Battery Cross-Operation-Condition Lifetime Prediction via Interpretable Feature Engineering Assisted Adaptive Machine Learning-Reference-Cited by-同舟云学术

Battery Cross-Operation-Condition Lifetime Prediction via Interpretable Feature Engineering Assisted Adaptive Machine Learning

Published:2023-07-10 Issue:8 Volume:8 Page:3269-3279
ISSN:2380-8195
Container-title:ACS Energy Letters
language:en
Short-container-title:ACS Energy Lett.

Author:

Tao Shengyu¹^ORCID,Sun Chongbo¹,Fu Shiyi²,Wang Yu²,Ma Ruifei¹,Han Zhiyuan¹,Sun Yaojie²,Li Yang¹,Wei Guodan¹^ORCID,Zhang Xuan¹,Zhou Guangmin¹^ORCID,Sun Hongbin³

Affiliation:

1. Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, China

2. School of Information Science and Technology, Fudan University, Shanghai 200433, China

3. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

Funder

Tsinghua Shenzhen International Graduate School

Shenzhen Science and Technology Innovation Program

Tsinghua Shenzhen International Graduate School???Shenzhen Pengrui Young Faculty Program of Shenzhen Pengrui Foundation

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment,Chemistry (miscellaneous)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsenergylett.3c01012

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