A High-speed Recurrent State Network with Noise Reduction for Multi-temperature State of Energy Estimation of Electric Vehicles Lithium-ion Batteries DOI
Y. Zou, Haotian Shi, Wen Cao

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135639 - 135639

Published: March 1, 2025

Language: Английский

A review of energy storage systems for facilitating large-scale EV charger integration in electric power grid DOI
Doğan Çelík, Muhammad Adnan Khan, Nima Khosravi

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 112, P. 115496 - 115496

Published: Jan. 29, 2025

Language: Английский

Citations

2
A lightweight two-stage physics-informed neural network for SOH estimation of lithium-ion batteries with different chemistries DOI

Chunsong Lin,

Longxing Wu, Xianguo Tuo

et al.

Journal of Energy Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

2
Transfer Learning‐Based Data‐Fusion Model Framework for State of Health Estimation of Power Battery Packs DOI Creative Commons
Zhiqiang Lyu, Xinyuan Wei, Longxing Wu

et al.

Battery energy, Journal Year: 2025, Volume and Issue: unknown

Published: March 11, 2025

ABSTRACT Accurate State of Health (SOH) estimation is critical for battery management systems (BMS) in electric vehicles (EVs). However, the absence a universal aging model power batteries presents significant challenges. This study leverages open‐source cell data set from University Maryland and focuses on private packs to address SOH estimation. Two features indicative capacity degradation are extracted constant current charging using incremental analysis (ICA). To handle nonlinearity feature coupling, flexible data‐driven proposed, employing dual Gaussian process regressions (GPRs) transfer learning enhance efficiency accuracy. Adaptive filtering via Particle filter (PF) further refines by integrating output capacity, resulting closed‐loop fusion approach precise Battery pack experiments validate proposed method, demonstrating that effectively improves The method achieves with mean root square error (RMSE) 0.87, underscoring its reliability precision.

Language: Английский

Citations

0
Battery SOC estimation with physics-constrained BiLSTM under different external pressures and temperatures DOI
Longxing Wu, Xinyuan Wei,

Chunsong Lin

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 117, P. 116205 - 116205

Published: March 14, 2025

Language: Английский

Citations

0
A High-speed Recurrent State Network with Noise Reduction for Multi-temperature State of Energy Estimation of Electric Vehicles Lithium-ion Batteries DOI
Y. Zou, Haotian Shi, Wen Cao

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135639 - 135639

Published: March 1, 2025

Language: Английский

Citations

0