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  • Power Management

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    Tim Kannegieter


    The main focus of power management in IoT is how to design a low power field device. In the past, the main components of any internet connected device were all power hungry, making operation by battery not practical. These include the microcontroller, sensors, connectivity interfaces and actuation. However, major advances in technology have reached a tipping point where devices operating on batteries for long periods of time (measured in years) is now feasible.

    This has been accompanied by a corresponding miniaturisation. The electronics in IoT devices can now be dwarfed by the size of the battery.

    Power management is important because it determines how long a battery powered device can last in a field, and often how expensive it might be. The embedded electronics might cost less than a dollar but the battery and subsections involved in the powering device, could be $30. Thus power is becoming a big limiting factor for how low we can go in terms of cost, size and longevity. Simply, it is more challenging to store energy than it is to pack transistors onto something, so the less power a device needs the less needs to be expended on storage systems.

    Key engineering challenges:

    A first step in power management is technology selection, in terms of the major components of the device (e.g.sensor, wireless interface, microcontroller, antenna etc). This can involve searching for and finding low power versions of each component which will still deliver the functionality required.

    The second step is to ensure that the system is designed intelligently, to minimise power consumption. For example, the system may be designed to take a reading daily or every few seconds. Some design have systems in active mode, taking measurements etc, far more often than necessary. The design should ensure that the devices electronics are sleeping for as much time as possible while still achieving the performance requirements. It can be woken to take the measurement and then put back to sleep.


    The total energy consumed by a device relates to how frequently a device is in active mode. Diagram curtesy of Simon Blyth, LX Group.

    The above diagram represents how power is being consumed when particular subsections are on. Some sections will be much power hungrier than others. For example, transmitting on a LoRA or Sigfox link will take much more energy going to be a much larger lack of energy than reading a small temperature sensor or accelerometer.

    In addition to the height of the power spikes above (amount of power drawn) there is also the width, or the amount of time that the subsection is on. With LPWAN technologies you can often select a bit rate, or how fast you actually communicate. Choosing a lower bit rate may often get you longer range in terms of RF performance. However, it comes at the cost of transmitting for longer. You are consuming more power because that impulse is longer.

    Another factor that, at a hardware layer, needs to be managed is drainage in sleep mode. Most components on an IOT device will have a lower power mode, which have a very low sleep current of just a few microamps. It is standard practice to design your system to put them into sleep mode when they are not being used. However, individually they all add up and can become a drain on power, particularly when the device has a lot of sensors taking different measurements.

    One technique to address this is to have real time clock, often within the processor, controlling a power rail. This can be used to completely disconnect all of the components from the battery. The only component drawing power then is the clock or the processor containing it, which periodically wakes up the rest of the system as required.

    One exception to this rule is when you can make use of a sensor to intelligently minimise the number of times you need to power the system up. Using rules which determine under what conditions it is worthwhile sending information, a sensor can be used to detect that condition and wake the system up. In this context, it may be worth keeping the sensor power up all the time.

    For example, in tracking applications an accelerometer can be used to determine if the item is actually moving or not. When moving the frequency of reporting may be every few minutes or hours. However, if it then it goes on a shelf and the sensor can tell it is not moving, the system can reduce the reporting to once or day or only to resume when it starts moving again.

    This is just one example of intelligent design of IoT devices to minimise power consumption and cost. Each application is unique and requires it own strategy to optimise the system.

    Another factor that needs to be considered in power management is whether to use high quality primary batteries (or mains) as the source of power or to make use of local sources of energy, including sun, heat, movement and ambient RF. This latter approach is known as energy harvesting and may be appropriate if the environment can provide enough energy and the IoT application can be designed with low enough power requirements.

    However, it should be borne in mind that energy scavenging systems actually have less life in the field than if you go with a primary source. This is because rechargeable batteries (e.g. lithium polymer, LIFE, sealed lead acid battery) reach the end of their life far quicker (often a few years) than high quality primary batteries (such as lithium manganese) that can be designed to last for 10 to 15 years.

    Calculating current draw

    To determine the capacity of the battery or requirements of the energy harvesting system, it is important to calculate accurately the expected current draw for a particular hardware design. This is important because it help’s determine how long a particular power cell can power a system.

    Designers need to calculate this using information from the component data sheets. This needs to be adjusted to what mode the device is operating that device in. This is done for each of the use cases for each subsection. Where the data is not known or clear, the worst case scenario is used. One important use case is when all of the subsystems are lit up simultaneously, which can be used to calculate the maximum the instantaneous current required.

    It is also important to take into account thermal effects. E.g. a battery data sheet might say it has 890 million amp hours. The different modes it is operated in and the environmental conditions will impact the actual effective capacity of the battery.

    A safety margin is then normally added and this will depend on the application and the impact of a battery going flat.

    This approach can be used to determine the overall batter capacity required to power a system and the predict its life expectancy.

    A good link on calculating IoT battery life is here.

    Sources: Material on this page has primarily been sourced from the following:


    Edited by Tim Kannegieter


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