Webcast this June 18th: Optimizing Battery Run and Charge Times of Today’s Mobile Wireless Devices

One thing for certain: Technological progress does not stand still for a moment and there is no place where this is any truer than for mobile wireless devices! Smart phones, tablets, and phablets have all but totally replaced yesterday’s mobile phones and other personal portable devices. They provide virtually unlimited information, connectivity, assistance, and all kinds of other capabilities anywhere and at any time.

However, as a consequence of all these greater capabilities and time spent being actively used is battery run time limitations. Battery run time is one of top dissatifiers of mobile device users. To help offset this manufacturers are incorporating considerably larger capacity batteries to get users through their day. I touched upon this several weeks ago with my earlier posting “Two New Keysight Source Measure Units (SMUs) for Battery Powered Device and Functional Test”. We developed higher power versions of our N678xA series SMUs in support of testing and development of these higher power mobile devices.

Ironically, a consequence of higher capacity batteries leads to worsening of another top user dissatifier, and that is battery charging time. Again, technological progress does not stand still! New specifications define higher power delivery over USB, which can be used to charge these mobile devices in less time. I also touched upon this just a few weeks ago with my posting “Updates to USB provide higher power and faster charging”. The power available over USB will no longer be the limiting factor on how long it takes to recharge a mobile device.

I have been doing a good amount of investigative work on these fronts which has lead me to put together a webcast “Optimizing Battery Run and Charge Times of Today’s Mobile Wireless Devices”. Here I will go into details about operation of these mobile devices during use and charging, and subsequent testing to validate and optimize their performance.  If you do development work on mobile devices, or even have a high level of curiosity, you may want to attend my webinar on June 18. Additional details about the webcast and registration are available at: “Click here for accessing webcast registration”. I hope you can make it!

Optimizing Mobile Device Battery Run-time Seminars

On many occasions in the past here both I, and my colleague, Gary, have written about measuring, evaluating, and optimizing battery life of mobile wireless battery powered devices. There is no question that, as all kinds of new and innovative capabilities and devices are introduced; battery life continues to become an even greater challenge.

I recently gave a two-part webcast entitled “Optimize Wireless Device Battery Run-time”. In the first part “Innovative Measurements for Greater Insights” a variety of measurement techniques are employed on a number of different wireless devices to illustrate the nature of how these devices operate and draw power from their batteries over time, and in turn how to go about making and analyzing the measurements to improve the device’s battery run-time. Some key points brought out in this first part include:

• Mobile devices operate in short bursts of activities to conserve power. The resulting current drain is pulsed, spanning a wide dynamic range. This can be challenging for a lot of traditional equipment to accurately measure.
• Not only is a high level of dynamic range of measurement needed for amplitude, but it is also needed on the time axis as well, for gaining deeper insights on optimizing a device’s battery run-time.
• Over long periods of time a wireless device’s activity tends to be random in nature. Displaying and analyzing long term current drain in distribution plots can quickly and concisely display and quantify currents relating to specific activities and sub-circuits that would otherwise be difficult to directly observe in a data log.
• The battery’s characteristics influence the current and power drawn by the device. When powering the device by other than its battery, it can be a significant source of error in testing if it does not provide results like that of when using the battery.

Going beyond evaluating and optimizing the way the device makes efficient use of its battery power, the second part, “The Battery, its End Use, and Its Management” brings out the importance of, and how to go about making certain you are getting the most of the limited amount of battery power you have available to you. Some key points for this second part include:

• Validating the battery’s stated capacity is a crucial first step both for being certain you are getting what is expected from the battery and serve as a starting reference point that you can correlate back to the manufacturer’s data.
• Evaluating the battery under actual end-use conditions is important as the dynamic loading a wireless device places on the battery often adversely affects the capacity obtained from the battery.
• Charging, for rechargeable batteries, must be carefully performed under stated conditions in order to be certain of in turn getting the correct amount of capacity back out of the battery. Even very small differences in charging conditions can lead to significant differences in charge delivered during the discharge of the battery.
• The wireless device’s battery management system (or BMS) needs to be validated for proper charging of the battery as well as suitability for addressing the particular performance needs of the device.

