Robust Vehicle State and Parameter Observation Adaptive Filtering Concept with Enhanced Robustness by Usage of Markov Chains DISSERTATION zur Erlangung des akademischen Grades eines Doktors der Ingenieurswissenschaften vorgelegt von Dipl.-Ing. Matthias Korte eingereicht bei der Naturwissenschaftlich-Technischen Fakultät der Universität Siegen Siegen 2016 Gutachter: 1. Gutachter Universität Siegen: Prof. Dr. Hubert Roth 2. Gutachter Universität Erlangen: Prof. Dr. Roppenecker Tag der mündlichen Prüfung: 06. Juni 2016 Acknowledgments This work was done during my job as a PhD student in the Functional Architecture at the Intedis GmbH & Co. KG in Würzburg. Most of this thesis was developed within the project eFuture that was founded by the European Commission. Main objective of eFuture was to invent a safe and efficient electric vehicle based on a Tata eVista. I would like to thank my supervisor at University Siegen Prof. Roth for the possibility of writing a PhD thesis and the support during my work. Thanks to Prof. Roppenecker from University Erlangen for being the second reviewer. For the friendly support and all the technical discussions I thank all colleagues at Intedis. A special thank goes out to Dr.-Ing. Frederic Holzmann who was my supervisor at Intedis and Dipl.-Ing. Gerd Kaiser with whom I worked very intensive during that time. Also Í am very grateful to all project partners for the fruitful collaboration in the eFuture project. Another special thanks goes out to my family and my girlfriend. They showed much patience and consideration especially during the writing time. iii Abstract The work presented here should fulfil the requirements for the granting of the degree of Doctor of Engineering at the University Siegen. It was completed within the EU funded project eFuture with the company Intedis. The goal of the project was to create an efficient and safe electric vehicle on the basis of a Tata eVista with help of a complete new architecture. A novel robust vehicle observer was designed for an optimal support of the integrated driver assistance systems. The concept for the observer is based upon an extended Kalman Filter using a non-linear vehicle model and the Dugoff tire model. Moreover, a parameter estimation and a plausibility check of the sensor signals were developed to increase the robustness of the observer. The estimation of the vehicle mass, the effective tire radii and the road adhesion were designed with an event-seeking characteristic in order to minimise the computational load. In the plausibility check delayed or faulty sensor signals are detected and corrected. Here the newly designed replacement of delayed or missing sensor signals by the concept of Markov Chains is pointed out. By this, the correctness of the output signals and the safety of the vehicle can be guaranteed for a defined time. Additionally, the evaluation of the stability limits and the driven distance of the vehicle are computed under the use of quantities that were calculated before. After the model based design the software was integrated on the hardware of the prototype. The functionality of this concept is given by results during dynamic test drives. v Zusammenfassung Die hier vorgestellte Arbeit soll die Anforderungen zur Verleihung des Doktortitels an der Universität Siegen erfüllen. Sie wurde im Rahmen des EU geförderten Projekts eFuture bei der Firma Intedis in Würzburg abgeleistet, in welchem ein sicheres und effizientes Elektrofahrzeug auf Basis eines Tata eVista dank eines neuen Konzeptes aufgebaut wur- de. Ein neuartiger robuster Fahrzeugbeobachter wurde entwickelt um die integrierten Fah- rerassistenzsysteme optimal zu unterstützen. Das Konzept des Beobachters basiert auf einem erweiterten Kalman Filter unter Verwendung eines nichtlinearen Fahrzeugmodells und des Dugoff Reifenmodells. Zusätzlich wurde eine Parameterschätzung sowie ein Plausibilitätscheck der Sensorsigna- le integriert, um die Robustheit des Beobachters zu erhöhen. Die Parameterschätzung von Fahrzeugmasse, effektiven Reifenradien und Haftreibung wurde mit Hinblick auf die Berechnungslast ereignisbasierend aufgebaut. Im Plausibilitätscheck werden sowohl feh- lerhafte oder verzögerte Signale detektiert als auch korrigiert. Hier ist das neu entworfene Ersetzen von verzögerten oder fehlenden Sensorsignalen auf Basis der Theorie der Mar- kov Ketten hervorzuheben. So kann auch bei einem Sensorausfall die Korrektheit der Ausgangssignale für einen gewissen Zeitraum und dadurch auch die Sicherheit des Fahr- zeugs unter Assistenzkontrolle garantiert werden. Die Evaluierung der Stabilitätsgrenzen für das Fahrzeug sowie die Berechnung der gefahrenen Strecke für das Kombiinstrument werden mit den zuvor ermittelten Größen durchgeführt. Nach der modellbasierten Ent- wicklung wurde die Software auf der Hardware des Prototypen integriert. Ergebnisse bei dynamischen Testfahrten zeigen die Funktionalität dieses Konzepts. vii List of publications 1. Improvement of EE Architecture Design using Functional Approach (B. Chretien, F. Holzmann, D. Gruyer, S. Glaser, M. Korte and S. Mammar), In Proceedings of the FISITA 2010 World Automotive Congress, Budapest, Hungary, June, 2010. 