Linköping Studies in Science and Technology Dissertations, No 1589 Model Based Vehicle Level Diagnosis for Hybrid Electric Vehicles Christofer Sundström Department of Electrical Engineering Linköping 2014 Linköping Studies in Science and Technology Dissertations, No 1589 Christofer Sundström christofer.se Division of Vehicular Systems Department of Electrical Engineering Linköping University SE–581 83 Linköping, Sweden Copyright © 2014 Christofer Sundström, unless otherwise noted. All rights reserved. Sundström, Christofer Model Based Vehicle Level Diagnosis for Hybrid Electric Vehicles ISBN 978-91-7519-356-4 ISSN 0345-7524 Hybrid powertrain illustration on the cover based on illustration by Lars Eriksson. Typeset with LATEX 2ε Printed by LiU-Tryck, Linköping, Sweden 2014 To Tilda, Elin and Siri Abstract When hybridizing a vehicle, new components are added that need to be monitored due to safety and legislative demands.
Diagnostic aspects due to powertrain hybridization are investigated, such as that there are more mode switches in the hybrid powertrain compared to a conventional powertrain, and that there is a freedom in choosing operating points of the components in the powertrain via the overall energy management and still fulfill the driver torque request. A model of a long haulage truck is developed, and a contribution is a new electric machine model. The machine model is of low complexity, and treats the machine constants in a different way compared to a standard model. It is shown that this model describes the power losses significantly better when adopted to real data, and that this modeling improvement leads to better signal separation between the non-faulty and faulty cases compared to the standard model.
To investigate the influence of the energy management design and sensor configuration on the diagnostic performance, two vehicle level diagnosis systems based on different sensor configurations are designed and implemented. It is found that there is a connection between the operating modes of the vehicle and the diagnostic performance, and that this interplay is of special relevance in the system based on few sensors. In consistency based diagnosis it is investigated if there exists a solution to a set of equations with analytical redundancy, i. there are more equations than unknown variables.
The selection of sets of equations to be included in the diagnosis system and in what order to compute the unknown variables in the used equations affect the diagnostic performance. A systematic method that finds properties and constructs residual generator candidates based on a model has been developed. Methods are also devised for utilization of the residual generators, such as initialization of dynamic residual generators, and for consideration of the fault excitation in the residuals using the internal form of the residual generators. For demonstration, the model of the hybridized truck is used in a simulation study, and it is shown that the methods significantly increase the diagnostic performance.
The models used in a diagnosis system need to be accurate for fault detection. Map based models describe the fault free behavior accurately, but fault isolability is often difficult to achieve using this kind of model. To achieve also good fault isolability performance without extensive modeling, a new diagnostic approach is presented. A map based model describes the nominal behavior, and another model, that is less accurate but in which the faults are explicitly included, is used to model how the faults affect the output signals.
The approach is exemplified by designing a diagnosis system monitoring the power electronics and the electric machine in a hybrid vehicle, and simulations show that the approach works well. v Populärvetenskaplig Sammanfattning Ett diagnossystem övervakar ett system för att fastställa om det är helt eller trasigt. Ett första steg är att upptäcka ett eventuellt fel, men det är även önskvärt att kunna peka ut vilken del av systemet som är trasigt. Övervakning av ett fordons drivlina är viktigt av flera anledningar, bland annat för att uppfylla lagkrav, säkerhetskrav, hög utnyttjandegrad, och effektiva reparationer.
När ett fordon hybridiseras, i den här avhandlingen genom att förbränningsmotorn kombineras med en elmaskin för fordonets framdrivning, så ökar systemets komplexitet och ställer därmed stora krav på det diagnossystem som övervakar fordonet. Det är vanligt att det finns ett diagnossystem för varje komponent i fordonets drivlina, men här studeras vilka fördelar det finns med att designa ett diagnossystem som övervakar ett flertal komponenter i fordonet. En speciell egenskap hos ett hybridiserat fordon är att det finns en frihet att välja om det är elmaskinen eller förbränningsmotorn som ska användas för att driva fordonet framåt. Därför är det intressant att studera hur designen av den övergripande energistyrningen påverkar möjligheten att felövervaka fordonet.
I avhandlingen används konsistensbaserad diagnos, vilket innebär att en matematisk modell över fordonet jämförs med sensorsignaler för att fastställa fordonets felstatus. För att undersöka hur olika designval påverkar diagnos- prestandan har en modell av en lastbil utvecklats och ett bidrag i avhandlingen är en ny elmaskinmodell. Det visas att den nya modellen beskriver maskinens förluster bättre än en standardmodell när dessa utvärderas på mätdata, samt att modelleringsförbättringen leder till en bättre signalseparation mellan det felfria fallet och när ett fel har uppstått i systemet. Flera olika diagnossystem har designats och implementerats i simuleringsmodellen.
Simuleringar visar bland annat att det finns en koppling mellan fordonets arbetspunkter och diagnosprestandan, samt att den kopplingen är av större betydelse när få sensorer är tillgängliga. Grunden i de utvecklade diagnossystemen är att konstruera residualgenera- torer, som här undersöker om lastbilsmodellen överensstämmer med sensormät- ningar. Det går att skapa tusentals residualgeneratorer baserat på en modell av ett komplext fysikaliskt system. Dessa har olika känslighet för att upptäcka fel i systemet, och därför har en metod som undersöker residualernas egenskaper baserat på en systemmodell utvecklats.
