An adaptive and interpretable SOH estimation method for lithium-ion batteries based-on relaxation voltage cross-scale features and multi-LSTM-RFR2

Guangzheng Lyu; Heng Zhang; Qiang Miao

doi:10.1016/j.energy.2024.132167

journal article Sep 01, 2024

An adaptive and interpretable SOH estimation method for lithium-ion batteries based-on relaxation voltage cross-scale features and multi-LSTM-RFR2

Guangzheng Lyu Heng Zhang

Qiang Miao

Energy Vol. 304 pp. 132167 · Elsevier BV

View at Publisher Save 10.1016/j.energy.2024.132167

Topics

No keywords indexed for this article. Browse by subject →

References

40

[1]

Chen "A new SOH estimation method for Lithium-ion batteries based on model-data-fusion" Energy (2024) 10.1016/j.energy.2023.129597

[2]

Bockrath "State of health estimation of lithium-ion batteries with a temporal convolutional neural network using partial load profiles" Appl Energy (2023) 10.1016/j.apenergy.2022.120307

[3]

Lee "State-of-health estimation of Li-ion batteries in the early phases of qualification tests: an interpretable machine learning approach" Expert Syst Appl (2022) 10.1016/j.eswa.2022.116817

[4]

Zhang "Cost-effective Lebesgue sampling long short-term memory networks for lithium-ion batteries diagnosis and prognosis" IEEE Trans Ind Electron (2021) 10.1109/tie.2021.3060675

[5]

Gong "State of health estimation for lithium-ion battery based on energy features" Energy (2022) 10.1016/j.energy.2022.124812

[6]

Lyu "Parallel state fusion LSTM-based early-cycle stage lithium-ion battery RUL prediction under lebesgue sampling framework" Reliab Eng Syst Saf (2023) 10.1016/j.ress.2023.109315

[7]

Xu "A physics-informed dynamic deep autoencoder for accurate state-of-health prediction of lithium-ion battery" Neural Comput Appl (2022)

[8]

Rapid and sustainable battery health diagnosis for recycling pretreatment using fast pulse test and random forest machine learning

Shengyu Tao, Ruifei Ma, Yuou Chen et al.

Journal of Power Sources 2024 10.1016/j.jpowsour.2024.234156

[9]

A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery

Xin Sui, Shan He, Søren B. Vilsen et al.

Applied Energy 2021 10.1016/j.apenergy.2021.117346

[10]

Thelen "Integrating physics-based modeling and machine learning for degradation diagnostics of lithium-ion batteries" EnSM (2022)

[11]

Tian "Electrode ageing estimation and open circuit voltage reconstruction for lithium ion batteries" EnSM (2021)

[12]

Jokar "Review of simplified Pseudo-two-Dimensional models of lithium-ion batteries" J Power Sources (2016) 10.1016/j.jpowsour.2016.07.036

[13]

Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method

Shiyi Fu, Shengyu Tao, Hongtao Fan et al.

Applied Energy 2024 10.1016/j.apenergy.2023.121991

[14]

Meng "Multi-scenarios transferable learning framework with few-shot for early lithium-ion battery lifespan trajectory prediction" Energy (2024) 10.1016/j.energy.2023.129682

[15]

Hong "State of health prediction for proton exchange membrane fuel cells combining semi-empirical model and machine learning" Energy (2024) 10.1016/j.energy.2024.130364

[16]

Ansari "Jellyfish optimized recurrent neural network for state of health estimation of lithium-ion batteries" Expert Syst Appl (2024) 10.1016/j.eswa.2023.121904

[17]

Zhang "RUL prediction and uncertainty management for multisensor system using an integrated data-level fusion and UPF approach" IEEE Trans Ind Inform (2020) 10.1109/tii.2020.3017194

[18]

Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries

Jin-Zhen Kong, Fangfang Yang, Xi Zhang et al.

