Berecibar, Maitane

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

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 1,193

Critical review of state of health estimation methods of Li-ion batteries for real applications

Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Van Mierlo, J., & Van den Bossche, P. (2016) · Renewable and Sustainable Energy Reviews

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 1,126

Random 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

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 727

A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter:

Li, Y., Abdel-Monem, M., Gopalakrishnan, R., Berecibar, M., Nanini-Maury, E., Omar, N., van den Bossche, P., & Van Mierlo, J. (2018) · Journal of Power Sources

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 531

State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application

Berecibar, M., Garmendia, M., Gandiaga, I., Crego, J., & Villarreal, I. (2016) · Energy

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 213

Online state of health estimation on NMC cells based on predictive analytics

Berecibar, M., Devriendt, F., Dubarry, M., Villarreal, I., Omar, N., Verbeke, W., & Van Mierlo, J. (2016) · Journal of Power Sources

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 202

Realistic lifetime prediction approach for Li-ion batteries

Sarasketa-Zabala, E., Martinez-Laserna, E., Berecibar, M., Gandiaga, I., Rodriguez-Martinez, L. M., & Villarreal, I. (2016) · Applied Energy

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 191

Online Health Diagnosis of Lithium-Ion Batteries Based on Nonlinear Autoregressive Neural Network

Khaleghi, S., Karimi, D., Beheshti, S. H., Hosen, M. S., Behi, H., Berecibar, M., & Van Mierlo, J. (2021) · Applied Energy

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 161

Beyond the State of the Art of Electric Vehicles: A Fact-Based Paper of the Current and Prospective Electric Vehicle Technologies

Van Mierlo, J., Berecibar, M., El Baghdadi, M., De Cauwer, C., Messagie, M., Coosemans, T., Jacobs, V., & Hegazy, O. (2021) · World Electric Vehicle Journal

Electric and Hybrid Vehicle TechnologiesElectric Vehicles and Infrastructure

Cited by 149

Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

Khaleghi, S., Firouz, Y., Berecibar, M., Mierlo, J. V., & Bossche, P. V. D. (2020) · Energies

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 44

State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network

Zhang, S., Hosen, M. S., Kalogiannis, T., Mierlo, J. V., & Berecibar, M. (2021) · World Electric Vehicle Journal

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 36