Date: September 18, 2025 (Thursday)
Time: 15:00 -16:30, Finnish Time (GMT+3)
Venue: Offline meeting room – Room 1023 AS4, Maarintie 8, Otaniemi, Espoo
Online via Zoom: https://aalto.zoom.us/j/69405853618
We are pleased to invite you to an exciting seminar on the topic of “Machine Learning-Assisted Lithium-ion Battery Diagnostics and Prognostics for Sustainable Remanufacturing, Reusing and Recycling.”
Lithium-ion batteries are central to the clean energy transition, yet their rapid degradation and uncertain lifetimes pose significant challenges for large-scale reuse and recycling. Traditional diagnostic methods often fall short in providing accurate, fast, and sustainable solutions.
On September 18, 2025, Dr. Shengyu Tao (Tsinghua University) will present cutting-edge research on how advanced machine learning methods enable accurate diagnostics and prognostics for lithium-ion batteries. The talk will highlight strategies for early trajectory prediction, state-of-health estimation, rapid diagnosis for recycling pretreatment, and collaborative sorting with federated learning.
This presentation builds on Dr. Tao’s influential publications:
[1] TAO S, ZHANG M, ZHAO Z, et al. Non-destructive degradation pattern decoupling for early battery trajectory prediction via physics-informed learning [J]. Energy & Environmental Science, 2025, 18(3): 1544-59.
[2] TAO S, MA R, ZHAO Z, et al. Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions [J]. Nature Communications, 2024, 15(1): 10154.
[3] TAO S, MA R, CHEN Y, et al. Rapid and sustainable battery health diagnosis for recycling pretreatment using fast pulse test and random forest machine learning [J]. Journal of Power Sources, 2024, 597: 234156.
[4] TAO S, LIU H, SUN C, et al. Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning [J]. Nature Communications, 2023, 14(1): 8032.
[5] TAO S, SUN C, FU S, et al. Battery Cross-Operation-Condition Lifetime Prediction via Interpretable Feature Engineering Assisted Adaptive Machine Learning [J]. ACS Energy Letters, 2023, 8(8): 3269-79.
[6] TAO S, GUO R, Lee J, et al. Immediate remaining capacity estimation of heterogeneous second-life lithium-ion batteries via deep generative transfer learning [J]. Energy & Environmental Science, 2025,18, 7413-7426.
The seminar will be highly relevant for professionals and researchers in energy storage, recycling, machine learning, and sustainable energy systems.
Let’s connect to explore intelligent pathways toward a circular economy for batteries and a sustainable energy future.