TY - JOUR A1 - Kleiner, Jan A1 - Stuckenberger, Magdalena A1 - Komsiyska, Lidiya A1 - Endisch, Christian T1 - Advanced monitoring and prediction of the thermal state of intelligent battery cells in electric vehicles by physics-based and data-driven modeling JF - Batteries N2 - Novel intelligent battery systems are gaining importance with functional hardware on the cell level. Cell-level hardware allows for advanced battery state monitoring and thermal management, but also leads to additional thermal interactions. In this work, an electro-thermal framework for the modeling of these novel intelligent battery cells is provided. Thereby, a lumped thermal model, as well as a novel neural network, are implemented in the framework as thermal submodels. For the first time, a direct comparison of a physics-based and a data-driven thermal battery model is performed in the same framework. The models are compared in terms of temperature estimation with regard to accuracy. Both models are very well suited to represent the thermal behavior in novel intelligent battery cells. In terms of accuracy and computation time, however, the data-driven neural network approach with a Nonlinear AutoregRessive network with eXogeneous input (NARX) shows slight advantages. Finally, novel applications of temperature prediction in battery electric vehicles are presented and the applicability of the models is illustrated. Thereby, the conventional prediction of the state of power is extended by simultaneous temperature prediction. Additionally, temperature forecasting is used for pre-conditioning by advanced cooling system regulation to enable energy efficiency and fast charging. UR - https://doi.org/10.3390/batteries7020031 KW - lithium-ion battery KW - electro-thermal model KW - smart cell KW - intelligent battery KW - neural network KW - temperature prediction Y1 - 2021 UR - https://doi.org/10.3390/batteries7020031 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13068 SN - 2313-0105 VL - 7 IS - 2 PB - MDPI CY - Basel ER - TY - JOUR A1 - Kleiner, Jan A1 - Stuckenberger, Magdalena A1 - Komsiyska, Lidiya A1 - Endisch, Christian T1 - Real-time core temperature prediction of prismatic automotive lithium-ion battery cells based on artificial neural networks JF - Journal of Energy Storage UR - https://doi.org/10.1016/j.est.2021.102588 KW - Lithium-ion battery KW - Battery modeling KW - Electro-thermal model KW - Thermal model KW - Neural network KW - Real-time application Y1 - 2021 UR - https://doi.org/10.1016/j.est.2021.102588 SN - 2352-1538 VL - 2021 IS - 39 PB - Elsevier CY - Amsterdam ER -