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Electrochemistry & Modelling Technology
Physics-informed battery modelling, turning the best of academia into robust state estimation. We select the most important elements of physics-based models for interpretability at the electrode level, but with a grounded pragmatism that ensures observability from real-world data. We understand the cell as a system of systems, allowing us to elegantly break-down degradation into multiple dimensions for lifetime insight, without getting bogged down in micro-scale electrochemistry.
Al & Data Science
Fusing machine learning with deep battery domain understanding
We believe in the fusion of probabilistic machine learning with a deep understanding of the battery domain. Our virtual sensors are derived from fundamental electrochemical principles for best-in-class life forecasting that minimises reliance on extensive training data.
Battery Systems Development Experience
Designed for the rigours of real-world data
Having worked on battery systems for the last decade, deployed into the toughest of environments, we understand the limitations of lab-based data when applied to real-world problems. Our Embedded algorithms are designed to run on standard automotive-grade hardware platforms, and our Cloud Platform features prognostics designed to detect real-world failure mechanisms at module and pack-level.