly Systems, Inc.

on NASA contract NAS-10203, to extract information from large volumes of space-

craft battery test data.

Novel statistical methods (application of cryptanalytic-like processes) have been

used to identify failure mechanisms from descriptions of failed cells, and to

predict cell life from data patterns hidden in the test data.

Computer programs have been written to reduce the cell test data for a desired

interval of time or for any selected measurement parameters, and to produce output

documentation easy to understand by the space craft design engineer.

The results of these programs include specific predictions of failure, many

thousands of cycles before actual failure, from less than i000 cycles of data.

INTRODUCTION

I. Background

Secondary batteries used for space power requirements have been investigated in many

test programs. Such battery-test programs include acceptance tests, cycling tests

(in which batteries are charge-discharge cycled within simulated space-mission

requirements), and life-test programs.

Despite the number of these tests, basic engineering information concerning the

performance of NiCd Batteries under space-flight operational conditions has been

difficult or impossible to obtain. Although large amounts of data have been

accumulated, interpretation of such data relative to mission requirements is

necessary. Sheer bulk of raw test data without adequate analysis does not help

the power engineer, who must make selections and decisions vital to real missions.

Examination of space-battery test programs indicated the need for developing improved

methods of (i) selecting cells for space batteries, (2) predicting battery/cell

performance within various operational requirements, and (3) determining the

magnitude, duration, and dynamics of a new test experiment.

The contract work now being reported is directed toward these objectives. Methods

of data reduction and analysis leading to indicators useful for both acceptance and

development of improved cells have been devised. Suggestions for better test programs

are included.

The data used in this contract come from just one test program--that st Crane N.A.D.

Clearly, the methods have broader applicability, but are well illustrated and checked

by the data used.

-I-

INTRODUCTION (cont'd)

Since about January, 1963, the Secondary Battery Section of the Quality Evaluation

Department at N.A.D., Crane, Indiana, has been monitoring and collecting data on

sealed nickel-cadmium cells cycled within various environmental parameters.

Cycling consists of charging and discharging cells, repetitively. For purposes of

this test, this charge-discharge time period is called an orbit period. These orbit

periods and their individual charge-discharge time periods are shown in Table I.

Every individual orbit period is not recorded. The cycles recorded are determined by

the test design as follows:

TABLE I.

Orbit

Period

In

Hours

1.5

3.0

8.0

24.0

DischargePeriod

In

Hours

0.5

0.5

1.0

1.0

ChargePeriod

In

Hours

1.0

2.5

7.0

23.0

Recording

Frequency

In

Cycles

32

16

12

8

The cells are connected in series to form packs of five or ten cells each. The packs

are cycled in the ambient temperature and depth of discharge combinations shown in

Table 2.

-2-

INTRODUCTION (Cont'd)

TABLE 2.

Depth

of

Discharge

15%

25%

40%

TEMPERATURE

-20

X

X

X

X

X

DE_REES CENT.

25

X

X

40

X

X

* 25° ambient was not as well regulated -- just sitting in an air conditioned room.

The packs being cycled are monitored. Individual cell voltage, cell temperature,

pack current, pack voltage, the monitoring time after the beginning of the cycle,

and pack identification are recorded. A maximum of eighteen battery packs may be

monitored simultaneously.

Each of a maximum set of eighteen battery packs is sequentially recorded. The

recording of all monitored information for all packs in a set may take one and one

half minutes.

Recordings are taken at monitor points during the orbit period. An example of

monitor points in a ninety minute orbit period follows:

Discharge

I 5 i0 15 20

I_ 30 minutes

25 28 1

Time In Minutes

Charge

t j !oI I 3i I I I I20 0 5 59,

-I60 minute s "--

For more detailed explanation of this cycling and recording see N.A.D. Crane Reports.

-3-


2. Purpose and Significance

The first purpose of this work is to develop methods, techniques, and computer programs

to extract useful and reliable information out of the space battery test data being

collected at N.A.D. Crane, Indiana. Several initial assumptions implicit in this

approach are:

i. That defects in cells are reflected in their operational

behavior long before failure.

2. That the optimum information elements which reflect these

defects are not known.

3. That the Crane data do in fact contain information which can

be discerned by appropriate reduction and analysis methods.

4. That new methods and techniques are needed, since those used in

the past have not been reliable or useful.

5. That the ultimate purpose of this work is to provide the power-

engineer with a method to assess the evaluated potentialities of

space-battery-cells, permiting cell selection within various space

mission operational requirements.

6. That the large volume of Crane data be reduced in order to critically

view selected portions of it.

7. That methods be established to facilitate this reduction and yet

not distort or eliminate relevant information in the data.

8. That results of this work be reported so as to facilitate the

application of new procedures to current battery technology.

Within the framework of analysis, improved methods for collection and handling of the

Crane data will be developed.

-4-


Inclusion of a computer in the data acquisition system is recommended. Also, the

requirements for an automated test procedure will be analyzed and reported.

This work is significant in that it initializes a new approach to the analysis and

evaluation of space battery test data.

-5-


3. Problem

A major problem area in this work was the handling of a very large volume of data --

600,000 punched cards. Some reduction of this volume had to be made to allow critical

scanning to detect differences which might be relevant. In making such reductions, a

prime aim is to avoid distortion or elimination of relevant features of the data.

When data reduction of this sort is possible, there are actually several advantages.

Reduction aids in:

i. Simplifying subsequent analysis.

2. Development of statistical methods for cell-failure prediction.

3. Reducing the cost of repeatedly processing the large original files

which may contain irrelevant or redundant information, or both_

4. Development of methods suited to an automated data management system,

including logging, reduction and analysis.

Reduction, in this case, produced 20,000 punched cards from the original total of 600,000

punched cards.

During the reduction process, certain problem areas were identified and dealt with.

These problem areas include:

i. Cards out of order.

2. Missing cards.

3. Duplicate cards.

4. Data errors.

The computer programs used to accomplish data reduction were designed to check for

various types of errors, to take action to eliminate those which were easily corrected,

and to produce error messages showing what type of error was encountered. With some

error types, for example -- "Cards out of order", an option permitted operator intervention

(to re-sort the cards), to correct error condition.

-6-


4. Method

The approach used in the development of this work was to consider all of the 600,000

data cards, the domain to be analyzed, and to work from this whole body of information

downwards into the individual pieces as differences were identified. This orientation,

examining the data and working back into it to identify and correlate its separate parts,

required the fewest assumptions.

It was determined that cell voltage first-differences included much vital information

regarding cell behavior and incipient failure voltage patterns. This determination

produced the 5-cycle Delta-Volt-Histogram Program, described in the Appendix of this

report. This program used the Crane data as input and produced, as output, 5-cycle

Delta, or first difference, Voltage Histograms. These Histograms contain all of the

voltage difference information for each identified cell for a set of 5 observed cycles

of information. This program extracted pertinant voltage data and effected a data

reduction, Crane data to 5-cycle data, of 30 to i.

