determine whether any of the candidate methods are capable of determining a corresponding battery condition value based at least in part on the mobile electronic device data and the information relating to each candidate method; and

in response to determining that one or more of the candidate methods is capable of determining one or more corresponding battery condition values, cause the one or more of the capable methods to be performed to thereby determine the one or more corresponding battery condition values.

According to at least one aspect of the present disclosure there is provided a server for use in determining a condition of a battery of a mobile electronic device, the server comprising a processing resource and a memory containing instructions which, when executed by the processing resource, cause the processing resource to:

- receive mobile electronic device data from the mobile electronic device;
- receive information relating to each candidate method of a plurality of candidate methods, each candidate method being configured for determining a value of a corresponding battery condition, wherein the battery condition value corresponding to each candidate method is representative of the condition of the battery of the mobile electronic device;
- determine whether any of the candidate methods are capable of determining a corresponding battery condition value based at least in part on the mobile electronic device data and the information relating to each candidate method; and
- in response to determining that one or more of the candidate methods is capable of determining one or more corresponding battery condition values, cause the one or more of the capable methods to be performed to thereby determine the one or more corresponding battery condition values.

According to at least one aspect of the present disclosure there is provided a system comprising the server and a database storing the information relating to each candidate method of the plurality of candidate methods, wherein, when executed by the processing resource of the server, the instructions cause the processing resource of the server to fetch the information relating to each candidate method of the plurality of candidate methods from the database.

It should be understood that any one or more of the features of any one of the foregoing aspects may be combined with any one or more of the features of any of the other foregoing aspects.

BRIEF DESCRIPTION OF THE DRAWINGS

A method and system for use in determining a condition of a battery of a mobile electronic device will now be described by way of non-limiting example only with reference to the following drawings of which:

FIG. 1 is a schematic of a mobile electronic device and a system for use in determining a condition of a battery of the mobile electronic device;

FIG. 2 is a generic example of historical battery charge data stored in a battery stats log file of the mobile electronic device of FIG. 1;

FIG. 3 is a generic example of historical battery drain data stored in a battery stats log file of the mobile electronic device of FIG. 1;

FIG. 4 is a flow chart illustrating a method for use in determining a condition of the battery of the mobile electronic device of FIG. 1;

FIG. 5 shows a method for determining a battery condition value of the mobile electronic device of FIG. 1 using historical battery charge data;

FIG. 6 shows step 208 of the method of FIG. 5 in more detail;

FIG. 7 shows an example of two overlapping battery charge intervals in a battery stats log file;

FIG. 8A shows pseudo code instructions for selecting an optimum method for use in determining a condition of the battery of the mobile electronic device of FIG. 1; and

FIG. 8B shows pseudo code instructions for comparing determined battery condition values with corresponding battery condition threshold values and determining whether the battery condition is compliant or non-compliant with a predetermined battery condition test.

DETAILED DESCRIPTION OF THE DRAWINGS

One of ordinary skill in the art will understand that one or more of the features of the embodiments described below with reference to the drawings may produce effects or provide advantages when used in isolation from one or more of the other features of the embodiments and that different combinations of the features are possible other than the specific combinations of the features of the embodiments described below.

Referring initially to FIG. 1 there is shown a mobile electronic device generally designated 2 and a system generally designated 20 for use in determining a condition of a battery of the mobile electronic device 2.

The mobile electronic device 2 includes the battery 4, a memory 6, a processing resource 8, and a user interface 10. The memory 6 stores an operating system in the form of an Android operating system 12 and mobile electronic device data 14. The mobile electronic device data 14 includes at least one of a make, a model, and a version of the Android operating system 12 of the mobile electronic device 2. The mobile electronic device data further includes data relating to the battery 4 of the mobile electronic device 2. Specifically, the mobile electronic device data 14 further includes historical battery performance data for the battery 4 of the mobile electronic device 2 in the form of a battery cycle information file and a battery stats log file stored in the memory 6 of the mobile electronic device 2. The battery stats log file may be output from a batterystats service of a dumpsys tool that runs on the mobile electronic device 2. The mobile electronic device data 14 may further include a make and/or a model of the battery 4 of the mobile electronic device 2.

The battery cycle information file includes a battery cycle count value representing the number of full discharge/charge cycles associated with the battery 4.

