A Comprehensive Review of Multiple Physical and Data-Driven Model Fusion Methods for Accurate Lithium-Ion Battery Inner State Factor Estimation DOI Creative Commons

Junjie Tao,

Shunli Wang,

Wen Cao

et al.

Batteries, Journal Year: 2024, Volume and Issue: 10(12), P. 442 - 442

Published: Dec. 13, 2024

With the rapid global growth in demand for renewable energy, traditional energy structure is accelerating its transition to low-carbon, clean energy. Lithium-ion batteries, due their high density, long cycle life, and efficiency, have become a core technology driving this transformation. In lithium-ion battery storage systems, precise state estimation, such as of charge, health, power, crucial ensuring system safety, extending lifespan, improving efficiency. Although physics-based estimation techniques matured, challenges remain regarding accuracy robustness complex environments. advancement hardware computational capabilities, data-driven algorithms are increasingly applied management, multi-model fusion approaches emerged research hotspot. This paper reviews application between models critically analyzes advantages, limitations, applicability models, evaluates effectiveness robustness. Furthermore, discusses future directions improvement model adaptability, performance under operating conditions, aiming provide theoretical support practical guidance developing management technologies.

Language: Английский

Thermal characteristics and management strategies for hybrid supercapacitors during charge and discharge DOI
Mingxia Wu, Can Zhang, Shengnan Xie

et al.

Ionics, Journal Year: 2025, Volume and Issue: unknown

Published: April 24, 2025

Language: Английский

Citations

0
A critical review on lithium ion battery modeling, battery management system and thermal runaway issues DOI
K. Dhananjay Rao, K. Venkateswara Rao, Pavani Ponnaganti

et al.

Electrical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 2, 2025

Language: Английский

Citations

0
Thermo-electric behavior analysis and coupled model characterization of 21,700 cylindrical ternary lithium batteries affected by cyclic aging DOI
Haopeng Chen, Tianshi Zhang,

Qing Gao

et al.

Sustainable Energy Technologies and Assessments, Journal Year: 2024, Volume and Issue: 71, P. 104013 - 104013

Published: Oct. 5, 2024

Language: Английский

Citations

0
A Comprehensive Review of Multiple Physical and Data-Driven Model Fusion Methods for Accurate Lithium-Ion Battery Inner State Factor Estimation DOI Creative Commons

Junjie Tao,

Shunli Wang,

Wen Cao

et al.

Batteries, Journal Year: 2024, Volume and Issue: 10(12), P. 442 - 442

Published: Dec. 13, 2024

With the rapid global growth in demand for renewable energy, traditional energy structure is accelerating its transition to low-carbon, clean energy. Lithium-ion batteries, due their high density, long cycle life, and efficiency, have become a core technology driving this transformation. In lithium-ion battery storage systems, precise state estimation, such as of charge, health, power, crucial ensuring system safety, extending lifespan, improving efficiency. Although physics-based estimation techniques matured, challenges remain regarding accuracy robustness complex environments. advancement hardware computational capabilities, data-driven algorithms are increasingly applied management, multi-model fusion approaches emerged research hotspot. This paper reviews application between models critically analyzes advantages, limitations, applicability models, evaluates effectiveness robustness. Furthermore, discusses future directions improvement model adaptability, performance under operating conditions, aiming provide theoretical support practical guidance developing management technologies.

Language: Английский

Citations

0