Analysis of the 5-cycle Histogram output enabled a cell-failure prediction technique

_escribed in Section III of this report.

Another computer program for reduction and analysis of the Crane data is the Superposed

Curve-pattern Indicator Program. This program creates overlayed or superposed sets of

charge-discharge cycle curves. These are then examined for patterns which might be

associated with a history of high occurrance in failed cells. This program is fully

described in Section II of this report.

-7-


5. Organization

The material in this report is presented in four Sections and five Appendices.

Section I covers data collection and Appendix A contains referenced data-collection

charts.

Section II covers data analysis and reduction and Appendix B contains referenced charts.

Section III covers cell failure prediction and Appendix C contains failure prediction

charts and other referenced material.

Section IV is conclusions and recommendations.

Appendix D presents a data reduction method; the 5 cycle-Delta-Volt-Histogram Program.

Appendix E contains cell failure histories.

-8-

SECTION I

DATA COLLECTION

Data Collection is defined as the acquisition and recording of test measurements from

the analog signal to the printed digital record.

Examination of Sample Data Collected in the Crane NICD Battery Life Tes_. Appendix A

contains the data sheets for 40 out of an elapsed time of 1200 cycles of data on ten

cells which represent Battery Pack #62. This pack was still on test as of December,

1966, and reached the elapsed cycle count of over 15,000 cycles. It is estimated that

the total data for the hundred-plus packs on test will involve some 18,000 pages of

data, such as those found in Appendix A. Some of the data sheets contained are repeated

here to assist in the discussion of the techniques used to obtain usefull information

from this vast store of data. Areas of interest on these data sheets have been marked

and annotated for their significance. It is important to note that the type of

inspection to be discussed is limited to a "one-measurement-at-a-time" kind of perusal.

It is equivalent to looking at curve plots of charge-discharge cycles one at a time;

however, the printed data in Appendix A does give some additional information requiring

computation plus some additional environmental and identifying information. From the

small sample of data presented in Appendix A, eight types of information "flagged" in

the data sheets are listed below.

Type of Observation

i. High End of Charge Reading

2. Low End of Discharge Reading

3. Data Transcription Errors

4. False Cut-In of Charge Limiter

5. End-of-Chg Cut-In of Chg. Lim.

6. Low Current on Pack

Place of Occurrence

Page:

i. 44 thru 48

2. 45

3. 46

4. 44

5. 45, 46, 47

6. 48

-9-

SECTION I (Cont' d_

It is interesting to note from the flagged data in Appendix A that cells exhibiting

these points of interest result in the identification of cells 2, 4, 5, I0 as being

suspect. Actually, cells 4, 5, 7, i0 failed on test, but thousands of cycles later.

In order to facilitate reading of Appendix A, a short description of the output is

presented here. The first column of figures on page 44 , (shown on the following

page) starting at the top left hand side of the page with 1.0, are time measurements

and fractions. The data for five cells are recorded on one punch card or one line

of the tabulation. When the pack contains ten cells, two punched cards are used

with the same time and cycle data given on both cards. The last number of each row

is always a 'I' or a '2' (for I0 cell packs), indicating the first card which holds

data for cells i thru 5, or the second card which holds the data for cells 6 thru I0.

Time periods range from 1.0 to 90.0 minutes for the 1½ hour orbits, with thirty

minutes for discharge and sixty minutes for charge. Discharge readings are made on

the average of every five minutes, and charge readings are made on the average of

every ten minutes. There is some variation in the clocking of these readings, so

that curve plotting and analysis complications arise. This clocking problem also

effects the number of points or readings obtained in some cycles. Interpolation

techniques could be used to correct this problem, but they were not considered in

this phase of the work for they involve assumptions not justified at this stage.

The clocking time distributions are illustrated on the following page (page 49

of Appendix B) in the top block for 3970 cycles of information. (Elapsed cycle

time). From such distributions, it is estimated that 60% of the clocking times are

correct for the middle portions of the charge and discharge curves. For the terminal

portions of the curves the readings are not as good. For future data acquisition

-I0-

1.4

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-11-

IST,TIME DIFF,

62 3970 93

62 3970 I

62 397O 3

62 3970 2

62 3970

62 3970 2 162 3970

62 3970 I

62 3970

62 3970 I

62 3970 2 I 1

62 3970

62 3970

62 3970 3

1 2 3 4 5 6 7

• l

210 7193511 8 ,

I 2 2 51744 5 l l

• 1 4 71261 3 5 1 1

I 1 2 52457 4 2 1 •

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2 1 21116241440 1 7 2 •110352714 7 1

8 9 101

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1 1 •

1 •

1 •

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I I I 3 I I I 4 2857 I I 2 •

• I I I I • I I I 1968 3 I ,

1 1 2 2 * 1 12065 3 3 1

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Ol DT

02 DT

03 DT

04 DT

05 DT

06 DT

07 DT08 DT

09 DT

10 DT

11 DT

12 DT

13 DT

14 DT

DISTRIBUTION OF TIME DIFFERENCE

-12-

SECTION I (Cont' d)

work, this is one area where much tighter control will be desired. Also, as a result

of work done to date, and conferences with others doing tests, it is clearly evident

that more points should be monitored.

The next column of numbers, starting with I. and ending with 31. represents the actual

cycle count from which the data was taken. The test required that approximately every

thirtieth cycle (every 2nd day) be monitored, which represents an initial loss of data

of over ninety percent. For life-test monitoring, this is not a restriction, but with

a computer analysis requirement this is not the place for any reduction to be imposed.

One test facility in the country, already monitors every cycle of data collected, and

transcribes this to magnetic tape. Even if one does not choose to print out every

cycle of data monitored, it may be stored on magnetic tape for permanent reference.

In the next column of figures of the Appendix A data, the number 62 represents the pack

number.

The next column of numbers, starting with 14.29, represents pack voltage. The pack

voltage is an operating parameter of the charge system. During this phase of the

analysis, no work was done with pack voltage information. It is important to use

this information in future work.

The next column, starting with 3.10, represents the pack current, in amperes, which was

set for an upper limit at a fixed level of about 3.0 for discharge and 1.7 for charge.

When the charge limiter cuts in, the current readings are depressed.

The next five columns which read across as: 139, 141, 140, 140, and 141, represent

the readings for cells 1 thru 5 multiplied by i00. The voltage readings for cells 6

thru I0 are found in the following row directly under those for cells 1 thru 5. Most

of the work in this report concerns these measurements. Packs have either five cells

or ten cells.

-13-

SECTION I (Cont'd_

An analysis of cell position in a pack, compared to failure distribution, did not

indicate any particular effect of cell position in the pack upon cell performance.

Voltage measurements on a clocked time increment are the customary procedures in all

test facilities but one. This one facility uses specific voltage change increments

computed from the total range to initiate readings. This dependence of measurement

clocking on precalculated conditions and levels is an interesting aspect of an on-

line test-data acquisition system.

