Abstract
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Jinglun Li, Yinuo Wang, Xiangjun Li, Zeyu Cheng, Yunlong Shang
Abstract
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The lithium-ion battery: state of the art and future perspectives
10.1016/j.rser.2018.03.002 · 2018
The future of energy storage: advancements and roadmaps for lithium-ion batteries
10.3390/ijms24087457 · 2023
A review on thermal runaway warning technology for lithium-ion batteries
10.1016/j.rser.2024.114882 · 2024
Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles
10.1016/j.apenergy.2020.115855 · 2020
Research advances on thermal runaway mechanism of lithium-ion batteries and safety improvement
2024
A review of thermal runaway mechanism, safety enhancement, monitoring and early warning of solid-state lithium batteries
10.1016/j.est.2025.118294 · 2025
Review on thermal runaway of lithium-ion batteries for electric vehicles
10.1007/s11664-021-09281-0 · 2022
Data-driven methods for early warning of battery thermal runaway: a review of multi-signal fusion and machine learning approaches
10.1016/j.est.2025.118043 · 2025
Thermal runaway process in lithium-ion batteries: a review
10.1016/j.nxener.2024.100186 · 2025
Provenance
crossref
Confidence 100%
openalex
Confidence 95%
datacite
Confidence 0%
Thermal runaway mechanism of lithium-ion battery with LiNi0.8Mn0.1Co0.1O2 cathode materials
10.1016/j.nanoen.2021.105878 · 2021
Thermal runaway of Lithium-ion batteries without internal short circuit
10.1016/j.joule.2018.06.015 · 2018
Thermal runaway mechanism of lithium ion battery for electric vehicles: a review
10.1016/j.ensm.2017.05.013 · 2018
Data-driven prediction of battery cycle life before capacity degradation
10.1038/s41560-019-0356-8 · 2019
Battery lifetime prognostics
10.1016/j.joule.2019.11.018 · 2020
Machine learning pipeline for battery state-of-health estimation
10.1038/s42256-021-00312-3 · 2021
State-of-charge estimation of LiFePO4 batteries in electric vehicles: a deep-learning enabled approach
10.1016/j.apenergy.2021.116812 · 2021
Degradation diagnostics for lithium ion cells
10.1016/j.jpowsour.2016.12.011 · 2017
Early warning method for thermal runaway of lithium-ion batteries under thermal abuse condition based on online electrochemical impedance monitoring
10.1016/j.jechem.2023.12.049 · 2024
Advanced fault diagnosis for Lithium-ion battery systems: a review of fault mechanisms, fault features, and diagnosis procedures
10.1109/mie.2020.2964814 · 2020
Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles
10.1016/j.apenergy.2016.12.143 · 2017
An online data-driven fault diagnosis and thermal runaway early warning for electric vehicle batteries
10.1109/tpel.2022.3173038 · 2022
Study on the extreme early warning method of thermal runaway utilizing li-ion battery strain
10.1016/j.apenergy.2025.125494 · 2025
Advanced ultrasonic detection of lithium-ion battery thermal runaway under various heating powers
10.1016/j.apenergy.2025.126328 · 2025
Thermal runaway classification and early warning for lithium-ion batteries based on voltage feature statistics and multi-model fusion
10.1016/j.applthermaleng.2025.128075 · 2025
An early fault detection method of series battery packs based on multi-feature clustering and unsupervised scoring
10.1016/j.energy.2025.135754 · 2025
Data-driven fault diagnosis in battery systems through cross-cell monitoring
10.1109/jsen.2020.3017812 · 2021
Active model-based fault diagnosis in reconfigurable battery systems
10.1109/tpel.2020.3012964 · 2021
Internal short circuit fault diagnosis for automotive lithium-ion batteries using an adaptive parameter identification model
10.1016/j.measurement.2025.117664 · 2025
Cylindrical battery fault detection under extreme fast charging: a physics-based learning approach
10.1109/tec.2021.3112950 · 2022
An integration framework based on deep learning and CFD for early detection of lithium-ion battery thermal runaway
10.1016/j.applthermaleng.2025.126460 · 2025
Realistic fault detection of li-ion battery via dynamical deep learning
10.1038/s41467-023-41226-5 · 2023
Thermal fault detection of lithium-ion battery packs through an integrated physics and deep neural network based model
10.1038/s44172-025-00409-2 · 2025
Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks
10.1016/j.apenergy.2023.121949 · 2023
Fault isolating and grading for li-ion battery packs based on pseudo images and convolutional neural network
10.1016/j.energy.2022.125867 · 2023
Advancing battery safety: integrating multiphysics and machine learning for thermal runaway prediction in lithium-ion battery module
10.1016/j.jpowsour.2024.235015 · 2024
Prediction of thermal runaway for a lithium-ion battery through multiphysics-informed DeepONet with virtual data
10.1016/j.etran.2024.100337 · 2024
Battery temperature anomaly early warning for electric vehicles under real driving conditions using a temporal convolutional network
10.1016/j.etran.2025.100445 · 2025
Mamba: linear-time sequence modeling with selective state spaces
