A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Existing machine learning models for battery degradation diagnosis are all developed with the charging and discharging curve as input.
“However, those models are all developed with the charging and discharging curve as input.”
From Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning (2020) · cites this paper as [34]
The paper departs from prior GPR works by employing impedance spectra as input and ARD to shed light on salient frequencies.
“Although GPR has been used in the literature in the context of Li-ion batteries17,33,34, we depart from those pioneering works by employing impedance spectra as input, as well employing ARD to shed light on salient frequencies.”
From Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning (2020) · cites this paper as [34]
Processed external features are normally extracted from differential charging curves under constant current rate, such as IC/DV curves and voltage gradient curves.
“Processed external features are normally extracted from differential charging curves under constant current rate, such as IC/DV curves [77,78] and voltage gradient curves (dV/dt) [79,80].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [80]
Cites
Gaussian Processes for Regression
Williams, C. K. I., & Rasmussen, C. E. (1995) · Aston Publications Explorer (Aston University) · Cited by 1,121 · see citing works
Prognostics methods for battery health monitoring using a bayesian framework
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831 · see citing works
Gaussian process regression for forecasting battery state of health
Richardson, R. R., Osborne, M. A., & Howey, D. A. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.05.004 · Cited by 732 · see citing works
Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., & Dietmayer, K. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.11.146 · Cited by 664 · see citing works
Hu, C. K., Youn, B. D., & Chung, J. (2011) · Applied Energy · doi:10.1016/j.apenergy.2011.08.002 · Cited by 561 · see citing works
Weng, C., Cui, Y., Sun, J., & Peng, H. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.02.012 · Cited by 561 · see citing works
Wu, J., Zhang, C., & Chen, Z. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.04.057 · Cited by 531 · see citing works
Self-citationTosun, N. (2005) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-004-2386-y · Cited by 471 · see citing works
Eddahech, A., Briat, O., Bertrand, N., Delétage, J.-Y., & Vinassa, J.-M. (2012) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2012.04.050 · Cited by 462 · see citing works
Liu, D., Pang, J., Zhou, J., Peng, Y., & Pecht, M. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2013.03.010 · Cited by 443 · see citing works
Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries
Andre, D., Appel, C., Soczka-Guth, T., & Sauer, D. U. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.001 · Cited by 428 · see citing works
Gaussian Processes for Ordinal Regression
Chu, W., & Ghahramani, Z. (2005) · Cambridge University Engineering Department Publications Database · Cited by 370 · see citing works
A naive Bayes model for robust remaining useful life prediction of lithium-ion battery
Ng, S. S. Y., Xing, Y., & Tsui, K. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.12.020 · Cited by 340 · see citing works
Dong, H., Jin, X., Lou, Y., & Wang, C. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.07.176 · Cited by 310 · see citing works
A novel health indicator for on-line lithium-ion batteries remaining useful life prediction
Zhou, Y., Huang, M., Chen, Y., & Tao, Y. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.04.119 · Cited by 289 · see citing works
Bole, B. M., Kulkarni, C. S., & Daigle, M. J. (2014) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2014.v6i1.2490 · Cited by 251 · see citing works
A novel state of health estimation method of Li-ion battery using group method of data handling
Wu, J., Wang, Y., Zhang, X., & Chen, Z. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.07.065 · Cited by 235 · see citing works
Self-citationOlivares, B. E., Cerda, M. A., Orchard, M. E., & Silva, J. F. (2012) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2012.2215142 · Cited by 217 · see citing works
Wei, Z., Tseng, K. J., Wai, N., Lim, T. M., & Skyllas‐Kazacos, M. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.09.123 · Cited by 214 · see citing works