The next five columns on the Appendix A data sheet, which read - .9, -I.I, - .8,

i0, - .8, are cell temperature readings for cells i thru 5. Similar measurements for

cells 6 thru I0 appear directly below in the following row. The strange readings

which occur at the end of charge appear (marked EOC) to be caused by signal noise from

the charge limiter cut-in. Temperatures were analyzed in this work in the hopes that

a correlation between voltage behavior and temperature behavior would help in the cell

performance prediction. It was observed, however, that voltage symptoms of trouble

always preceded any temperature ones, so that most of the temperature work was

restricted to an analysis of the accuracy of temperature measurement control.

The last column of data which starts with -4.51 represents the ambient temperature

plus a final digit which is a card designation number as described before.

1.3 Computer Programs Prepared For The Data Collection Phase.

The following computer programs were written for the data collection phase of the

project:

Program Name and Description Where Written

I. Voltage Cycle Plot Program (A RCA-301 program i. RCA - ist phase

which takes edited charge-discharge readings

and draws the plot)

-14-

SECTION I (Cont'd)

Program Name and Description

. Temperature Plot Program (An IBM-1620

program which takes cell and pack temp.

measurements and lists the points of

plot on a page grid

. Test Data Edit Programs (These programs

are composed of a set of six. Three by

RCA to handle data on magnetic tape formated

for Fortran, Machine language, and retrieval

purposes, resp.) Three are by Mauchly Systems

to print out test data with all card handling

and data errors removed, a separate listing of

all errors and corrections, and a Fortran format

data edit program.

. Paper-Tape to Card Transcription Program (This

program takes the digitalized test data from

paper tape into the IBM-1620 computer, or IBM-

360 and produces punched card outputs.

. Card to Magnetic Tape Conversion Program (This

program converts the Crane cards to RCA-301 and

IBM-7090 magnetic tape.)

Where Written

2. Mauchly Systems

2rid Phase

. RCA-Ist phase

Mauchly Systems

(2nd phase)

4. Crane-2nd & Ist

phase

5. RCA-Ist phase

U of Pa-2nd phase

1.4 Conclusions and Recommendations for Data Collections

As a result of the work done to date, the following recommendations are made:

i.) Every cycle of data should be monitored and recorded on magnetic tape.

There is no reason with data acquisition equipment presently available

to eliminate any data at the source.

2.) The number of points per curve which are monitored should be increased

to the maximum compatible to the flow rate of the acquisition system.

(One organization conducting an on-line test has allocated 20 points

for a half hour discharge.)

3.) A computer should be included in the Data Acquisition System. It should

feature a programmable clock, two magnetic tape channels, a word size of

16 bits and an input bit rate capable of handling 2500 measurements per

minute. (Three decimal digits/measurement.)

-15-

SECTION I (Cont' d)

4.) Increase the parameters measured on each cell. These should include

internal pressure, impedance, and capacitance calculations on line)

percent of over-charge, auxiliary electrode voltages, reference

electrode voltages, and perhaps some special parameters.

5.) Include a standard cell test-point to automatically check the analog-

digital calibration.

6.) Include an automatic shut-off feature for individual cells that exceed

selected thresholds, with a manual or automatic switch-in option.

7.) In addition to the measured information on each charge-discharge cycle

which is stored for the cells, a Header Block of initial input data should

be included which provides background information on the cell, such as,

manufacturer's rated characteristics, test conditions under which the cell

is being operated, design features, and a performance rating for cell at

certain cycle life times. This Header Block will then permit automatic

cell selection and automatic report summaries.

-16-

SECTION II

DATA ANALYSIS AND REDUCTION

Data analysis and reduction is defined as the identification and extraction of

essential elements of information. In this case, reduction produced 20,000 punched

cards from a total of 600,000 punched cards.

The formal analysis of the test data is important, since cell design and acceptance

has been completed for the cells under test. Failure mechanisms and cell defects

can be identified and analyzed. This information can be applied to the design and

manufacture of new cells to effect a longer operational life. A major objective

of this analysis is the development of statistical methods for prediction of cell

and/or pack failure as a function of failure characteristics and operating constraints.

In addition, other important objectives which are closely associated with the data

analysis are also to be considered. These additional objectives include:

I.) The association of data characteristics to certain failure

modes.

2.) The development of methods for presentation of battery data

for more informative reporting of results.

3.) "To develop' improved methods for the collection and handling

of the Crane data.

4.) "To develop'mathematical models related to battery tests as

practicable and useful to the battery user.

5.) The determination of operational test system limitations under

various environmental and operational extremes.

6.) The mechanization of an automatic life-test reporting system

for periodic management review and evaluation.

-17-

SECTION II (Cont'd)

These new objectives, however, affect the whole data collection system since not only

are more parameters desirable, but also, more measurements are required to permit more

precise control.

Other purposes for analyzing and reducing the data are to develop an Automated Data

Management System and to reduce the cost of repeatedly processing large volumes of

redundant information.

The problem associated with the selection of any procedure to reduce the data is to

make certain that essential information is not lost in the data reduction process.

This requires that clean distinction between redundant data and new information be

made. The important question is: What are the correct elements of information?

It also involves several initial assumptions:

I.) That defects in cells are reflected in their operational behavior

long before failure.

2.) That the optimum information elements which reflect these defects

are not known.

3.) That the Crane Data is sufficient to establish a new perspective

in Battery Data Analysis.

It must always be kept in mind that no statistical technique can reveal information

not inherent in the data.

The approach used in the development of this work was to consider all of the 600,000 data

cards the domain to be analyzed, and to work from this whole body of information down-

wards into the pieces as differences were identified. This orientation required the

fewest assumptions.

-18-

SECTION II (Cont'd)

Another problem one encounters in trying to implement this approach is that of

handling the large volume of data. Some reduction of this volume must be made in

order to critically view a portion of it, and yet this reduction must not distort

or eliminate any part of the data. The technique selected to accomplish this for

cell voltage histories was one of superposing charge-discharge curves upon one

another. If, in fact, ten or more charge-discharge curves were all the same, then

the resultant superposed curve of all of them would look exactly the same as any

one of them. Pages B 49 thru B-50 of Appendix B illustrates 93 superposed curves

for ten cells of pack 62. This data corresponds to the data described in Appendix

A-45 thru A-48 , except that it represents ten times as much more.

In order to continue with references to actual test data, a short description of

Page B-49 (Pages B-49 to B-50 reproduced on the following page) of Appendix B will

now be provided. A complete set of the data sheets is provided in Appendix B of this

report.

