The DOI matches its Crossref record; 39 of the 40 references it cites were found in a registry or an index.
Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression
Abstract
Abstract Since a battery pack consists of hundreds of cells in series and parallel, inconsistencies between cells make it difficult to create an explicit model to simulate its behaviors effectively. Therefore, the widely used and sophisticated model-based methods (such as Kalman filters) are difficult to apply to SOC (state of charge) estimation of battery packs. In this paper, a data-driven method based on Gaussian process regression (GPR) is proposed to provide a feasible solution. Its superiority includes the ability to approximate nonlinearity accurately, nonparametric modeling, and probabilistic predictions. First, a feature extraction strategy, including data preprocessing, correlation analysis, and principal component analysis, is employed to obtain a compacted input set with a high correlation with SOC. Second, the squared exponential kernel function is used, and the automatic relevance determination is applied to optimize the weights of features. Third, besides the regular GPR model, an autoregressive GPR model is also constructed to further improve estimation accuracy and confidence. The experimental results verify that the autoregressive model has better SOC estimation performance than the regular model, and its estimation error under different dynamic cycles, temperatures, aging conditions, and even extreme conditions is lower than 3.9%, and the confidence interval is also much narrower.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“Some methodologies, including fuzzy adaptive federated filtering[125] and GPR[60], have been developed to address these challenges.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [60]
Citations
Cites
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
A review on the key issues for lithium-ion battery management in electric vehicles
Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.060 · Cited by 4,837
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768
A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions
Schulz, E., Speekenbrink, M., & Krause, A. (2018) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2018.03.001 · Cited by 1,735
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
Waag, W., Fleischer, C., & Sauer, D. U. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.02.064 · Cited by 1,059
Xing, Y., He, W., Pecht, M., & Tsui, K. L. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.07.008 · Cited by 977
State estimation for advanced battery management: Key challenges and future trends
Hu, X., Feng, F., Liu, K., Zhang, L., Xie, J., & Liu, B. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109334 · Cited by 857
Self-citationYang, D., Zhang, X., Pan, R., Wang, Y., & Chen, Z. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.03.015 · Cited by 765
Data Preprocessing For Supervised Leaning
Kotsiantis, S. B., Kanellopoulos, D. N., & Pintelas, P. (2007) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1082415 · Cited by 669
Research works printed but not linked: 2
- Plett GL. Efficient battery pack state estimation using bar-delta filtering. Conference Efficient battery pack state estimation using bar-delta filtering.
- Liu X, He Y, Chen Z. State-of-Charge estimation for power Li-ion battery pack using V min-EKF. Conference State-of-Charge estimation for power Li-ion battery pack using V min-EKF. IEEE, p. 27-31.
Cited by
Sorted by citations
Overview of batteries and battery management for electric vehicles
Liu, W., Placke, T., & Chau, K. T. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.03.016 · Cited by 776
Kumar, R. R., Bharatiraja, C., Udhayakumar, K., Devakirubakaran, S., Sekar, K. S., & Mihet‐Popa, L. (2023) · IEEE Access · doi:10.1109/access.2023.3318121 · Cited by 497
