Validation of a Battery Management System based on AUTOSAR via FMI on a HiL platform

Leonard Janczyk
Dassault Systèmes Deutschland GmbH, Munich, Germany

Klemens Esterle
Dassault Systèmes Deutschland GmbH, Munich, Germany

Stephan Diehl
Dassault Systèmes Deutschland GmbH, Munich, Germany

Michael Seibt
Dassault Systèmes Deutschland GmbH, Munich, Germany

Arthur Gauthier
Dassault Systèmes SE, Plouzané, France

Viry Guillaume
Dassault Systemes KK, Tokyo, Japan

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp1612487

Ingår i: The First Japanese Modelica Conferences, May 23-24, Tokyo, Japan

Linköping Electronic Conference Proceedings 124:12, s. 87-94

Visa mer +

Publicerad: 2016-05-18

ISBN: 978-91-7685-749-6

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


In systems which are sourcing their electric energy from a battery system, such as electric or hybrid electric vehicles, it is of crucial importance to monitor the battery’s condition in order to ensure its usability and longevity. The battery management system (BMS) is a control unit which supervises the physical variables in order to assess the condition of the battery. For the development and testing of control units in the automotive industry, such as the BMS, the AUTOSAR standard was introduced, which separates application code from platform-specific software. By using AUTOSAR tools and the model exchange via the Functional Mock-up Interface (FMI), this paper shows how BMS algorithms can be validated and tested in several abstraction layers. A sub-function of the algorithm is tested first in the Modelica-based system simulation tool Dymola on a personal computer and then on Hardware-in-the-Loop (HiL) platform which emulates the hardware of an automotive ECU. In order to provide realistic inputs of the physical variables, a battery model in Modelica is built using the Dymola add-on Battery Library by Dassault Systèmes. In order to run on the HiL platform the battery model is implemented such that it is real-time compliant. For both, the BMS algorithm and the battery model, it is described along the process which adjustments need to be made when switching from the simulation framework to the HiL platform.


battery model, battery management system, AUTOSAR, FMI, ASim, MiL, SiL, HiL, XiL, Co-Simulation


AUTOSAR. 2016. Technical Overview. February 06. http://www.autosar.org/about/technical-overview/.

Bertsch, Christian, Jonathan Neudorfer, Elmar Ahle, Siva Sankar Arumugham, Karthikeyan Ramachandran, and Andreas Thuy. 2015. "FMI for physical models on automotive embedded targets." 11th International Modelica Conference. Versailles, France. 43-50. doi: 10.3384/ecp1511843.

Blochwitz, Otter, Bausch, Clauß, Elmqvist, Junghans, Mauss, et al. 2011. "The Functional Mockup Interface for Tool independent Exchange of Simulation Models." 8th International Modelica Conference. Dresden, Germany.

dSpace GmbH. 2016. "DS1006 Processor Board." Technical Details. February 06. http://www.dspace.com/en/pub/home/products/hw/modular_hardware_introduction/processor_boards/ds1006.cfm.

Gerl, Johannes, Leonard Janczyk, Imke Dr Krüger, and Nils Modrow. 2014. "A Modelica Based Lithium Ion Battery Model." 10th International Modelica Conference. Lund, Sweden: Linköping University Electronic Press. 335-341.

He, Yongsheng, Liu Wei, and Koch J. Brian. 2010. "Battery algorithm verfication and development using hardware-in-the-loop testing." Journal of Power Sources (195): 2969-2974. doi: 10.1016/j.powersour.2009.11.036.

Intel Corporation. 2016. "Intel® Core™ i7-4810MQ Processor." Specifications. February 06. http://ark.intel.com/products/78937/Intel-Core-i7-4810MQ-Processor-6M-Cache-up-to-3_80-GHz.

Jossen, Andreas, and Wolfgang Weydanz. 2006. Moderne Akkumulatoren richtig einsetzen. Reichhardt Verlag.

Citeringar i Crossref