Cited by 127Battery Modeling and ManagementControl Systems and IdentificationAdvancements in Battery Materials

The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index.

A novel Gaussian model based battery state estimation approach: State-of-Energy

He, HongWen, Zhang, YongZhi, Xiong, Rui, Wang, Chun·2015·Applied Energy

DOI 10.1016/j.apenergy.2015.04.062

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.apenergy.2015.04.062

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

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.2004.02.032 · Cited by 1,927
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.2004.02.031 · Cited by 1,385
Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach
He, H., Xiong, R., & Fan, J. (2011) · Energies · doi:10.3390/en4040582 · Cited by 1,180
Self-citation
State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model
He, H., Xiong, R., Zhang, X., Sun, F., & Fan, J. (2011) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2011.2132812 · Cited by 825
Self-citation
A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation
Hu, C. K., Youn, B. D., & Chung, J. (2011) · Applied Energy · doi:10.1016/j.apenergy.2011.08.002 · Cited by 561
Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2006) · Journal of Power Sources · doi:10.1016/j.jpowsour.2006.06.004 · Cited by 547
A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles
Xiong, R., Sun, F., Chen, Z., & He, H. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.07.061 · Cited by 529
Self-citation
Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2006) · Journal of Power Sources · doi:10.1016/j.jpowsour.2006.06.003 · Cited by 390
Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering
Lee, J.-M., Nam, O., & Cho, B. H. (2007) · Journal of Power Sources · doi:10.1016/j.jpowsour.2007.03.072 · Cited by 332