DIST. TIME DIFF., represents the distribution of timing intervals for each of fourteen

points monitored on a charge-discharge curve. The first seven rows are associated

with the discharge portion of the curve, and the last seven rows are associated with

the charge portion of the curve. The numbers, from i to I0, on the top row refer to

actual minutes between two consecutive points monitored. The 3970 which is repeated

for every row is the number of elapsed cycles reached in order to obtain 93 cycles of

information. Crane monitors every thirty-second cycle of measurement in general. The

numbers found between the cycle number, 3970, and the monitoring point numbers, i thru

14, on the right hand side of the page are counts of frequencies of occurrence of time

-19-

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-21-

SECTION II (Cont'd)

intervals for each monitoring point. For example, under 5 on the top row, which

refers to the five-minute period between two points monitored, there appear the

numbers: 8, 44, 61, 57, 60 and 40, vertically. These numbers represent the number

of times five-minute intervals occurred between readings for the 2nd, 3rd, 4th, 5th,

6th, and 7th readings of discharge. If the timing control had been exact_ all these

numbers should be 93. Likewise, for charge, which should be all ten-minute intervals,

the actual distribution is shown in rows 9 thru 13. This particular distribution

table is associated with the next data blocks which are numbered on the right most

column of each block. These numbers correspond to cell numbers for pack 62.

The remainder of the charts on page B- 51 thru B- 58 are similar in construction,

but they cover smaller and smaller segments of test data. The voltage levels of i00

(equals 1.00), 120 (equals 1.20), 140 (equals 1.40) and 160 (equals 1.60) are listed

at the top of each cell data, and the numbers taken two at a time which represents a

voltage change of 0.02 volts are the frequency distributions of the voltages at the

scalar level for the 93 charge discharge curves. In other words, the curves are

represented here in a digital form, where each point on a graph is a counter which adds

one to itself every time a plot passes through it.

Page B- 59 to B- 62 , represent ten selected superposed curve profiles selected.

Cells 2, 4 and 5 extend above 1.60 volts at the end of charge. Cells i, 3, 6, and

I0 have counts above the 1.60 volt level, but the highest count on the ordinate which

determines the profile is not found above 1.60 volts. Cells I, 3, 7, 8 and 9 show ai

drop at the end of charge. Cells 4, 5 and i0 show a lower end of discharge. Cell I0

shows a complete bottoming-out at 1.00v. This is explained by the fact that cell #i0

failed at 2995 cycles, which is within the elapsed cycle range taken for the data.

-22-

SECTION II (Cont'd)

If the ten cell profiles on pages 49 thru 62 are compared the following points are

noticed:

I.) The normal family of superposed curves vary around the high values

for each point between counts of 50 and 60. (example, cell #i)

2.) The voltage ranges for cells which do not fail are 1.38v to 1.18v for

discharge, and 1.18v to 1.52v for charge. (example, cell #I - Note

that pack 62 is at 0 degrees ambient T)

3.) High counts for voltages above 1.60 at the end of charge are associated

with cells which fail. (exm_ple, cells 4, 5)

4.) The high counts for suspect cells are lower than for normal cells.

(example, cells 4 and i0)

5.) High counts for voltages less than 1.18v are associated at the end of

discharge with cells which fail. (example, cell 5, i0)

6.) A variation in the voltage level for the respective high count location

appears associated with cells which fail. (example, cells 5, 10-

From this characteristic, the first difference method for detecting curve

variations was developed. This technique will be described later in

this section)

Items 2, 3, and 5, above, have already been observed in the section on Collection and

are standard points of interest throughout industry. Items I, 4 and 6, however, are

associated with sets of curves, rather than single measurement observations. They

concern curve profile changes in a set, the measurement of these changes along the

whole charge-discharge cycle, and the distribution of the changes. In order to detect

such things, a computer is essential. The techniques developed, to date, to implement

these 'set variation measurements' for curves are unsophisticated and they are intended

-23-

SECTION II (Cont'd)

to lead to a basis for the development of sophisticated probabilistic methods to

measure changes in curve-sets. For example, if the superposed counts were changed

to probability constants derived from 'n' cycles of data then the next 'k' cycles

could be used to produce new probability constants which could be compared against

the original ones. In addition, the point-to-point transition probabilities could

be calculated and compared against norms to determine the sequential variations some-

what similar to point 6 above.

Pages B-59 through B-62 covers all the available cycles for pack 62 up through 3970

total elapsed cycles. If this material is divided into four sections; the first

quarter of life, the midpoint of life, the third quarter of life and the last quarter

of life, serves to illustrate how cell symptoms are reflected respectively. Pages

B-59 through B-62 of Appendix B represents the same material as 49 through 50 ,

but is divided into four, approximately 25 cycle sets; B- 57 through B-58 is the

last quarter of the data. Cell #I0 is dead, as is seen from the fact that all the counts

are under I00 volts. From the low end of discharge, cells 4, 5 and 7 look suspect.

Pages B- 55 through B-56 represents the third quarter of the life. Cell #i looks

ideal; cell #2 is too scattered, probably from bad data; cells 3, 4 and 5 all have

high end of charges, and low end of discharges; cells 6,7, 8, and 9 look alright but

not as good as cell #I; and cell #i0 is beginning to fail, especially on the discharge

portion of the curve. Pages B-53 through B-54 continue to show the same performance,

and so does the first quarter of data on pages B-51 through B-52

The interesting conclusion to be made from an examination of the superposed curve sets

found on pages B-51 through B-58 is fact that the suspect cells which actually did

fail looked suspect throughout the whole history of the data. Also that cell #i

looked the best throughout its history.

-24-

SECTION II (Cont'd)

In addition to the observations made concerning pack 62 (described on page 9), a

summary of all types of suspect features with illustrations for the superposed curve-

sets is listed here:

i.) The end of charge must be at least greater than 0.06v more than

the start of discharge. (see pack 27, cell 4, page B-70 )

2.) The end of charge should be above 1.40v. (same as one, above)

3.) The end of charge should not be above 1.60v (Pk 52, cell 7, B- 65)

4.) Dispersion during charge should not be more than 0.12v. (Pack 27,

cell 4, page B- 70 ) & (pack 52, cell 7, page B-65 )

5.) There should be no zeros in charge or discharge. (Pack 26, cell 6,

page B- 69, - discharge is coded on the chart as #13)

6.) The charge limiter should not cut-in below 1.45v. This happens

because the charge limiter is triggered by the pack voltage, not

the individual cell voltage. However, if certain cells can

continually be cut-off on charge, they may never hsve a chance to

charge up. For this reason, this point must be watched. (example,

Pack 26, cell i0, B-70 )

7.) The end of charge voltage should not drop more than 0.06v after

the charge limiter cuts-in. (example, Pack 49, cell i pp B-65)

8.) The superposed curve-set should not have 'side shoots' in the

profile. (example, pack 62, cell i0, pp B- 56)

9.) The start of discharge should not be lower than 1.30v. (example,

pack 16, cell 7, page B-66 )

I0.) The end of discharge should not be lower than 1.15v (example, pack 16,

cell 7, pp. B- 66)

Ii.) Dispersion during charge should not be more than 0.08v. (example,

pack 52, cell 2, page B-65 )

-25-

Table I

Index Chart for Environmental

Effects on Charge-Discharge Cycle

Pack#/Cell# Page Temperatures Depth of Disch. Effect

40 25 0 15% 25% 40%

37/5

85/4

26/8

3/1

13/3

49/10

96/1

71

71

69

69

69

71

68

68

68

14/6

2/9

lowered

dispersed

lowered

dispersed

lowered

dispersed

normal

normal

higher

lower

lower

lower

All of the above effects refer to cells which did not fail. In other words, not only

failure mechanisms cause changes in the voltage cycle characteristics, but also certain

environmental changes. Thresholds must be changed accordingly. Life times are

affected. On page B- 70 , cell 4. of pack 27, the superposed curve sets are shown

for a cell which did not fail, but was at the point of failure when the pack itself

failed. Also attached is the curve set for the same cell 3000 cycles earlier. In

orbits where the length of discharge varies from relatively short periods to very

long times, it will be very important to obtain results from an analysis of the life

test data, since the interplay of failure mechanisms and environmental changes are at

a maximum. This study has not analyzed the effect of percent overcharge on the

voltage histories.

