Machine learning for battery systems applications: Progress, challenges, and opportunities

Z Nozarijouybari, HK Fathy - Journal of Power Sources, 2024 - Elsevier
Abstract Machine learning has emerged as a transformative force throughout the entire
engineering life cycle of electrochemical batteries. Its applications encompass a wide array …

Review on state of charge estimation techniques of lithium-ion batteries: A control-oriented approach

N Ghaeminezhad, Q Ouyang, J Wei, Y Xue… - Journal of Energy …, 2023 - Elsevier
Energy storage has become one of the most critical issues of modern technology. In this
regard, lithium-ion batteries have proven effective as an energy storage option. To optimize …

[HTML][HTML] Intelligent fault diagnosis methods toward gas turbine: A review

LIU **aofeng, C Yingjie, L **ong, W Jianhua… - Chinese Journal of …, 2024 - Elsevier
Fault diagnosis plays a significant role in conducting condition-based maintenance and
health management for gas turbines (GTs) to improve reliability and reduce costs. Various …

Characterization and identification towards dynamic-based electrical modeling of lithium-ion batteries

C Fan, K Liu, Y Ren, Q Peng - Journal of Energy Chemistry, 2024 - Elsevier
Lithium-ion batteries are widely recognized as a crucial enabling technology for the
advancement of electric vehicles and energy storage systems in the grid. The design of …

[HTML][HTML] Systematic analysis of the impact of slurry coating on manufacture of Li-ion battery electrodes via explainable machine learning

MF Niri, C Reynolds, LAAR Ramírez, E Kendrick… - Energy Storage …, 2022 - Elsevier
The manufacturing process strongly affects the electrochemical properties and performance
of lithium-ion batteries. In particular, the flow of electrode slurry during the coating process is …

Data‐Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects

S Ji, J Zhu, Y Yang, G Dos Reis, Z Zhang - Small Methods, 2024 - Wiley Online Library
Battery characterization and prognosis are essential for analyzing underlying
electrochemical mechanisms and ensuring safe operation, especially with the assistance of …

Multilevel data-driven battery management: From internal sensing to big data utilization

Z Wei, K Liu, X Liu, Y Li, L Du… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
A battery management system (BMS) is essential for the safety and longevity of lithium-ion
battery (LIB) utilization. With the rapid development of new sensing techniques, artificial …

[HTML][HTML] A review of the applications of explainable machine learning for lithium–ion batteries: From production to state and performance estimation

M Faraji Niri, K Aslansefat, S Haghi, M Hashemian… - Energies, 2023 - mdpi.com
Lithium–ion batteries play a crucial role in clean transportation systems including EVs,
aircraft, and electric micromobilities. The design of battery cells and their production process …

Explainable neural network for sensitivity analysis of lithium-ion battery smart production

K Liu, Q Peng, Y Liu, N Cui… - IEEE/CAA Journal of …, 2024 - ieeexplore.ieee.org
Battery production is crucial for determining the quality of electrode, which in turn affects the
manufactured battery performance. As battery production is complicated with strongly …

Advancing lithium-ion battery health prognostics with deep learning: A review and case study

M Massaoudi, H Abu-Rub… - IEEE Open Journal of …, 2024 - ieeexplore.ieee.org
Lithium-ion battery prognostics and health management (BPHM) systems are vital to the
longevity, economy, and environmental friendliness of electric vehicles and energy storage …