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Holistic thermal management strategies for electric vehicles

: Hütter, M.; König, W.; Kuitunen, S.


Watzenig, Daniel (Ed):
Comprehensive Energy Management - Safe Adaptation, Predictive Control and Thermal Management
Cham: Springer International Publishing, 2017
ISBN: 978-3-319-57444-8 (Print)
ISBN: 978-3-319-57445-5 (Online)
ISBN: 3-319-57444-2
Aufsatz in Buch
Fraunhofer IVI ()

Thermal management systems in vehicles ensure the demand-based supply and extraction of the heating and cooling energy that is needed to fulfil the specific requirements of components regarding their operating temperatures and to guarantee thermal convenience inside the cabin. Due to the limited energy capacity of fully electric vehicles, the energy efficiency of thermal management systems is a key factor for the sustainability of electromobility. In order to improve the efficiency of the overall system through a holistic approach, it is necessary to use the existing heat sources and lower the primary energy demand. The thermal management system that is currently being developed within the EU-funded iCOMPOSE project follows this approach. The most important element of the project is a centralized multicore computing unit in which the vehicle’s essential control functions are implemented, these functions being traction control and power split between battery and supercapacitors in addition to thermal management. The obvious aim, therefore, is the development and implementation of a resource-conserving model predictive controller for all thermal functionalities. On the basis of a specially developed library of cooling and air-conditioning components, a simulation environment for the development and testing of the controller was built. In the process, three different system configurations were explicitly analyzed using a LOTUS Evora 414E in order to reference the controller in compliance with current development trends. The following article introduces the overall project concept before discussing the development of the component models in detail. Afterwards, the simulation of the three reference systems using the example of one of these systems will be described. The development and testing process for the model predictive controller will follow. The article concludes with a summary of the results and an outlook on existing optimization opportunities.