Show all 27 references

Sorted by citations

Thermal runaway mechanism of lithium ion battery for electric vehicles: A review
Feng, X., Ouyang, M., Liu, X., Lu, L., Xia, Y., & He, X. (2017) · Energy storage materials · doi:10.1016/j.ensm.2017.05.013 · Cited by 4,383
A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems
Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110015 · Cited by 1,394
Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries
Shrivastava, P., Soon, T. K., Idris, M. Y. I. B., & Mekhilef, S. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.06.040 · Cited by 704
A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation
Patil, M. A., Tagade, P., Hariharan, K. S., Kolake, S. M., Song, T., Yeo, T., & Doo, S. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.08.119 · Cited by 631
Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine
Feng, X., Weng, C., He, X., Han, X., Lu, L., Ren, D., & Ouyang, M. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2927120 · Cited by 505
Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles
Zheng, Y., Ouyang, M., Han, X., Lu, L., & Li, J. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.11.094 · Cited by 493
A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles
Wang, Z., Feng, G., Zhen, D., Gu, F., & Ball, A. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.113 · Cited by 441
Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
Chen, D., Hong, W., & Zhou, X. (2022) · IEEE Access · doi:10.1109/access.2022.3151975 · Cited by 420
State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles
Zhang, R., Xia, B., Li, B., Cao, L., Lai, Y., Zheng, W., Wang, H., & Wang, W. (2018) · Energies · doi:10.3390/en11071820 · Cited by 320
A comparison of methodologies for the non-invasive characterisation of commercial Li-ion cells
Barai, A., Uddin, K., Dubarry, M., Somerville, L., McGordon, A., Jennings, P., & Bloom, I. (2019) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2019.01.001 · Cited by 346
Optimizing rooftop photovoltaic distributed generation with battery storage for peer-to-peer energy trading
Nguyen, S., Peng, W., Sokolowski, P., Alahakoon, D., & Yu, X. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.07.042 · Cited by 292
Combined State of Charge and State of Energy Estimation of Lithium-Ion Battery Using Dual Forgetting Factor-Based Adaptive Extended Kalman Filter for Electric Vehicle Applications
Shrivastava, P., Soon, T. K., Idris, M. Y. I., Mekhilef, S., & Adnan, S. B. R. S. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3051655 · Cited by 283
Lithium-Ion Battery Pack State of Charge and State of Energy Estimation Algorithms Using a Hardware-in-the-Loop Validation
Zhang, Y., Xiong, R., He, H., & Shen, W. (2016) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2016.2603229 · Cited by 236
Self-citation
Digital twin and cloud-side-end collaboration for intelligent battery management system
Wang, Y., Xu, R., Zhou, C., Kang, X., & Chen, Z. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.11.006 · Cited by 222
Review on technological advancement of lithium-ion battery states estimation methods for electric vehicle applications
Shrivastava, P., Naidu, P. A., Sharma, S., Panigrahi, B. K., & Garg, A. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107159 · Cited by 205
State Estimation Models of Lithium-Ion Batteries for Battery Management System: Status, Challenges, and Future Trends
Zhou, L., Lai, X., Li, B., Yao, Y., Yuan, M. H., Weng, J., & Zheng, Y. (2023) · Batteries · doi:10.3390/batteries9020131 · Cited by 176
A Review on lithium-ion battery thermal management system techniques: A control-oriented analysis
Ghaeminezhad, N., Wang, Z., & Ouyang, Q. (2022) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2022.119497 · Cited by 170
A Practical Lithium-Ion Battery Model for State of Energy and Voltage Responses Prediction Incorporating Temperature and Ageing Effects
Li, K., Wei, F., Tseng, K. J., & Soong, B.-H. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2779411 · Cited by 160
Remaining life prediction of lithium-ion batteries based on health management: A review
Song, K., Hu, D., Tong, Y., & Yue, X. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106193 · Cited by 158
A fractional-order model-based state estimation approach for lithium-ion battery and ultra-capacitor hybrid power source system considering load trajectory
Wang, Y., Gao, G., Li, X., & Chen, Z. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2019.227543 · Cited by 147
Physics-informed machine learning model for battery state of health prognostics using partial charging segments
Kohtz, S., Xu, Y., Zheng, Z., & Wang, P. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109002 · Cited by 147
Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales
Dai, H., Xu, T., Zhu, L., Wei, X., & Sun, Z. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.10.020 · Cited by 141
An online model-based method for state of energy estimation of lithium-ion batteries using dual filters
Dong, G., Chen, Z., Wei, J., Zhang, C., & Wang, P. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.10.011 · Cited by 139
A novel method for lithium-ion battery state of energy and state of power estimation based on multi-time-scale filter
Zhang, X., Wang, Y., Wu, J., & Chen, Z. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.02.117 · Cited by 137
Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology
Li, S., He, H., & Li, J. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.03.154 · Cited by 136
Self-citation
Probability 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
A novel method for state of energy estimation of lithium-ion batteries using particle filter and extended Kalman filter