In Figure 1 the actual charging regiment was captured on a mobile phone battery being charged by its BMS. There turned out to be a number of notable differences in comparison to when the battery was charged using a standard charging regiment.

Figure 1: Validating BMS charge regiment on a GSM/GPRS mobile phone

If you are interested in learning more about optimizing wireless device battery run-time this two part seminar is now available on-demand at:

Optimize Wireless Device Battery Run-time: Two Part Webcast Series

I think you will enjoy them!

Establishing Measurement Integration Time for Leakage Currents

The proliferation of mobile wireless devices drives a corresponding demand for components going into these devices. A key attribute of these components is the need to have low levels of leakage current during off and standby mode operation, to extend the battery run-time of the host device. I brought up the importance of making accurate leakage currents quickly in an earlier posting “Pay Attention to the Impact of the Bypass Capacitor on Leakage Current Value and Test Time”(click here to review). Another key aspect about making accurate leakage currents quickly is establishing the proper minimum required measurement integration time. I will go into factors that govern establishing this time here.

Assuming the leakage current being drawn by the DUT, as well as any bypass capacitors on the fixture, have fully stabilized, the key thing with selecting the correct measurement integration time is getting an acceptable level of measurement repeatability. Some experimentation is useful in determining the minimum required amount of time. The primary problem with leakage current measurement is one of AC noise sources present in the test set up. With DC leakage current being just a few micro amps or less these noises are significant. Higher level currents can be usually measured much more quickly as the AC noises are relatively negligible in comparison. There are a variety of potential noise sources, including radiated and conducted from external sources, including the AC line, and internal noise sources, such as the AC ripple voltage from the DC source’s output. This is illustrated in Figure 1 below. Noise currents directly add to the DC leakage current while noise voltages become corresponding noise currents related by the DUT and test fixture load impedance.

Figure 1: Some noise sources affecting DUT current measurement time

Using a longer measurement time integrates out the peak-to-peak random deviations in the DC leakage current to provide a consistently more repeatable DC measurement result, but at the expense of increasing overall device test time. Measurement repeatability should be based on a statistical confidence level, which I will do into more detail further on. Using a measurement integration time of exactly one power line cycle (1 PLC) of 20 milliseconds (for 50 Hz) or 16.7 milliseconds (for 60 Hz) cancels out AC line frequency noises. Many times a default time of 100 milliseconds is used as it is an integer multiple of both 20 and 16.7 milliseconds. This is fine if overall DUT test time is relatively long but generally not acceptable when total test time is just a couple of seconds, as is the case with most components. As a minimum, setting the measurement integration time to 1 PLC is usually the prudent thing to do when short overall DUT test time is paramount.

Reducing leakage current test time below 1 PLC means reducing any AC line frequency noises to a sufficiently low level such that they are relatively negligible compared to higher frequency noises, like possibly the DC source’s wideband output ripple noise voltage and current. Proper grounding, shielding, and cancellation techniques can greatly reduce noise pickup. Paying attention to the choice and size of bypass capacitors used on the test fixture is also important. A larger-than-necessary bypass capacitor can increase measured noise current when the measuring is taking place before the capacitor, which is many times the case. Establishing the requirement minimum integration time is done by setting a setting an acceptable statistical confidence level and then running a trial with a large number of measurements plotted in a histogram to assure that they fall within this confidence level for a given measurement integration time. If they did not then the measurement integration time would need to be increased. As an example I ran a series of trials to determine what the acceptable minimum required integration time was for achieving 10% repeatability with 95% confidence for a 2 micro amp leakage current. AC line noises were relatively negligible. As shown in Figure 2, when a large series of measurements were taken and plotted in a histogram, 95% of the values fell within +/- 9.5% of the mean for a measurement integration time of 1.06 milliseconds.

