Design and testing for better power consumption and battery life in smartphones

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© 2011 Intuigence Group –internal use only

WHITE PAPER

Mobile Device / Application Power Profiling Testing and Design considerations in mobile Devices & Apps

for power performance and battery life

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Table of contents

Introduction ................................................................................................................................ 3

Battery operated mobile device ................................................................................................. 4

Batteries in mobile devices ..................................................................................................... 4

Batteries used in mobile devices ............................................................................................ 5

Smart battery packs in mobile devices ................................................................................. 11

Power profiling and power management states .................................................................. 13

Power profiling approaches in smartphones ........................................................................ 14

Component level power measurement ............................................................................ 14

Device level power measurement: ................................................................................... 20

Power measurement lab, tools and equipments ..................................................................... 24

Design considerations for Power Performance ........................................................................ 26

Battery life and battery recovery effect ............................................................................. 26

Power efficient GUI user interface ..................................................................................... 28

Making displays more power efficient .............................................................................. 29

Intuigence Power profiling and battery life testing service ...................................................... 31

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Introduction

For many operators around the world, Smartphones and in particular those based on iOS and

Android have provided a flexible, appealing and robust platform to transition their

subscribers from traditional voice and messaging use base to more data intensive services

and applications. This has lead to an explosive growth in number and volume of smartphones

being made by OEMs and launched by operators. There’s been equally large number of

mobile applications that are being developed for these platforms.

The experience of the last few years have shown us that the single most important factor

determining success or failure of a smartphone and a mobile application launch is the user

experience it delivers. Beyond functional features, the two main components of user

experience that need to be evaluated and tested prior to launch are usability and battery

consumption and Intuigence has a long standing in developing methods to objectively and

efficiently measure and test both.

In an attempt to put together a concise guide on batteries in mobile devices, this document

outlines background knowledge, experimental findings and some lessons learned on battery

testing and, power profiling of mobile devices and applications, and design considerations for

optimizing battery life in smartphones. We hope you enjoy reading this document and find it

useful.

If we can be of any help in your device and/or mobile application launch programs for battery

life testing, power profiling and related areas, give us a shout. We’d love to hear from you.

Moe Tanabian

Managing Partner

Intuigence Group

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Battery operated mobile device1

With the omnipresence of mobile devices in our daily life, more companies are developing

and launching battery powered devices such as smartphones that are used for a wide range

of purposes. Smartphones are used for mobile internet, music play, social networking, online

and offline gaming and many other innovative uses through the expanding use of mobile

applications.

One of the challenging issues of smartphones is energy consumption management. In order

to support users’ mobility and their various use scenarios of their devices, it is necessary to

make available light and reliable battery powered devices with batteries that last longer.

Since the advances in battery technology have been much slower than the market evolution

and other device technologies, device makers are resorting to find other innovative and

effective solutions to extend battery lifetime at reasonable cost.

Batteries in mobile devices

Smartphones are becoming the choice conduit for delivering a wide array of network

services. They are pervasive, they are within consumers’ buying power and they are

customizable and flexible. Over the last few years, new smartphones are becoming more

powerful, screens are getting bigger, CPUs are getting faster, and network speeds are

reaching those of wired devices. And all these advances are embraced by device makers and

mobile service providers alike. Consumers also seem that they can’t get enough of these new

shiny gadgets. They use mobile devices to watch movies and streams videos, browse the

web, send and receive messages, keep in touch with their social circle, navigate their way to

their destination, and play collaborative games. And of course make phone calls.

All of these new activities and superior technical specifications of new mobile devices is

putting an unprecedented demand on the energy source inside smartphones: the battery.

As mentioned, we are making significant progress on almost all aspects and technical

capabilities of modern mobile devices, but we are still using batteries with technology of the

last decade. It is not uncommon to have to charge a mobile device several times a day if it is

used continually during the day. The days of charging a mobile phone once a week, is long

behind us.

Device OEMs and mobile operators who provide connectivity and other services to mobile

users are realizing the solution is in making these devices more power efficient and more

intelligent in conserving battery energy.

1 Many parts in this section are referenced with permission from www.battery university.com

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More and more companies are trying to understand and innovate ways for hardware and

software components inside mobile devices to consume less energy while performing their

function.

Batteries used in mobile devices

There are several types of batteries that power today’s mobile devices. Nickel or Lithium

based batteries are very common; smartphones mostly use lithium based batteries.