-26-

SECTION II (Cont'd)

The second major computer program used to analyze the collection data described in

Section I of this report, is the first-difference histogram program. This program

generates a histogram for five charge-discharge cycles at a time. It is a tabulation

of all the differences between each set of two consecutive points of voltage data

monitored. The ordinate is increments in voltage measurements, shown in steps of 0.I

volts, and the abcissa is a count of the occurrences for each voltage measurement.

Pages 104 through 107 are the histogram tabulations for the same data as is

represented by the superposed curves on pages B-49 through B-50 Taken together,

these two programs cover both slope changes and 'curve-to-curve' changes, which,

in turn are used for prediction. It is anticipated that these programs will be

very useful with carefully controlled input data.

Superposed curve-pattern indicator program:

This program searches juxtaposed, overlayed or superposed sets of

charge-discharge cycles for patterns which are associated with a

history of high occurance in failed cells. It has some advantages

over the first differences techniques. Environmental effects show

up better, and so do end of charge and end of discharge problems.

It also has the advantage of direct meaning to the battery specialist.

* This program was described in the previous contract.

-27-

SUPERPOSITIONEDCURVES

i. The twelve superpositioned discharge-charge curves (Appendix B, Page _68

thru Page B -71) were selected as being typical within individual environ-

mental groups.

2. The environmental conditions are specified to the left of each set. They

are depth of discharge, in percent; ambient temperature, in degrees Centi-

grade; cycle orbit period, in hours.

3. Curve set identifiers:

a. The first three digits specify battery pack number.

b. The next four digits specify cycle of the last cycle in set number.

c. The column headed TP specifies time period.

d. The column headed CLNspecifies cell number.

e. The dotted lines headed 120, 140, 160 specify the levels of 1.20 volts,

1.40 volts, and 1.60 volts respectively.

4. In these sets of superpositioned curves the ordinate is voltage and the

abcissa is time.

5. Time periods one through seven are the discharge portion. Time periods eight

through fourteen are the charge portion.

-28-

SECTION II (Cont'd)

Other types of observations that could be made but are not which are not "flagged"

in the data collection of Appendix A are:

Type of Observation

i. LOW VOLTAGE ON START OF DISCHARGE

2. HIGH VOLTAGE ON START OF DISCHARGE

3. HIGH CURRENT ON CHARGE

4. NO CHARGE LIMITER CUT-IN

5. HIGH CURRENT ON DISCHARGE

6. LOW MIDPOINT VOLTAGE ON DISCHARGE

7. HIGH RATIO OF END OF CHARGE TO START OF DISCHARGE FOR CONSECUTIVE CYCLES

8. LOW PACK VOLTAGE

Consideration should be given to the screening of cells selected for the packs, so

that matched cells from some short pre-test program following acceptance testing

are assigned to the same pack. Cells of a lower capacity should be assigned to

another pack, and finally a random mix of cells (all from the same manufacturer)

should be assigned to a third pack. In this way, one can determine if pack

performance can be improved by pre-test cell selection.

-29-

ANALYSIS - SECTION II

Failure Cycle Patterns

Battery and cell failure data is included with other test data in the publication

"Monthly Progress Report on National Aeronautics and Space Administration Space

Cell Test Program." This publication is produced by the Secondary Battery Section

of the Quality Evaluation Department at N.A.D. Crane, Indiana, who is conducting

the test program. The basic cell failure information contained in this publication

was the basis of the tables in this report Section and in Appendix E.

The cycle number at which failure was observed was tabulated for each failed

cell within each pack that had one or more cell failures (to December, 1966).

Table A , Pagesll0 throughll3, shows the cycle numbers of cell failure in the body

of the table. The leftmost column, headed P, is the pack number. The second through

sixth columns, headed M, C, D, T, O, contain coded information for each numbered

pack. Table A , Pagell0, lists the coding structure for the table. Table A indicates

the incidence of failure and serves as a reference.

Table B , Pagesll4throughll6, shows cell failures within I000 cycle groupings. The

row headings indicate pack number and the column headings indicates the I000 cycle

group within which the cell(s) failed. The body of the table contains the actual

cell numbers, i through i0 for ten-cell packs, of failed cells within each identified

pack. A cell number listed in a column headed "Less than 5000 cycles" means that

that cell failed somewhere between 4000 and 4999 cycles of operation.

Two patterns appear in Table B (i.) The bunching of failures under 5000 cycles, and

(2.) The grouping within one pack of three or more cell failures before 2000 cycles.

Further study of Table B , shows that the position of a cell in a pack is not related

to its success or failure.

-30-

ANALYSIS - SECTION II (Cont'd)

The bunching of cell failures under 5000 cycles can be examined in more detail by referral

to Table T which follows. Failure frequencies are shown for 500 cycle intervals for the

first I0,00 cycles of operation, not by pack number but by test condition parameters.

Table T is actually three sub-tables with con_non column headings (cycle interval number).

The Tables break down this set of frequencies by (I) depth of discharge in percentage,

(2) by orbit period in hours, (3) and by ambient temperature in degrees centigrade. A

failed cell is entered into its proper failure cycle interval group three times; once

for each of the three test condition parameters. A few packs were cycled in a twenty-

four hour orbit period with fifty percent depth of discharge. These were not included

in Table T. It can be observed that there is less bunching (I) for 15% discharge than

for 25% or 40% discharge, (2) for 1.5 hour orbits than for 3.0 hour orbits, and (3)

ofor 0°C than for 25°C or for 40 C.

TABLE T

Number of cells that failed within cycle intervals:

500 cycle intervals -- cycle 0001 --- 10500

i 2 3 4 5 6 7 8 9 I0 Ii 12 13 14 15 16 17 18 19 20 21

Depth 40%

Of 25%

Disch.15%

Orbit 3.0

Hours i. 5

Amb. 40

Temp. 25

Cent. 0

09 08 04

14 12 i0

04 02 09

i0 16 16

17 06 07

14 Ii 12

II ii 07

02 O0 04

12 07 04

12 13 18

Ii 05 12

15 09 08

20 16 26

19 Ii 18

16 12 ii

O0 02 05

02 07 06

14 12 18

04 01 08

06 12 15

14 08 17

I

07 09 14

09 II 13

04 00 05

08 04 00

06 00 00

03 04 00

05 02 00

12 06 00

08 03 00

09 04 00

00 01 00

00 01 00

01 00 00

02 00 02

00 00 00

03 01 02

02 00 02

01 01 00

00 00 00

00 00 00

03 03 03

03 02 02

00 00 00

06 05 05

03 O0 02

02 04 01

01 01 02

00 00 00

00 02 03

04 02 02

00 00 00

04 04 05

04 01 00

00 00 03

00 03 02

-31-

ANALYSIS - SECTION II (Cont'd)

As additional reference, Table C , Pagesll7throughl21, show coded failure characteristics

for each failed cell in every identified pack. This information was extracted from the

previously mentioned "Monthly Progress Report", which contains cell post-mortem autopsy

data. These data were encoded as twenty-one possible failure characteristic types,

represented by the letters A through U. Table C , Pagell7, shows the code structure.

