Energy Storage Testing with ML


Using machine learning, we're helping energy storage companies test their devices in days rather than months.

How it's done

Our Process

With machine learning models, we're able to predict the ageing of supercapacitors based off of factors like voltage, temperature and current. With just a fraction of data routinely collected by manufacturers, we can cut down testing times by 96% — from 3 months to 3.5 days.

Model #1: Neural Networks

Given a capacitor's duty cycle and equivalent series resistance, our architecture is able to make predictions for cycle life. These two pieces of data are fed through a three-layer artificial neural network and an estimate for cycle life is calculated, allowing us to more easily forecast how a supercapacitor will age.

Model #2: Bayesian NP Time-Series

Bayesian models can use both past and real-time data to make predictions. Our model is non-parametric — it can be fed an infinite amount of parameters and determines the relationships between them against time. By inputting ranges of voltage, temperature and current, we're able to successfully predict capacitance and ESR over time.

What Drives Us

  • If we’re producing more supercapacitors, we can help widely deploy this as an energy storage system around the world. This is with the hopes that we can create a renewable future with cheap and scalable energy storage systems.

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    Alishba Imran Co-Founder, XVolta
  • Energy is what separates man from beast — I believe everyone on the planet deserves access. By helping scale next-gen energy storage, we're getting one step closer to achieving that dream.

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    Aadil Ali Co-Founder, XVolta
  • We're essentially putting full-blown labs on laptops. I can't think of anything I'd rather be working on.

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    Samarth Athreya Co-Founder, XVolta
  • By accelerating the prediction of ageing in their devices, I'm confident that we can help supercapacitor companies produce 26x more and go to market at least 4 months faster.

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    Shagun Maheshwari Co-Founder, XVolta
Contact Information
Where to Find Us

Toronto, ON

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