Lai, X., Huang, Y., Han, X., Gu, H., & Zheng, Y. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103269 · Cited by 113
An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles
Wang, Y., Zhang, C., & Chen, Z. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.11.087 · Cited by 112
A comprehensive review of battery thermal management systems for electric vehicles: Enhancing performance, sustainability, and future trends
Togun, H., Basem, A., Dhabab, J. M., Mohammed, H. I., Sadeq, A. M., Biswas, N., Abdulrazzaq, T., Hasan, H. A., Homod, R. Z., & Talebizadehsardari, P. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.11.093 · Cited by 115
Active battery cell equalization based on residual available energy maximization
Diao, W., Xue, N., Bhattacharjee, V., Jiang, J., Karabaşoğlu, O., & PECHT, M. G. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.07.137 · Cited by 107
A Relative State of Health Estimation Method Based on Wavelet Analysis for Lithium-Ion Battery Cells
Xu, J., Mei, X., Wang, X., Fu, Y., Zhao, Y., & Wang, J. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.3001836 · Cited by 96
Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: State-of-energy
Lin, C. W., Mu, H., Xiong, R., & Cao, J. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.05.065 · Cited by 94
Self-citation
On-line remaining energy prediction: A case study in embedded battery management system
Wang, Y., Chen, Z., & Zhang, C. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.05.081 · Cited by 94
Novel methods for estimating lithium-ion battery state of energy and maximum available energy
Zheng, L., Zhu, J., Wang, G., He, T., & Wei, Y. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.06.031 · Cited by 92
Soft Sensors for State of Charge, State of Energy, and Power Loss in Formula Student Electric Vehicle
Purohit, K., Srivastava, S., Nookala, V., Joshi, V., Shah, P., Sekhar, R., Panchal, S., Fowler, M. W., Fraser, R. A., Tran, M., et al. (2021) · Applied System Innovation · doi:10.3390/asi4040078 · Cited by 91
Power capability evaluation for lithium iron phosphate batteries based on multi-parameter constraints estimation
Wang, Y., Pan, R. N., Liu, C., Chen, Z., & Ling, Q. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.11.019 · Cited by 86
Estimation of Lithium-Ion Batteries State-Condition in Electric Vehicle Applications: Issues and State of the Art
Laadjal, K., & Cardoso, A. J. M. (2021) · Electronics · doi:10.3390/electronics10131588 · Cited by 89
Real-time estimation of state-of-charge in lithium-ion batteries using improved central difference transform method
Xuan, D.-J., Shi, Z., Chen, J., Zhang, C., & Wang, Y. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119787 · Cited by 83
Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects
Lai, 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
A novel capacity estimation method based on charging curve sections for lithium-ion batteries in electric vehicles
Zheng, Y., Wang, J., Qin, C., Lu, L., Han, X., & Ouyang, M. (2019) · Energy · doi:10.1016/j.energy.2019.07.059 · Cited by 78
Remaining energy estimation for lithium-ion batteries via Gaussian mixture and Markov models for future load prediction
Niri, M. F., Truong, B. N. M., Truong, D. Q., Hosseinzadeh, E., Yu, T. F., & Marco, J. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101271 · Cited by 76
State of charge estimation of Lithium-ion battery using an improved fractional-order extended Kalman filter
Solomon, O. O., Zheng, W., Chen, J., & Zhu, Q. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104007 · Cited by 75
Online dynamic equalization adjustment of high-power lithium-ion battery packs based on the state of balance estimation
Wang, S., Shang, L., Li, Z., Deng, H., & Li, J. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.01.013 · Cited by 75
Parameter identification of a lithium‐ion battery based on the improved recursive least square algorithm
Ren, B., Xie, C., Sun, X., Zhang, Q., & Yan, D. (2020) · IET Power Electronics · doi:10.1049/iet-pel.2019.1589 · Cited by 71
Identification of fractional-order equivalent circuit model of lithium-ion battery for improving estimation of state of charge
Wang, J., Yu, W., Cheng, G., & Chen, L. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108043 · Cited by 66
Advancing Electric Vehicle Battery Analysis With Digital Twins in Intelligent Transportation Systems
Saba, I., Ullah, M., & Tariq, M. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3361807 · Cited by 65
A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles
Xu, J., Wang, J., Li, S., & Cao, B. (2016) · Sensors · doi:10.3390/s16081328 · Cited by 64
State of charge estimation by multi-innovation unscented Kalman filter for vehicular applications
Sassi, H. B., Errahimi, F., & Es-Sbai, N. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101978 · Cited by 62
A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part II: Parameter identification and state of energy estimation for LiFePO 4 battery
Li, X., Pan, K., Fan, G., Lu, R., Zhu, C., Rizzoni, G., & Canova, M. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.09.048 · Cited by 62
State of Energy Estimation for Lithium-Ion Battery Pack via Prediction in Electric Vehicle Applications
An, F., Jiang, J., Zhang, W., Zhang, C., & Fan, X. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3125194 · Cited by 61
Battery remaining discharge energy estimation based on prediction of future operating conditions
Ren, D., Lu, L., Shen, P., Feng, X., Han, X., & Ouyang, M. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.100836 · Cited by 60
A joint state-of-health and state-of-energy estimation method for lithium-ion batteries through combining the forgetting factor recursive least squares and unscented Kalman filter