A maximum of seven failure characteristics per cell is permitted so that the failure

history of one entire pack can be represented by one punched card. This maximum was

sufficient to record all the failure data that was presented.

The body of Table C shows coded failure characteristic data for each cell identified by

the column headings. Row headings contain pack and manufacturer identification.

-32-

SECTIONIII

CELLFAILUREPREDICTION

Prediction in this application is defined as the determination of those cell voltage

pattern changeswhich permit an early differentiation of cells which fail from cells

which do not fail.

The pu_oses of developing prediction capability from life cycle test data are:

i.) To establish a higher level of confidence in an operational

battery system.

2.) To establish a basis for better identification of failure modes,

by providing an early warning of failure, so that the cell may be

removed and inspected.

3.) To provide a basis for estimating the effects of unscheduled

operational changes upon the life of the mission.

4.) To evaluate the relative advantages of the available manufacturers

products.

5.) To indicate the requirements for new design goals.

6.) To utilize more efficiently the costly but essential life test data.

The basis of the prediction techniques developed in this report follows from the

work done in the Data Analysis and Reduction section. Here new elements of information

were developed through voltage curve slope studies and through curve set studies.

At first, the information elements which were first difference histograms and the

superposed curves were classified into types depending upon the different character-

istics which appeared in them. Then they were subclassified by the environmental

conditions under which the cells were tested. Finally, the failure histories were

-33-

SECTION III (Cont'd)

taken to subclassify again the total information. The mode of failure information

obtained by cutting open failed cells was so uncertain that no attempt was made to

apply this information to the data. A correlation was then made between the

characteristics associated with the cells that failed against the characteristics

associated with the cells that did not fail. Those characteristics which showed a

high correlation with failed cells were prograr_ned as indicators which were used

for prediction.

The prediction programs used the reduced data as input.

programs.

I.)

There are four prediction

Histogram Indicator Program:

This program searches the first-difference histograms for

established patterns in the charge or discharge tallies which

are known to be associated with failure. A description of the

actual indicators is included in Appendix C. When these patterns

are identified, a weight is associated with the cell which accumu-

lates for each new pattern identified in the cell history. When these

weights reach a certain threshold, the cell is predicted to fail.

The thresholds must be changed for different environmental conditions

in accordance with the results described in Section II. The lead

times to failure are the differences between the actual elapsed

cycle time of failure, and the elapsed cycle time when the

prediction was made. No attempt has been made to predict the lead

time to failure, since the data has not been collected with enough

control to permit this. For example, the monitoring point times

and the elapsed cycle intervals not recorded are not consistently

-34-


constant. This means the rate of change of cell performance

cannot be related to a well structured reference system to

permit lead time predictions, and extrapolation at this point

is not advisable. Nevertheless, Pages C-72 through C-92 of

Appendix C show an average lead time of 5400 elapsed cycles

where predictions are made after I000 elapsed cycles of data,

and a lead time of 1300 elapsed cycles where predictions are

made before 1500 elapsed cycles of data. It is also true that

those cells where predictions could be made during the first

1500 cycles, usually failed relatively early in life. An

example of the output of this program is presented on pages

C-94 through C-97

2.) Histogram Weight-Gaming Program:

Although the above program involved human decisions to

establish the data pattern material to indicate defective

cells, the implementation or the automatic application of these

rules to the data was of such a nature, that the rules could be

changed without requiring any change to the program. This meant

that random changes to the rules could be programmed in the form

of an arbitrary assignment of weights which would in turn accomplish

the same thing as comparing portions of the charge discharge curve

against each other to predict failure. The resulting predictions

could then be compared to the actual failure data in order to

assess the merits of the randomly changed rules. In this way, an

-35-


interesting check on the human choices could be made. The results,

although still incomplete, have turned up some interesting con-

clusions. In general, all of the manually selected rules do result

in better than average success; however, the machine has found one

which competes equally with the manual ones. Essentially, this

rule compares the upper middle portion of the discharge curve with

the lower middle portion of the discharge curve. Appendix C, Page

C-93 contain data and the tabular results from this program. One

of the advantages of this program is that it permits an automatic

setting of the thresholds used for prediction. The desired percent

success can be fixed, and the program will only select those

indicators which meet or exceed the performance level required of

the system. This type of gaming is only possible on the computer

since the amount of routine clerical work is too large to merit

manual costs. The gaming process to identify indicators of failure

can take into consideration different operational environments. In

this way the best conditions for testing can be established.

-36-

INDICATORS - SECTION III

Delta-Volt Histograms are constructed for each cell for both charge and discharge.

These Histograms are the result of summing the Delta Volt levels zero to nine for

a specific number of cycles within a cyclic group. All delta volt occurrences

above 9 DV are counted as 9 DV. All delta volt occurrences that are negative are

counted as DVO. These Histograms are in effect a distribution of a specific number

of Histograms.

On a per cell basis, the number of occurrences of delta volts 1 through 9 are

multiplied by their individual values. These nine values are then summed to

produce the area under the Histogram. This procedure is carried out for both

charge and discharge. We now have a numerical value for the area under each entire

Histogram.

This area is then subdivided into three individual areas. The summed value of

DV 1-2-3, summed value of DV 4-5-6, the summed value of DV 7-8-9. For clarity we

will call these three areas DV low, DV mid, DV high, respectively.

Indicator - Discharge

Indicator #i - If DV high equals 9

Indicator #2 - If DV high equals>9 - -

count i against cell

count 2 against cell

Indicator - Discharge

Indicator #3 - If DV high is more than 1/4 the value of DV low - - -

count against cell

-37-

INDICATORS - SECTION III (Cont'd)

Indicator

Indicator #4

Indicator #5

Indicator #6

- Discharge

- If DV low is + 15 from DV mid - - - count 1 against cell.

If DV low and DV mid differ by more than +15 count 2

against cell.

- If DV mid is higher in value than DV low count I against cell.

Indicator

Indicator #7

Indicator #8 - If DV high is more than 1/2 the value of DV low

against cell.

Indicator - Charge - Discharge

Indicator #9

- Charge

- If DV low is 4 or more times larger or 1/4 less than DV mid

count i against cell.

count I

- If DV mid charge is more than twice or less than 1/2 the value of

DV mid (discharge) - - - count I against cell.

