Cited by 178Battery Modeling and ManagementControl Systems and IdentificationElectric and Hybrid Vehicle Technologies

The DOI matches its Crossref record; 31 of the 32 references it cites were found in a registry or an index.

Adaptive unscented Kalman filter based state of energy and power capability estimation approach for lithium-ion battery

Zhang, Weige, Shi, Wei, Ma, Zeyu·2015·Journal of Power Sources

DOI 10.1016/j.jpowsour.2015.04.148

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.jpowsour.2015.04.148

Cites

31 of 31 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?

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.2004.02.033 · Cited by 1,768
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
The state of the art of electric and hybrid vehicles
Chan, C. C. (2002) · Proceedings of the IEEE · doi:10.1109/5.989873 · Cited by 1,102
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
Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles
Chiang, Y., Sean, W., & Ke, J.-C. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.01.005 · Cited by 393
High-Performance Battery-Pack Power Estimation Using a Dynamic Cell Model
Plett, G. L. (2004) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2004.832408 · Cited by 290
Adaptive Partial Differential Equation Observer for Battery State-of-Charge/State-of-Health Estimation Via an Electrochemical Model
Moura, S. J., Chaturvedi, N. A., & Krstić, M. (2013) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4024801 · Cited by 262
A modified model based state of charge estimation of power lithium-ion batteries using unscented Kalman filter
Tian, Y., Xia, B., Sun, W., Xu, Z., & Zheng, W. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.07.143 · Cited by 229
A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles
Xiong, R., Sun, F., Gong, X., & Gao, C. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.09.006 · Cited by 229

Flagged first, then by citations

Show all 32 references

1 of the 32 references couldn’t be matched to a known paper.