Lipu, M. S. H., Hannan, M. A., Karim, T. F., Hussain, A., Saad, M. H. M., Ayob, A., Miah, M. S., & Mahlia, T. M. I. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.126044 · Cited by 478
Wang, Z., Feng, G., Zhen, D., Gu, F., & Ball, A. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.113 · Cited by 441
Gao, Y., Liu, K., Zhu, C., Zhang, X., & Zhang, D. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3066946 · Cited by 422
Mitigation strategies for Li-ion battery thermal runaway: A review
Xu, B. S., Lee, J., Kwon, D., Kong, L., & PECHT, M. G. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111437 · Cited by 395
Hossain Lipu, M. S., Hannan, M. A., Hussain, A., Ayob, A., Saad, M. H. M., Karim, T. F., & How, D. N. T. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.124110 · Cited by 378
Adaikkappan, M., & Nageswari, S. (2021) · International Journal of Energy Research · doi:10.1002/er.7339 · Cited by 326
Zhang, G., Wei, X., Tang, X., Zhu, J., Chen, S., & Dai, H. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110790 · Cited by 318
Ma, L., Hu, C., & Cheng, F. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102440 · Cited by 308
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
Self-citationRen, Z. G., & Du, C. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.01.108 · Cited by 292
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
Self-citationPredictive Battery Health Management With Transfer Learning and Online Model Correction
Che, Y., Deng, Z., Lin, X., Hu, L., & Hu, X. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3055811 · Cited by 261
Self-citationXu, Z., Wang, J., Lund, P. D., & Zhang, Y. (2021) · Energy · doi:10.1016/j.energy.2021.122815 · Cited by 258
Lai, X., Huang, Y., Gu, H., Deng, C., Han, X., Feng, X., & Zheng, Y. (2021) · Energy storage materials · doi:10.1016/j.ensm.2021.05.010 · Cited by 256
Lai, X., Huang, Y., Deng, C., Gu, H., Han, X., Zheng, Y., & Ouyang, M. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111162 · Cited by 251
Shu, X., Shen, J., Li, G., Zhang, Y., Chen, Z., & Liu, Y. (2021) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2021.3074638 · Cited by 230
Jiang, L., Deng, Z., Tang, X., Hu, L., Lin, X., & Hu, X. (2021) · Energy · doi:10.1016/j.energy.2021.121266 · Cited by 218
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
Self-citationElectrochemical Technologies for Lithium Recovery from Liquid Resources: A Review
Wang, J., Yue, X., Wang, P., Yu, T., Du, X., Hao, X., Abudula, A., & Guan, G. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111813 · Cited by 211
Kong, J., Yang, F., Zhang, X., Pan, E., Peng, Z., & Wang, D. (2021) · Energy · doi:10.1016/j.energy.2021.120114 · Cited by 204
Perspectives 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 205
Greenbank, S., & Howey, D. A. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3106593 · Cited by 201
Hannan, M. A., How, D. N. T., Lipu, M. S. H., Mansor, M., Ker, P. J., Dong, Z. Y., Sahari, K. S. M., Tiong, S. K., Muttaqi, K. M., Mahlia, T. M. I., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-98915-8 · Cited by 196
Liu, Y., He, Y., Bian, H., Guo, W., & Zhang, X. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104664 · Cited by 210
Self-citationLithium-ion batteries remaining useful life prediction based on BLS-RVM
Chen, Z., Shi, N., Ji, Y., Niu, M., & Wang, Y. (2021) · Energy · doi:10.1016/j.energy.2021.121269 · Cited by 197
Lipu, M. S. H., Ansari, S., Miah, M. S., Meraj, S. T., Hasan, K., Shihavuddin, A., Hannan, M. A., Muttaqi, K. M., & Hussain, A. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105752 · Cited by 199
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
Connecting battery technologies for electric vehicles from battery materials to management
Zhao, G., Wang, X., & Negnevitsky, M. (2022) · iScience · doi:10.1016/j.isci.2022.103744 · Cited by 198
SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture
