Insights and reviews on battery lifetime prediction from research to practice

X Qu, D Shi, J Zhao, MK Tran, Z Wang, M Fowler… - Journal of Energy …, 2024 - Elsevier
The rising demand for energy storage solutions, especially in the electric vehicle and
renewable energy sectors, highlights the importance of accurately predicting battery health …

Strategies for reducing automobile fuel consumption

CA Romero, P Correa, EA Ariza Echeverri, D Vergara - Applied Sciences, 2024 - mdpi.com
In recent times, the significance of advancing road transportation technologies has notably
increased. This is mainly driven by the escalating need for road transportation systems that …

Large-scale field data-based battery aging prediction driven by statistical features and machine learning

Q Wang, Z Wang, P Liu, L Zhang, DU Sauer… - Cell Reports Physical …, 2023 - cell.com
Accurately predicting battery aging is critical for mitigating performance degradation during
battery usage. While the automotive industry recognizes the importance of utilizing field data …

Advancing state of charge management in electric vehicles with machine learning: A technological review

A Mousaei, Y Naderi, IS Bayram - IEEE Access, 2024 - ieeexplore.ieee.org
As the share of electric vehicles increases, electric vehicles are exposed to broader of
driving conditions (eg, extreme weather), which reduce the performance and driving ranges …

Exploiting domain knowledge to reduce data requirements for battery health monitoring

J Tian, L Ma, T Zhang, T Han, W Mai, CY Chung - Energy Storage Materials, 2024 - Elsevier
Rechargeable batteries are becoming increasingly significant in decarbonising the world.
For their widespread usage, to monitor and predict the battery health status has been …

Dynamic cycling enhances battery lifetime

A Geslin, L Xu, D Ganapathi, K Moy, WC Chueh… - Nature Energy, 2024 - nature.com
Laboratory ageing campaigns elucidate the complex degradation behaviour of most
technologies. In lithium-ion batteries, such studies aim to capture realistic ageing …

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

A Thelen, X Huan, N Paulson, S Onori, Z Hu… - npj Materials …, 2024 - nature.com
Diagnosing lithium-ion battery health and predicting future degradation is essential for
driving design improvements in the laboratory and ensuring safe and reliable operation over …

Multi-year field measurements of home storage systems and their use in capacity estimation

J Figgener, J van Ouwerkerk, D Haberschusz, J Bors… - Nature Energy, 2024 - nature.com
Home storage systems play an important role in the integration of residential photovoltaic
systems and have recently experienced strong market growth worldwide. However …

Electrochemical characterization tools for lithium-ion batteries

S Ha, G Pozzato, S Onori - Journal of Solid State Electrochemistry, 2024 - Springer
Lithium-ion batteries are electrochemical energy storage devices that have enabled the
electrification of transportation systems and large-scale grid energy storage. During their …

[HTML][HTML] Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data

K Nakano, S Vögler, K Tanaka - Advances in Applied Energy, 2024 - Elsevier
The widespread adoption of electric vehicles (EVs) underscores the urgent need for
innovative approaches to estimate their lithium-ion batteries' state of health (SOH), which is …