Figure 2: 2 Leakage current measurement repeatability histogram example

Leakage current measurements by nature take longer to measure due to their extremely low levels. Careful attention to minimizing noise and establishing the minimum required measurement integration time contributes toward improving the test throughput of components that take just seconds to test.

Using Current Drain Measurements to Optimize Battery Run-time of Mobile Devices

One power-related application area I do a great deal of work on is current drain measurements and analysis for optimizing the battery run-time of mobile devices. In the past the most of the focus has been primarily mobile phones. Currently 3G, 4G and many other wireless technologies like ZigBee continue to make major inroads, spurring a plethora of new smart phones, wireless appliances, and all kinds of ubiquitous wireless sensors and devices. Regardless of whether the device is overly power-hungry due to running data-intensive applications or power-constrained due to its ubiquitous nature, there is a need to optimize its thirst for power in order to get the most run-time from its battery. The right kind of measurements and analysis on the device’s current drain can yield a lot of insight on the device’s operation and efficiency of its activities that are useful for the designer in optimizing its battery run-time. I recently completed an article that appeared in Test & Measurement World, on-line back in November and then in print in their Dec 2011- Jan 2012 issue. Here is a link to the article:
http://www.tmworld.com/article/520045-Measurements_optimize_battery_run_time.php

A key factor in getting current drain measurements to yield the deeper insights that really help optimize battery run-time is the dynamic range of measurement, both in amplitude and in time, and then having the ability to analyze the details of these measurements. The need for a great dynamic range of measurement stems from the power-savings nature of today’s wireless battery powered devices. For power-savings it is much more efficient for the device to operate in short bursts of activities, getting as much done as possible in the shortest period of time, and then go into a low power idle or sleep state for an extended period of time between these bursts of activities. Of course the challenge for the designer to get his device to quickly wake up, stabilize, do its thing, and then just as quickly go back to sleep again is no small feat! As one example the current drain of a wireless temperature transmitter for its power-savings type of operation is shown in Figure 1.


Figure 1: Wireless temperature transmitter power-savings current drain

The resulting current drain is pulsed. The amplitude scale has been increased to 20 µA/div to show details of the signal’s base. This particular device’s current drain has the following characteristics:
• Period of ~4 seconds
• Duty cycle of 0.17%
• Currents of 21.8 mA peak and 53.7 µA average for a crest factor of ~400
• Sleep current of 7 µA
This extremely wide dynamic range of amplitude is challenging to measure as it spans about 3 ½ decades. Both DC offset error and noise floors of the measurement equipment must be extremely low as to not limit needed accuracy and obscure details.

Likewise being able to examine details of the current drain during the bursts of activities provides insights about the duration and current drain level of specific operations within the burst. From this you can make determinations about efficiencies of the operations and if there is opportunity to further optimize them. As an example, in standby operation a mobile phone receives in short bursts about every 0.25 to 1 seconds to check for incoming pages and drops back into a sleep state in between the receive (RX) bursts. An expanded view of one of the RX current drain bursts is shown in figure 2.


Figure 2: GPRS mobile phone RX burst details

There are a number of activities taking place during the RX burst. Having sufficient measurement bandwidth and sampling time resolution down to 10’s of µsec provides the deeper insight needed for optimizing these activities. The basic time period for the mobile phone standby operation is on the order of a second but it is usually important to look at the current drain signal over an extended period of time due to variance of activities that can occur during each of the RX bursts. Having either a very deep memory, or even better, high speed data logging, provides the dynamic range in time to get 10’s of µsec of resolution over an extended period of time, so that you can determine overall average current drain while also being able to “count the coulombs” it takes for individual, minute operations, and optimize their efficiencies.

Anticipate seeing more here in future posts about mobile wireless battery-powered devices, as it relates to the “DC” end of the spectrum. In the meantime, while you are using your smart phone or tablet and battery life isn’t quite meeting your expectation (or maybe it is!), you should also marvel at how capable and compact your device is and how far it has come along in contrast to what was the state-of-the-art 5 and 10 years ago!