Using machine learning, we're helping energy storage companies test their devices in days rather than months.
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.
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.
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.
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.
We're essentially putting full-blown labs on laptops. I can't think of anything I'd rather be working on.
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.