A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries
Wang, Y., Zhang, C., & Chen, Z. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.08.081 · Cited by 200 · see citing works
Self-citationGaussian Processes for Ordinal Regression
ChuWei, & GhahramaniZoubin (2005) · Journal of Machine Learning Research · doi:10.5555/1046920.1088707 · Cited by 190 · see citing works
Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques
Saha, B., & Goebel, K. (2008) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero.2008.4526631 · Cited by 187 · see citing works
Gaussian process regression for multivariate spectroscopic calibration
Chen, T., Morris, J., & Martin, E. (2007) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2006.09.004 · Cited by 175 · see citing works
A Review of SOH Estimation Methods in Lithium-ion Batteries for Electric Vehicle Applications
Cheng, L., Tang, A., & Wang, W. (2015) · Energy Procedia · doi:10.1016/j.egypro.2015.07.199 · Cited by 174 · see citing works
Physically-based reduced-order capacity loss model for graphite anodes in Li-ion battery cells
Jin, X., Vora, A. P., Hoshing, V., Saha, T., Shaver, G. M., Garcı́a, R. E., Wasynczuk, O., & Varigonda, S. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.12.099 · Cited by 168 · see citing works
Method for estimating capacity and predicting remaining useful life of lithium-ion battery
Hu, C. K., Jain, G., Tamirisa, P. A., & Gorka, T. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.03.086 · Cited by 162 · see citing works
Xian, W., Long, B., Li, M., & Wang, H. (2013) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2013.2276473 · Cited by 143 · see citing works
Zhang, X., Wang, Y., Yang, D., & Chen, Z. (2016) · Energy · doi:10.1016/j.energy.2016.08.109 · Cited by 143 · see citing works
Self-citationDu, J., Liu, Z., Wang, Y., & Wen, C. (2016) · Control Engineering Practice · doi:10.1016/j.conengprac.2016.05.014 · Cited by 132 · see citing works
Self-citationProbability based remaining capacity estimation using data-driven and neural network model
Wang, Y., Yang, D., Zhang, X., & Chen, Z. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.03.054 · Cited by 120 · see citing works
Self-citationTorai, S., Nakagomi, M., Yoshitake, S., Yamaguchi, S., & Oyama, N. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.11.070 · Cited by 104 · see citing works
Cheng, Y., Lü, C., Li, T., & Tao, L. (2015) · Energy · doi:10.1016/j.energy.2015.07.022 · Cited by 75 · see citing works
Sun, P., & Zhenpo, W. (2016) · Energy Procedia · doi:10.1016/j.egypro.2016.06.086 · Cited by 17 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193 · see citing works
“Processed external features are normally extracted from differential charging curves under constant current rate, such as IC/DV curves [77,78] and voltage gradient curves (dV/dt) [79,80].”
BackgroundZhang, Y., Tang, Q., Zhang, Y., Wang, J., Stimming, U., & Lee, A. A. (2020) · Nature Communications · doi:10.1038/s41467-020-15235-7 · Cited by 889 · see citing works
“We use a zero mean function and a linear (LIN) covariance function Although GPR has been used in the literature in the context of Li-ion batteries 17,33,34 , we depart from those pioneering works by employing impedance spectra as input, as well employing ARD to shed light on salient frequencies.”
MethodLiu, K., Shang, Y., Ouyang, Q., & Widanage, W. D. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2973876 · Cited by 838 · see citing works
Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation
Zhu, J., Wang, Y., Huang, Y., Gopaluni, R. B., Cao, Y., Heere, M., Mühlbauer, M. J., Mereacre, L., Dai, H., Liu, X., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29837-w · Cited by 835 · see citing works
Self-citationRandom forest regression for online capacity estimation of lithium-ion batteries
Li, Y., Zou, C., Berecibar, M., Nanini-Maury, E., Chan, J. C.-W., van den Bossche, P., Van Mierlo, J., & Omar, N. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.09.182 · Cited by 727 · see citing works
Machine learning pipeline for battery state-of-health estimation
Roman, D., Saxena, S., Robu, V., PECHT, M. G., & Flynn, D. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00312-3 · Cited by 689 · see citing works
Artificial Intelligence Applied to Battery Research: Hype or Reality?
