Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteries

Researchers have developed neural posterior estimation (NPE) to improve the scalability and accuracy of inverse parameter inference for Li-ion batteries, reducing computational time from minutes to milliseconds. This method enables real-time applications while maintaining or surpassing the accuracy of traditional Bayesian calibration, though it may introduce higher voltage prediction errors in some cases. Developers can now explore this approach using an open-source implementation available on GitHub.

Read the full article at arXiv cs.LG (ML)

---

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.