One battery, five decisions, five models.
A battery's value and risk come down to a handful of decisions: is this cell safe, when should the car charge, where should the next charger go, does this pack deserve a second life, and can we trust its history? Each project below takes on one of them with an open, working ML prototype. Together they form a small network that shares data along the lifecycle.
Beyond batteries
Standalone projects in other fields, each with its own stack.
How these are built
Every model is compared with what people would do without ML: fixed thresholds, persistence forecasts, "test everything", "charge on plug-in".
Feature attributions, Shapley values and uncertainty bands are shown next to every prediction, so a decision can be challenged.
The data comes from physics-inspired simulators. Each page says what's simulated and what real data would be needed to validate it.
Plain Node.js with zero dependencies. The ML toolkit (random forests, isolation forests, ridge, k-means, kNN, dynamic programming) is written from scratch and runs on the server and in the browser. Printable QR cards →