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

Accurately estimating a battery's state of health (SOH) helps preventbattery-powered applications from failing unexpectedly. With the superiority ofreducing the data requirement of model training for new batteries, transferlearning (TL) emerges as a promising machine learning approach that appliesknowledge learned from a source battery, which has a large amount of data.However, the determination of whether the source battery model is reasonableand which part of information can be transferred for SOH estimation are rarelydiscussed, despite these being critical components of a successful TL. Toaddress these challenges, this paper proposes an interpretable TL-based SOHestimation method by exploiting the temporal dynamic to assist transferlearning, which consists of three parts. First, with the help of dynamic timewarping, the temporal data from the discharge time series are synchronized,yielding the warping path of the cycle-synchronized time series responsible forcapacity degradation over cycles. Second, the canonical variates retrieved fromthe spatial path of the cycle-synchronized time series are used fordistribution similarity analysis between the source and target batteries.Third, when the distribution similarity is within the predefined threshold, acomprehensive target SOH estimation model is constructed by transferring thecommon temporal dynamics from the source SOH estimation model and compensatingthe errors with a residual model from the target battery. Through a widely-usedopen-source benchmark dataset, the estimation error of the proposed methodevaluated by the root mean squared error is as low as 0.0034 resulting in a 77%accuracy improvement compared with existing methods.

Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization