Sunday, December 3, 2023

Utilizing Data For Better Battery Systems

By Yashasvini Razdan

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iRasus Technologies is leveraging the power of data analytics to identify the barriers that sedate the growth and adoption of electric batteries and develop a single platform interface with tools to integrate standards and protocols into one seamless experience for the consumer

Lithium-ion batteries have transformed the energy space, but even as the nascent sector struggles to secure its footing in a world awakening to the effects of carbon emissions, operational challenges continue to constrict its growth.

In the midst of this, Gurugram-based battery analytics startup iRasus Technologies is effectively wielding the power of data analytics to reduce risk, improve efficiency, and increase trust in batteries.

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Founded in 2018 by Arjun Sinha Roy and Anirudh Ramesh, iRasus has designed its flagship platform named Preksha, which leverages the efficiency in visualization, analysis, and predictive models of energy data in the cleantech domain for the mobility and stationary battery storage segments.

irasus Technologies’ flagship product, Preksha

The data generated from the sensors inside the battery is collected through an Internet-of-Things layer and an analytics layer and is fed back into these business processes. “The sensors present inside of the battery are either a part of the BMS or a part of the telematics unit, that determine the completeness and accuracy of the data. The measurements are captured from the sensors periodically and there is no large time duration when no data is reported. That is taken care of by the telematics unit,” says Ramesh.

Further elaborating on the method of data processing, Ramesh explains, “The system comes with the ability to collect these measurements from the sensors and then send them over a telecom link. A 2G/3G/4G transceiver is present inside of the telematics hardware, which acts as a gateway to dispatch both electrical as well as geographical data to the backend. Since sensor data is inherently noisy, it’s filtered so that it can be utilized in a format that is useful for analytics.”


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