Lombardo, T., Duquesnoy, M., El-Bouysidy, H., Årén, F., Gallo‐Bueno, A., Jørgensen, P. B., Bhowmik, A., Demortière, A., Ayerbe, E., Alcaide, F., et al. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00108 · Cited by 511 · see citing works
Li, X., Yuan, C., Li, X., & Wang, Z. (2019) · Energy · doi:10.1016/j.energy.2019.116467 · Cited by 485 · see citing works
Review on state-of-health of lithium-ion batteries: Characterizations, estimations and applications
Yang, S., Zhang, C., Jiang, J., Zhang, W., Zhang, L., & Wang, Y. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.128015 · Cited by 435 · see citing works
Self-citationThe challenge and opportunity of battery lifetime prediction from field data
Sulzer, V., Mohtat, P., Aitio, A., Lee, S., Yeh, Y. T., Steinbacher, F., Khan, M. U. A., Lee, J. W., Siegel, J. B., Stefanopoulou, A. G., et al. (2021) · Joule · doi:10.1016/j.joule.2021.06.005 · Cited by 424 · see citing works
Deng, Z., Hu, X., Lin, X., Che, Y., Xu, L., & Guo, W. (2020) · Energy · doi:10.1016/j.energy.2020.118000 · Cited by 423 · see citing works
A Novel Deep Learning Framework for State of Health Estimation of Lithium-Ion Battery
Fan, Y., Xiao, F., Li, C., Yang, G., & Tang, X. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101741 · Cited by 415 · see citing works
A deep learning method for online capacity estimation of lithium-ion batteries
Shen, S., Sadoughi, M., Chen, X., Hong, M., & Hu, C. K. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.100817 · Cited by 399 · see citing works
A review on prognostics and health management (PHM) methods of lithium-ion batteries
Meng, H., & Li, Y. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109405 · Cited by 393 · see citing works
Li, X., Wang, Z., Zhang, L., Zou, C., & Dorrell, D. D. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.10.069 · Cited by 388 · see citing works
Liu, K., Li, Y., Hu, X., Lucu, M., & Widanage, W. D. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2941747 · Cited by 383 · see citing works
Tan, Y., & Zhao, G. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2946551 · Cited by 376 · see citing works
Gou, B., Xu, Y. H., & Feng, X. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3014932 · Cited by 376 · see citing works
Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning
Hu, X., Che, Y., Lin, X., & Onori, S. (2020) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2020.3017090 · Cited by 376 · see citing works
Jiang, B., Zhu, J., Wang, X., Wei, X., Shang, W., & Dai, H. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119502 · Cited by 375 · see citing works
Online capacity estimation of lithium-ion batteries with deep long short-term memory networks
Li, W., Sengupta, N., Dechent, P., Howey, D. A., Annaswamy, A. M., & Sauer, D. U. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228863 · Cited by 365 · see citing works
Venkatesan, C., Patil, C. K., Karthick, A., Dharmaraj, G., Rahim, R., & Ghosh, A. (2021) · World Electric Vehicle Journal · doi:10.3390/wevj12010038 · Cited by 364 · see citing works
Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data
Deng, Z., Hu, X., Li, P., Lin, X., & Bian, X. (2021) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2021.3134701 · Cited by 364 · see citing works
Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries
Liu, K., Hu, X., Wei, Z., Li, Y., & Jiang, Y. (2019) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2019.2944802 · Cited by 358 · see citing works
Ma, G., Zhang, Y. A., Cheng, C., Zhou, B., Hu, P., & Yuan, Y. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113626 · Cited by 343 · see citing works
Li, Q., Li, D., Zhao, K., Wang, L., & Wang, K. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104215 · Cited by 335 · see citing works
Rauf, H., Khalid, M., & Arshad, N. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111903 · Cited by 331 · see citing works
Li, X., Wang, Z., & Yan, J. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2019.03.008 · Cited by 317 · see citing works
Early prediction of battery lifetime via a machine learning based framework
Fei, Z., Yang, F., Tsui, K., Li, L., & Zhang, Z. (2021) · Energy · doi:10.1016/j.energy.2021.120205 · Cited by 302 · see citing works
Battery health estimation with degradation pattern recognition and transfer learning
Deng, Z., Lin, X., Cai, J., & Hu, X. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2022.231027 · Cited by 301 · see citing works
Liu, Y., Wang, L., Li, D., & Wang, K. (2023) · Protection and Control of Modern Power Systems · doi:10.1186/s41601-023-00314-w · Cited by 291 · see citing works
Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles
Deng, Z., Xu, L., Liu, H., Hu, X., Duan, Z., & Xu, Y. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120954 · Cited by 273 · see citing works
Meng, H., Geng, M., & Han, T. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109288 · Cited by 260 · see citing works
Chen, Z., Zhao, H., Zhang, Y., Shen, S., Shen, J., & Liu, Y. (2021) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.230892 · Cited by 250 · see citing works
Self-citationZhou, D., Li, Z., Zhu, J., Zhang, H., & Hou, L. (2020) · IEEE Access · doi:10.1109/access.2020.2981261 · Cited by 242 · see citing works
Review of battery state estimation methods for electric vehicles-Part II: SOH estimation
Demirci, O., Taşkın, S., Schaltz, E., & Demirci, B. A. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.112703 · Cited by 241 · see citing works
A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods
Yao, L., Xu, S., Tang, A., Zhou, F., Hou, J., Xiao, Y., & Fu, Z. (2021) · World Electric Vehicle Journal · doi:10.3390/wevj12030113 · Cited by 237 · see citing works
Wen, Y., Rahman, M. F., Xu, H., & Tseng, T.-L. (2021) · Measurement · doi:10.1016/j.measurement.2021.110276 · Cited by 231 · see citing works
Lin, M., Wu, D., Meng, J., Wu, J., & Wu, H. (2021) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.230774 · Cited by 228 · see citing works
Impedance-based forecasting of lithium-ion battery performance amid uneven usage
Jones, P., Stimming, U., & Lee, A. A. (2022) · Nature Communications · doi:10.1038/s41467-022-32422-w · Cited by 228 · see citing works
Jia, J., Liang, J., Shi, Y., Wen, J., Pang, X., & Zeng, J. (2020) · Energies · doi:10.3390/en13020375 · Cited by 223 · see citing works
Deng, Y., Ying, H., Jiaqiang, E., Zhu, H., Wei, K., Chen, J., Zhang, F., & Liao, G. (2019) · Energy · doi:10.1016/j.energy.2019.03.177 · Cited by 222 · see citing works
Yang, F., Wang, D., Xu, F., Huang, Z., & Tsui, K. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228654 · Cited by 216 · see citing works
Liu, J., & Chen, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2905740 · Cited by 215 · see citing works
Self-citationGeneral Discharge Voltage Information Enabled Health Evaluation for Lithium-Ion Batteries
Deng, Z., Hu, X., Lin, X., Xu, L., Che, Y., & Hu, L. (2020) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2020.3040010 · Cited by 212 · see citing works
Shu, X., Shen, S., Shen, J., Zhang, Y., Li, G., Chen, Z., & Liu, Y. (2021) · iScience · doi:10.1016/j.isci.2021.103265 · Cited by 210 · see citing works
Self-citationHealth Prognosis for Electric Vehicle Battery Packs: A Data-Driven Approach
Hu, X., Che, Y., Lin, X., & Deng, Z. (2020) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2020.2986364 · Cited by 206 · see citing works
Kong, J., Yang, F., Zhang, X., Pan, E., Peng, Z., & Wang, D. (2021) · Energy · doi:10.1016/j.energy.2021.120114 · Cited by 204 · see citing works
Self-citationPerspectives and challenges for future lithium-ion battery control and management
Wang, Y., Zhang, X., Li, K., Zhao, G., & Chen, Z. (2023) · eTransportation · doi:10.1016/j.etran.2023.100260 · Cited by 202 · see citing works
Self-citationTagade, P., Hariharan, K. S., Ramachandran, S., Khandelwal, A., Naha, A., Kolake, S. M., & Han, S. H. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2019.227281 · Cited by 201 · see citing works
Intelligent state of health estimation for lithium-ion battery pack based on big data analysis
Song, L., Zhang, K., Liang, T., Han, X., & Zhang, Y. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101836 · Cited by 197 · see citing works
Cai, L., Meng, J., Stroe, D., Peng, J., Luo, G., & Teodorescu, R. (2020) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.2987383 · Cited by 194 · see citing works
State-of-charge estimation and remaining useful life prediction of supercapacitors
Liu, C., Li, Q., & Wang, K. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111408 · Cited by 194 · see citing works
Liu, C., Wang, Y., & Chen, Z. (2018) · Energy · doi:10.1016/j.energy.2018.10.131 · Cited by 189 · see citing works
Self-citationMathews, I., Xu, B., He, W., Barreto, V., Buonassisi, T., & Peters, I. M. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115127 · Cited by 178 · see citing works
Dang, W., Liao, S., Yang, B., Yin, Z., Liu, M., Yin, L., & Zheng, W. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106469 · Cited by 175 · see citing works