Residualsignalerna i ett diagnossystem efterbehandlas och metoder för detta har konstruerats. En metod har även utvecklats för att kombinera en noggrann modell för det felfria fallet med en annan modell för samma fysikaliska system, men som beskriver hur de olika felen påverkar systemet. Detta leder till att det är möjligt att upptäcka fel i det övervakade systemet, och samtidigt även specificera vilken komponent som är felaktig, utan detaljerad modellering. För att demonstrera dessa metoder har en simuleringsstudie med lastbilsmodellen utförts där det visas att metoderna signifikant förbättrar diagnotikprestandan.
vii Acknowledgments First of all I would like to express my gratitude to my supervisor Professor Lars Nielsen for letting me join his research group and for all his support during these years. My second supervisor Erik Frisk is acknowledged for the many discussions about diagnosis and good comments for improving paper manuscripts. Daniel Eriksson and Emil Larsson are acknowledged for different discussions about diagnosis in general throughout these years. Carl Svärd is acknowledged for letting me use his implementation of the residual generator selection algorithm that is modified in Paper C, and Mattias Krysander and Per Öberg for electric machine modeling discussions.
During this period I have had the pleasure to collaborate with Daniel Eriksson, Mattias Krysander, Xavier Llamas Comellas, Tomas Nilsson, Peter Nyberg, and Martin Sivertsson in different projects. Lars Eriksson is also acknowledged for involving me in an undergraduate course about vehicle propulsion in which the discussions with the students have given me many new insights about vehicle hybridization. The industrial involvement in the project is valuable and Tobias Axelsson, Mattias Nyberg, Tobias Pettersson, Marcus Stigsson, and Nils-Gunnar Vågstedt are acknowledged for this. The colleagues at vehicular systems are acknowledged for creating a nice and pleasant atmosphere to work in.
The never-ending discussions with Oskar Leufvén has given me insights in all from forestry to turbochargers, but most importantly lots of fun. Finally, I would like to thank my family for all your support and encourage- ment. I would also like to express my gratitude to you Therése and to Tilda, Elin, and Siri, for bringing all your love and happiness into my life. ix Contents 1 Introduction 1 1.2 Outline and Contributions.
8 Publications 11 A Overall Monitoring and Diagnosis for Hybrid Vehicle Power- trains 13 1 Introduction .4 Controller and energy management .5 Driving cycles and simulation results. 24 3 Sensor configurations and theoretical maximum fault isolability. 28 4 Diagnosis systems design. 32 5 Results and discussion.
41 xi xii Contents B A New Electric Machine Model and its Relevance for Vehicle Level Diagnosis 43 1 Introduction. 46 2 Electric machine model .3 Parametrization of the models. 55 4 Evaluation on diagnosis system. 64 C Selecting and Utilizing Sequential Residual Generators in FDI Applied to Hybrid Vehicles 67 1 Introduction.
70 2 Residual generator construction .1 Sequential Residual Generation by Structural Analysis .4 Modification to the Algorithm to Handle Dynamic Consis- tency Relations. 78 4 Selection of Consistency Relation .1 Avoiding algebraic loops by consistency relation selection 79 4.2 Properties of the sequential residual generators candidates 82 4.3 Summary and discussion. 83 5 Methods for Utilization of Residual Generators and Test Quantities 84 5.1 Avoid differentiating in the consistency relation using a state transformation .2 Initialization of states .3 Consider fault excitation when computing test quantities 86 6 Illustrative Designs and Simulation Study .1 Properties of diagnosis systems used in simulation study .2 Model used in the diagnosis system .3 Initialization of states when restarting residual generators 90 6.4 Two approaches for when to update dynamic test quantities 91 Contents xiii 6.5 Simulations on driving cycle .1 Same tests in ICDS and MCDS. 104 D Diagnostic Method Combining Map and Fault Models Applied on a Hybrid Electric Vehicle 105 1 Introduction .1 Contributions and outline.
108 2 Models of the electric machine .1 Map based model. 110 3 Combining the map and analytical models for fault modeling .1 Finding an expression for ∆Tem .2 Finding an expression for ∆Pem,l. 114 4 Isolability gain by combining models .1 Theoretical fault isolability using map based model .2 Theoretical fault isolability using a combined model. 115 5 Design of a diagnosis system .1 State space formulation of the model .3 Design of residual generators and test quantities.
129 Chapter 1 Introduction There are possibilities to increase the efficiency of automotive powertrains using hybrid technology. The largest relative fuel saving can be obtained in city buses and garbage trucks with many start and stops, but also a small relative saving in the fuel consumption for long haulage trucks results in a large amount of saved fuel (Bradley, 2000). When hybridizing a vehicle, new components are added compared to a conventional vehicle, e. electric machines, battery pack, and power electronics (Husain, 2003; Guzzella and Sciarretta, 2013), and these components need to be accurately monitored (Diallo et al.
One reason for monitoring the powertrain is safety. Faults in the electrical components could be fatal due to the high voltage in the system. Another issue is to avoid that unintended torque is applied to the vehicle. Such faults are included in the functional safety of the vehicle, and there is an increased consideration to this field by the automotive industry.
There is a global standard, ISO 26262, that provides processes and methods for the design, development, and manufacturing of vehicles, with the goal to determine the Automotive Safety Integrity Level (ASIL) in a systematic way (ISO, 2011). The ISO 26262 standard is not mandatory for heavy trucks, but this is likely to be changed by 2016 (Dardar et al. In addition to safety, fault detection and isolation is important to decrease the vehicle ownership cost and to maximize the up-time of the vehicle. Fault detection can e.
protect other components from breaking down if a fault occurs in a powertrain component. It is especially important to protect the battery that is expensive and may degrade fast (Chen et al. if there are large power flows in the battery. High power in the electrical components could be caused by a fault in the power electronics or the electric machine.
Further, more efficient repair is possible if a fault is isolated, i. it is stated what component that is broken and to some extent also in what way.