A battery and its performance can be characterized by the following metrics:

Energy density (Power per weight unit, e.g. mA hour per Kg)

Number of charging cycles the battery can take during its life time

Charge rate characteristics

Discharge rate characteristics

Each battery type and chemistry provides different strengths and weaknesses across these

factors. There are four types of batteries that are in use today in mobile devices; and each

has its strengths and weaknesses which makes it suitable for particular applications.

Nickel Cadmium (NiCad) - This is a mature battery technology, has been around

for long and its behavior is well understood. The main drawback of NiCad

batteries is low energy density (Wh/Kg). They are more suited where long battery

life, high discharge rate and price economy are priority. Military equipments and

some public safety mobile devices use NiCad batteries.

Nickel Metal Hybrid (NiMH) – This is a more advanced Nickel based battery and

compared to NiCd has higher energy density at the expense of shorter cycle life.

Many cordless phones are powered by NiMH battries.

Lithium Ion (Li-ion) – The newest and fastest growing battery technology. Li-ion

batteries are smaller and lighter than Nickel based batteries (higher energy

density) and they are more expensive. They are the main type of battery used in

smartphones and other handheld mobile devices.

Lithium Ion Polymer (Li-ion Polymer) – A lower-end version of Li-ion battery with

smaller profile and more simplified packaging. It has the same energy density as

Li-ion batteries.

Table 1 shows a comparative data sheet for different types of batteries.

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Table 1- battery chemistry comparison

Lithium based batteries are powering most of modern mobile devices. Lithium is the lightest

of all metals, and has the greatest electrochemical potential and provides the largest energy

density per weight. Li-ion batteries are safe in the hands of consumers provided that they are

not overheated during charging and discharging stages.

They are low maintenance batteries and they don’t develop memory. Li-ion batteries have a

low self discharge cycle which makes them ideal for mobile devices. They can sit on the shelf

for a while without much discharge leakage. The high voltage per cell of these batteries allow

for manufacturing a practical battery pack with only one cell thus simplifying production

process.

Gravimetric Energy

Density(Wh/kg)45-80 60-120 110-160 100-130

Internal Resistance 100 to 2001 200 to 3001 150 to 2501 200 to 3001

(includes peripheral

circuits) in mΩ6V pack 6V pack 7.2V pack 7.2V pack

Fast Charge Time 1h typical 2-4h 2-4h 2-4h

Overcharge Tolerance moderate low very low low

Self-discharge /

Month (room temperature)20%4 30%4 10%5 ~10%5

Cell Voltage(nominal) 1.25V6 1.25V6 3.6V 3.6V

Load Current

- peak 20C 5C >2C >2C

- best result 1C 0.5C or low er 1C or low er 1C or low er

-40 to -20 to -20 to 0 to

60°C 60°C 60°C 60°C

Maintenance Requirement 30 to 60 days 60 to 90 days not req. not req.

Commercial use since 1950 1990 1991 1999

Cycle Life (to 80% of initial

capacity)15002 300 to 5002,3 500 to 10003

Operating

Temperature(discharge

only)

300 to 500

NiCd NiMH Li-ion Li-ion polymer

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The negative electrode of most recent Li-ion batteries is made of coke or graphite. Graphite

has proven a better choice since it provides better discharge voltage curve and a sharper

knee bend at the end of the discharge. At the same time, graphite delivers most of the stored

energy by only having to be discharged to 3V/cell whereas coke has to be discharged to

2.4V/cell to get similar run time. Figure 1 shows the discharge characteristics of Li-ion

batteries with coke and graphite negative electrodes.

Figure 1 – Discharge characteristics of Li-ion battery

Charging Li-ion batteries – Li-ion batteries need to be charged under strictly limited and

controlled voltage. Most modern Li-ion batteries need to be charged under 4.2V/cell charge

regime. The tolerance for charge variation is very low and around +/-0.05V/cell.

The time to charge Li-ion batteries when charged at 1C (C is the battery capacity e.g. 1500

mAh) is three hours. At this rate the battery remains cool during the charge process. Full

charge is achieved when the voltage has reached the upper voltage threshold and the charge

current has fallen off to about 3% of the charge current. An important point to remember

when designing or testing devices for Li-ion batteries is that increasing charge current does

not shorten the charge time significantly. Figure 2 shows the charging voltage/current

behavior of Li-ion batteries.