Fan, X., Zhang, W., Zhang, C., Chen, A., & An, F. (2022) · Energy · doi:10.1016/j.energy.2022.124612 · Cited by 180
Alsuwian, T. M., Ansari, S., Zainuri, M. A. A. M., Ayob, A., Hussain, A., Lipu, M. S. H., Alhawari, A. R. H., Almawgani, A. H. M., Almasabi, S., & Hindi, A. T. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.123123 · Cited by 182
Liu, K., Peng, Q., Che, Y., Zheng, Y., Li, K., Teodorescu, R., Widanage, W. D., & Barai, A. (2022) · Advances in Applied Energy · doi:10.1016/j.adapen.2022.100117 · Cited by 172
Self-citationWang, Y.-X., Chen, Z., & Zhang, W. (2022) · Energy · doi:10.1016/j.energy.2022.123178 · Cited by 166
Guo, S., & Ma, L. (2023) · Energy · doi:10.1016/j.energy.2022.125872 · Cited by 162
Sherkatghanad, Z., Ghazanfari, A., & Makarenkov, V. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111524 · Cited by 162
State of charge estimation of lithium batteries: Review for equivalent circuit model methods
Tao, Z., Zhao, Z., Wang, C., Huang, L., Jie, H., Li, H., Hao, Q., Zhou, Y., & See, K. Y. (2024) · Measurement · doi:10.1016/j.measurement.2024.115148 · Cited by 157
Pan, D., Liu, X., Li, H., Huang, Z., Zhang, K., Shao, L., & Abedinia, O. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111287 · Cited by 146
Aging mechanisms, prognostics and management for lithium-ion batteries: Recent advances
Wang, Y., Xiang, H., Soo, Y.-Y., & Fan, X. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.114915 · Cited by 142
State of charge estimation of lithium-ion batteries based on PSO-TCN-Attention neural network
Li, F., Zuo, W., Zhou, K., Li, Q., & Huang, Y. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.110806 · Cited by 145
Cavus, M., Dissanayake, D. A., & Bell, M. C. (2025) · Energies · doi:10.3390/en18051041 · Cited by 171
Chen, J., Zhang, Y., Li, W., Cheng, W., & Zhu, Q. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105396 · Cited by 132
A review on various optical fibre sensing methods for batteries
Han, G.-C., Yan, J.-Z., Guo, Z.-W., Greenwood, D., Marco, J., & Yu, Y. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111514 · Cited by 121
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
Hong, J., Liang, F., Yang, H., Zhang, C., Zhang, X., Zhang, H., Wang, W., Li, K., & Yang, J. (2024) · eTransportation · doi:10.1016/j.etran.2024.100322 · Cited by 125
Stacked bidirectional LSTM RNN to evaluate the remaining useful life of supercapacitor
Liu, C., Zhang, Y., Sun, J., Cui, Z., & Wang, K. (2021) · International Journal of Energy Research · doi:10.1002/er.7360 · Cited by 126
Jamei, M., Ahmadianfar, I., Olumegbon, I. A., Karbasi, M., & Asadi, A. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.102067 · Cited by 120
Electric vehicles and charging infrastructure in Turkey: An overview
Gönül, Ö., Duman, A. C., & Güler, Ö. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110913 · Cited by 125
Li, F., Zuo, W., Zhou, K., Li, Q., Huang, Y., & Zhang, G. (2023) · Energy · doi:10.1016/j.energy.2023.130025 · Cited by 122
Jiang, Z., Shi, Q., Wei, Y., Hanlin, W., Bingzhao, G., & He, L. (2021) · Energy · doi:10.1016/j.energy.2021.120805 · Cited by 118
State of health prognostics for series battery packs: A universal deep learning method
Che, Y., Deng, Z., Li, P., Tang, X., Khosravinia, K., Lin, X., & Hu, X. (2022) · Energy · doi:10.1016/j.energy.2021.121857 · Cited by 116
Self-citationLiu, X., Li, K., Wu, J., He, Y., & Liu, X. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102655 · Cited by 114
Oyewole, I., Chehade, A., & Kim, Y. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118726 · Cited by 118
Gao, Y., Zhu, C., Zhang, X., & Guo, B. (2020) · Energy · doi:10.1016/j.energy.2020.119688 · Cited by 111
He, L., Wang, Y., Wei, Y., Wang, M., Hu, X., & Shi, Q. (2021) · Energy · doi:10.1016/j.energy.2021.122627 · Cited by 110
Self-citationBattery monitoring and prognostics optimization techniques: Challenges and opportunities