FIG. 2 illustrates the format of the battery stats log file which includes historical battery charge data in the form of a first set of historical battery charge data points corresponding to a first battery charge interval 101 and a second set of historical battery charge data points corresponding to a second battery charge interval 102.

Each historical battery charge data point of the first battery charge interval 101 comprises time information and corresponding battery charging current and battery charge information. The time information takes the form of relative time values or time stamps expressed as a date and a time in hours, minutes and seconds, t_start1, . . . t_j, . . . t_finish1. The battery charging current information comprises an absolute value of charging current expressed in mA, I_start1, . . . I_j, . . . I_finish1supplied to the battery at each time t_start1, . . . t_j, . . . t_finish1respectively. The battery charge information comprises a fractional value q_start1, . . . q_i, . . . q_finish1of the actual charge capacity Q_actof the battery expressed as a fraction or percentage of the actual charge capacity Q_actof the battery at each time t_start1, . . . t_finish1respectively.

Similarly, each historical battery charge data point of the second battery charge interval 102 comprises time information and corresponding battery charging current and battery charge information. The time information takes the form of relative time values or time stamps expressed as a date and a time in hours, minutes and seconds, t_start2, . . . t_j, . . . t_finish2. The battery charging current information comprises an absolute value of charging current expressed in mA, I_start2, . . . I_j, I_finish2supplied to the battery at each time t_start2, . . . t_j, . . . t_finish2respectively. The battery charge information comprises a fractional value q_start2, . . . q_i, . . . q_finish2of the actual charge capacity Q_actof the battery expressed as a fraction or percentage of the actual charge capacity Q_actof the battery at each time t_start2, . . . t_j, . . . t_finish2respectively.

The battery stats log file also includes battery charging status information such as a battery charging status flags 110, 112, indicating when battery charging started, and a battery charging status flags 120, 122 indicating when battery charging finished.

One of skill in the art will understand that, an actual battery stats log file may include other information not shown in FIG. 2 or that an actual battery stats log file may present the historical battery charge data in a different format to that shown in FIG. 2.

FIG. 3 illustrates a specific example of a further portion of the battery stats log file which includes the historical battery drain data. Specifically, as shown in FIG. 3, the historical battery drain data includes a value for the design capacity Q_desof the battery 4 which is expressed in mAh and which is identified in FIG. 3 using the label “Capacity”. For the specific data shown in FIG. 3, the design capacity Q_desof the battery 4 is 2700 mAh. The historical battery drain data also includes a range of values for the cumulative charge consumed by the battery 4 over a drain period identified in FIG. 3 using the label “actual drain”, wherein the drain period is defined as a time period elapsed since the last full charge of the battery 4. Specifically, the range of values for the cumulative charge consumed by the battery 4 over the drain period includes a lower limit min(ΣΔQ) expressed in mAh and an upper limit max(ΣΔQ) expressed in mAh. For the specific data shown in FIG. 3, min(ΣΔQ)=4887 mAh and max(ΣΔQ)=5427 mAh. The historical battery drain data further includes a cumulative percentage drop in charge ΣΔp over the drain period which is identified in FIG. 3 using the label “Amount discharged (upper bound)”, where each percentage drop in charge Δp is expressed as a percentage drop in charge relative to a full charge of the battery e.g. if during a drain period, a percentage charge of the battery 4 drops from 100% to 20% (i.e. a percentage drop of 80%), the battery is then charged so that the percentage charge of the battery 4 rises from 20% to 70%, and the percentage charge of the battery 4 then drops from 70% to 30% (i.e. a percentage drop of 40%), then the cumulative percentage drop in charge ΣΔp over the drain period is 120%. For the specific data shown in FIG. 3, the cumulative percentage drop in charge ΣΔp over the drain period is 201%.

One of skill in the art will understand that, in general, the historical battery drain data values in the battery stats log file may be different to the specific historical battery drain data values shown in FIG. 3, the battery stats log file may include other information not shown in FIG. 3, and that the battery stats log file may present the historical battery drain data in a different format to that shown in FIG. 3.

Referring back to FIG. 1, the memory 6 also stores an app 16 which is configured to determine a battery condition value which is representative of a condition of the battery 4 of the mobile electronic device 2, as will be described in more detail below.

