AI for Energy Storage

Artificial intelligence is emerging as a key enabler of battery digital twins and next-generation battery management systems. This presentation highlights recent advances in AI-assisted battery diagnostics and prognostics, including non-invasive core temperature estimation, physics-guided degradation prediction, and AI-based fault detection. By combining electrochemical sensing, thermal analysis, and machine learning, these approaches provide improved visibility into battery health, safety, and remaining life, enabling safer, more reliable, and longer-lasting energy storage systems for transportation and grid applications.?

Lithium-ion battery thermal runaway poses significant safety risks in electric vehicles, energy storage systems, and consumer electronics. This presentation introduces an acoustic-based early warning framework for detecting thermal runaway initiation using deep learning. The approach identifies precursor signatures that emerge prior to catastrophic failure, enabling earlier detection and intervention compared with conventional monitoring methods. Results demonstrate robust detection performance across multiple battery conditions, highlighting the potential of acoustic sensing as a non-invasive and scalable solution for future battery safety monitoring and hazard mitigation.

While deep learning achieves state-of-the-art battery state-of-health estimation, the emerging EU Digital Battery Passport introduces a critical constraint: model auditability. Black-box predictions, however accurate, cannot satisfy the transparency and traceability requirements that regulators, OEMs, and recyclers will demand. This presentation proposes a counterfactual explainability framework that maps model outputs onto interpretable, condition-level degradation drivers—identifying precisely which operating scenarios accelerate or preserve battery health. Structured for DBP compliance, these explanations deliver actionable decision support across warranty assessment, second-life qualification, and end-of-life routing, closing the gap between predictive performance and regulatory-grade transparency.