Lai, X., Weng, J., Huang, Y., Yuan, M. H., Yao, Y., Han, X., & Zheng, Y. (2022) · Measurement · doi:10.1016/j.measurement.2022.112187 · Cited by 61
Distance to empty soft sensor for ford escape electric vehicle
Sekhar, R., Shah, P., Panchal, S., Fowler, M. W., & Fraser, R. A. (2022) · Results in Control and Optimization · doi:10.1016/j.rico.2022.100168 · Cited by 57
State of Charge Estimation for Lithium-Ion Battery with a Temperature-Compensated Model
Yang, S., Deng, C. L., Zhang, Y., & He, Y. (2017) · Energies · doi:10.3390/en10101560 · Cited by 54
Self-citation
Model‐based state of X estimation of lithium‐ion battery for electric vehicle applications
Shrivastava, P., Tey, K. S., Idris, M. Y. I., Mekhilef, S., & Adnan, S. B. R. S. (2022) · International Journal of Energy Research · doi:10.1002/er.7874 · Cited by 53
Energy state of health estimation for battery packs based on the degradation and inconsistency
Diao, W., Jiang, J., Zhang, C., Liang, H., & PECHT, M. G. (2017) · Energy Procedia · doi:10.1016/j.egypro.2017.12.248 · Cited by 48
Butler-Volmer equation-based model and its implementation on state of power prediction of high-power lithium titanate batteries considering temperature effects
Jiang, J., Liu, S., Ma, Z., Wang, L. Y., & Wu, K. (2016) · Energy · doi:10.1016/j.energy.2016.10.087 · Cited by 47
Joint estimation of the state-of-energy and state-of-charge of lithium-ion batteries under a wide temperature range based on the fusion modeling and online parameter prediction
Xia, L., Wang, S., Yu, C., Fan, Y., Li, B., & Xie, Y. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105010 · Cited by 47
Accurate lithium-ion battery modeling with inverse repeat binary sequence for electric vehicle applications
Zhu, R., Duan, B., Zhang, C., & Gong, S. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113339 · Cited by 47
Battery pack consistency modeling based on generative adversarial networks
Fan, X., Zhang, W., Sun, B., Zhang, J., & He, X. (2021) · Energy · doi:10.1016/j.energy.2021.122419 · Cited by 45
An Accurate Co-Estimation of Core Temperature and State of Charge for Lithium-Ion Batteries With Electrothermal Model
Liu, X., Li, Y., Kang, Y., Zhao, G., Duan, B., & Zhang, C. (2023) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2023.3304754 · Cited by 43
A Multi-Timescale Estimator for Lithium-Ion Battery State of Charge and State of Energy Estimation Using Dual H Infinity Filter
Xu, W. Z., Xu, J., Lang, J., & Yan, X. (2019) · IEEE Access · doi:10.1109/access.2019.2959396 · Cited by 43
Remaining driving range prediction for electric vehicles: Key challenges and outlook
Mei, P., Karimi, H. R., Huang, C., Chen, F., & Yang, S. (2023) · IET Control Theory and Applications · doi:10.1049/cth2.12486 · Cited by 42
Comparison of SOC Estimation between the Integer-Order Model and Fractional-Order Model Under Different Operating Conditions
Jin, G., Li, L., Xu, Y., Hu, M., Fu, C., & Qin, D. (2020) · Energies · doi:10.3390/en13071785 · Cited by 39
Artificial intelligence-driven rechargeable batteries in multiple fields of development and application towards energy storage
Li, Z., Zhang, S., Huang, H., Liu, R., Cai, M., Bian, Y., Chang, L., & Du, H. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108926 · Cited by 39
An integrated online adaptive state of charge estimation approach of high-power lithium-ion battery packs
Wang, S., Fernández, C., Shang, L., Li, Z., & Yuan, H. (2017) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331217694681 · Cited by 35
Community shared ES-PV system for managing electric vehicle loads via multi-agent reinforcement learning
Talihati, B., Fu, S., Zhang, B., Zhao, Y., Wang, Y., & Sun, Y. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.125039 · Cited by 32
Multi-state joint estimation of series battery pack based on multi-model fusion
Liu, F., Yu, D., Su, W., & Bu, F. (2023) · Electrochimica Acta · doi:10.1016/j.electacta.2023.141964 · Cited by 31
Battery state estimation methods and management system under vehicle–cloud collaboration: A Survey
Mei, P., Karimi, H. R., Xie, J., Chen, F., Ou, L., Yang, S., & Huang, C. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.114857 · Cited by 29
A novel fusion model based online state of power estimation method for lithium-ion capacitor
Chen, W., Cheng, X., Chen, M., Jiang, K., & Wang, K. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102387 · Cited by 29
Distributed Intelligent Battery Management System Using a Real-World Cloud Computing System
García, E., Quiles, E., & Correcher, A. (2023) · Sensors · doi:10.3390/s23073417 · Cited by 26
Multi-state joint estimation for a lithium-ion hybrid capacitor over a wide temperature range
Li, X., Long, T., Tian, J., & Tian, Y. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228677 · Cited by 26
A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health
Mei, P., Karimi, H. R., Chen, F., Yang, S., Huang, C., & Qiu, S. (2022) · Sensors · doi:10.3390/s22239474 · Cited by 25
Utilizing Artificial Intelligence Techniques for Modeling Minimum Miscibility Pressure in Carbon Capture and Utilization Processes: A Comprehensive Review and Applications
Amar, M. N., Djema, H., Ourabah, K., Alqahtani, F. M., & Ghasemi, M. (2024) · Energy & Fuels · doi:10.1021/acs.energyfuels.4c02249 · Cited by 24
Arrhenius Equation-Based Model to Predict Lithium-Ions Batteries’ Performance
Zeng, L., Hu, Y., Lu, C., Pan, G., & Li, M. (2022) · Journal of Marine Science and Engineering · doi:10.3390/jmse10101553 · Cited by 23
Remaining discharge energy prediction for lithium-ion batteries over broad current ranges: A machine learning approach
Tu, H., Borah, M., Moura, S. J., Wang, Y., & Fang, H. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124086 · Cited by 22