The system 20 includes a battery profiler cloud 22 which includes a server in the form of a battery profiler server 24 and a database in the form of a battery profiler database 26. The battery profiler server 24 includes a processing resource 24a and a memory 24b containing instructions which, when executed by the processing resource 24a, cause the processing resource 24a to perform various operations as will be described in more detail below. The battery profiler database 26 stores information relating to each candidate method of a plurality of candidate methods, each candidate method being configured for determining a corresponding battery condition value, wherein the battery condition value corresponding to each candidate method is representative of the condition of the battery 4 of the mobile electronic device 2. The battery condition value may, for example, be a battery cycle count value of the battery 4; a ratio of a present or actual charge capacity of the battery 4 to a design charge capacity of the battery 4 expressed as a percentage or as a fractional value; a proportion of a life-time of the battery 4 which remains expressed as a percentage or as a fractional value; a proportion of a life-time of the battery 4 which has elapsed expressed as a percentage or as a fractional value; or a present or actual wear value of the battery 4 expressed as a percentage or as a fractional value. The information relating to each candidate method may include instructions for performing the candidate method and a battery condition threshold value corresponding to the candidate method. The battery profiler server 24 is configured for two-way communication with the mobile electronic device 2 and for two-way communication with the battery profiler database 26.

Referring to FIG. 4 there is shown a method generally designated 50 for use in determining a condition of the battery 4 of the mobile electronic device 2. The method 50 begins at step 52, with a user of the mobile electronic device 2 running the app 16. Running the app 16 causes the app 16 to extract at least some of the mobile electronic device data 14 from the mobile electronic device 2 via one or more application programming interfaces (APIs) and to transmit at least some of the mobile electronic device data 14 from the mobile electronic device 2 to the battery profiler server 24 at step 54. Specifically, at step 54, the app 16 extracts at least one of the make, the model and the version of the Android operating system 12 of the mobile electronic device 2 from the memory 6 of the mobile electronic device 2 and transmits at least one of the make, the model and the operating system of the mobile electronic device 2 to the battery profiler server 24.

At step 56, the method continues with the processing resource 24a of the battery profiler server 24 fetching information relating to each candidate method of the plurality of candidate methods from the battery profiler database 26, each candidate method being configured for determining a corresponding battery condition value. Specifically, the processing resource 24a of the battery profiler server 24 fetches instructions from the battery profiler database 26 for performing each of the candidate methods stored in the battery profiler database 26.

At step 58, the method continues with the processing resource 24a of the battery profiler server 24 determining whether any of the candidate methods are capable of determining a corresponding battery condition value based at least in part on the mobile electronic device data 14 and the information relating to each candidate method. Specifically, the processing resource 24a of the battery profiler server 24 identifies a set of input parameters required by the instructions for performing each of the candidate methods stored in the battery profiler database 26 and compares the required set of input parameters for each of the candidate methods with a set of parameter values known to be stored in a memory of any mobile electronic device which has the same make, model and version of the Android operating system as the mobile electronic device 2. If the required set of input parameters for any candidate method matches the set of parameter values known to be stored in a memory of any mobile electronic device having the same make, model and version of the Android operating system as the mobile electronic device 2, then the processing resource 24a of the battery profiler server 24 identifies the candidate method concerned as a method which is capable of determining a corresponding battery condition value for the battery 4 of the mobile electronic device 2 based on the historical battery performance data which must be stored in the memory 6 of the mobile electronic device 2. For the specific example of the mobile electronic device 2, the processing resource 24a of the battery profiler server 24 uses at least one of the make, model and version of the Android operating system of the mobile electronic device 2 to identify that the memory 6 of the mobile electronic device 2 must have historical battery performance data in the form of a battery cycle information file specifying a battery cycle count value and a battery stats log file including both historical battery charge data and historical battery drain data as described with reference to FIGS. 1-3. Step 58 then results in the processing resource 24a of the battery profiler server 24 identifying three candidate methods from the plurality of methods stored in the battery profiler database 26, each candidate method being capable of determining a corresponding battery condition value for the battery 4 of the mobile electronic device 2, namely a battery cycle method for determining a battery cycle count as the corresponding battery condition value, a battery charge method for determining a corresponding battery condition value based on a present or actual charge capacity of the battery derived from the historical battery charge data, and a battery drain method for determining a corresponding battery condition value based on a present or actual charge capacity of the battery derived from the historical battery drain data. Each of these three capable methods will be described in detail below.