2. Torque Vectoring with a feedback and feed forward controller - applied to a through the road hybrid electric vehicle (G. Kaiser, F. Holzmann, B. Chretien, M. Korte and H. Werner), In Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, June, 2011. 3. Development of an adaptive vehicle observer for an electric vehicle (M. Korte, F. Holzmann, V. Scheuch and H. Roth), In Proceedings of the European Electric Vehicle Congress (EEVC), Brussels, Belgium, 2011. 4. Two-Degree-of-Freedom LPV Control for a through-the-Road Hybrid Electric Vehi- cle via Torque Vectoring (Q. Liu, G. Kaiser, S. Boonto, H. Werner, F. Holzmann, B. Chretien and M. Korte), In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), Orlando, FL, USA, December 2011. 5. Design of a robust plausibility check for an adaptive vehicle observer in an electric vehicle (M. Korte, G. Kaiser, V. Scheuch, F. Holzmann and H. Roth) In Proceedings of the 16th Advanced Microsystems for Automotive Appli- cations(AMAA), Berlin, Germany, May 2012. 6. Robust Vehicle Observer to Enhance Torque Vectoring in an EV (M. Korte, F. Holzmann, G. Kaiser and H. Roth,), In Proceedings of the 5th Fachtagung: Steuerung und Regelung von Mo- toren und Fahrzeugen (AUTOREG), Baden-Baden, Germany, June 2013. 7. Design of a Robust Adaptive Vehicle Observer Towards Delayed and Missing Ve- hicle Dynamics Sensor Signals by Usage of Markov Chains (M. Korte, G. Kaiser, F. Holzmann and H. Roth) In Proceedings of the 2013 American Control Conference (ACC), Washington D.C., USA, June 2013. ix 0. List of publications 8. Torque Vectoring for an Electric Vehicle - Using an LPV Drive Controller and a Torque and Slip Limiter (G. Kaiser, Q. Liu, C. Hoffmann, M. Korte and H. Werner), In Proceedings of the 51st IEEE Conference on Decision and Control(CDC), Maui, Hawaii, USA, December 2012. 9. Torque Vectoring for a Real, Electric Car – Implementing an LPV Controller (G. Kaiser, M. Korte, Q. Liu, C. Hoffmann and H. Werner), In Proceedings of the 19th World Congress of the International Federation of Automatic Control(IFAC), Cape Town, South Africa, August 2014. x Contents Abstract v Zusammenfassung vii List of publications ix Nomenclature xv Acronyms....................................... xv List of symbols ....................................xvi 1 Introduction 1 1.1eFutureProject................................. 2 1.2 Hardware Description ............................. 4 1.2.1 Vehicle ................................. 4 1.2.2 Sensors ................................. 5 1.3 Function Description .............................. 10 1.4 State-of-the-art and Innovations . ..................... 11 1.4.1 Vehicle state and parameter observation ............... 11 1.4.2 Handling of signal loss . ........................ 11 1.5 Objective and organisation of work ...................... 12 2 Vehicle simulation model 15 2.1Vehiclemodel.................................. 16 2.1.1 Vehicle dynamics . ........................... 16 2.1.2 Components . ........................... 18 2.1.3 Model calibration ............................ 26 2.2 Vehicle dynamics controller .......................... 30 2.2.1 Stability controller ........................... 31 2.2.2 Assistance controller .......................... 35 2.3 Driver model .................................. 38 2.3.1 Driving scenarios ............................ 39 3 Vehicle Observer 43 3.1 Filter and estimation concepts ........................ 43 3.1.1 Linear stochastic systems ....................... 45 3.1.2 Kalman filter .............................. 47 3.1.3 Evaluation of most proper Kalman-filter ............... 57 xi Contents 3.2 Vehicle observer structure ........................... 60 3.2.1 Data Flow and signal definition .................... 61 3.3 Plausibility Check ............................... 65 3.3.1 Signal Conversion ........................... 66 3.3.2 Detection Mechanisms ......................... 66 3.3.3 Correction Mechanisms . ..................... 70 3.3.4 Confidence calculation ......................... 71 3.3.5 Vehicle observer activation ...................... 72 3.4 Extended Kalman Filter Algorithm ...................... 73 3.4.1 Build up and functionality ....................... 73 3.4.2 Slip and Side slip Calculation ..................... 73 3.4.3 Dugoff