Reproducibility of battery state of health estimation using short discharge tests across independent experimental platforms

Age-related development of cell-to-cell variation under various operating conditions in commercial NMC/graphite lithium-ion cells

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Lithium-ion battery data: from production to prediction

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Selecting the appropriate features in battery lifetime predictions

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State of health estimation for lithium-ion batteries based on voltage segment and transformer

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State of health and remaining useful life prediction of lithium-ion batteries based on a disturbance-free incremental capacity and differential voltage analysis method

10.1016/j.est.2023.107161 · doi-reference

Characterization of aging mechanisms and state of health for second-life 21700 ternary lithium-ion battery

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Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition

10.1016/j.apenergy.2020.115074 · doi-reference

Rapid estimation of static capacity based on machine learning: a time-efficient approach

10.3390/batteries10060191 · doi-reference

Cell-to-cell variation beyond parameter analysis-identification and correlation of processes in lithium-ion batteries using a combined distribution of relaxation times analysis

10.1016/j.jpowsour.2023.233677 · doi-reference

Quantifiability of inherent cell-to-cell variations of commercial lithium-ion batteries

10.1016/j.etran.2021.100129 · doi-reference

Performance metrics for the comparison of lithium ion cell aging experiments

10.1016/j.enrev.2023.100025 · doi-reference

Transfer state of health estimation based on cross-manifold embedding

10.1016/j.est.2021.103555 · doi-reference

State of health estimation of lithium-ion batteries using autoencoders and ensemble learning

10.1016/j.est.2022.105708 · doi-reference

Refined lithium-ion battery state of health estimation with charging segment adjustment

10.1016/j.apenergy.2024.124077 · doi-reference

Rapid and flexible battery capacity estimation using random short-time charging segments based on residual convolutional networks

10.1016/j.apenergy.2023.121925 · doi-reference

Data-driven battery state of health estimation based on random partial charging data

10.1109/tpel.2021.3134701 · doi-reference

Towards a smarter battery management system: a critical review on battery state of health monitoring methods

10.1016/j.jpowsour.2018.10.019 · doi-reference

Advanced diagnostics to evaluate heterogeneity in lithium-ion battery modules

10.1016/j.etran.2020.100045 · doi-reference

Sorting, regrouping, and echelon utilization of the large-scale retired lithium batteries: a critical review

10.1016/j.rser.2021.111162 · doi-reference

Development of cell selection framework for second-life cells with homogeneous properties

10.1016/j.ijepes.2018.08.015 · doi-reference

Procedure for assessing the suitability of battery second life applications after EV first life

10.3390/batteries8090122 · doi-reference

Extending the electric vehicle battery first life: performance beyond the current end of life threshold

10.1016/j.heliyon.2024.e26066 · doi-reference

Electric vehicle adoption and planning: the increasing importance of the built environment

10.1016/j.jtrangeo.2025.104115 · doi-reference

Evaluating Norway’s electric vehicle incentives

10.1016/j.eneco.2025.108490 · doi-reference

Hidden effects and externalities of electric vehicles

10.1016/j.enpol.2024.114335 · doi-reference