In response to the processing resource 24a of the battery profiler server 24 determining that one or more of the candidate methods are capable of determining a corresponding battery condition value, the method continues at step 60 with the processing resource 24a of the battery profiler server 24 transmitting instructions for performing one or more of the capable methods to the mobile electronic device 2. The app 16 then causes the processing resource 8 to perform one or more of the capable methods to thereby determine one or more corresponding battery condition values at step 62.

For example, where the processing resource 8 performs the battery cycle method, the processing resource 8 simply looks up the battery cycle count value stored in the battery cycle information file in the memory 6 of the mobile electronic device 2 and returns the battery cycle count value as at least one of the determined battery condition values.

Where the processing resource 8 performs the battery charge method, the processing resource 8 performs the method 200 described with reference to FIGS. 5-7. The method 200 begins at step 202 with the processing resource 8 accessing the design capacity Q_desof the battery 4 from the battery stats log file stored in the memory 6 of the mobile electronic device 2.

The method continues at step 206 with the processing resource 8 using the battery charging status flags 110, 112, 120, 122 to identify one or more battery charge intervals during which the battery 4 was charged.

As will be described in more detail below, the method continues at step 208 with the processing resource 8 identifying one or more battery charge ranges within each of the one or more battery charge intervals and determining, for each battery charge range, an estimate Q_act(k) of the actual charge capacity Q_actof the battery, where k is an index assigned to each battery charge range according to the numerical order in which the fractional values of the actual capacity Q_actof the battery in the battery charge ranges occur.

At step 210, the processing resource 8 estimates the actual capacity Q_actof the battery 4 by averaging the estimates Q_act(k) of the actual capacity of the battery 4 for all of the battery charge ranges according to the formula:

Q
act

=

1

N
k

⁢

∑

∀
k

Q
act

(
k
)

where N_kis the total number of battery charge ranges. It is known that the charge behaviour of a battery of a mobile electronic device is generally non-linear such that the accuracy of the measurement of the battery charge information may vary across the full range of fractional values of the actual capacity Q_actof the battery 4. Consequently, averaging the estimates Q_act(k) of the actual capacity of the battery 4 for all of the battery charge ranges in this way may improve the accuracy of the estimate of the actual capacity Q_actof the battery 4 because it may average out or reduce any variations in the accuracy of the battery charge information across the full range of fractional values of the actual capacity Q_actof the battery 4. It is also thought that the accuracy of the measurement of the battery charge information may vary with usage of the mobile electronic device 2 during charging. Averaging the estimates Q_act(k) of the actual capacity of the battery 4 for all of the battery charge ranges in this way, may also help to account for any variation in the accuracy of the measurement of the battery charge information with usage of the mobile electronic device 2 during charging, thereby helping to improve the accuracy of the estimate of the actual capacity Q_actof the battery 4.

At step 212, the processing resource 8 returns one or more of the following quantities as one or more of the battery condition values:

- the ratio Q_act/Q_des;
- the percentage 100×Q_act/Q_des;
- a percentage wear value W_pdetermined according to:

W
p

=

100
×

(

1
-

Q
act

Q
des

)

;

- and
- a fractional wear value W_fdetermined according to:

W
f

=

1
-

Q
act

Q
des

.

Step 208 of the method of FIG. 5 will now be described with reference to FIGS. 6 and 7. At step 220, the processing resource 8 identifies any common and any unique battery charge ranges. Specifically, the processing resource 8:

- identifies any common battery charge ranges in which the fractional values of the actual capacity Q_actof the battery 4 of one battery charge interval overlap with the fractional values of the actual capacity Q_actof the battery 4 of any one or more other battery charge intervals; and
- identifies any unique battery charge ranges in which none of the fractional values of the actual capacity Q_actof the battery 4 of one battery charge interval overlap with the fractional values of the actual capacity Q_actof the battery 4 of any one or more other battery charge intervals.

For example, referring to FIG. 7 there are shown two overlapping battery charge intervals: a first battery charge interval for which the fractional values of the actual capacity Q_actof the battery range from 0% to 60% and a second battery charge interval for which the fractional values of the actual capacity Q_actof the battery range from 30% to 90%. Accordingly, the processing resource 8 identifies three battery charge ranges:

- i. a first battery charge range, battery charge range 1, which has fractional values of the actual capacity Q_actof the battery ranging from 0% to 30% and which is unique to the first battery charge interval;
- ii. a second battery charge range, battery charge range 2, which has fractional values of the actual capacity Q_actof the battery ranging from 30% to 60% and which is common to the first and second battery charge intervals; and
- iii. a third battery charge range, battery charge range 3, which has fractional values of the actual capacity Q_actof the battery ranging from 60% to 90% and which is unique to the second battery charge interval.

