A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries-Reference-Cited by-同舟云学术

A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries

Published:2021-11 Issue: Volume:7 Page:5562-5574
ISSN:2352-4847
Container-title:Energy Reports
language:en
Short-container-title:Energy Reports

Author:

Wang Shunli^ORCID,Jin Siyu^ORCID,Bai Dekui,Fan Yongcun,Shi Haotian,Fernandez Carlos

Publisher

Elsevier BV

Subject

General Energy

Reference153 articles.

1. Multi-agent microgrid energy management based on deep learning forecaster;Afrasiabi;Energy,2019

2. A demand-supply matching-based approach for mapping renewable resources towards 100% renewable grids in 2050;Al-Ghussain;Ieee Access,2021

3. An integrated photovoltaic/wind/biomass and hybrid energy storage systems towards 100% renewable energy microgrids in university campuses;Al-Ghussain;Sustain. Energy Technol. Assess.,2021

4. Lithium-ion battery degradation indicators via incremental capacity analysis;Anseán;Ieee Trans. Ind. Appl.,2019

5. Theory of battery ageing in a lithium-ion battery: Capacity fade, nonlinear ageing and lifetime prediction;Atalay;J. Power Sources,2020

Cited by 149 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A review of expert hybrid and co-estimation techniques for SOH and RUL estimation in battery management system with electric vehicle application;Expert Systems with Applications;2024-07

2. Optimal parameter identification strategy applied to lithium-ion battery model for electric vehicles using drive cycle data;Energy Reports;2024-06

3. A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities;Mechanical Systems and Signal Processing;2024-03

4. The effect of electric vehicle charging demand variability on optimal hybrid power systems with second-life lithium-ion or fresh Na–S batteries considering power quality;Energy;2024-02

5. Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects;Renewable and Sustainable Energy Reviews;2024-02