The role of machine learning enabled diagnostics in a circular battery economy
Other text in scientific journal, 2026
Machine learning-enabled battery diagnostics transform scarce and heterogeneous field battery data into reliable state indicators, enabling informed decision-making across reuse, recycling, and remanufacturing stages. By linking safety, economic value, and environmental performance, diagnostics function as critical information infrastructure for an efficient, scalable and sustainable battery circular economy.
Author
Shengyu Tao
Chalmers, Electrical Engineering, Systems and control
Xuan Zhang
Tsinghua University
Changfu Zou
Chalmers, Electrical Engineering, Systems and control
Chem Circularity
3051-2948 (ISSN)
Vol. 1 100005Multiphysics modelling and monitoring of lithium-ion cells for next-generation management
Swedish Research Council (VR) (2023-04314), 2024-01-01 -- 2027-12-31.
E-powertrain predictive maintenance using physics informed learning (TEAMING)
European Commission (EC) (101131278), 2023-12-01 -- 2027-11-30.
Areas of Advance
Energy
Subject Categories (SSIF 2025)
Electrical Engineering, Electronic Engineering, Information Engineering
DOI
10.1016/j.checir.2026.100005