Referring back to FIG. 6, the method continues at step 222 with the processing resource 8 determining, for each common battery charge range (e.g. battery charge range 2 of FIG. 5) and for each battery charge interval to which the common battery charge range is common (e.g. the first and second battery charge intervals of FIG. 7), an estimate Q_act(i, k) of the actual capacity of the battery according to the formula:

Q
act

(

i
,
k

)

=

Δ
⁢

Q
⁡
(

i
,
k

)

×

100
/

[

p
final

(

i
,
k

)

-

p
initial

(

i
,
k

)

]

- where p_initial(i, k) is the initial fractional value of the actual capacity of the battery expressed as a percentage at the beginning of the k^thbattery charge range in the i^thbattery charge interval;
- where p_final(i, k) is the final fractional value of the actual capacity of the battery expressed as a percentage at the end of the k^thbattery charge range in the i^thbattery charge interval; and
- where ΔQ(i, k) is the total charge supplied to the battery over the k^thbattery charge range in the i^thbattery charge interval.

For the example shown in FIG. 7, there is only one common battery charge range, namely battery charge range 2, and two battery charge intervals, namely the first and second battery charge intervals. Accordingly, step 222 results in the processing resource 8 determining:

p
inital

(

1
,
2

)

=

30
⁢

and
⁢

p
final

(

1
,
2

)

=
60

;
and

p
inital

(

2
,
2

)

=

30
⁢

and
⁢

p
final

(

2
,
2

)

=
60.

The processing resource 8 also determines, for each common battery charge range (e.g. battery charge range 2 of FIG. 7) and for each battery charge interval to which the common battery charge range is common (e.g. the first and second battery charge intervals of FIG. 7), the total charge ΔQ (i, k) supplied to the battery 4 over the k^thbattery charge range in the i^thbattery charge interval according to:

Δ
⁢
Q
⁢

(

i
,
k

)

=

I
avg

(

i
,
k

)

×

T
⁡
(

i
,
k

)

where t_initial(i, k) is the average battery charging current supplied to the battery 4 over the k^thbattery charge range in the i^thbattery charge interval, and T(i, k) is the duration of the k^thbattery charge range in the i^thbattery charge interval determined according to the formula:

T
⁡
(

i
,
k

)

=

t
final

(

i
,
k

)

-

t
initial

(

i
,
k

)

where t_initial(i, k) is the relative time value or time stamp corresponding to the first battery charge data point of the k^thbattery charge range in the i^thbattery charge interval, and t_final(i, k) is the relative time value or time stamp corresponding to the last battery charge data point of the k^thbattery charge range in the i^thbattery charge interval.

For the example shown in FIG. 7, there is only one common battery charge range, namely battery charge range 2. Accordingly, for the example shown in FIG. 7, the processing resource 8 determines:

Δ
⁢

Q
⁡
(

1
,
2

)

=

I
avg

(

1
,
2

)

×

T
⁡
(

1
,
2

)

Δ
⁢

Q
⁡
(

2
,
2

)

=

I
avg

(

2
,
2

)

×

T
⁡
(

2
,
2

)

where:

- I_avg(1,2) is the average current determined for battery charge range 2 from the battery charging data in the first battery charge interval;
- T(1,2) is the duration associated with battery charge range 2 from the time data in the first battery charge interval;
- I_avg(2,2) is the average current determined for battery charge range 2 from the battery charging data in the second battery charge interval; and
- T(2,2) is the duration associated with battery charge range 2 from the time data in the second battery charge interval.

The processing resource 8 determines, for each common battery charge range and for each battery charge interval to which the common battery charge range is common, the average battery charging current I_avg(i, k) supplied to the battery 4 over the k^thbattery charge range in the i^thbattery charge interval according to the formula:

I
avg

(

i
,
k

)

=

1

N
j

(

i
,
k

)

⁢

∑

∀

j
∈
i

,
k

I
j

where I_jis the battery charging current supplied to the battery corresponding to the j^thhistorical battery charge data point, ∀j∈i, k indicates that the summation should be performed over all of the historical battery charge data points in the k^thbattery charge range in the i^thbattery charge interval, and N_j(i, k) is the total number of historical battery charge data points in the k^thbattery charge range in the i^thbattery charge interval.

