Capacity prediction method of lithium-ion battery in production process based on eXtreme Gradient Boosting DOI
Zhengyu Liu, Rui Xu, Hao Wang

et al.

Ionics, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 30, 2024

Language: Английский

Improved particle swarm optimization-adaptive dual extended Kalman filtering for accurate battery state of charge and state of energy joint estimation with efficient core factor feedback correction DOI
Shunli Wang, Yue Wu,

Heng Zhou

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135686 - 135686

Published: March 1, 2025

Language: Английский

Citations

1
Performance prediction of vacuum membrane distillation system based on multi-layer perceptron neural network DOI

Zetian Si,

Zhuohao Li,

Ke Li

et al.

Desalination, Journal Year: 2025, Volume and Issue: unknown, P. 118593 - 118593

Published: Jan. 1, 2025

Language: Английский

Citations

0
A study of adaptive extended Kalman filter with different sliding window lengths for lithium-ion battery state-of-charge estimation DOI
Xin Li,

Di Dong,

Zhipeng Hu

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 118, P. 116276 - 116276

Published: March 22, 2025

Language: Английский

Citations

0
Online Core Temperature Estimation for Lithium-Ion Batteries via an Aging-Integrated ECM-1D Coupled Model-Based Algorithm DOI Creative Commons
Yiqi Jia, Lorenzo Brancato, Marco Giglio

et al.

Batteries, Journal Year: 2025, Volume and Issue: 11(4), P. 160 - 160

Published: April 18, 2025

Thermal management is pivotal for ensuring the safe and efficient operation of LIBs under dynamic conditions. Accurate core temperature monitoring remains a key BTMS challenge predicting thermal distributions mitigating TR risks. This study proposes real-time estimation framework integrating joint EKF with an electro-thermal-aging model (ECM-1D). Using only surface voltage measurements, it simultaneously estimates temperature, SOC, capacity bidirectional electro-thermal coupling. The hybrid approach pre-calibrates temperature/SOC/SOH-dependent parameters offline while updating online. Validation extreme conditions (high-rate cycling, aging, ISCs) demonstrates 60% lower RMSE during high-rate maximum error below 0.9 K, 58.9% reduction in SOC aging versus existing methods. reliably tracks trends despite parasitic heat signal noise, enabling earlier critical warnings. provides foundation prevention, advancing battery safety EV grid storage applications. Future extensions could integrate physical mechanisms enhance fault detection capabilities.

Language: Английский

Citations

0
Thermal management for EV power batteries based on INFO-ADRC algorithm DOI
Shuwen Zhou,

Siwen Zhao,

Xinrui Duan

et al.

International Journal of Green Energy, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 18

Published: April 8, 2025

Language: Английский

Citations

0
Capacity prediction method of lithium-ion battery in production process based on eXtreme Gradient Boosting DOI
Zhengyu Liu, Rui Xu, Hao Wang

et al.

Ionics, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 30, 2024

Language: Английский

Citations

0