2023
Unresolved referenced work
2017
Decoupled weight decay regularization
2017
Battery temperature anomaly early warning for electric vehicles under real driving conditions using a temporal convolutional network
10.1016/j.etran.2025.100445 · doi-reference
Prediction of thermal runaway for a lithium-ion battery through multiphysics-informed DeepONet with virtual data
10.1016/j.etran.2024.100337 · doi-reference
Advancing battery safety: integrating multiphysics and machine learning for thermal runaway prediction in lithium-ion battery module
10.1016/j.jpowsour.2024.235015 · doi-reference
Fault isolating and grading for li-ion battery packs based on pseudo images and convolutional neural network
10.1016/j.energy.2022.125867 · doi-reference
Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks
10.1016/j.apenergy.2023.121949 · doi-reference
Thermal fault detection of lithium-ion battery packs through an integrated physics and deep neural network based model
10.1038/s44172-025-00409-2 · doi-reference
Realistic fault detection of li-ion battery via dynamical deep learning
10.1038/s41467-023-41226-5 · doi-reference
An integration framework based on deep learning and CFD for early detection of lithium-ion battery thermal runaway
10.1016/j.applthermaleng.2025.126460 · doi-reference
Cylindrical battery fault detection under extreme fast charging: a physics-based learning approach
10.1109/tec.2021.3112950 · doi-reference
Internal short circuit fault diagnosis for automotive lithium-ion batteries using an adaptive parameter identification model
10.1016/j.measurement.2025.117664 · doi-reference
Active model-based fault diagnosis in reconfigurable battery systems
10.1109/tpel.2020.3012964 · doi-reference
Data-driven fault diagnosis in battery systems through cross-cell monitoring
10.1109/jsen.2020.3017812 · doi-reference
An early fault detection method of series battery packs based on multi-feature clustering and unsupervised scoring
10.1016/j.energy.2025.135754 · doi-reference
Thermal runaway classification and early warning for lithium-ion batteries based on voltage feature statistics and multi-model fusion
10.1016/j.applthermaleng.2025.128075 · doi-reference
Advanced ultrasonic detection of lithium-ion battery thermal runaway under various heating powers
10.1016/j.apenergy.2025.126328 · doi-reference
Study on the extreme early warning method of thermal runaway utilizing li-ion battery strain
10.1016/j.apenergy.2025.125494 · doi-reference
An online data-driven fault diagnosis and thermal runaway early warning for electric vehicle batteries
10.1109/tpel.2022.3173038 · doi-reference
Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles
10.1016/j.apenergy.2016.12.143 · doi-reference
Advanced fault diagnosis for Lithium-ion battery systems: a review of fault mechanisms, fault features, and diagnosis procedures
10.1109/mie.2020.2964814 · doi-reference
Early warning method for thermal runaway of lithium-ion batteries under thermal abuse condition based on online electrochemical impedance monitoring
10.1016/j.jechem.2023.12.049 · doi-reference
Degradation diagnostics for lithium ion cells
10.1016/j.jpowsour.2016.12.011 · doi-reference
State-of-charge estimation of LiFePO4 batteries in electric vehicles: a deep-learning enabled approach
10.1016/j.apenergy.2021.116812 · doi-reference
Machine learning pipeline for battery state-of-health estimation
10.1038/s42256-021-00312-3 · doi-reference
Battery lifetime prognostics
10.1016/j.joule.2019.11.018 · doi-reference
Data-driven prediction of battery cycle life before capacity degradation
10.1038/s41560-019-0356-8 · doi-reference
Thermal runaway mechanism of lithium ion battery for electric vehicles: a review
10.1016/j.ensm.2017.05.013 · doi-reference
Thermal runaway of Lithium-ion batteries without internal short circuit
10.1016/j.joule.2018.06.015 · doi-reference
Thermal runaway mechanism of lithium-ion battery with LiNi0.8Mn0.1Co0.1O2 cathode materials
10.1016/j.nanoen.2021.105878 · doi-reference
Thermal runaway process in lithium-ion batteries: a review
10.1016/j.nxener.2024.100186 · doi-reference
Data-driven methods for early warning of battery thermal runaway: a review of multi-signal fusion and machine learning approaches
10.1016/j.est.2025.118043 · doi-reference
Review on thermal runaway of lithium-ion batteries for electric vehicles
10.1007/s11664-021-09281-0 · doi-reference
A review of thermal runaway mechanism, safety enhancement, monitoring and early warning of solid-state lithium batteries
10.1016/j.est.2025.118294 · doi-reference
Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles
10.1016/j.apenergy.2020.115855 · doi-reference
A review on thermal runaway warning technology for lithium-ion batteries
10.1016/j.rser.2024.114882 · doi-reference
The future of energy storage: advancements and roadmaps for lithium-ion batteries
10.3390/ijms24087457 · doi-reference
The lithium-ion battery: state of the art and future perspectives
10.1016/j.rser.2018.03.002 · doi-reference