Semeraro, C., Caggiano, M., Olabi, A. G., & Dassisti, M. (2022) · Energy · doi:10.1016/j.energy.2022.124538 · Cited by 111
A review of state-of-health estimation for lithium-ion battery packs
Li, Q., Song, R., & Wei, Y. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116078 · Cited by 106
Coppitters, D., Paepe, W. D., & Contino, F. (2020) · Energy · doi:10.1016/j.energy.2020.118798 · Cited by 103
Zafar, M. H., Khan, N. M., Houran, M. A., Mansoor, M., Akhtar, N., & Sanfilippo, F. (2024) · Energy · doi:10.1016/j.energy.2024.130584 · Cited by 100
Wang, L., Zhao, X., Deng, Z., & Yang, L. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106275 · Cited by 99
Self-citationLi, F., Min, Y., Zhang, Y., Zhang, Y., Zuo, H., & Bai, F. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109787 · Cited by 101
Memon, S. A., Upadhyay, D. S., & Patel, R. N. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103446 · Cited by 98
State of Charge Estimation for Lithium-Ion Batteries Based on TCN-LSTM Neural Networks
Hu, C., Cheng, F., Ma, L., & Li, B. (2022) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ac5cf2 · Cited by 100
Rahmani, P., Chakraborty, S., Mele, I., Katrašnik, T., Bernhard, S., Prüfling, S., Wilkins, S., & Hegazy, O. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.235827 · Cited by 110
Yu, H., Zhang, L., Wang, W., Li, S., Chen, S., Yang, S., Li, J., & Liu, X. (2023) · Energy · doi:10.1016/j.energy.2023.127846 · Cited by 96
A Reduced-Order Electrochemical Model for All-Solid-State Batteries
Deng, Z., Hu, X., Lin, X., Xu, L., Li, J., & Guo, W. (2020) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2020.3026962 · Cited by 96
Self-citationMahmoodzadeh, A., Nejati, H. R., & Mohammadi, M. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104305 · Cited by 95
Wang, Q., Ye, M., Wei, M., Lian, G., & Li, Y. (2022) · Energy · doi:10.1016/j.energy.2022.125718 · Cited by 96
Development and Demonstration of Microgrid System Utilizing Second-Life Electric Vehicle Batteries
Lacap, J., Park, J. W., & Beslow, L. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102837 · Cited by 97
A Novel State of Charge Approach of Lithium Ion Battery Using Least Squares Support Vector Machine
Li, J., Ye, M., Wei, M., Xu, X., & Jiao, S. (2020) · IEEE Access · doi:10.1109/access.2020.3033451 · Cited by 92
Zhang, K., Jiang, L., Deng, Z., Xie, Y., Couture, J., Lin, X., Zhou, J., & Hu, X. (2023) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2023.3234770 · Cited by 92
Self-citationLi, C., Zhang, H., Ding, P., Yang, S., & Bai, Y. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113576 · Cited by 96
Lipu, M. S. H., Miah, M. S., Jamal, T., Rahman, T., Ansari, S., Rahman, M. S., Ashique, R. H., Shihavuddin, A., & Shakib, M. N. (2023) · Vehicles · doi:10.3390/vehicles6010002 · Cited by 103
Meng, J., Yue, M., & Diallo, D. (2022) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2022.3209629 · Cited by 90
Zou, R., Duan, Y., Wang, Y., Pang, J., Liu, F., & Sheikh, S. R. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106298 · Cited by 88
Chen, L., Song, Y., Lopes, A. M., Bao, X., Zhang, Z., & Lin, Y. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2023.3291501 · Cited by 85
Ma, L., & Zhang, T. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.01.036 · Cited by 84
Almaita, E., Alshkoor, S., Abdelsalam, E., & Almomani, F. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104761 · Cited by 84
Zhang, Q., Li, X., Du, Z., & Liao, Q. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102743 · Cited by 82
Li, X., Xu, J., Hong, J., Tian, J., & Tian, Y. (2020) · Energy · doi:10.1016/j.energy.2020.118858 · Cited by 81
Sensitivity Analysis and Joint Estimation of Parameters and States for All-Solid-State Batteries
Deng, Z., Hu, X., Lin, X., Kim, Y., & Li, J. (2021) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2021.3050987 · Cited by 81
