Data driven battery aging prediction

Research Project, 2020 – 2023

Description missing.

Participants

Collaborations

Funding

Publications

State of health estimation for lithium-ion batteries under arbitrary usage using data-driven multi-model fusion

Detecting abnormality of battery lifetime from first-cycle data using few-shot learning

Data-driven battery aging diagnostics and prognostics

Early prediction of battery life by learning from both time-series and histogram data

Practical battery state of health estimation using data-driven multi-model fusion

Offline and Online Blended Machine Learning for Lithium-Ion Battery Health State Estimation

Combining offline and online machine learning to estimate state of health of lithium-ion batteries

A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data

More information

Latest update