Optimizing Battery RUL Prediction of Lithium-Ion Batteries Based on Harris Hawk Optimization Approach Using Random Forest and LightGBM-Reference-Cited by-同舟云学术

Optimizing Battery RUL Prediction of Lithium-Ion Batteries Based on Harris Hawk Optimization Approach Using Random Forest and LightGBM

Published:2023 Issue: Volume:11 Page:87034-87046
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Jafari Sadiqa¹^ORCID,Byun Yung-Cheol²^ORCID

Affiliation:

1. Department of Electronic Engineering, Jeju National University, Jeju, South Korea

2. Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science and Technology, Jeju National University, Jeju, South Korea

Funder

the Korea Technology and Information Promotion Agency for SMEs

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10216301.pdf?arnumber=10216301

Reference40 articles.

1. Lithium-ion battery capacity and remaining useful life prediction using board learning system and long short-term memory neural network

2. A Hybrid Method for the Prediction of the Remaining Useful Life of Lithium-Ion Batteries With Accelerated Capacity Degradation

3. An Integrated Method of the Future Capacity and RUL Prediction for Lithium-Ion Battery Pack

4. An Empirical Capacity Degradation Modeling and Prognostics of Remaining Useful Life of Li-ion Battery using Unscented Kalman Filter

5. Semi-Supervised Self-Learning-Based Lifetime Prediction for Batteries

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