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Figure 2 – Charge behavior of Li-ion batteries

Discharge characteristics of Li-ion batteries – While charging behavior of a battery is

important for when designing chargers, the discharging characteristics are important for

designing devices and applications that the batteries will power. To better illustrate this

battery property we introduce a battery model. A battery can simply be modeled as a source

of voltage and an internal resistor as it is shown in Figure 3.

Figure 3 – A simple battery model

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In this model, Voc is the open circuit voltage and Ri is the internal resistance of the battery. Cl

and Rl respectively represent load capacitance and load resistance. Ri (internal battery

resistance) and its variability play an important role in discharging behavior of a battery.

Another important parameter characterizing battery discharge is the depth of discharge.

Depth of discharge is the lowest battery voltage before recharge has been applied. It is

important for Li-ion a battery to be recharged before its voltage drops below 2.5V.

When a Li-ion battery is fully discharged, it forms a copper shunt and charging it at 1C will

cause excessive heating, and potential battery explosion. On the other hand, a fully

discharged Li-ion battery, or one that its voltage has dropped below 1.5V, needs to be

recharged with a special charger that can initiate the charge at 0.1C. In this situation the

battery should probably be discarded.

Figure 4 - Discharge profiles of Li-ion and NiMH batteries

Similarly, discharging Lithium based battery below 2.5V may trigger the battery cut-off

protection circuit and the battery may become unusable.

Modern mobile devices are very demanding on their batteries. Depending on the amount of

concurrency that is allowed in the device design, momentary pulsed load can cause a brief

voltage drop which may push the voltage below cut-off point. The higher the internal battery

resistance, it is likelier for the battery to get into this situation. We will discuss considerations

for concurrency and impulsive loads later in the document.

Impulsive load can also dramatically reduce the number of charge cycles a battery can

provide. A Li-ion battery with steady current within its dominated C can provide 700 charge

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cycles and still maintaining 80% of its capacity. The same battery under impulsive load can

provide merely 300 charge cycles. The reason for this is that batteries are chemical machines

and their response time to get to steady state is slower than power variations that a modern

mobile device exerts.

On the opposite end of this, is another important factor that will be very useful for designing

mobile devices and mobile applications for longer battery life, the recovery effect. Recovery

effect means that battery can regain some of the lost charge capacity during its idle period.

Later in the document we will see that how we can potentially leverage this property of

batteries in designing more sophisticated and more intelligent battery packs Nd smarter

hardware and software in a way to intentionally allow the battery to rest by providing these

idle periods and to recover some of its lost capacity.

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Smart battery packs in mobile devices

Most of the consumer mobile devices we use today need to inform the user the battery’s

state of charge (SoC). For a device to do this, it needs to communicate with the battery and

query its conditions. Regular (or dumb) batteries don’t understand these queries. On the

other hand smart battery packs or smart batteries have internal circuitry to understand the

device’s query and provide the device with SoC of the battery. Depending on how much

complexity is put into making a battery smarter, there are different types of smart batteries.

The most basic smart battery pack often contains a chip to identify its chemistry and tell the

charger which charge algorithm to apply. Some batteries claim to be smart because they

provide protection against overcharging, under-discharging and short circuiting.

A more widely accepted definition for Smart battery states that a smart battery should be

able to provide SoC indicators.

Most small mobile devices, including all smartphones, use a type of smart battery that is

known as Single Wire Bus terminal. In this type of battery as it is shown in Figure 5, all the

data communication between the device and the charger on one side and the battery on the

other side channels through one wire. A Single Wire Bus battery may have three or four

terminating points. For safety reasons some manufacturers use a forth pin for temperature

sensing.

Figure 5 - Single Wire Bus smart battery

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There is also a more sophisticated smart battery architecture which is mainly used for

laptops, medical devices, data collection devices and other more sophisticated industrial

uses. It’s called SMBus architecture and uses a bi-directional two wire I2C data

communication link. The second wire is used for clock synchronization.

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Power profiling and power management states

Battery life and power consumption optimization is one of the most challenging aspects of

designing and launching a smartphone device. As noted earlier, smartphones have larger

screens, faster CPUs, faster network connections and are used for a lot more than just voice

calls and messaging and all this drains batteries faster than ever. This is why a complete,

objective and efficient way to test battery life and power consumption is always an essential

part of every mobile device or application launch process.

