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

The acid-base flow battery: Tradeoffs between energy density, efficiency, and stability DOI Creative Commons
Nadia Boulif,

R. C. Evers,

Jelle Driegen

et al.

Applied Energy, Journal Year: 2025, Volume and Issue: 383, P. 125327 - 125327

Published: Jan. 17, 2025

Language: Английский

Citations

0
Analysis and comparison of SOC estimation techniques for Li-ion batteries DOI
Manal M. Zaki, Mohamed A. El-Beltagy,

Ahmed E. Hammad

et al.

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

Published: Feb. 13, 2025

Language: Английский

Citations

0
A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis DOI Creative Commons
Seyed Saeed Madani, Yasmin Shabeer, François Allard

et al.

Batteries, Journal Year: 2025, Volume and Issue: 11(4), P. 127 - 127

Published: March 26, 2025

Lithium-ion batteries experience degradation with each cycle, and while aging-related deterioration cannot be entirely prevented, understanding its underlying mechanisms is crucial to slowing it down. The aging processes in these are complex influenced by factors such as battery chemistry, electrochemical reactions, operational conditions. Key stressors including depth of discharge, charge/discharge rates, cycle count, temperature fluctuations or extreme conditions play a significant role accelerating degradation, making them central analysis. Battery directly impacts power, energy density, reliability, presenting substantial challenge extending lifespan across diverse applications. This paper provides comprehensive review methods for modeling analyzing aging, focusing on essential indicators assessing the health status lithium-ion batteries. It examines principles modeling, which vital applications portable electronics, electric vehicles, grid storage systems. work aims advance technology promote sustainable resource use variables influencing durability. Synthesizing wide array studies identifies gaps current methodologies highlights innovative approaches accurate remaining useful life (RUL) estimation. introduces emerging strategies that leverage advanced algorithms improve predictive model precision, ultimately driving enhancements performance supporting their integration into various systems, from vehicles renewable infrastructures.

Language: Английский

Citations

0
State of health estimation for lithium-ion batteries using a hybrid Mixture of Gaussian and Laplacian extreme learning machine algorithm DOI
Pallabi Kakati, Devendra Dandotiya, Rajiv Ranjan Singh

et al.

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

Published: April 15, 2025

Language: Английский

Citations

0
Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected Vehicles DOI Creative Commons
Hongzhao Li, Hongsheng Jia, Ping Xiao

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(9), P. 2144 - 2144

Published: April 22, 2025

Accurately estimating the State of Charge (SOC) power batteries is crucial for Battery Management Systems (BMS) in new energy intelligent connected vehicles. It directly influences vehicle range, management efficiency, and safety lifespan battery. However, SOC cannot be measured with instruments; it needs to estimated using external parameters such as current, voltage, internal resistance. Moreover, represent complex nonlinear time-varying systems, various uncertainties—like battery aging, fluctuations ambient temperature, self-discharge effects—complicate accuracy these estimations. This significantly increases complexity estimation process limits industrial applications. To address challenges, this study systematically classifies existing algorithms, performs comparative analyses their computational accuracy, identifies inherent limitations within each category. Additionally, a comprehensive review technologies utilized BMS by automotive OEMs globally conducted. The analysis concludes that advancing multi-fusion frameworks, which offer enhanced universality, robustness, hard real-time capabilities, represents primary research trajectory field.

Language: Английский

Citations

0
A comprehensive review, perspectives and future directions of battery characterization and parameter estimation DOI
Tasadeek Hassan Dar,

Satyavir Singh

Journal of Applied Electrochemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 18, 2024

Language: Английский

Citations

2
State of health estimation based on PSO-SA-LSTM for fast-charge lithium-ion batteries DOI
Liangliang Wei,

Qi Diao,

Yiwen Sun

et al.

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

Published: Nov. 25, 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