These counts are then totaled for each cell. Cells that have highest count will fail

earliest.

-38-

SECTION IV CONCLUSIONS

The initial assumptions, set forth in Section II of this report, have proven to be

correct. These assumptions are:

i. That defects in cells are reflected in their operational behavior

long before failure.

2. That the optimum information elements which reflect these defects

are not known.

3. That the Crana data are sufficient to establish a new perspective

in battery data analysis.

Computer programs, developed in this work and described elsewhere in this report,

have been used to analyze specific portions of the Crane data and have shown useful

predictive ability regarding cell failure. These programs, which detect certain

voltage behavior patterns and voltage magnitude ranges, have predicted cell failure,

with a high percent of reliability, many thousands of cycles before actual failure.

To produce a time factor, relative to cell failure prediction, will require an increase

in the cycling parameters measured and an increase in the accurately timed monitor

points per voltage curve.

This work has shown that cycling environmental parameters affect both the cell voltage

characteristics and cell life. These prime effects, described in this report, have

been applied to various statistical techniques with excellent results; a high percentage

of incipient cell failures were detected early in battery pack life. However, further

improvement of predictive techniques relative to pack and cell failure may require that

the data base be broadened to include additional parameters. Although one cannot say

in advance just which such measurements will prove most useful, some of the candidates

-39-

CONCLUSIONS (Cont'd)

are internal pressure, cell impedance, percent of overcharge, etc.

Voltage behavior patterns and environmental parameter effects were identified and

isolated within the Crane data by working downward from the mass of data into

individual sub-sets which displayed cause and effect relationships. After noting

suggested relationships, randomly selected pack test histories were examined for

the same relationships. Thus, relationships derived from a sub-set of the data

were validated on independent data. Voltage behavior patterns, environmental

parameter effects, and various inter- and intra-relationships do constitute

information elements contained within the structure of the Crane data.

It is stated that this method has established a new perspective in battery data

analysis. As one instance, this work suggests that the resultant charge-discharge

curve may be determined as a function of operational and environmental parameters.

In addition, the present work supports proposed new battery tests wherein only data

"flagged" by pre-set thresholds and indicators would be recorded for analysis.

The ultimate purpose of the work done in this contract is to develop methods leading

to timely and accurate correlation of various space mission power requirements with

well defined potentialities and limitations of all battery cell types for which test

information can be secured.

-40-

RECOMMENDATIONS

The present Crane Data Acquisition Systembecomessaturated somewhereabout three

times its present data flow rate. The addition of pressure measurements, the

increase in the numberof test points monitored per time unit, and the collection

of data during every cycle of testing will increase the data flow rate at least

two hundred times. To accomplish this extension of mission, a computer in the

data acquisition system appears to be unadvoidable.

With a computer in the data acquisition system, bottle-necks of data storage and

access, and data flow-rate cspacities disappear; and the data collection system

now becomes defined by requirements imposed from the following four areas of

consideration:

i. The Simulated Operational Environment

Considerations of this area determine the design of the test

experiment, the number of cells in a pack, the number of packs

in the test, the types of cells, the orbit regime, the parameters

measured, and the environmental and operational ranges to be

tested or controlled.

2. The Data Acquisition Equipment

Consideration of this area determines the precision limits on

measurements, the rate and volume of maximum data flow, the

accuracy of timing controls, the channels available for measure-

ment, the minimum interval for data taking, the calibration and

control of the analog-to-digital conversion, the channel interrupt

or cell removal options, the operational thresholds and controls,

the adaptability of the acquisition system to changes in requirements,

the sensitivity to noise, and the safeguards against damage to cells

on test.

-41-

RECOMMENDATIONS (Cont 'd)

3. The Data Processing Equipment

Consideration of this area determines the speed at which data

can be received from the acquisition equipment, added to,

(i.e., identifiers, past performance indicators, calculated

variables such as pack voltage, Watt hour efficiencies, cell

impedance, etc.), stored and/or reduced and edited for output

to the printer or various storage mediums. The processing

equipment has two major functions. The first is an on-line

data management and control function, and the other is an

analysis function. It is probable that these two functions

are not compatible in the same machine. The analysis function

requires a large memory and high speed on an off-line basis for

data retrieval and statistical analysis; whereas, the data-

management function requires a slower but continuous on-line

operation. Although a large machine could handle the data-

management function, the necessity to remain on-line and shifting

back and forth from data-management to analysis is an inefficient

and expensive way to accomplish the job. Even the programming for

these two functions require different categories of personnel.

4. The Analysis Methods

Considerations of this area determine the size and the speed of

the processing equipment, the amount of high-speed memory, the

type of back-up storage for efficient retrieval, the data-indexing

system, the reliability of prediction, and the sophistication of

the overall test system.

-42-

RECOMMENDATIONS (Cont'd)

In the Crane test experiment, the battery pack number was arbitrarily assigned, and

therefore has no functional meaning other than an identification tag. It would be

helpful if this number were a code which would include the identification of the

manufacturer, the cell-rated characteristics, and the cell design features; in order

to permit the computer to use this information for extracting classes of cells with

specific co_mnon characteristics. Also, in the same respect, a code should be

included for the cell operating characteristics.

One other consideration for future data collection would be the desirability of

obtaining Watt-hour and ampere-hour efficiency calculations to be included

information for the analysis work.

-43-

APPENDIX A

DATA COLLECTION CHARTS

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TIME DIFFERENCE/

SUPERPOSITION CURVE SET PROGRAM

NAB- P3

This program takes a pre-determined number of cycles of Crane data and

plots the voltage/time curve of each cycle one on top of the other.

The ordinate of the graph is voltage from less than 1. ZOV. to more than

i. 60V.

The abcissa of the graph is time, divided into fourteen time points; seven

in discharge and seven in charge.

The first plotted curve places a 'one' at its voltage level for each of the

time points. Each successive curve in the group repeats this same process

on top of the first curve. Where the voltage levels of each curve coincide within

each time point, each plotted point is incremented by the number of coincidences,

to give the total number of occurrences of that position.

The output of this program is a physical/numerical representation of a

set of voltage/time curves showing their voltage levels and time differences

within the discharge and charge.

The time difference/superposition curve set program makes possible the

examination of many voltage/time curves at one time. The examination and

comparison of sets of these curves gives the ability to see the effects of varying

cycling parameters and to discriminate against 'noise'.

It is not possible to separate out the individual curves of a set.

-63-

DISTRIBUTION OF TIME DIFFERENCES

This is a part of the superpositioned curve set program. It establishes

fourteen time periods and plots the number of occurrences of varying times

within each.

The output is a graph. The ordinate has fourteen time periods, seven for

discharge and seven for charge. The abcissa is time, in minutes, from less

than one minute to more than I0 minutes.

This graph is a distribution of time differences. It shows the number and

distribution of time occurrences, within fourteen time periods, for the set of

superpositioned voltage/time curve sets that follow.

There is no correction of the time intervals of the original input data.

