Physics Informed ML to Reduce Accelerated Life Testing for Li-Ion Batteries by 75%

Estimating End of Life (EoL) for a battery is often a complex, expensive, and time-intensive process. Traditional, accelerated life testing (ALT) methods rely heavily on cycler testing, which can take months to run and demand significant resources. Even then, these methods often miss the real-world degradation patterns that impact battery performance over time.

Discover how our physics informed machine learning models provides faster and reliable insights, helping you predict battery life. Whether you’re a battery engineer, data scientist, or product manager, learn firsthand how to leverage these models to reduce costs, ensure battery reliability, and make data-driven decisions with confidence.

About oorja: oorja is a deep-tech startup revolutionizing battery modeling with cutting-edge physics-based models combined with machine learning. By focusing on simplifying the complexities of battery engineering, oorja empowers users around the world to innovate faster and more efficiently.