Measurement stability and test result reproducibility: Different measurement of power

consumed by a component of a smartphone or by the device itself can vary from test to test.

Many factors introduce certain levels of variability even in controlled lab environments. To

account for this variability and to make measurement results more stable and achieve

statistical stability, power readings need to be done for a period of time (t), at the sampling

frequency of (f) and repeated for a number of repetitions (n) with a total number of readings

of N as shown in the following formula. Choosing the right values for N, t, f and n are largely

dependent of the test case, the tester’s skill, the measurement equipment accuracy and the

test environment. For example device level test cases which run usage scenarios require

more run repetitions.

N = t x f x n

N: The total number of required power readings for stable test results

If the tester has access to a high sampling frequency measurement tools, a stable result for a

test case can be achieved faster and by smaller number of runs. Our experiments show that

in general, for device level measurements, a sampling frequency of 300 reading per second,

measurement period of 1min and 32 runs for each test case produce stable results and small

and acceptable standard deviations.

Device power consuming states: Before getting to examining different power profiling

methods, an important consideration to measure power is to get power consumption

readings at all different device states. Most power management modules in smartphone

change the state of the device based on its activity level. A modern mobile device can

transition among the three possible power consuming states:

Suspended state: A mobile device typically spends significant amount of time

in a state that it is not being actively used. In this state the application

processor is idle, and the communication processor and subsystem only

perform a minimal amount of activity to stay connected to the network to be

able to receive calls, text messages and emails. This is called the suspended

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state of the mobile device and power management module of the mobile

operating system often aggressively suspends all activity to memory meaning

that the state of all ongoing activities are written into RAM and the device is

put into low powered sleep mode. Android is a good example of a platform

with very aggressive power management policies. Power readings in this

state tend to be fluctuating mostly due to impulsive activity of the radio and

networking subsystem in the absence all other components. For example the

power reading of a test device can be around 70mW with a relative standard

deviation (RSD) of 9%.

Idle state: A device is in idle if it is fully awake but no application is running

and the backlight is off. Power readings at the aggregate battery point in this

state are generally stable, and again most of the fluctuations are due to the

networking activity. In an Android test device, most of the measured power

is related to the display subsystem. For example, LCD alone can consume

close to 50% of the measured power, and with backlight added on, this can

increase to 80%.

Active usage state: This is the state that the device is performing a task,

either in a call, receiving or transmitting data, interacting with user within an

application environment, etc. Power measurements in this state require

considering factors such as user habits, usage scenarios, use cases and time

of day usage to develop an objective and complete picture of the power

consumption behavior of the device while in use.

Power profiling approaches in smartphones

For smartphones, there are generally two approaches to test and measure power

consumption and consequently measure the expected battery life. Each of these methods

has its advantages and disadvantages and each is more appropriate for different situations.

Component level power measurement

In this approach, the power reading is done for a particular component – e.g. the audio

component – and the aggregate device power measure is the sum of the measured power

consumptions for each important component. Important components are the ones that have

material effect on power consumption of a device.

Since the power reading is done for each component independently, this method

produces more accurate and more reproducible measurements.

It’s more complicated, it takes more efforts, time and it is more expensive

Detailed device hardware and software documentations are necessary to find the

right test points for each component, which often makes it difficult in many cases.

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With all the above considerations, this approach is more suited for device OEMs

and platform developers. They can easily perform component level power readings

since they have access to detailed design aspects of the device. Component level

measurement is mostly used in the design phase.

Figure 6 shows different components of a typical smartphone and how the power

consumption of a component – in the case the audio apparatus of the device – can be

measured using a power sensing resistor.

To achieve an objective measurement when testing multiple devices and to be able to make

fair comparisons and benchmarks, each important component needs to be driven with an

appropriate load that is similar across all devices under test. In other words, the proper

definition of the test cases for each component level measurement needs to explain the

component deriving load. This is critical in making the power measurements and

benchmarking objective.

Let’s take a look at each of these important components (subsystems) and examine what

considerations we need to take into account for defining test conditions. These particular

considerations are tailored for Android platform but can be easily used for other operating

systems.

Figure 6 - Component level power consumption measurements

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Display subsystem

Power measurement for the display subsystem can be tricky, particularly when the objective

of the measurement is to benchmark several devices. A rule of thumb is to measure power

for the following scenarios (test cases):

1. With the backlight off, measure power for a complete black screen

2. With the backlight off, measure power for a complete white screen

3. With the backlight off, measure power consumed to play a pre-selected video. For example, the test video clip can start from a black screen with white noise (static) sliding in slowly to cover the entire screen over the period of few minutes and then slowly sweeping the screen back to a complete white screen. Figure 7 shows three example screens for display power readings and testing.