Energy 2021 10.1016/j.energy.2021.120114

[19]

Li "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles" Energy (2022)

[20]

Jones "Impedance-based forecasting of lithium-ion battery performance amid uneven usage" Nat Commun (2022) 10.1038/s41467-022-32422-w

[21]

Lee "A convolutional neural network model for SOH estimation of Li-ion batteries with physical interpretability" Mech Syst Signal Process (2023) 10.1016/j.ymssp.2022.110004

[22]

Lin "A multi-feature-based multi-model fusion method for state of health estimation of lithium-ion batteries" J Power Sources (2022) 10.1016/j.jpowsour.2021.230774

[23]

Lyu "An interpretable state of health estimation method for lithium-ion batteries based on multi-category and multi-stage features" Energy (2023)

[24]

Li "Random forest regression for online capacity estimation of lithium-ion batteries" Appl Energy (2018) 10.1016/j.apenergy.2018.09.182

[25]

Lin "Improving state-of-health estimation for lithium-ion batteries via unlabeled charging data" EnSM (2023)

[26]

Ruan "Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging" Appl Energy (2023) 10.1016/j.apenergy.2023.120751

[27]

Attia "Closed-loop optimization of fast-charging protocols for batteries with machine learning" Nature (2020) 10.1038/s41586-020-1994-5

[28]

Data-driven prediction of battery cycle life before capacity degradation

Kristen A. Severson, Peter M. Attia, Norman Jin et al.

Nature Energy 2019 10.1038/s41560-019-0356-8

[29]

Fan "Battery capacity estimation using 10-second relaxation voltage and a convolutional neural network" Appl Energy (2023) 10.1016/j.apenergy.2022.120308

[30]

Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation

Jiangong Zhu, Yixiu Wang, Yuan Huang et al.

Nature Communications 2022 10.1038/s41467-022-29837-w

[31]

Jiang "An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range" Energy (2023) 10.1016/j.energy.2022.125802

[32]

Baghdadi "State of health assessment for lithium batteries based on voltage–time relaxation measure" Electrochim Acta (2016) 10.1016/j.electacta.2016.02.109

[33]

Hofmann "The ΔQ-method: state of health and degradation mode estimation for lithium-ion batteries using a mechanistic model with relaxed voltage points" J Power Sources (2024) 10.1016/j.jpowsour.2024.234107

[34]

Saha (2007)

[35]

Saha "Prognostics methods for battery health monitoring using a Bayesian framework" IEEE Trans Instrum Meas (2008) 10.1109/tim.2008.2005965

[36]

Lu "Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning" EnSM (2022)

[37]

Qian "State-of-health (SOH) evaluation on lithium-ion battery by simulating the voltage relaxation curves" Electrochim Acta (2019) 10.1016/j.electacta.2019.02.055

[38]

Fang "A state of health estimation method for lithium-ion batteries based on voltage relaxation model" Energies (2019) 10.3390/en12071349

[39]

Li "Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond" Knowl Inf Syst (2022)

[40]

Pei "Development of a voltage relaxation model for rapid open-circuit voltage prediction in lithium-ion batteries" J Power Sources (2014) 10.1016/j.jpowsour.2013.12.083

Metrics

36

Citations

40

References

Details

Published: Sep 01, 2024
Vol/Issue: 304
Pages: 132167
License: View

Authors

Funding

National Natural Science Foundation of China Award: 52205118

China Postdoctoral Science Foundation Award: 2022M722247

Natural Science Foundation of Sichuan Province Award: 2022NSFSC1942

Cite This Article

Guangzheng Lyu, Heng Zhang, Qiang Miao (2024). An adaptive and interpretable SOH estimation method for lithium-ion batteries based-on relaxation voltage cross-scale features and multi-LSTM-RFR2. Energy, 304, 132167. https://doi.org/10.1016/j.energy.2024.132167

An adaptive and interpretable SOH estimation method for lithium-ion batteries based-on relaxation voltage cross-scale features and multi-LSTM-RFR2

You May Also Like