Self-citationLai, X., Huang, Y., Gu, H., Han, X., Feng, X., Dai, H., Zheng, Y., & Ouyang, M. (2021) · Energy · doi:10.1016/j.energy.2021.121754 · Cited by 81
Estimating battery state of charge using recurrent and non-recurrent neural networks
Vidal, C., Malysz, P., Naguib, M., Emadi, A., & Kollmeyer, P. J. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103660 · Cited by 84
Zou, W., Li, J., Yang, Q., Wan, X., He, Y., & Lan, H. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120667 · Cited by 79
Strong robustness and high accuracy in predicting remaining useful life of supercapacitors
Liu, C., Li, D., Wang, L., Li, L., & Wang, K. (2022) · APL Materials · doi:10.1063/5.0092074 · Cited by 79
Xu, P., Hu, X., Liu, B., Ouyang, T., & Chen, N. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3131725 · Cited by 78
Self-citationTian, Y., Huang, Z., Tian, J., & Li, X. (2021) · Energy · doi:10.1016/j.energy.2021.121917 · Cited by 76
Chen, H., Zhang, T., Gao, Q., Han, Z., Jin, Y., Li, L., Yang, K., Xu, Y., Liu, X., Xu, X., et al. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.134583 · Cited by 76
An RUL prediction approach for lithium-ion battery based on SADE-MESN
Ji, Y., Chen, Z., Shen, Y., Yang, K., Wang, Y., & Cui, J. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107195 · Cited by 74
Takyi-Aninakwa, P., Wang, S., Zhang, H., Yang, X., & Fernandez, C. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120043 · Cited by 75
Li, Y., Ye, M., Wang, Q., Lian, G., & Xia, B. (2024) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2024.100163 · Cited by 78
Zhang, S., Zhang, C., Jiang, S., & Zhang, X. (2022) · Energy · doi:10.1016/j.energy.2022.123423 · Cited by 74
Zhang, G., Xia, B., Wang, J., Ye, B., Chen, Y., Yu, Z., & Li, Y. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104211 · Cited by 73
Yang, K., Tang, Y., & Zhang, Z. (2021) · Energies · doi:10.3390/en14041054 · Cited by 71
Díaz‐González, F., Chillón-Antón, C., Llonch-Masachs, M., Arellano, S. G., Rull‐Duran, J., Bergas, J., & Bullich‐Massagué, E. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104446 · Cited by 77
Jiang, B., Tao, S., Wang, X., Zhu, J., Wei, X., & Dai, H. (2023) · Energy · doi:10.1016/j.energy.2023.127890 · Cited by 70
Wu, J., Fang, C., Jin, Z., Zhang, L., & Xing, J. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104666 · Cited by 68
Hosseininasab, S., Momtaheni, N., Pischinger, S., Günther, M., & Bauer, L. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109011 · Cited by 67
Wang, C., Li, R., Cao, Y. F., & Li, M. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.235272 · Cited by 67
Che, Y., Deng, Z., Tang, X., Lin, X., Nie, X., & Hu, X. (2022) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-021-00668-y · Cited by 64
Self-citation
Metadata
- Authors
- Deng, Z., Hu, X., Lin, X., Che, Y., Xu, L., & Guo, W.
- Affiliations
- Chongqing University (CN)
- Ontario Tech University (CA)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Hu, Xiaosong
- Lin, Xianke
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 205
- Pages
- 118000
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W3033613562
- Semantic Scholar
- a491ddd78e3d1e5acaa49684b5aa2f2270b68d97
- Cited by
- 423
- References
- 40
- Citations by year
- 2026: 42 · 2025: 79 · 2024: 70 · 2023: 70 · 2022: 77 · 2021: 73 · 2020: 12
- FWCI
- 21.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Natural Science Foundation Project of Chongqing, Chongqing Science and Technology Commission
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Keywords
- state of charge estimation, lithium-ion battery packs, Gaussian process regression, data-driven estimation, cell inconsistency, feature extraction, principal component analysis, automatic relevance determination, Gaussian kernel, autoregressive models, probabilistic forecasting, battery degradation, temperature effects
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
