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

Hybrid Electrochemical Circuit Modeling and Variable Forgetting Factor Sliding Window Recursive Least Squares Linearization Chaos Firefly Optimization Full-Parameter Identification for Lithium-Ion Batteries DOI

Liangwei Cheng,

Shunli Wang, Lei Zhou

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
SOH-KLSTM: A hybrid Kolmogorov-Arnold Network and LSTM model for enhanced Lithium-ion battery Health Monitoring DOI
Imen Jarraya, Safa Ben Atitallah, Fatimah Alahmed

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 122, P. 116541 - 116541

Published: April 15, 2025

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