For the example shown in FIG. 7, there is only one common battery charge range, namely battery charge range 2, and two battery charge intervals, namely the first and second battery charge intervals. The processing resource 8 determines the average battery charging current I_avg(1,2) supplied to the battery 4 over battery charge range 2 in the first battery charge interval according to the formula:

I
avg

(

1
,
2

)

=

1

N
j

(

1
,
2

)

⁢

∑

∀

j
∈
i

=
1

,

k
=
2

I
j

where I_jis the battery charging current supplied to the battery corresponding to the i^thhistorical battery charge data point, ∀j∈i=1, k=2 indicates that the summation should be performed over all of the historical battery charge data points in battery charge range 2 in the 1^stbattery charge interval, and N_j(1,2) is the total number of historical battery charge data points in battery charge range 2 in the 1^stbattery charge interval.

Similarly, the processing resource 8 determines the average battery charging current I_avg(1,2) supplied to the battery over battery charge range 2 in the second battery charge interval according to the formula:

I
avg

(

2
,
2

)

=

1

N
j

(

2
,
2

)

⁢

∑

∀

j
∈
i

=
2

,

k
=
2

I
j

where I_jis the battery charging current supplied to the battery corresponding to the j^thhistorical battery charge data point, ∀j∈i=2, k=2 indicates that the summation should be performed over all of the historical battery charge data points in battery charge range 2 in the 2^ndbattery charge interval, and N_j(2,2) is the total number of historical battery charge data points in battery charge range 2 in the 2^ndbattery charge interval.

As shown in FIG. 6, the method continues at step 224 with the processing resource 8 determining, for each common battery charge range, an average of the estimates Q_act(i, k) of the actual capacity of the battery 4 over each battery charge interval to which the common battery charge range is common so as to provide an improved estimate Q_act(k) of the actual capacity Q_actof the battery for the k^thbattery charge range according to the formula:

Q
act

(
k
)

=

1

N
icom

(
k
)

⁢

∑

∀

i
∈

i
com

Q
act

(

i
,
k

)

where ∀i∈i_comindicates that the summation should be performed over all of the battery charge intervals to which the k^thbattery charge range is common and N_icom(k) is the number of battery charge intervals to which the k^thbattery charge range is common. Averaging the estimates of the actual capacity of the battery 4 in this way across each battery charge interval to which each common battery charge range is common, may help to account for any variation in the usage of the mobile electronic device during charging in the common battery charge range in different battery charge intervals, thereby helping to improve the accuracy of the estimate of the actual capacity of the battery 4.

For the example shown in FIG. 7, there is only one common battery charge range, namely battery charge range 2, and two battery charge intervals. Accordingly, for the example shown in FIG. 7 at step 224, the processing resource 8 simply averages the estimates Q_act(1,2) and Q_act(2,2) to determine an estimate of the actual capacity of the battery for battery charge range 2 according to the formula:

Q
act

(
2
)

=

1
2

⁢

∑

i
=
1

,
2

Q
act

(

i
,
2

)

Averaging the estimates of the actual capacity of the battery 4 in this way over a battery charge range for different battery charge intervals, helps to account for any variation in the usage of the mobile electronic device 2 during charging, thereby helping to improve the accuracy of the estimate of the actual capacity of the battery 4.

At step 226 of FIG. 6, the processing resource 8 determines, for each unique battery charge range, an estimate Q_act(k) of the actual capacity of the battery according to the formula:

Q
act

(
k
)

=

Δ
⁢

Q
⁡
(

i
,
k

)

×

/

[

p
final

(

i
,
k

)

-

p
initial

(

i
,
k

)

]

- where p_initial(i, k) is the initial fractional value of the actual capacity of the battery 4 expressed as a percentage at the beginning of the k^thbattery charge range in the i^thbattery charge interval;
- where p_final(i, k) is the final fractional value of the actual capacity of the battery 4 expressed as a percentage at the end of the k^thbattery charge range in the i^thbattery charge interval; and
- where ΔQ(i, k) is the total charge supplied to the battery over the k^thbattery charge range in the i^thbattery charge interval.