Estimation of the state of health (SOH) of batteries using discrete curvature feature extraction
Goh, H. H., Lan, Z., Zhang, D., Dai, W., Kurniawan, T. A., & Goh, K. C. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104646 · Cited by 173 · see citing works
Li, Y., Stroe, D., Cheng, Y., Sheng, H., Sui, X., & Teodorescu, R. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.102122 · Cited by 172 · see citing works
Li, X., Yuan, C., & Wang, Z. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228358 · Cited by 170 · see citing works
Thelen, A., Huan, X., Paulson, N. H., Onori, S., Hu, Z., & Hu, C. K. (2024) · npj Materials Sustainability · doi:10.1038/s44296-024-00011-1 · Cited by 170 · see citing works
Xia, F., Wang, K., & Chen, J. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107161 · Cited by 168 · see citing works
Meng, J., Cai, L., Luo, G., Stroe, D., & Teodorescu, R. (2018) · Microelectronics Reliability · doi:10.1016/j.microrel.2018.07.025 · Cited by 168 · see citing works
Lv, Y., Yang, X., & Zhang, G. (2020) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2020.115747 · Cited by 167 · see citing works
Challenges and opportunities for second-life batteries: Key technologies and economy
Gu, X., Bai, H., Cui, X., Zhu, J., Zhuang, W., Li, Z., Hu, X., & Song, Z. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.114191 · Cited by 165 · see citing works
Liu, K., Ashwin, T. R., Hu, X., Lucu, M., & Widanage, W. D. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110017 · Cited by 164 · see citing works
Zhang, S., Guo, X., Dou, X., & Zhang, X. (2020) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2020.100752 · Cited by 163 · see citing works
Self-citationWu, Y., Xue, Q., Shen, J., Lei, Z., Chen, Z., & Liu, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2972344 · Cited by 160 · see citing works
Self-citationMa, G., Xu, S., Yang, T., Du, Z., Zhu, L., Ding, H., & Yuan, Y. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3176925 · Cited by 160 · see citing works
Miranda, D., Gonçalves, R., Wuttke, S., Costa, C. M., & Lanceros-Méndez, S. (2023) · Advanced Energy Materials · doi:10.1002/aenm.202203874 · Cited by 159 · see citing works
Gou, B., Xu, Y. H., & Feng, X. (2020) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2020.3029295 · Cited by 158 · see citing works
Li, Y., Sheng, H., Cheng, Y., Stroe, D., & Teodorescu, R. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115504 · Cited by 156 · see citing works
Constant current charging time based fast state-of-health estimation for lithium-ion batteries
Lin, C., Xu, J., Shi, M., & Mei, X. (2022) · Energy · doi:10.1016/j.energy.2022.123556 · Cited by 156 · see citing works
Benchmark Synthetic Training Data for Artificial Intelligence-based Li-ion Diagnosis and Prognosis
Dubarry, M., & Beck, D. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228806 · Cited by 154 · see citing works
Obregon, J., Han, Y.-R., Ho, C. W., Mouraliraman, D., Lee, C. W., & Jung, J. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.106680 · Cited by 152 · see citing works
Life prediction of lithium-ion batteries based on stacked denoising autoencoders
Xu, F., Yang, F., Fei, Z., Huang, Z., & Tsui, K. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107396 · Cited by 150 · see citing works
Thelen, A., Lui, Y. H., Shen, S., Laflamme, S., Hu, S., Ye, H., & Hu, C. (2022) · Energy storage materials · doi:10.1016/j.ensm.2022.05.047 · Cited by 148 · see citing works
Lai, X., Qiao, D., Zheng, Y., Ouyang, M., Han, X., & Zhou, L. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.12.210 · Cited by 141 · see citing works
Battery prognostics and health management from a machine learning perspective
Zhao, J., Feng, X., Pang, Q., Wang, J., Lian, Y., Ouyang, M., & Burke, A. F. (2023) · Journal of Power Sources · doi:10.1016/j.jpowsour.2023.233474 · Cited by 140 · see citing works
Advances in battery state estimation of battery management system in electric vehicles
Jiang, M., Li, D., Li, Z., Chen, Z., Yan, Q., Fu, L., Yu, C., Jiang, B., Wei, X., Yan, W., et al. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.234781 · Cited by 137 · see citing works
Self-citationXu, H. L., & Shen, M. (2021) · International Journal of Energy Research · doi:10.1002/er.7150 · Cited by 136 · see citing works
Data-Driven Transfer-Stacking-Based State of Health Estimation for Lithium-Ion Batteries
Wu, J., Cui, X., Meng, J., Peng, J., & Lin, M. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2023.3247735 · Cited by 136 · see citing works
