IoT asset trackers have become an indispensable part for many industries. However, tracking assets can become costly if a tracker runs out of battery and stops relaying data. Another challenge is replacing depleted batteries, particularly for large industries which need to allocate time and resources on maintaining thousands of trackers. Besides operational nuisance, this process is detrimental to the environment, especially considering that it can be avoided.
The number of IoT devices worldwide is growing rapidly; and so is the volume of batteries needed to facilitate this growth. With an expected 100 billion wasted AA-batteries in 2050, this is roughly equivalent to 300 Olympic size swimming pools. Evidently, there is a growing environmental problem, one that IoT companies need to take responsibility for. But how do we deal with such an immense amount of toxic waste? The solution begins with creating low power devices that make more efficient use of the energy they have at their disposal. Then, as a result of the lower power requirements, it can function with the voltage generated by attaching a solar panel and harvesting (more) sustainable energy sources, such as sun or indoor lights.
Recommended ITech News: Cyberhaven Unveils Full Context Blocking to Transform Stagnant and Ineffective DLP Market
However, simply integrating a solar panel is not enough to build an effective solar-powered tracker. Typically, solar panels need to generate a certain level of power for the energy to be stored. Therefore, selecting an appropriately sized solar panel and matching it with highly versatile configurable Power Management Integrated Circuit PMIC is essential for harvesting enough energy for the device function.
The solution
Dealing with the plethora of challenges associated, solar-powered asset tracking requires a multi-faceted approach:
- Firstly, the device needs to be low power, meaning that its components need to use minimal power, while the embedded software manages onboard processes to only use power when necessary.
- Secondly, the device needs to harvest solar energy as efficiently as possible, even in conditions where there is little sun or indoor light.
- Finally, the device needs to store the energy it accumulates to operate when light isn’t available.
IoT Industry leader SODAQ decided to take on this challenge. SODAQ’s expertise in low power asset tracking and e-peas’ energy harvesting knowledge enabled the creation of the TRACK Solar: a small, solar-powered tracker designed for a variety of outdoor asset tracking use cases.
Recommended ITech News: WealthTech Company Advisor360°® Demonstrates Its Commitment To Clients’ Data Security In Successful Completion Of SOC2 And HIPAA Audits
SODAQ TRACK Solar
Utilizing the e-peas’ AEM10941, TRACK Solar is capable of operating on a 6.9 x 6.9 cm Monocrystalline Solar Panel. Harvested solar energy is stored in a 2400mAh lithium-ion battery, which enables the tracker to operate for up to a month without sunlight. The AEM10941 module plays an important role as it allows the TRACK SOLAR to harvest solar energy at very low voltages, resulting in a smaller solar panel and thus a smaller form factor. Additionally, the e-peas chip helps reduce the depth-of-discharge for the battery, leading to increased battery life and overall product longevity. TRACK Solar is designed to last for an estimated five years in the field, but it is likely to last much longer depending on the environmental conditions it is exposed to over its lifetime.
The result of the merging expertise of the two companies makes TRACK Solar capable of monitoring the location of non-powered objects for years on end, without the need for battery replacement. In addition to the hardware, TRACK users gain access to a cloud platform environment enabling them to view the location of assets, configure devices and administer over-the-air updates. Device configurations can be managed from SODAQ’s dashboard environment, whereby users can tailor the tracker to their use case by controlling motion triggered messages and the frequency at which messages are sent.
Recommended ITech News: Connecticut Becomes Third State to Incentivize Cybersecurity Best Practices for Businesses
Where can it be used? Examples of existing use cases
TRACK Solar is ideal for any outdoor asset tracking use case where sunlight is available. Objects that can be tracked include equipment such as compressors, welding machines, generators and other equipment one might find on a construction site. Integrating SODAQ TRACK not only enables construction workers to find equipment faster, but allows operators to monitor the running hours of their equipment and identify opportunities to optimize workflow.
Airports are another operator that can benefit from this technology, particularly to alleviate the difficulties of monitoring ground support equipment. Integrating the tracker with dollies, tugs and refuelers allows airport operators to save time when trying to find these valuable assets on-site. Using pre-existing geofences, operators can keep overview of equipment and prevent assets from leaving their designated working zones.
“With the TRACK Solar we are already saving the equivalent of more than 50 non-rechargeable batteries per device over a 5 year lifespan, but what is even more exciting is that we are able to go batteryless by using e-peas AEM10941 and a supercapacitor that holds the charge from the solar panel in the moments where it isn’t catching sunlight. This means we will go completely batteryless, having an unlimited number of charge cycles and therefore a much more sustainable device.” – Ollie Smeenk, CEO SODAQ
Recommended ITech News: Disruptive Platform Special Security Services Launches Intuitive Security Scheduler
5 benefits of using harvested energy in asset tracking:
–Â No battery changes for an estimated period of more than 5 years
–Â Reduced maintenance costs
–Â Reduced negative environmental impact
–Â Increased product lifetime
–Â Ideal for uses in environments with sunlight or indoor light
Recommended ITech News: Algolux Closes $18.4 Million Series B Round for Robust Computer Vision