For the example of FIG. 7 there are two unique battery charge ranges, namely battery charge ranges 1 and 3. Accordingly step 226 results in the processing resource 8 determining:

p
inital

(

1
,
2

)

=

0
⁢

and
⁢

p
final

(

1
,
1

)

=
30

;
and

p
inital

(

2
,
3

)

=

60
⁢

and
⁢

p
final

(

2
,
3

)

=
90.

The processing resource 8 also determines, for each unique battery charge range, the total charge ΔQ(k) supplied to the battery over the k^thbattery charge range in the i^thbattery charge interval according to:

Δ
⁢

Q
⁡
(
k
)

=

I
avg

(

i
,
k

)

×

T
⁡
(

i
,
k

)

where I_avg(i, k) is the average battery charging current supplied to the battery over the k^thbattery charge range in the i^thbattery charge interval, and T(i, k) is the duration of the k^thbattery charge range in the i^thbattery charge interval determined according to the formula:

T
⁡
(

i
,
k

)

=

t
final

(

i
,
k

)

-

t
initial

(

i
,
k

)

where t_initial(i, k) is the relative time value or time stamp corresponding to the first battery charge data point of the k^thbattery charge range in the i^thbattery charge interval, and t_final(i, k) is the relative time value or time stamp corresponding to the last battery charge data point of the k^thbattery charge range in the i^thbattery charge interval.

For the example shown in FIG. 7, there are two unique battery charge ranges, namely battery charge ranges 1 and 3. Accordingly, for the example shown in FIG. 7, the processing resource 8 determines the total charge ΔQ(1) supplied to the battery over battery charge range 1 in the 1^stbattery charge interval according to:

Δ
⁢

Q
⁡
(
1
)

=

I
avg

(

1
,
1

)

×

T
⁡
(

1
,
1

)

and the total charge ΔQ(3) supplied to the battery over battery charge range 3 in the 2^ndbattery charge interval according to:

Δ
⁢

Q
⁡
(
3
)

=

I
avg

(

2
,
3

)

×

T
⁡
(

2
,
3

)

The processing resource 8 determines, for each unique battery charge range, the average battery charging current I_avg(i, k) supplied to the battery over the k^thbattery charge range in the i^thbattery charge interval according to the formula:

I
avg

(

i
,
k

)

=

1

N
j

(

i
,
k

)

⁢

∑

∀

j
∈
i

,
k

I
j

where I_jis the battery charging current supplied to the battery corresponding to the j^thhistorical battery charge data point, ∀j∈i, k indicates that the summation should be performed over all of the historical battery charge data points in the k^thbattery charge range in the i^thbattery charge interval, and N_j(i, k) is the total number of historical battery charge data points in the k^thbattery charge range in the i^thbattery charge interval.

For the example shown in FIG. 7, there are two unique battery charge ranges, namely battery charge ranges 1 and 3, wherein battery charge range 1 is unique to the first battery charge interval and battery charge range 3 is unique to the second battery charge interval. The processing resource 8 determines, for unique battery charge range 1, the average battery charging current I_avg(1,1) supplied to the battery over battery charge range 1 in the first battery charge interval according to the formula:

I
avg

(

1
,
1

)

=

1

N
j

(

1
,
1

)

⁢

∑

∀

j
∈
i

=
1

,

k
=
1

I
j

where I_jis the battery charging current supplied to the battery corresponding to the j^thhistorical battery charge data point, ∀j∈i=1, k=1 indicates that the summation should be performed over all of the historical battery charge data points in battery charge range 1 in the 1^stbattery charge interval, and N_j(1,1) is the total number of historical battery charge data points in battery charge range 1 in the 1^stbattery charge interval.

Similarly, the processing resource 8 determines, for unique battery charge range 3, the average battery charging current I_avg(2,3) supplied to the battery over battery charge range 3 in the second battery charge interval according to the formula:

I
avg

(

2
,
3

)

=

1

N
j

(

2
,
3

)

⁢

∑

∀

∈
i

=
2

,

k
=
3

I
j

where I_jis the battery charging current supplied to the battery corresponding to the j^thhistorical battery charge data point, ∀j∈i=2, k=3 indicates that the summation should be performed over all of the historical battery charge data points in battery charge range 3 in the 2^ndbattery charge interval, and N_j(2,3) is the total number of historical battery charge data points in battery charge range 3 in the 2^ndbattery charge interval.

