Physics

Pure mathematics

Government (linguistics)

Healthy aging

Mathematics

Accelerated aging

Machine learning

Engineering

Philosophy

Artificial intelligence

Power (physics)

Economic shortage

Reliability engineering

Scale (ratio)

Computer science

Linguistics

Battery (electricity)

Quantum mechanics

Gerontology

Field (mathematics)

Medicine

Patterns

Batteries are crucial for building a clean and sustainable society, and their performance is highly affected by aging status. Reliable battery health assessment, however, is currently restrained by limited access to sufficient aging data, resulting from not only complicated battery operations but also long aging experimental time (several months or even years). Refining industrial datasets (e.g., the field data from electric vehicle batteries) unlocks opportunities to acquire large-volume aging data with low experimental efforts. We introduce the potential of combining industrial data with accelerated aging tests to recover high-quality battery aging datasets, through a migration-based machine learning. A comprehensive dataset containing 8,947 aging cycles with 15 operational modes is collected for evaluation. While saving up to 90% experimental time, aging data can be recovered with ultra-low error within 1%. It provides an alternative solution to significantly improve data shortage issues for assessment of battery and energy storage aging.

Recovering large-scale battery aging dataset with machine learning