Enhanced quantile regression long short-term memory hybrid neural network for the state of charge point and interval estimation of lithium-ion batteries

doi:10.1016/j.energy.2025.137201

Hakkımızda SSS Gizlilik Şartlar İletişim

Enhanced quantile regression long short-term memory hybrid neural network for the state of charge point and interval estimation of lithium-ion batteries (2025) | RDL Network

Back

Home
Publications
Enhanced quantile regression long short-term memory hybrid neural network for the state of charge point and interval estimation of lithium-ion batteries

Shared by

Frede Blaabjerg

Aalborg University

Enhanced quantile regression long short-term memory hybrid neural network for the state of charge point and interval estimation of lithium-ion batteries

Article 2025 en

Discussion(0)

No comments yet. Be the first to comment.

Publication Info

DOI: 10.1016/j.energy.2025.137201
Year: 2025
Published: —
Language: en

Article Details

Volume: 332
Link Of The Paper: https://doi.org/10.1016/j.energy.2025.137201

Timeline

Created:June 19, 2026

Related publications

Article2024

An improved multi-innovation error compensation-long-short-term memory network modeling method for high-precision state of charge estimation of lithium-ion batteries

Wu Qiqiao, Carlos Fernández, Josep Maria Guerrero

Article2024

Improved Multi-head Bi-directional Long and Short-term Memory Temporal Convolutional Network for Lithium-ion Batteries State of Charge Estimation in Energy Storage Systems

Article2024

An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures

Article2023

Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries

Article2025

A novel extended Kalman filter-guided long short-term memory algorithm for power lithium-ion battery state of charge estimation at multiple temperatures