Sorted by citations

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
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
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
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
Self-citation
Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends
Dai, H., Jiang, B., Hu, X., Lin, X., Wei, X., & PECHT, M. G. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110480 · Cited by 408
A Novel Fractional Order Model for State of Charge Estimation in Lithium Ion Batteries
Xiong, R., Tian, J., Shen, W., & Sun, F. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2880085 · Cited by 291
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
A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management
Hasib, S. A., Islam, S., Chakrabortty, R. K., Ryan, M. J., Saha, D. K., Ahamed, M. H., Moyeen, S. I., Das, S. K., Ali, M. F., Islam, M. R., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3089032 · Cited by 281
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
Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery
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
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
Noise-Immune Model Identification and State-of-Charge Estimation for Lithium-Ion Battery Using Bilinear Parameterization
Wei, Z., Dong, G., Zhang, X., Pou, J., Quan, Z., & He, H. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2962429 · Cited by 203
A multi-fault diagnostic method based on an interleaved voltage measurement topology for series connected battery packs
Kang, Y., Duan, B., Zhou, Z., Shang, Y., & Zhang, C. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2019.01.058 · Cited by 192
Online state of charge estimation and open circuit voltage hysteresis modeling of LiFePO4 battery using invariant imbedding method
Dong, G., Wei, J., Zhang, C., & Chen, Z. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.10.092 · Cited by 189
A method for state of energy estimation of lithium-ion batteries based on neural network model
Dong, G., Zhang, X., Zhang, C., & Chen, Z. (2015) · Energy · doi:10.1016/j.energy.2015.07.120 · Cited by 187
A novel fractional order model based state-of-charge estimation method for lithium-ion battery
Mu, H., Xiong, R., Zheng, H., Chang, Y., & Chen, Z. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.07.003 · Cited by 186
Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms
Wei, Y., & Wu, D. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108947 · Cited by 188
Disturbance-Immune and Aging-Robust Internal Short Circuit Diagnostic for Lithium-Ion Battery
Hu, J. P., He, H., Wei, Z., & Li, Y. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3063968 · Cited by 161
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
Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter
Baccouche, I., Jemmali, S., Manaï, B., Omar, N., & Amara, N. E. B. (2017) · Energies · doi:10.3390/en10060764 · Cited by 160
Online Estimation of Power Capacity With Noise Effect Attenuation for Lithium-Ion Battery
Wei, Z., Zhao, J., Xiong, R., Dong, G., Pou, J., & Tseng, K. J. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2878122 · Cited by 143
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
A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty
Li, Y., Wang, C., & Gong, J. (2016) · Energy · doi:10.1016/j.energy.2016.05.047 · Cited by 136
Towards health-aware energy management strategies in fuel cell hybrid electric vehicles: A review
Kandidayeni, M., Trovão, J. P. F., Soleymani, M., & Boulon, L. (2022) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.01.064 · Cited by 126
A Review of Equivalent Circuit Model Based Online State of Power Estimation for Lithium-Ion Batteries in Electric Vehicles
Guo, R., & Shen, W. (2021) · Vehicles · doi:10.3390/vehicles4010001 · Cited by 121
Kalman filter for onboard state of charge estimation and peak power capability analysis of lithium-ion batteries
Dong, G., Wei, J., & Chen, Z. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.08.065 · Cited by 117
Model based insulation fault diagnosis for lithium-ion battery pack in electric vehicles
Wang, Y., Tian, J., Chen, Z., & Liu, X. (2018) · Measurement · doi:10.1016/j.measurement.2018.09.007 · Cited by 111
Data-Driven Battery Health Prognosis Using Adaptive Brownian Motion Model
Dong, G., Yang, F., Wei, Z., Wei, J., & Tsui, K. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2948018 · Cited by 110
A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique
Li, Y., Wang, C., & Gong, J. (2017) · Energy · doi:10.1016/j.energy.2017.11.079 · Cited by 103
Lithium-Ion Battery State of Charge and State of Power Estimation Based on a Partial-Adaptive Fractional-Order Model in Electric Vehicles
