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Design and testing for better power consumption and battery life in smartphones
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A guide on power objective measurement and battery life testing in smartphones
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Design and testing for better power consumption and battery life in smartphones
1.
[1]
WHITE PAPER Mobile Device / Application Power Profiling Testing and Design considerations in mobile Devices & Apps for power performance and battery life © 2011 Intuigence Group –internal use only
2.
[2]
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 © 2011 Intuigence Group –internal use only
3.
[3] 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 © 2011 Intuigence Group –internal use only
4.
[4] 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 © 2011 Intuigence Group –internal use only
5.
[5] 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. © 2011 Intuigence Group –internal use only
6.
[6]
NiCd NiMH Li-ion Li-ion polymer Gravimetric Energy 45-80 60-120 110-160 100-130 Density(Wh/kg) Internal Resistance 100 to 2001 200 to 3001 150 to 2501 200 to 3001 (includes peripheral 6V pack 6V pack 7.2V pack 7.2V pack circuits) in m Ω Cycle Life (to 80% of initial 15002 300 to 5002,3 500 to 10003 300 to 500 capacity) Fast Charge Time 1h typical 2-4h 2-4h 2-4h Overcharge Tolerance moderate low very low low Self-discharge / 20%4 30%4 10%5 ~10%5 Month (room tem perature) Cell Voltage (nom inal) 1.25V 6 1.25V 6 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 Operating -40 to -20 to -20 to 0 to Temperature (discharge only) 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 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. © 2011 Intuigence Group –internal use only
7.
[7] 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. © 2011 Intuigence Group –internal use only
8.
[8]
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 © 2011 Intuigence Group –internal use only
9.
[9] 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 © 2011 Intuigence Group –internal use only
10.
[10] 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. © 2011 Intuigence Group –internal use only
11.
[11] 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 © 2011 Intuigence Group –internal use only
12.
[12] 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. © 2011 Intuigence Group –internal use only
13.
[13] 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=txfxn 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 © 2011 Intuigence Group –internal use only
14.
[14]
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. © 2011 Intuigence Group –internal use only
15.
[15]
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 © 2011 Intuigence Group –internal use only
16.
[16] 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 © 2011 Intuigence Group –internal use only
17.
[17] 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. © 2011 Intuigence Group –internal use only
18.
[18] 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 © 2011 Intuigence Group –internal use only
19.
[19] 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. Figure 9 – Sound meter 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. © 2011 Intuigence Group –internal use only
20.
[20] 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. © 2011 Intuigence Group –internal use only
21.
[21] 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*. © 2011 Intuigence Group –internal use only
22.
[22] 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 © 2011 Intuigence Group –internal use only
23.
[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 © 2011 Intuigence Group –internal use only
24.
[24] 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 © 2011 Intuigence Group –internal use only
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[25]
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 © 2011 Intuigence Group –internal use only
26.
[26] 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 b approximation of the battery life L under constant load can be approximated by L = a / I , 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 © 2011 Intuigence Group –internal use only
27.
[27] 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. © 2011 Intuigence Group –internal use only
28.
[28] 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 © 2011 Intuigence Group –internal use only
29.
[29] 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 © 2011 Intuigence Group –internal use only
30.
[30] 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. © 2011 Intuigence Group –internal use only
31.
[31] 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. © 2011 Intuigence Group –internal use only
32.
[32] 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. © 2011 Intuigence Group –internal use only
33.
[33] Contact Information
Moe Tanabian Managing Partner clients@intuigencegroup.com US HQ Intuigence Group LLC 269 S. Beverly Dr., Suite 1127 Beverly Hills, CA 90212 United States Phone: +1 888 763 5171 info@intuigencegroup.com www.intuigencegroup.com © 2011 Intuigence Group –internal use only
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