Liebertseder, JohannesJohannesLiebertsederWunsch, SusannSusannWunschSonner, ChristineChristineSonnerBerg, Lars-FredrikLars-FredrikBergDoppelbauer, MartinMartinDoppelbauerTübke, JensJensTübke2023-05-222023-05-222023https://publica.fraunhofer.de/handle/publica/44206510.1109/CogMob55547.2022.10118237The accurate prediction of the battery temperature in an electric vehicle is crucial for an effective thermal management of the battery system. Here, a nonlinear autoregressive exogenous network is used to model the complex thermal behavior of a battery cell. It is trained with conventional driving data and uses input parameters that are easy to obtain. Its accuracy is proven for a wide range of temperatures, showing the simple, general and robust applicability of the approach.enTemperature distributionComputational modelingTraining dataPredictive modelsThermal managementElectric vehiclesData modelsconference paper