Returning to step 210 shown in FIG. 5, for the example of FIG. 7, the processor 6 estimates the actual capacity Q_actof the battery by averaging the estimates Q_act(k) of the actual capacity of the battery 4 for all of the battery charge ranges k=1, 2 and 3 according to the formula:

Q
act

=

1
3

⁢

∑

k
=
1

,
2
,
3

Q
act

(
k
)

It is known that the charge behaviour of the battery 4 of the mobile electronic device 2 is generally non-linear and it is thought that the accuracy of the measurement of the battery charge information may vary across the full range of fractional values of the actual capacity Q_actof the battery 4. Consequently, averaging the estimates Q_act(k) of the actual capacity of the battery 4 for all of the battery charge ranges in this way may improve the accuracy of the estimate of the actual capacity Q_actof the battery 4 because it may average out or reduce any variations in the accuracy of the battery charge information across the full range of fractional values of the actual capacity Q_actof the battery 4. It is also thought that the accuracy of the measurement of the battery charge information may vary with usage of the mobile electronic device 2 during charging. Averaging the estimates Q_act(k) of the actual capacity of the battery 4 for all of the battery charge ranges in this way, may also help to account for any variation in the accuracy of the measurement of the battery charge information with usage of the mobile electronic device 2 during charging, thereby helping to improve the accuracy of the estimate of the actual capacity Q_actof the battery 4.

Where the processing resource 8 performs the battery drain method, the processing resource 8 determines one or more of the battery condition values based at least in part on the historical battery drain data. Specifically, with reference to the description of the historical battery drain data of FIG. 3 above, the processing resource 8 accesses the design capacity Q_desof the battery 4, the lower limit min(ΣQ) for the cumulative charge consumed over the drain period, the upper limit max(ΣQ) for the cumulative charge consumed over the drain period, from the battery stats log file stored in the memory 6 of the mobile electronic device 2. The processing resource 8 then determines an average value avg(ΣQ) for the cumulative charge consumed over the drain period according to

avg
⁡
(

∑
Q

)

=

1
2

[

min
⁡
(

∑
Q

)

+

max
⁡
(

∑
Q

)

]

.

For the specific battery drain data shown in FIG. 3, this results in an average value avg(ΣQ) for the cumulative charge consumed over the drain period of

1
2

[

4887
+
5427

]

=

5157
⁢

mAh
.

Where the historical battery drain data includes a cumulative percentage drop in charge Σp over the drain period, the processing resource 8 determines an actual capacity Q_actof the battery 4 according to Q_act=avg(ΣQ)×100/Σp. For the specific battery drain data shown in FIG. 3, this results in Q_act=5157×100/201=2566 mAh.

The processing resource 8 then returns one or more of the following quantities as one or more of the battery condition values: the ratio Q_act/Q_des; the percentage 100×Q_act/Q_des; a percentage wear value W_pdetermined according to W_p=100×(1−Q_act/Q_des); and a fractional wear value W_fdetermined according to W_f=1−Q_act/Q_des. For the specific battery drain data shown in FIG. 3, this results in the processing resource 8 returning one or more of the following: a ratio Q_act/Q_des=2566/2700=0.95; a percentage 100×Q_act/Q_des=95%; a percentage wear value W_p=5%; or a fractional wear value W_fof 0.05.

Referring back to FIG. 4, following step 62, the app 16 causes the user interface 10 to display each determined battery condition value and/or causes the mobile electronic device 2 to transmit each determined battery condition value to the battery profiler server 24 or a further server such as a report server (not shown in FIG. 1) at step 64.

It will be appreciated by one of ordinary skill in the art that various modifications may be made to the foregoing system and method without departing from the scope of the present invention as defined by the claims. For example, a first alternative method may include an additional step performed between steps 58 and 60 of FIG. 4, which additional step includes the processing resource 24a of the battery profiler server 24 selecting an optimum one of the one or more capable methods based at least in part on the mobile electronic device data 14 and the information relating to each candidate method, wherein the battery condition value determined using the optimum one of the one or more capable methods is known to provide a more accurate representation of the condition of the battery of the mobile electronic device than the battery condition value determined using any other one of the capable methods. In this regard, the processing resource 24a of battery profiler server 24 may select the optimum method from the one or more capable methods according to a priori knowledge of an accuracy of each capable method. For example, it is known that a battery condition value determined using the battery cycle method is more accurate than a battery condition value determined using the battery charge method which is, in turn, more accurate than a battery condition value determined using the battery drain method. Accordingly, if the processing resource