Habilitation à Diriger des Recherches Guillaume Colin 5 décembre 2013

1. Optimisation du contrôle de la chaînede traction des véhicules automobilesOptimization of powertrain management for automotivevehicles Habilitation à Diriger des Recherches Guillaume Colin 5 décembre 2013

2. Outline • Extended curriculum Vitae • General context • Research Activities • Supervisory control : Optimal approach • Control : Robust or Predictive approach • Observer : Polytopic approach • Conclusion & future prospects

3. Curriculum vitae ESSTIN engineer Master in Automatic control Scientific Excellence bonus (PES) Habilitation defense PhD in Energetics 2011 2003 2007 2013 2006 ATER Monitor Assistant Professor A. Ivanco M. Debert C. Deng PhDStudents J. El Hadef 33 yearsold Married 2 children P. Michel T. Miro-Padovani A. Lamara

4. Supervision, production, partnerships • Supervision • 7 PhD students (340%) • 3 already defended (160%) and 1 in january (60%)! • 6 Masters (450 %) • Production • 17 international papers and 31 international conferences • 1 book chapter • 3 patents • Academic Partnership • Supelec Paris (from 2012) • CRAN (from 2003) • IMS (from 2006) • LAMIH (2004-2007) • ETH Zurich (from 2008)

5. Teaching Activities • Main teaching activities (L2, M1, M2) • Automatic control, Informatics, Signal treatment, Powertrain Control and internal combustion engine • Around 250 h/year from License 2 to Master 2 • Applied control to automotive (M2)

6. Administrative responsabilities • Teaching • Elected to the council of PolytechOrléans • Co-responsible of the international Master Automotive Engineering for Sustainable Mobility (AESM) • Co-responsible of the speciality VSE of PolytechOrléans • Correspondent for PolytechOrléans of Master VTD (IFP School, ENS Cachan, Supelec, Centrale) • Research • Research group on Automatic control and automotive (GTAA) • Correspondent for University of Orléans of the Society of Automotive Engineers (SIA) • 7 Industrial Contracts from 2006 (418k€)

7. Global view of my researches My activities ENERGETICS (62) AUTOMATIC (61) Physical models Control oriented models Hybrid Vehicle Optimal Control Predictive Control Model Based Internal Combustion engine Powertrain Robust Control Observers Battery

8. General context : why my work? • Energy demand increases • Big part of oil used for transport • Number of vehicles increases  Reduce fuel consumption and pollutant emissions with drivability is a challenge! Source : IAE, 2013 Source : FMI & world bank data

9. How to reduce energy demand for mobility ? • Global view of energy conversion for vehicles • well-to-tank • tank-to-vehicle • vehicle-to-kms  An efficient energy management is important to obtain maximum efficiency! (Guzzella, Sciarretta, Vehicule Propulsion Systems)

10. Where is my work? • General powertrain control scheme • Supervisory control • Dynamic control • Observer

11. Outline • Extended curriculum Vitae • General context • Research Activities • Supervisory control : Optimal approach • Control : Robust or Predictive approach • Observer : