Nonlinear Model Predictive Control of a Thermal Management System for Electrified Vehicles using FMI

Torben Fischer
Fraunhofer Institute for Chemical Technology (ICT), Project Group New Drive Systems, Germany

Tom Kraus
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany

Christian Kirches
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany

Frank Gauterin
Institute of Vehicle System Technology, Karlsruhe Institute of Technology (KIT), Germany

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp17132255

Ingår i: Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017

Linköping Electronic Conference Proceedings 132:27, s. 255-264

Visa mer +

Publicerad: 2017-07-04

ISBN: 978-91-7685-575-1

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Energy-efficient thermal management systems for Emobility help to decrease energy consumption and increase range. Due to transient external conditions and the increasing system complexity, optimization-based control approaches are required in order to harness the full potential of such systems. In (Fischer et al., 11th Int. Modelica Conf, 2015), we have presented a model-based development cycle for a thermal management system in Emobility to this end. In this article, we build upon this work to describe the use of this model within a nonlinear model predictive control (NMPC) approach. The main benefits of using an advanced optimization-based control system in this application are a) the ability to control the battery temperature and the cabin temperature simultaneously, b) the increased energy efficiency achieved by exploiting the predictive character of the optimizationbased control approach, c) the possibility to include operational limits as constraints in the optimization problems and d) the fast reaction to disturbances or model parameter changes. We evaluate the merit of the proposed advanced control system by way of a comparison to conventional PID controller.


thermal management system, nonlinear model predictive control, Functional Mock-up Int


A. Afram and F. Janabi-Sharifi. Theory and applications of HVAC control systems: A review of model predictive control (MPC). Building and Environment, 72:343–355, 2014.

J. Albersmeyer. Adjoint based algorithms and numerical methods for sensitivity generation and optimization of large scale dynamic systems. PhD thesis, Heidelberg University, 2010.

I. Bauer, H.G. Bock, and J.P. Schlöder. DAESOL – a BDF-code for the numerical solution of differential algebraic equations. Internal report, IWR, SFB 359, Heidelberg University, 1999.

T. Blochwitz, M. Otter, M. Arnold, C. Bausch, C. Clauss, H. Elmqvist, A. Junghanns, J. Mauss, M. Monteiro, T. Neidhold1, D. Neumerkel, H. Olsson, J.-V. Peetz, and S. Wolf. The functional mockup interface for tool independent exchange of simulation models. 8th Int. Modelica Conf., 2011.

H.G. Bock and K.J. Plitt. A Multiple Shooting algorithm for direct solution of optimal control problems. In Proceedings of the 9th IFAC World Congress, pages 242–247, Budapest, 1984. Pergamon Press.

H.G. Bock, M. Diehl, P. Kühl, E. Kostina, J.P. Schlöder, and L.Wirsching. Numerical Methods for Efficient and Fast Nonlinear Model Predictive Control. In R. Findeisen, F. Allgöwer, and L. T. Biegler, editors, Assessment and future directions of Nonlinear Model Predictive Control, volume 358 of LNCIS, pages 163–179. Springer, 2005.

J. Bonilla, S. Dormido, and F. E. Cellier. Switching moving boundary models for two-phase flow evaporators and condensers. Communications in Nonlinear Science and Numerical Simulation, 20:743–768, 2015.

L. del Re, F. Allgöwer, L. Glielmo, C. Guardiola, and I. Kolmanovsky. Automotive Model Predictive Control. Springer, 2010.

M. Diehl. Real-Time Optimization for Large Scale Nonlinear Processes. PhD thesis, Universität Heidelberg, 2001.

H. Esen, T. Tashiro, D. Bernardini, and A. Bemporad. Cabin heat thermal management in hybrid vehicles using model predictive control. 22nd Med. Conf. Contr. Autom. (MED), 2014.

T. Fischer, F. Götz, L. Berg, H.-P. Kollmeier, and F. Gauterin. Model-based development of a holistic thermal management system for an electric car with a high temperature fuel cell range extender. 11th Int. Modelica Conference, 2015.

R. Franke. Formulation of dynamic optimization problems using modelica and their efficient solution. 2nd International Modelica Conference, pages 315–323, 2002.

J.V. Frasch, L. Wirsching, S. Sager, and H.G. Bock. Mixed-Level Iteration Schemes for Nonlinear Model Predictive Control. In Proc. IFAC Conf. on NMPC, 2012.

M. Gräber, C. Kirches, D. Scharff, and W. Tegethoff. Using functional mock-up units for nonlinear model predictive control. 9th International Modelica Conference, 2012.

J.M. Jensen and H. Tummescheit. Moving boundary models for dynamic simulations of two-phase flows. 2nd International Modelica Conference, pages 235–244, 2002.

A.Y. Karnik, A. Fuxman, P. Bonkoski, M. Jankovic, and J. Pekar. Vehicle powertrain thermal management system using model predictive control. SAE International, 2016.

C. Kirches. Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. In H.G. Bock, W. Hackbusch, M. Luskin, and R. Rannacher, editors, Advances in Numerical Mathematics. Springer Vieweg, Wiesbaden, July 2011.

C. Kirches, L. Wirsching, H.G. Bock, and J.P. Schlöder. Efficient Direct Multiple Shooting for Nonlinear Model Predictive Control on Long Horizons. J. Proc. Contr., 22(3):540–550, 2012.

C. Kirches, H.G. Bock, J.P. Schlöder, and S. Sager. Mixedinteger NMPC for predictive cruise control of heavy-duty trucks. In European Control Conference, pages 4118–4123, Zurich, Switzerland, July 17-19 2013.

D.B. Leineweber, I. Bauer, A.A.S. Schäfer, H.G. Bock, and J.P. Schlöder. An Efficient Multiple Shooting Based Reduced SQP Strategy for Large-Scale Dynamic Process Optimization (Parts I and II). Comp. Chem. Eng., 27:157–174, 2003.

J. Nocedal and S.J. Wright. Numerical Optimization. Springer Verlag, Berlin Heidelberg New York, second edition, 2006.

Citeringar i Crossref