Guo, R., & Shen, W. (2022) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3220881 · Cited by 99
Modeling and Estimation for Advanced Battery Management
Lin, X., Kim, Y., Mohan, S. N., Siegel, J. B., & Stefanopoulou, A. G. (2019) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-053018-023643 · Cited by 99
State of power estimation of lithium-ion battery based on fractional-order equivalent circuit model
Liu, C., Hu, M., Jin, G., Xu, Y., & Zhai, J. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102954 · Cited by 98
Power capability prediction for lithium-ion batteries using economic nonlinear model predictive control
Zou, C., Klintberg, A., Wei, Z., Fridholm, B., Wik, T., & Egardt, B. S. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.06.034 · 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
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
SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm
Zeng, M., Zhang, P., Yang, Y., Xie, C., & Shi, Y. (2019) · Energies · doi:10.3390/en12163122 · Cited by 85
A review of multi-state joint estimation for lithium-ion battery: Research status and suggestions
Liu, F., Yu, D., Shao, C., Liu, X., & Su, W. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109071 · Cited by 82
State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy
Li, X., Xu, J., Hong, J., Tian, J., & Tian, Y. (2020) · Energy · doi:10.1016/j.energy.2020.118858 · Cited by 81
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
The state of health estimation of lithium-ion batteries based on data-driven and model fusion method
Huang, P., Gu, P., Kang, Y., Zhang, Y., Duan, B., & Zhang, C. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.132742 · Cited by 82
Load-responsive model switching estimation for state of charge of lithium-ion batteries
Tang, X., Gao, F., Zou, C., Yao, K., Hu, W., & Wik, T. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.057 · Cited by 80
Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter
Dong, G., Wei, J., Chen, Z., Sun, H. W., & Yu, X. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.08.040 · Cited by 80
Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery
Wei, Z., Zhao, J., Zou, C., Lim, T. M., & Tseng, K. J. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.09.034 · Cited by 77
Parameter Identification of Lithium-Ion Batteries Model to Predict Discharge Behaviors Using Heuristic Algorithm
Li, J., Zou, L., Tian, F., Dong, X., Zou, Z., & Yang, H. (2016) · Journal of The Electrochemical Society · doi:10.1149/2.0861608jes · Cited by 76
A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation
Zhang, S., Zhang, C., Jiang, S., & Zhang, X. (2022) · Energy · doi:10.1016/j.energy.2022.123423 · Cited by 74
Online Estimation of State of Power for Lithium-Ion Batteries in Electric Vehicles Using Genetic Algorithm
Lu, J., Chen, Z., Yang, Y., & Ming, L. V. (2018) · IEEE Access · doi:10.1109/access.2018.2824559 · Cited by 74
Lithium-ion Battery Instantaneous Available Power Prediction Using Surface Lithium Concentration of Solid Particles in a Simplified Electrochemical Model
Zheng, L., Zhu, J. G., Wang, G., Lu, D. D., & He, T. (2018) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2018.2791965 · Cited by 69
Comprehensive co-estimation of lithium-ion battery state of charge, state of energy, state of power, maximum available capacity, and maximum available energy
Shrivastava, P., Soon, T. K., Idris, M. Y. I., Mekhilef, S., & Adnan, S. B. R. S. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106049 · Cited by 65
A new approach to consider the influence of aging state on Lithium-ion battery state of power estimation for hybrid electric vehicle
Esfandyari, M., Esfahanian, V., Yazdi, M. R. H., Nehzati, H., & Shekoofa, O. (2019) · Energy · doi:10.1016/j.energy.2019.03.176 · Cited by 63
Residual Statistics-Based Current Sensor Fault Diagnosis for Smart Battery Management
Hu, J. P., Bian, X., Wei, Z., Li, J., & He, H. (2021) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2021.3131696 · 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
Self-citation
An improved adaptive estimator for state-of-charge estimation of lithium-ion batteries
Zhang, W., Wang, L., Wang, L., & Liao, C. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.09.016 · Cited by 61
Self-citation
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
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 by finite difference extended Kalman filter with HPPC parameters identification
He, L., Hu, M., Wei, Y., Liu, B., & Shi, Q. (2019) · Science China Technological Sciences · doi:10.1007/s11431-019-1467-9 · Cited by 55
Sequential Monte Carlo Filter for State-of-Charge Estimation of Lithium-Ion Batteries Based on Auto Regressive Exogenous Model
