A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges-Reference-Cited by-同舟云学术

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A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges

Published:2024-10 Issue: Volume:97 Page:630-649
ISSN:2095-4956
Container-title:Journal of Energy Chemistry
language:en
Short-container-title:Journal of Energy Chemistry

Author:

Xie Yanxin,Wang Shunli^ORCID,Zhang Gexiang,Takyi-Aninakwa Paul,Fernandez Carlos^ORCID,Blaabjerg Frede^ORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference180 articles.

1. Toward Autonomous Multi-UAV Wireless Network: A Survey of Reinforcement Learning-Based Approaches

2. Access to advanced sodium-ion batteries by presodiation: Principles and applications

3. Proven reserve oil and renewable energy nexus: Efficacy of policy stringency

4. Lithium nexus: Energy, geopolitics, and socio-environmental impacts in Mexico's Sonora project

5. A review of understanding electrocatalytic reactions in energy conversion and energy storage systems via scanning electrochemical microscopy

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