English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recent data-driven approaches have shown great potential in early predictionof battery cycle life by utilizing features from the discharge voltage curve.However, these studies caution that data-driven approaches must be combinedwith specific design of experiments in order to limit the range of agingconditions, since the expected life of Li-ion batteries is a complex functionof various aging factors. In this work, we investigate the performance of thedata-driven approach for battery lifetime prognostics with Li-ion batteriescycled under a variety of aging conditions, in order to determine when thedata-driven approach can successfully be applied. Results show a correlationbetween the variance of the discharge capacity difference and the end-of-lifefor cells aged under a wide range of charge/discharge C-rates and operatingtemperatures. This holds despite the different conditions being used not onlyto cycle the batteries but also to obtain the features: the features arecalculated directly from cycling data without separate slow characterizationcycles at a controlled temperature. However, the correlation weakensconsiderably when the voltage data window for feature extraction is reduced, orwhen features from the charge voltage curve instead of discharge are used. Asdeep constant-current discharges rarely happen in practice, this imposes newchallenges for applying this method in a real-world system.

Promise and Challenges of a Data-Driven Approach for Battery Lifetime Prognostics