Abstract
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.
Highlights
In the past few decades, the transport mobility sector, and especially the automotive industry, has experienced considerable changes
In order to compare the temperature estimation of the real-time thermal models to the target results of the reference system in Section 3.1, an independent dataset of the reference system based on the ADAC electric vehicle cycle is chosen
For the temperature estimation at the electronics, the dynamic changes in the electronics heat generation in combination with the three representative thermal masses lead to a maximum deviation of 2.7 K for the Thermal Equivalent Circuit Model (TECM)
Summary
In the past few decades, the transport mobility sector, and especially the automotive industry, has experienced considerable changes. The use of sensors integrated in mass-produced electronics in combination with thermal models enables the core temperature of the battery cells to be monitored. This avoids the need to integrate additional temperature sensors into the cell, as proposed by [5,7], which would lead to increased production costs and safety issues [8]. There are currently no models that use NARX networks for core temperature modeling of large format cells, neither for conventional, nor for intelligent batteries Since both approaches, physical-based and data-driven modeling, seem to be adequate as thermal battery models, it is important to compare the modeling approaches related to the application in Battery Electric Vehicles (BEVs). An predictive cooling system regulation is presented that enables pre-conditioning for fast charging
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