Dual-time scale collaborative estimation of SOC and SOH for lithium-ion batteries based on FOMIRUKF-EKF DOI

Wenqi Guo,

Qingfan Wang,

Guishu Li

и другие.

Computers & Electrical Engineering, Год журнала: 2024, Номер 123, С. 110048 - 110048

Опубликована: Дек. 31, 2024

Язык: Английский

Capacity estimation of lithium-ion batteries based on segment IC curve data dimensionality reduction and reconstruction methods DOI
Jianping Wen, Chenze Wang, Zhuang Zhao

и другие.

Ionics, Год журнала: 2025, Номер 31(3), С. 2457 - 2471

Опубликована: Янв. 11, 2025

Язык: Английский

Процитировано

0

A physics-enhanced online joint estimation method for SOH and SOC of lithium-ion batteries in eVTOL aircraft applications DOI

Fusheng Jiang,

Yi Ren, Ting Tang

и другие.

Journal of Energy Storage, Год журнала: 2025, Номер 112, С. 115567 - 115567

Опубликована: Янв. 30, 2025

Язык: Английский

Процитировано

0

Systematic overview of equalization methods for battery energy storage systems DOI
Xiangwei Guo, Gang Chen, Liangjun Zhao

и другие.

Journal of Power Sources, Год журнала: 2025, Номер 640, С. 236766 - 236766

Опубликована: Март 13, 2025

Язык: Английский

Процитировано

0

A Joint Estimation Method for the SOC and SOH of Lithium-Ion Batteries Based on AR-ECM and Data-Driven Model Fusion DOI Open Access
Zhiyuan Wei,

Xiaowen Sun,

Yiduo Li

и другие.

Electronics, Год журнала: 2025, Номер 14(7), С. 1290 - 1290

Опубликована: Март 25, 2025

Accurate estimations of State-of-Charge (SOC) and State-of-Health (SOH) are crucial for ensuring the safe efficient operation lithium-ion batteries in Battery Management Systems (BMSs). This paper proposes a novel joint estimation method integrating an Autoregressive Equivalent Circuit Model (AR-ECM) with data-driven model to address strong coupling between SOC SOH. First, multi-strategy improved Ivy algorithm (MSIVY) is utilized optimize hyperparameters Hybrid Kernel Extreme Learning Machine (HKELM). Key voltage interval features, including split voltage, differential capacity, current–voltage product, extracted filtered using sliding window approach enhance SOH prediction accuracy. The estimated subsequently incorporated into AR-ECM state-space equations, where enhanced particle swarm optimization optimizes parameters. Finally, Extended Kalman Filter (EKF) applied achieve collaborative SOC–SOH estimation. Experimental results demonstrate that proposed achieves errors below 1% under 2% on public datasets, showcasing its robust generalization capability real-time performance.

Язык: Английский

Процитировано

0

Multi-model deep learning-based state of charge estimation for shipboard lithium batteries with feature extraction and Spatio-temporal dependency DOI

Yanxi Qiu,

Shuli Wen, Qiang Zhao

и другие.

Journal of Power Sources, Год журнала: 2024, Номер 629, С. 235983 - 235983

Опубликована: Дек. 9, 2024

Язык: Английский

Процитировано

2

Technical and economic sizing of custom electric vehicles with mobile electricity storage facilities for the provision of energy services in urban areas DOI Creative Commons
Krzysztof Zagrajek, Mariusz Kłos, Jarosław Korzeb

и другие.

Journal of Energy Storage, Год журнала: 2024, Номер 106, С. 114909 - 114909

Опубликована: Дек. 3, 2024

Язык: Английский

Процитировано

0

Dual-time scale collaborative estimation of SOC and SOH for lithium-ion batteries based on FOMIRUKF-EKF DOI

Wenqi Guo,

Qingfan Wang,

Guishu Li

и другие.

Computers & Electrical Engineering, Год журнала: 2024, Номер 123, С. 110048 - 110048

Опубликована: Дек. 31, 2024

Язык: Английский

Процитировано

0