4. Measure power with black screen and different levels of backlight. Android provides

a range of backlight brightness between 1 and 255 but the brightness control user

interface allows only for changing the brightness between 30 and 255. Measure

power for backlight at the lowest level, mid level and the brightest level.

5. In a dark room, with the help of an intensity controllable light source, measure

power at few different luminance levels (light brightness is measured in Lumens, so

you might need a lumens meter). This test case measures the effectiveness of the

Automatic Ambient Light Adjustment mechanism of the device for power

conservation.

Figure 7 - Three test screens for display power reading

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Radio and Network subsystem

For radio and networking subsystem, power measurements are performed for the three

main radio components of the device; namely the Wi-Fi radio, the cellular radio and the

Bluetooth radio. A simple and objective test case for the Wi-Fi and cellular radios can be

downloading a file via HTTP. In Android this can easily be done by using wget. A good test file

for the Wi-Fi measurement can contain 15 MB of random data. For the cellular radio,

depending on the technology and the connection speed, a smaller size file might be more

appropriate.

It is important to note that networking power consumption is the sum of the power that the

radio draws and the power that is consumed by the CPU and the RAM components for the

baseband and higher level protocols processing. CPU and RAM draw more power when the

data throughput is higher.

Another important test case to measure power consumption behavior of the networking

component is the effect of the signal strength. A simple test case is to shield the device in a

metal box (e.g. a still or aluminum box with ~2mm thickness). The shielding can drop the

signal strength by around 10dBm for the cellular radio and around 2dBm for the Wi-Fi radio,

and as one might expect shielding has much lesser effect on Wi-Fi throughput and power

consumption.

Similar test cases can be designed to measure power consumption of the Bluetooth radio, for

different supported profiles. For example, the delta between consumed power when a music

clip played through stereo corded headphones and when it is played through stereo

Bluetooth headset in A2DP profile can be a good representative of the Bluetooth module’s

power consumption.

CPU and RAM

To measure CPU and RAM power consumption, we need to develop, or select, benchmark

pieces of code. Part of the benchmark code needs to be CPU intensive, and part memory

usage activity intensive. There are ready made benchmark codes available but most of them

are developed for desktop computers which have higher computing resources than

smartphones. A very well known benchmark code that is widely used is SPEC CPU2000 which

is developed by Standard Performance Evaluation Corporation (SPEC). SPEC is a nonprofit

organization and was founded in 1988. Its members include Apple, Dell, IBM, Intel, Microsoft

and Oracle. Some of the SPEC programs that are developed for testing CPU performance and

written in Java can be used for benchmarking Android smartphones. These include equack,

vpr, gzip, crafty and mcf. Modules equack, vpr and gzip are CPU intensive so the CPU power

consumption dominates RAM power consumption. Modules crafty and mcf are memory

intensive so RAM power consumption dominates the CPU’s.

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When measuring CPU and RAM power consumption, it is important to take CPU dynamic

frequency scaling into account. Dynamic frequency scaling (also known as CPU throttling) is a

technique commonly used in mobile devices, by which the CPU’s clock frequency is

automatically adjusted to conserve power or to control excessive heat. If possible,

measurements should be performed at fixed core frequencies, such as 400 MHz, or 1GHz.

Another test condition for benchmarking CPU and RAM is to measure CPU and RAM power

consumption when the device is in idle state.

Some newer mobile devices are equipped with dual-core CPUs. A dual core device does not

necessarily consume more power than a single core CPU device. In fact in many cases the

opposite is the case. In a dual core device, the power manager can utilize more flexible

dynamic voltage and frequency scaling policies to conserve power more effectively. Figure 8

shows a scenario that a dual core CPU device can consume 40% less power than a single core

CPU device for completing the same workload2.