Toward a function realization of multi‐scale modeling for lithium‐ion battery based on CHAIN framework
Ma, B., Yu, H., Yang, L., Liu, Q., Xie, H., Chen, S., Zhang, Z., Zhang, C., Zhang, L., Wang, W., et al. (2022) · Rare Metals · doi:10.1007/s12598-022-02138-3 · Cited by 20
Electric fleet charging management considering battery degradation and nonlinear charging profile
Shi, J., Zeng, T., & Moura, S. J. (2023) · Energy · doi:10.1016/j.energy.2023.129094 · Cited by 20
State of Energy Estimation of Lithium Titanate Battery for Rail Transit Application
Zhai, G., Liu, S., Wang, Z., Zhang, W., & Ma, Z. (2017) · Energy Procedia · doi:10.1016/j.egypro.2017.03.681 · Cited by 19
State of health forecasting of Lithium-ion batteries operated in a battery electric vehicle fleet
von Bülow, F., Wassermann, M., & Meisen, T. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108271 · Cited by 19
Understanding the Energy Potential of Lithium‐Ion Batteries: Definition and Estimation of the State of Energy
Quade, K. L., Jöst, D., Sauer, D. U., & Li, W. (2023) · Batteries & Supercaps · doi:10.1002/batt.202300152 · Cited by 17
An SOE estimation model considering electrothermal effect for LiFePO4/C battery
Lin, S., Song, W., Lv, J., Feng, Z., Zhang, Y., & Li, Y. (2017) · International Journal of Energy Research · doi:10.1002/er.3818 · Cited by 16
Self-citation
Lithium-ion battery State-of-Latent-Energy (SoLE): A fresh new look to the problem of energy autonomy prognostics in storage systems
Rozas, H., Troncoso-Kurtovic, D., Ley, C. P., & Orchard, M. E. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102735 · Cited by 16
Cloud-Battery management system based health-aware battery fast charging architecture using error-correction strategy for electric vehicles
Bose, B., Shaosen, S., Li, W., Gao, L., Wei, K., & Garg, A. (2023) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2023.101197 · Cited by 15
A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency
Fang, Q., Wei, X., & Dai, H. (2019) · Energies · doi:10.3390/en12060987 · Cited by 15
Net Hydrogen Consumption Minimization of Fuel Cell Hybrid Trains Using a Time-Based Co-Optimization Model
Meng, G., Wu, C., Zhang, B., Xue, F., & Lu, S. (2022) · Energies · doi:10.3390/en15082891 · Cited by 14
Improved back-propagation neural network-multi-information gain optimization Kalman filter method for high-precision estimation of state-of-energy in lithium-ion batteries
Shi, H., Wu, Q., Wang, S., Cao, W., Li, Y., Fernández, C., & Huang, Q. (2025) · Energy · doi:10.1016/j.energy.2025.138214 · Cited by 13
Study of the influencing factors on the discharging performance of lithium-ion batteries and its index of state-of-energy
Li, K., Tseng, K. J., & Moraleja, L. (2016) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2016.7793567 · Cited by 12
Overview of “Mechanical Abuse-thermal Runaway” of Electric Vehicle Power Battery and Its Safety Prevention and Control Technology
Tongxin, S., Zhenpo, W., Hong, J., Qu, C., Zhang, J., Yangjie, Z., & Yankai, H. (2022) · Journal of Mechanical Engineering · doi:10.3901/jme.2022.14.252 · Cited by 12
A Comprehensive Review of Key Technologies for Enhancing the Reliability of Lithium-Ion Power Batteries
Ren, Y., Jin, C., Fang, S., Yang, L., Wu, Z., Wang, Z., Peng, R., & Gao, K. (2023) · Energies · doi:10.3390/en16176144 · Cited by 12
Accurate Remaining Available Energy Estimation of LiFePO4 Battery in Dynamic Frequency Regulation for EVs with Thermal-Electric-Hysteresis Model
Zhang, Z., Lu, L., Li, Y., Wang, H., & Ouyang, M. (2023) · Energies · doi:10.3390/en16135239 · Cited by 11
Evaluation of the model-based state-of-charge estimation methods for lithium-ion batteries
Zhang, Y., Xiong, R., & He, H. (2016) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec.2016.7520184 · Cited by 11
Self-citation
Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat
Meng, B., Wang, Y., Mao, J., Liu, J., Xu, G., & Dai, J. (2018) · Energies · doi:10.3390/en11030586 · Cited by 11
A Critical Review of the State Estimation Methods of Power Batteries for Electric Vehicles
Zhang, Q., Rong, H., Zhao, D., Pei, M., & Dong, X. G. (2025) · Energies · doi:10.3390/en18143834 · Cited by 10
Performance evaluation of lithium battery pack based on MATLAB simulation with lumped parameter thermal model
Zeng, L., Hu, Y., Lu, C., & Pan, G. (2023) · Energy Science & Engineering · doi:10.1002/ese3.1477 · Cited by 9
Research on fire safety control and early warning mechanism for hybrid lithium-ion supercapacitors
Wu, M., Zhang, C., & Xie, S. (2024) · Ionics · doi:10.1007/s11581-024-05581-7 · Cited by 9
An electrical model capable of estimating the state of energy for lithium-ion batteries used in energy storage systems
Li, K., & Tseng, K. J. (2016) · IEEE Annual Southern Power Electronics Conference · doi:10.1109/spec.2016.7846167 · Cited by 9
Accurate battery models matter: Improving battery performance assessment using a novel energy management architecture
Wang, H., Pourmousavi, S., Soong, W. L., Zhang, X.-N., & Yuan, R. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.236216 · Cited by 8
Advanced battery modeling and State-of-Charge estimation of lithium-ion batteries: A comprehensive review of modeling approaches, parameterization, and operational challenges
Rout, S., Das, S., Kumar, K. M. S., Dhanamjayulu, C., & Muyeen, S. M. (2026) · Energy Strategy Reviews · doi:10.1016/j.esr.2026.102192 · Cited by 9
Bayesian filtering techniques for state of charge and state of health estimation in lithium-ion batteries for electric vehicles: Methods, challenges, and future directions
Kham, S. L., & Aunsri, N. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128015 · Cited by 9