State of Health estimation for lithium-ion batteries using Random Forest and Gated Recurrent Unit
Wang, X., Hu, B., Su, X., Xu, L., & Zhu, D. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109796 · Cited by 135 · see citing works
State of health estimation of lithium-ion battery with improved radial basis function neural network
Wu, J., Fang, L., Dong, G., & Lin, M. (2022) · Energy · doi:10.1016/j.energy.2022.125380 · Cited by 133 · see citing works
Battery state of health modeling and remaining useful life prediction through time series model
Lin, C. P., Lin, C.-P., Cabrera, J., Yang, F., Ling, M.-H., Tsui, K., & Bae, S.-J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115338 · Cited by 129 · see citing works
Sun, X., Zhang, Y., Zhang, Y., Wang, L., & Wang, K. (2023) · Energies · doi:10.3390/en16155682 · Cited by 129 · see citing works
Wang, F., & Mamo, T. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.08.073 · Cited by 127 · see citing works
Jia, C., Tian, Y., Shi, Y., Jia, J., Wen, J., & Zeng, J. (2023) · Energy · doi:10.1016/j.energy.2023.129401 · Cited by 126 · see citing works
Mass load prediction for lithium-ion battery electrode clean production: A machine learning approach
Liu, K., Wei, Z., Yang, Z., & Li, K. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.125159 · Cited by 125 · see citing works
Basia, A., Simeu-Abazi, Z., Gascard, E., & Zwolinski, P. (2021) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2021.02.004 · Cited by 125 · see citing works
Prognostics of battery cycle life in the early-cycle stage based on hybrid model
Zhang, Y., Peng, Z., Guan, Y., & Wu, L. (2021) · Energy · doi:10.1016/j.energy.2021.119901 · Cited by 123 · see citing works
Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features
Son, S., Jeong, S., Kwak, E., Kim, J., & Oh, K. (2021) · Energy · doi:10.1016/j.energy.2021.121712 · Cited by 123 · see citing works
State of health estimation based on modified Gaussian process regression for lithium-ion batteries
Wang, J., Deng, Z., Yu, T., Yoshida, A., Xu, L., Guan, G., & Abudula, A. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104512 · Cited by 123 · see citing works
Niri, M. F., Rashid, M., Sansom, J. E. H., Sheikh, M. A., Widanage, D., & Marco, J. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106295 · Cited by 123 · see citing works
A novel graph-based framework for state of health prediction of lithium-ion battery
Yao, X., Chen, G., PECHT, M. G., & Chen, B. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106437 · Cited by 123 · see citing works
Duan, W., Song, S., Xiao, F., Chen, Y., Peng, S., & Song, C. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107322 · Cited by 123 · see citing works
Li, R., Hong, J., Zhang, H., & Chen, X. (2022) · Energy · doi:10.1016/j.energy.2022.124771 · Cited by 122 · see citing works
Lin, M., Yan, C., Wang, W., Dong, G., Meng, J., & Wu, J. (2023) · Energy · doi:10.1016/j.energy.2023.127675 · Cited by 122 · see citing works
Chen, Z., Xue, Q., Xiao, R., Liu, Y., & Shen, J. (2019) · IEEE Access · doi:10.1109/access.2019.2930680 · Cited by 120 · see citing works
Self-citationPredicting battery end of life from solar off-grid system field data using machine learning
Aitio, A., & Howey, D. A. (2021) · Joule · doi:10.1016/j.joule.2021.11.006 · Cited by 118 · see citing works
Battery state of health estimation under dynamic operations with physics-driven deep learning
Tang, A., Xu, Y., Hu, Y., Tian, J., Nie, Y., Yan, F., Tan, Y., & Yu, Q. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123632 · Cited by 118 · see citing works
- Authors
- Yang, D., Zhang, X., Pan, R., Wang, Y., & Chen, Z.
- Affiliations
- University of Science and Technology of China (CN)
- Corresponding author
- Chen, Zonghai
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 384
- Pages
- 387-395
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W2793702125
- Cited by
- 765
- References
- 33
- Citations by year
- 2026: 65 · 2025: 101 · 2024: 124 · 2023: 144 · 2022: 116 · 2021: 98 · 2020: 71 · 2019: 35 · 2018: 11
- FWCI
- 28.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Power Sources (publishedVersion)
- Funders
- National Natural Science Fund of China (61375079)
- CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellow (2017LH007)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- state of health estimation, lithium-ion batteries, Gaussian process regression, charging curves, battery degradation, grey relational analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