Figure 8 - Single core and dual core CPU power consumption

2 Courtesy of NVidia corporation

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Audio subsystem

To test the audio component of a smartphone, the easiest way is to use

its media player functionality to play an audio load. A good example of

an audio load would be a 10 minute 44 KHz MP3 stereo music file, which

is close to real world usage of listening to a song track. The output can

be directed to a set of stereo headphones. The measurement should be

done with backlight off – similar to real usage scenario --, with cellular

radio on, with volume set at different levels, such as 10%, 50% and

maximum. Volume setting can introduce subjectivity and inaccuracy in

measurements. To avoid it, the volume level indicators of each device

can be calibrated using a sound level meter similar to Figure 9 and a

monotonic audible file to find volume level positions corresponding to

the same dB levels in all of the test devices.

GPS component

Measuring the power consumption of the GPS component can be done by taking a simple

approach. To perform the power reading, turn the module on and run an application that

makes use of the GPS for coordinate readings. For the Android the simplest app is GPS Status,

as shown in Figure 10, which is freely available on Android Market. Our experiments show

that the power consumed by GPS receiver is for the large part stable and to a good degree

independent of the received signal – i.e. using internal or external antenna does not make a

material change in power consumption--. Using external antenna increases power

consumption by only a few percentage points. The number of acquired satellites also seems

to have no effect on drawn power by the GPS unit.

Power measurements for navigation applications though are much more complex and

involve many scenarios and use cases in certain test conditions to make them accurate and

reproducible.

Figure 9 – Sound meter

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Figure 10 - Android GPS Status 2 App to measure GPS component power consumption

Device level power measurement:

In this approach the power is measured at the aggregate point of battery connection for

variety of scenarios of the device usage. Device level power measurement is widely used as

the de facto method for mobile device power profiling and battery life testing.

This method is easier, and more practical for most cases particularly for

operators and application developers

Depending on how accurately the device under test is prepared for the test,

the results may vary from run to run so it may be necessary to repeat tests to

achieve statistical significance and stability

There are many considerations when testing battery consumption at the device level. One

important factor is that most use cases for power measurement are interlinked with cloud

based service. This introduces a lot of variability, unpredictability and uncertainty into

measurements which for the most part make them much less accurate and reproducible.

Another consideration is to take into consideration the target user segment that will be using

the device or the application. Power consumption of a device, and consequently its battery

life can greatly vary depending on the usage pattern.

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The cloud effect on power measurement

The ultimate objective of power measurement is to accurately identify the energy a device

consumes to perform a task. A task such as a phone call, a Youtube video playback,

sending/receiving emails, web browsing, etc. In the lab, we can control the test environment

and always create similar test conditions before power measurements. But when we

measure consumed power at the device aggregate battery point for each of these tasks, we

largely depend on services that are provided by servers somewhere in the cloud (the

internet).

The problem is, cloud services can change their behavior and these changes have significant

implications on power consumption of the device, making measurement no reproducible. For

example when playing a Youtube video, the server often adjusts the resolution based on the

client’s capabilities and the connection throughput. Another example is that many web

servers adapt to the device and connection parameters and deliver web page contents with

different layout and parameters.

Another issue is that many of today’s smartphone applications constantly and in an ad-hoc

way exchange data with backend servers that are not user driven or user controllable, to

perform their functions. Google Maps and almost all other location based service (LBS)

applications are good examples. This is an important consideration when designing test cases

and test environment preconditions for power measurements for each use scenario.

A way to get around this and minimize the cloud variability effect is to re-create the use

scenario in a controlled lab environment. We can run the use scenario in real conditions –

through the cloud service – and capture traces of the device activity, and related parameters

for the network activity, dynamic capture of the content over the period of the test, device

conditions at the time of the test such as screen resolution, backlight level, timestamps,

delays etc, and then reproduce the scenario on a server in the lab with those exact

parameters. This will make the test environment more controllable and the results more

reliable and reproducible.

To recreate the input sequence of actions in Android, while running the real cloud based test,

all the user input interactions traces – such as user touch on screens, pushing buttons, etc –

can be captured in a file and then for the rerun, the file can be written to /dev/input/event*

at the time of re-test in the lab. Linux kernel can re-run the sequence by reading this

information from /dev/input/event*.