Authors: He, H., Zhang, Y., Xiong, R., & Wang, C.
Affiliations: Beijing Institute of Technology (CN)
Corresponding author: He, HongWen
Xiong, Rui
ORCID: He, HongWen
Zhang, YongZhi
Xiong, Rui
Wang, Chun
Venue: Applied Energy
Publisher: Elsevier BV
Published: 2015-08
Volume: 151
Pages: 41-48
ISSN: 0306-2619 (ISSN-L)
DOI: 10.1016/j.apenergy.2015.04.062
OpenAlex: W2012660988
Cited by: 127
References: 27
Citations by year: 2026: 7 · 2025: 9 · 2024: 11 · 2023: 18 · 2022: 16 · 2021: 12 · 2020: 9 · 2019: 12 · 2018: 5 · 2017: 12 · 2016: 13 · 2015: 3
FWCI: 11.36
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Applied Energy (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: National Science & Technology Pillar Program (2013BAG05B00)
Beijing Institute of Technology Research Fund Program for Young Scholars
Excellent young scholars Research Fund of Beijing Institute of Technology
Topics: Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
Keywords: battery management systems, electric vehicles, open-circuit voltage, genetic algorithm parameter selection, Akaike information criterion, central difference Kalman filter, lithium-ion batteries, LiFePO4, LiMn2O4

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

A novel Gaussian model based battery state estimation approach: State-of-Energy

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks