Improved LSTM based state of health estimation using random segments of the charging curves for lithium-ion batteries
Author:
Publisher
Elsevier BV
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment
Reference42 articles.
1. Estimation of state-of-charge and state-of-health for lithium-ion battery based on improved firefly optimized particle filter;Ouyang;J. Energy Storage,2023
2. A novel back propagation neural network-dual extended Kalman filter method for state-of-charge and state-of-health co-estimation of lithium-ion batteries based on limited memory least square algorithm;Wang;J. Energy Storage,2023
3. Accelerated state of health estimation of second life lithium-ion batteries via electrochemical impedance spectroscopy tests and machine learning techniques;Faraji-Niri;J. Energy Storage,2023
4. Multi-objective building energy system optimization considering EV infrastructure;Park;Appl. Energy,2023
5. Concept review of a cloud-based smart battery management system for lithium-ion batteries: feasibility, logistics, and functionality;Tran;Batteries,2022
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1. Ultra-high-accuracy state-of-charge fusion estimation of lithium-ion batteries using variational mode decomposition;Energy;2024-11
2. Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability;Energy;2024-10
3. Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries;Energy and AI;2024-09
4. A novel hybrid neural network-based SOH and RUL estimation method for lithium-ion batteries;Journal of Energy Storage;2024-09
5. Technological trajectory analysis in lithium battery manufacturing: Based on patent claims perspective;Journal of Energy Storage;2024-09
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