Preindl, Matthias

Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries

Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R., & Emadi, A. (2018) · IEEE Transactions on Industrial Electronics

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 906

State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

Chemali, E., Kollmeyer, P. J., Preindl, M., & Emadi, A. (2018) · Journal of Power Sources

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 818

Making the Case for Electrified Transportation

Bilgin, B., Magne, P., Malysz, P., Yang, Y., Pantelic, V., Preindl, M., Korobkine, A., Jiang, W., Lawford, M., & Emadi, A. (2015) · IEEE Transactions on Transportation Electrification

Electric and Hybrid Vehicle TechnologiesElectric Vehicles and Infrastructure

Cited by 455

Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review

Li, A. G., West, A. C., & Preindl, M. (2022) · Applied Energy

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 93

Multi-objective optimization and comparative evaluation of Si soft-switched and SiC hard-switched automotive DC-DC converters

Waffler, S., Preindl, M., & Kolar, J. W. (2009) · 2009 35th Annual Conference of IEEE Industrial Electronics

Advanced DC-DC ConvertersMultilevel Inverters and Converters

Cited by 43

Turn-off energy minimization for soft-switching power converters with wide bandgap devices

Agrawal, B., Preindl, M., & Emadi, A. (2017) · 2017 IEEE International Conference on Industrial Technology (ICIT)

Advanced DC-DC ConvertersSilicon Carbide Semiconductor Technologies

Cited by 10