Machine learning for a sustainable energy future
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …
demands advances—at the materials, devices and systems levels—for the efficient …
State-of-health estimation of lithium-ion batteries based on electrochemical impedance spectroscopy: a review
Lithium-ion batteries (LIBs) are crucial for the large-scale utilization of clean energy.
However, because of the complexity and real-time nature of internal reactions, the …
However, because of the complexity and real-time nature of internal reactions, the …
Artificial intelligence applied to battery research: hype or reality?
T Lombardo, M Duquesnoy, H El-Bouysidy… - Chemical …, 2021 - ACS Publications
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …
A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
Towards long lifetime battery: AI-based manufacturing and management
Technologies that accelerate the delivery of reliable battery-based energy storage will not
only contribute to decarbonization such as transportation electrification, smart grid, but also …
only contribute to decarbonization such as transportation electrification, smart grid, but also …
Machine learning: an advanced platform for materials development and state prediction in lithium‐ion batteries
Lithium‐ion batteries (LIBs) are vital energy‐storage devices in modern society. However,
the performance and cost are still not satisfactory in terms of energy density, power density …
the performance and cost are still not satisfactory in terms of energy density, power density …
A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems
With the rapid development of new energy electric vehicles and smart grids, the demand for
batteries is increasing. The battery management system (BMS) plays a crucial role in the …
batteries is increasing. The battery management system (BMS) plays a crucial role in the …
The challenge and opportunity of battery lifetime prediction from field data
Accurate battery life prediction is a critical part of the business case for electric vehicles,
stationary energy storage, and nascent applications such as electric aircraft. Existing …
stationary energy storage, and nascent applications such as electric aircraft. Existing …
Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling
Designing and deployment of state-of-the-art electric vehicles (EVs) in terms of low cost and
high driving range with appropriate reliability and security are identified as the key towards …
high driving range with appropriate reliability and security are identified as the key towards …
Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved
challenge that limits technologies such as consumer electronics and electric vehicles. Here …
challenge that limits technologies such as consumer electronics and electric vehicles. Here …