Longevity of battery-driven IoT devices is crucial especially for the users of these devices. Just put yourself in their shoes. Recharging a wearable device, whether it is a hearing-aid, fitness tracker, or a gadget is annoying.
Or even worse, a tracking device for valuable or sensitive goods which stops working during transport because the battery is drained is more than annoying. It is the worst case scenario.
So what can you do as a company, project leader, or design engineer to get the most out of batteries and have happy customers?
Correlate Current and Activity
Certainly, you put a lot of effort into selecting the most suitable components with sleep mode capabilities, choosing the best battery available and wrote a firmware which makes use of all the fancy capabilities of your components.
Nonetheless, you can do more. Measuring the current consumption of IoT devices with a high temporal resolution and correlate it to the different working modes such as sleep mode, active mode, RF transmission, etc.
It is pretty obvious that such an analysis of current consumption and IoT device activity provides a lot of insights:
- Which status consumes most of the electricity?
- What time is required for each status?
- How long will batteries last?
Such kind of insights would tremendously help to unveil potential for optimizing current consumption and extending the lifetime of batteries.
However, obtaining this kind of correlated data with traditional test and measurement is far from trivial, error-prone, requires a lot of time to set up and costly.
Certainly, you do not want to spend your scarce resources on the endeavour of developing such a homemade measurement system.
IoT Device Battery Life Optimization Solution
To enable IoT device developers, to gain insights into the correlation between current consumption and device activity quickly, Keysight Technologies developed the IoT Battery Life Optimization Solution.
The solution synchronizes RF and DC events with current consumption allowing to pinpoint critical events consuming the most current.
An automated in-depth analysis of the acquired current consumption and RF activity calculates activity occupancy time and its current consumption contribution. On top, the solution provides a battery life estimation.