Timing looseness in the original data can cause a great spread in the

voltage/time curve sets. This spread can cause difficulty and error in various

predictive theories.

In the future tests, time control should be better regulated.

-64-

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APPENDIX D

5 CYCLE-DELTA VOLT-HISTOGRAM PROGRAM

NAB-P1

5 CYCLE-DELTA VOLT-HISTOGRAM PROGRAM

Input to this program is Crane Battery Test Data.

Output is sets of five cycle tally histograms of first difference voltages on

a per cell basis for both discharge and charge.

Each output card is identified by pack number. Each card contains the

starting and ending cycle numbers of its cycle group. Each card contains the

voltage first difference history, both discharge and charge, of one identified

cell for the indicated cycle group.

Each card contains the minimum and maximum temperatures recorded, for

its identified cell, for the cycle group.

The format of each output card is as follows:

C.C. 1 - X - Denoting ten cell pack.

V - Denoting five cell pack.

C.C. 3-5: Pack Number

C.C. 7-i0: Group Starting Cycle Number

C.C. 12-15: Group Ending Cycle Number

C.C. 17-18: Number of Cycles Contained in this Cycle Group

C.C. 20-39: Ten, two digit fields starting with Delta volts equal zero

to Delta Volts equal nine. Each of the two digit fields records

the occurrences of Delta volt values for its respective level.

For example--the first two digit field records the number of

occurrences of the zero Delta volt level; the second two digit

field records the number of occurrences of the one Delta volt

level. This section is for discharge.

-98-

NAB- P1

C.C. 41 -43:

C.C. 44-63:

C.C. 65-67:

C.C. 70-76:

C.C. 78-79:

C.C. 80:

Records the total number of Delta volt occurrences in the dis-

charge section.

The same as C.C. 20-39 but for charge.

The same as C.C. 41-43 but for charge.

Two, three digit fields recording the maximum and minimum

temperatures for the cell for its cycle group.

Records the individual cell number.

The letter "P" denoting that this output was checked and found

error free by the error checks within the program.

ERROR CHECKS AND ERROR OUTPUT:

I. Duplication and sequence errors:

This program checks the Crane Battery Test Data for

proper sequence and duplicate card errors. When such

errors are detected, the program, through switching options,

notifies the operator, through card punch or console type-

writer, which errors have occurred. The operator then has

the chance to eliminate several types of errors.

These error messages are as follows:

Cycle number decreased.

Time Decreased--followed by a message stating that New

Time Accepted.

Duplicate Time, Card I.

No Card 1 Before (card#Z)

No Card Z Before (the next #I card)

Duplicate 5 pack card.

-99-

NAB-P1

One Misplaced Card

May Cause Z or More

Error Messages: These error messages also contain pack,

cycle, and time identification.

Other Error Types:

The following error messages are output the same as above.

Column 80 type wrong.

Trouble Card.

Wrong for this Pack.

. "Reject" Cards:

These are caused by negative voltage differences in the

charge cycle. No error messages are output.

Appendix E, Page 102 through 107 are examples of NAB-P1 output.

-i00-

SUPERTALLY

Supertally is a dual purpose program that can output either one Delta volt

histogram or a histogram of histograms.

Input to Supertally is NAB Pass 1 output.

Supertally computes the number of occurrences, from zero to twenty-four,

of Delta volt values zero to nine. More than twenty-four occurrences is counted

as twenty-four. Control cards specify from which cyclic group output is desired.

Output is per cell with complete pack, cell, and cyclic group identification.

Discharge and charge are separated and outputted concurrently.

If the input to Supertally is a single histogram in numerical form, the

output is a physical representation of that histogram.

If the input to Supertally is many histograms in numerical form, the output

is a distribution of occurrences of Delta volt values. From this distribution a

mean can be calculated for use as a threshhold for certain indicator and other

programs.

An example of "Supertally" is in Appendix E, Pagel08 through Pagel09

-I01-

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-109-

APPENDIX E

FAILURE CHARTS

GLOSSARY

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FAILURE CHARACTERISTICS

Low Voltage Charge.

Low Voltage Discharge.

Separator: Deteriorated, Dissolved, Burned, Pinpoint Penetration, Short.

Plate Material Shorted Through Separator.

Separator Impregnated With Negative Plate Material.

Migration of Positive and/or Negative Plate Material.

Extraneous Material Between Plates.

Deposit on Positive and/or Negative Terminals.

Blistering on Positive Plate(s).

Plate(s) Stuck To Case.

Excess Scoring of Case.

High Pressure, Bulge, Convex Side(s).

Concave Side(s), Short(s) Due To Internal Shift.

Broken Seal(s): Ceramic, Glass.

Ceramic Short.

Electrolyte Leak, Weight Loss, Separator Dry, Electrolyte Shorted Out Cell.

Tab(s): Burned, Broken, Welds Weak.

Third Electrode Shorted to Plate.

Cell Blew Up.

Circuit: Short, Open.

High Voltate Charge.

TABLE C-I

-117-

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-121-

GLOSSARY

Depth of Discharge:

The percent of the manufacturer specified ampere-hour capacity

of each cell which is intended to be removed from each cell in

the battery pack during discharge.

Orbit Period:

Total time, in hours, for one charge-discharge period during

automatic cycling.

Ampere-hour Capacity:

The ampere-hour capacity of the individual cells in a battery

pack as rated by the manufacturer.

Ambient Temperature:

The environmental temperature at which battery packs are cycled.

Charge or Voltage Limit:

The maximum intended average voltage, per cell, during the charge

period. Expressed in volts.

Charge or Voltage Limiter:

A device which limits the voltage to which the entire battery pack is

charged. It is set at the number of cells in the pack multiplied by the

individual cell voltage limit.

NiCd :

Nickel - Cadmium

-122-

GLOSSARY (Cont'd)

Histogram:

A chart showing, on its horizontal scale, a sequence of values or class

intervals into which the data may fall, and on its vertical scale the

frequency with which the data falls in each such class interval.

Threshold:

A predetermined numeric value having the function of an acceptance and/

or cut-off point.

Indicator:

A symbol or level-number used in a data-division entry to indicate level

or condition.

Cell Failure:

Any cell whose voltage level, at the end of discharge, is below 0.5

volts.

First Difference or Delta Voltage:

The positive or negative voltage quantity resulting when the measured

cell voltage at a given monitoring point is subtracted from the measured

voltage at the same monitoring point for the immediately previous time of

data-taking within the same cycle. (Note that first observation time in a

cycle has no prior observation in same cycle.)

Observed Cycle:

In the Crane test, every charge-discharge cycle is not recorded. The

cycle recording frequency is dependent on pack orbit period. For example,

in a 1.5 hour orbit regime, every 32nd cycle is recorded; in a 24 hour

orbit regime, every 8th cycle is recorded. Observed cycle is synonymous

with recorded cycle.

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Computer Methods For The Reduction - NASA · Computer Methods For ... Sheer bulk of raw test data without adequate analysis does not help the power ... Inclusion of a computer in