Dong, G., Chen, Z., & Wei, J. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2890499 · Cited by 54
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
Advances in Battery Modeling and Management Systems: A Comprehensive Review of Techniques, Challenges, and Future Perspectives
Madani, S. S., Shabeer, Y., Nair, A. P., Fowler, M. W., Panchal, S., Ziebert, C., Chaoui, H., Dou, S. X., See, K. W., Mekhilef, S., et al. (2025) · Batteries · doi:10.3390/batteries11110426 · Cited by 54
On-board adaptive model for state of charge estimation of lithium-ion batteries based on Kalman filter with proportional integral-based error adjustment
Wei, J., Dong, G., & Chen, Z. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.08.101 · Cited by 50
Modelling Li-ion batteries using equivalent circuits for renewable energy applications
Navas, S. J., González, G. M. C., Pino, J., & Guerra, J. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.03.103 · Cited by 49
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
Self-citation
Renewable integration and energy storage management and conversion in grid systems: A comprehensive review
Ahmad, A. B., Ooi, C. A., Ali, O., Charin, C., Maharum, S. M. M., Swadi, M., & Salem, M. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.02.008 · Cited by 57
Recent advancements in battery state of power estimation technology: A comprehensive overview and error source analysis
Guo, R., & Shen, W. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.114294 · Cited by 50
A New Method for State of Charge Estimation of Lithium-Ion Battery Based on Strong Tracking Cubature Kalman Filter
Xia, B., Wang, H., Wang, M., Sun, W., Xu, Z., & Lai, Y. (2015) · Energies · doi:10.3390/en81212378 · Cited by 43
Optrone: Maximizing Performance and Energy Resources of Drone Batteries
Kim, J., Choi, Y., Jeon, S., Kang, J., & Cha, H. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2020.3012790 · Cited by 41
An enhanced multi-constraint state of power estimation algorithm for lithium-ion batteries in electric vehicles
Guo, R., & Shen, W. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104628 · Cited by 40
State of charge estimation based on a new dual-polarization-resistance model for electric vehicles
Zhao, X., Cai, Y., Yang, L., Deng, Z., & Qiang, J. (2017) · Energy · doi:10.1016/j.energy.2017.06.094 · Cited by 40
A novel state-of-energy simplified estimation method for lithium-ion battery pack based on prediction and representative cells
An, F., Zhang, W., Sun, B., Jiang, J., & Fan, X. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107083 · Cited by 38
Self-citation
Multi-interest adaptive unscented Kalman filter based on improved matrix decomposition methods for lithium-ion battery state of charge estimation
Wang, S., Huang, P., Lian, C., & Liu, H. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.234547 · Cited by 38
Peak power prediction for series-connected LiNCM battery pack based on representative cells
Zhou, Z., Kang, Y., Shang, Y., Cui, N., Zhang, C., & Duan, B. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.05.144 · Cited by 37
Constrained Bayesian dual-filtering for state of charge estimation of lithium-ion batteries
Dong, G., Wei, J., & Chen, Z. (2018) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2018.02.005 · Cited by 36
Remaining available energy prediction for lithium-ion batteries considering electrothermal effect and energy conversion efficiency
Chen, Y., Yang, X., Luo, D., & Wen, R. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102728 · Cited by 37
Power capability prediction for lithium-ion batteries based on multiple constraints analysis
Pan, R. N., Wang, Y., Zhang, X., Yang, D., & Chen, Z. (2017) · Electrochimica Acta · doi:10.1016/j.electacta.2017.04.004 · Cited by 35
Lyapunov-based state of charge diagnosis and health prognosis for lithium-ion batteries
Wei, J., Dong, G., & Chen, Z. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.07.024 · Cited by 35
Hybrid and combined states estimation approaches for lithium-ion battery management system: Advancement, challenges and future directions
Lipu, M. S. H., Rahman, M. S. A., Mansor, M., Ansari, S., Meraj, S. T., & Hannan, M. A. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.112107 · Cited by 34
Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs
Jiang, B., Dai, H., Wei, X., Zhu, L., & Sun, Z. (2017) · Energies · doi:10.3390/en10030390 · Cited by 31
Model based state-of-energy estimation for LiFePO4 batteries using unscented particle filter
Chang, J., Chi, M., & Shen, T. (2020) · Journal of Power Electronics · doi:10.1007/s43236-020-00051-5 · Cited by 31
Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints
Shun, X., Hu, G., Huang, R., Guo, F., & Zhou, P. (2018) · Energies · doi:10.3390/en11020283 · Cited by 30
