Remaining useful life prediction based on denoising technique and deep neural network for lithium-ion capacitors-Reference-Cited by-同舟云学术

Remaining useful life prediction based on denoising technique and deep neural network for lithium-ion capacitors

Published:2020-08 Issue: Volume:5 Page:100078
ISSN:2590-1168
Container-title:eTransportation
language:en
Short-container-title:eTransportation

Author:

Yang Hao,Wang Penglei,An Yabin,Shi Changli,Sun Xianzhong^ORCID,Wang Kai,Zhang Xiong,Wei Tongzhen,Ma Yanwei

Funder

National Natural Science Foundation of China

Strategic Priority Research Program of Chinese Academy of Sciences

Dalian National Laboratory For Clean Energy (DNL) Cooperation Fund, the CAS

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Transportation,Automotive Engineering

Reference42 articles.

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2. Advanced diagnostics to evaluate heterogeneity in lithium-ion battery modules;Tanim;eTransportation,2020

3. Electrical, mechanical, and electromagnetic shielding properties of silver nanowire-based transparent conductive films;Zhang;Phys Status Solidi A,2018

4. Electrochemical performances and capacity fading behaviors of activated carbon/hard carbon lithium ion capacitor;Sun;Electrochim Acta,2017

5. Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review;Li;Renew Sustain Energy Rev,2019

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