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Use scenario in device level power consumption testing

In a typical smartphone use, power measurements should be performed for the following use

scenarios as shown in Figure 11:

Voice call

Camera use

Email

Web browsing

Audio playback

Social networking use cases such as Facebook, Twitter

Video playback

Figure 11 - Usage scenarios to be considered for power measurement at device level

[23]


Power consumption measurement and user segments

The consumed power by a device, and consequently its battery life can largely depend on the usage behavior and user patterns. For example, the frequency of sending SMS and/or IM by a teenage user can prevent the power manger to transition the device into sleep mode, and thus draw more battery than another use case, e.g. constantly being on a voice call for which, power management can be leveraged. To do this, power measurement test cases need to be designed and executed for the right user segment(s) that the device is targeting. How to segment mobile users can depend on the particular application. A widely used user segmentation in the industry is one done by Experian Simmons. In this segmentation mobile users are divided into five segments:

Basic users (does not apply to smartphones)

Mobile professionals

Mobirati

Social connectors

Pragmatic adopters

This segmentation is illustrated in Figure 12.

Figure 2 – Mobile subscriber user segmentation

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Power measurement lab, tools and equipments To measure power consumption efficiently, few basic tools and equipments are needed to

make the measurements repeatable and accurate. As it is shown in Figure 13, these basic

tools and equipments include:

High sampling frequency Digital Multi Meter

Accurate and configurable power supply

Logging Ammeters and Volt meters

Current sensing resistors

Servers to imitate cloud services

Radio shielded box, or faraday cage

Darkroom, light intensity measurement and sound level measurement

equipments

Figure 13 - Some equipments used in power profiling and battery life testing

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To measure power at the aggregate point of battery, we need to get access to battery

terminals without disrupting the device’s functionality. There are intrusive ways of

soldering wires to device battery contact terminals, but a better way is to build a dummy

battery adapter.

A battery adapter is basically a battery made for the device, with lithium cells taken out

and instead, wires are soldered to be connected to an external power supply to supply

power to device while measuring consumed power. The battery adapter can then be

inserted in the device as a normal battery would. Figure 14 shows a battery adapter for

an HTC Android phone.

Figure 14 - A hand-made battery adapter to power an HTC Android device using extrnal

power supply

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Design considerations for Power Performance

Battery is a complex behaving element and at the same time it’s the life line of a mobile

device. Hardware and software designers need to know batteries and how they behave in

order to develop and implement strategies for conserving energy and prolong battery life in

hardware and software. The good news is that with little knowledge of behavior of battery as

an electrochemical machine, designers can maximize usage of the stored energy in the

battery and prolong its discharge life.

Battery life and battery recovery effect

Earlier we saw that Li-ion batteries have a non-linear discharge curve. The capacity of a

battery is measured in ampere-hour (Ah). For a battery of voltage (V), and the charge

capacity of (C), V x C is a measure of the energy stored in the battery.

In the case of a constant current I, the lifetime (L) of a battery with capacity C is calculated as L = C / I. This assumes a linear discharge curve. To account for non-linearity, a simple

approximation of the battery life L under constant load can be approximated by L = a / I b ,

where a > 0 and b > 1. Current I can also represent the average of variable current load, i(t).

This suggests that all load profiles with the same constant or average current load lead to the same battery life. However experimental results show that this is not the case, because of the recovery effect that was described earlier in the document.

For a variable load, i(t), the battery life time can be approximated by the following:

Where accounts the battery life gain due to the recovery effect. This shows that

device designers can potentially prolong the battery life by designing hardware and software

applications and their interactions in a way that allow for the battery to have rest periods, in

order to regain some of its lost capacity. This is particularly important in the case of running

several concurrent applications, and presents an opportunity for the power manager system

in the device to schedule application processes to leverage this property of the battery.

In practice, the needed rest periods to produce material energy gains are long, sometime in

the order of few minutes. This makes leveraging recovery effect infeasible in single battery

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devices and to allow for long enough recovery time, the device may need to be equipped

with multiple batteries or a multi-cell battery. An intelligent scheduler can swap batteries in

and out of duty to achieve the best possible recovery effect gain.

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Power efficient GUI user interface

A mobile device GUI user interface is where all the user interactions go through, and since it always fully utilizes the screen, it’s a major power consuming part of the device software. An intelligently designed GUI can make it more power efficient. With a few considerations when designing GUI elements for a mobile device, the software designer can make user interaction more efficient. And as a result of this user efficiency in completing a task, conserve energy. As noted above, GUIs are direct users of display, which is a very power hungry component in a mobile device, and any savings by making user interactions shorter and more efficient, can result to less use of display. GUI is generally designed to perform input tasks, output tasks and hybrid task (a combination of input and output tasks). There are several GUI parameters that can directly or indirectly make it more power efficient.

Cognitive speed: One way that a designer can make user interactions more efficient is by

reducing Cognitive Latency. Cognitive latency is the time that the user needs to understand

the number of GUI elements present on the screen (i.e. number of selections). If there are N

distinct and equally possible selections, then the reaction time required to make a choice is

given by the Hick-Hayman law as:

reaction time = a + b log2N

Where a and b are constants. A very effective way to reduce cognitive latency is decreasing

the number of options from which the user can make a selection. Split menu is a good

example of a GUI element with low cognitive latency.

Perceptual capacity: Better visibility of the GUI elements being presented to the user lowers

required user interaction time. Font type, font size, color, GUI component size and color and

optimal contrast ratio are all examples of ways to improve perceptual capacity.

Hot keys: Hot keys can shorten the time the user and the device need to interact to complete

a task. A very good example is the hard search button on Android devices which directly

takes the user to the browser and Google page. Hot keys can also be implemented as soft

touch buttons on the screen with similar efficiency gains.

User input cache: This method is widely used in desktop computers but still it is not much

utilized in mobile devices. In this method, user entered data are cached and can be used

again to avoid repetitive data entry. Web browser content placement, e.g. in personal data

entry forms is a good example. Adaptive auto completion in text entry mode is also another

example.

Direct GUI power reduction: There are other ways to make GUIs more power efficient. Some

of these methods directly reduce power consumption of the device. For example, using low

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energy color schemes can reduce energy consumption. In general in TFT-LCD displays, darker

color schemes consume less power; and power consumption in OLED based displays is

proportional to the number of on-pixels and their luminance.

Another method to reduce GUI power consumption is to minimize frequent screen changes

to reduce GPU’s (Graphical Processing Unit) activity and also to lower display’s power

consumption.

Making displays more power efficient

By understanding how an LCD display and its controller apparatus work, designers can

implement creative ways in their system level and application software to reduce display’s

power consumption. A color TFT-LCD display which is still the most common type in

smartphones is composed of the following key components, as it is illustrated in figure 14:

LCD panel

Frame buffer memory

LCD controller

Backlight inverter and luminance lamp

Figure 12 - TFT LCD system components

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There are a number of ways to make the LCD display system more power efficient. Two of these techniques are backlight control and frame buffer compression. Backlight control: A simple way to lower backlight energy consumption is to adapt the backlight’s luminance intensity according to level of the ambient light. This technique is widely used in most modern smartphones including most Android devices and is fairly simple to implement. Another more sophisticated method is to perform concurrent brightness and contrast scaling and adaptive image compensation to give the user the same perceived image contrast, with lower backlight intensity. Some experiments show that this method can lower the backlight’s power consumption 20-80% without compromising user experience. Frame buffer compression: The energy consumption of the frame buffer and its associated busses is directly proportional to the number of frame buffer accesses during the sweep operation. Frame buffer compression reduces the number of frame buffer accesses and consequently lowers the display’s power consumption.

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Intuigence Power profiling and battery life testing

service Service: Full Power profiling and battery life measurement testing for one mobile device for typical usage scenarios:

Building battery adaptor to connect to battery testing equipments

Analyzing and Identifying relevant usage scenarios

Developing power measurement test cases

Executing test cases and capturing test results

Statistical and post processing of the test results

Time: The turnaround time is typically two weeks from the arrival of the device in our lab How it works: You Fedex us the device, we perform the test and analysis and we send you the device back and walk you through the final report in a meeting or a conference call.

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ACKNOWLEDGEMENT

1. Many of the concepts related to power saving in smartphones, display power savings, concurrency issues in battery performance, and other topics in this publication are the conclusion of several discussions with Khosro Rabii of Qualcomm Corporations.

2. Some of the illustrations and some information in this publication are referenced with permission from “Batteries in a Portable Worlds” by Isidor Buchmann and www.batteryuniversity.com

OTHER REFERENCES

1. A Survey of Software Based Energy Saving Methodologies for Handheld Wireless Communication Devices, by Kshirasagar Naik.

2. An Analysis of Power Consumption in a Smartphone, by Aaron Caroll et al.

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Contact Information

Moe Tanabian Managing Partner [email protected]

US HQ Intuigence Group LLC 269 S. Beverly Dr., Suite 1127 Beverly Hills, CA 90212 United States Phone: +1 888 763 5171

[email protected]

www.intuigencegroup.com

Design and testing for better power consumption and battery life in smartphones

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