Rapid Prediction of the Open-Circuit-Voltage of Lithium Ion Batteries Based on an Effective Voltage Relaxation Model
Yang, J. R., Du, C., Wang, T., Gao, Y., Cheng, X., Zuo, P., Ma, Y., Wang, J., Yin, G., Xie, J., et al. (2018) · Energies · doi:10.3390/en11123444 · Cited by 30
A Review of Parameter Identification and State of Power Estimation Methods for Lithium-Ion Batteries
Ma, C., Wu, C. Q., Wang, L., Chen, X., Liu, L., Wu, Y., & Ye, J. (2024) · Processes · doi:10.3390/pr12102166 · Cited by 32
Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis
Wang, Y., Meng, D., Chang, Y., Zhou, Y., Li, R., & Zhang, X. (2021) · International Journal of Energy Research · doi:10.1002/er.7175 · Cited by 29
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 multi-timescale framework for state monitoring and lifetime prognosis of lithium-ion batteries
Wei, J., & Chen, C. (2021) · Energy · doi:10.1016/j.energy.2021.120684 · Cited by 26
A Novel State Estimation Approach Based on Adaptive Unscented Kalman Filter for Electric Vehicles
Li, J., Ye, M., Jiao, S., Wei, M., & Xu, X. (2020) · IEEE Access · doi:10.1109/access.2020.3030260 · Cited by 25
Real-time state of charge and capacity estimations of vanadium redox flow battery based on unscented Kalman filter with a forgetting factor
Zhao, X., Nam, J., Jung, H., & Jung, S. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109146 · Cited by 25
Available power prediction limited by multiple constraints for LiFePO4 batteries based on central difference Kalman filter
Xie, J., Ma, J., & Chen, J. (2018) · International Journal of Energy Research · doi:10.1002/er.4227 · Cited by 25
Online Estimation of Open Circuit Voltage Based on Extended Kalman Filter with Self-Evaluation Criterion
Qiao, X., Wang, Z., Hou, E., Liu, G., & Cai, Y. (2022) · Energies · doi:10.3390/en15124373 · Cited by 24
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
Determination of Lithium-Ion Battery Capacity for Practical Applications
Bašić, H., Bobanac, V., & Pandžić, H. (2023) · Batteries · doi:10.3390/batteries9090459 · Cited by 21
A Novel Discharge Mode Identification Method for Series-Connected Battery Pack Online State-of-Charge Estimation Over A Wide Life Scale
Liu, S.-Q., Wang, J., Liu, Q., Tang, J. W., Liu, H., Zhou, Y., & Pan, X. (2020) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3001020 · Cited by 21
A Real-Time Blockchain-Based State Estimation System for Battery Energy Storage Systems
Mohammadi, F., Sanjari, M. J., & Saif, M. (2022) · IEEE Kansas Power and Energy Conference (KPEC) · doi:10.1109/kpec54747.2022.9814731 · Cited by 21
Adaptive Importance Sampling Unscented Kalman Filter based SAR image super resolution
Kanakaraj, S., Nair, P. (. ). M. S., & Kalady, S. (2019) · Computers & Geosciences · doi:10.1016/j.cageo.2019.104310 · Cited by 21
Peak Power Estimation of Vanadium Redox Flow Batteries Based on Receding Horizon Control
Xiong, B., Dong, S., Li, Y., Tang, J., Su, Y., & Gooi, H. B. (2022) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2022.3152588 · Cited by 20
Estimation of Lithium-Ion Battery SOC Model Based on AGA-FOUKF Algorithm
Fang, C., Jin, Z., Wu, J., & Liu, C. (2021) · Frontiers in Energy Research · doi:10.3389/fenrg.2021.769818 · Cited by 18
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
Self-citation
Prediction for the Remaining Useful Life of Lithium–Ion Battery Based on RVM-GM with Dynamic Size of Moving Window
Nan, J., Deng, B., Cao, W., & Tan, Z. (2022) · World Electric Vehicle Journal · doi:10.3390/wevj13020025 · Cited by 20

Authors: Zhang, W., Shi, W., & Ma, Z.
Affiliations: Beijing Jiaotong University (CN)
Corresponding author: Zhang, Weige
ORCID: Zhang, Weige
Shi, Wei
Ma, Zeyu
Venue: Journal of Power Sources
Publisher: Elsevier BV
Published: 2015-09
Volume: 289
Pages: 50-62
ISSN: 0378-7753 (ISSN-L)
DOI: 10.1016/j.jpowsour.2015.04.148
OpenAlex: W652828899
Cited by: 178
References: 32
Citations by year: 2026: 6 · 2025: 6 · 2024: 10 · 2023: 19 · 2022: 22 · 2021: 18 · 2020: 13 · 2019: 22 · 2018: 22 · 2017: 25 · 2016: 9 · 2015: 6
FWCI: 21.35
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Journal of Power Sources (publishedVersion)
Funders: National High Technology Research and Development Program of China (2013BAG24B02)
Topics: Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
Keywords: lithium-ion batteries, state of energy estimation, adaptive unscented Kalman filter, joint state estimation, response surface model, lithium iron phosphate batteries, battery degradation, electric vehicles, operating 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

Adaptive unscented Kalman filter based state of energy and power capability estimation approach for lithium-ion battery

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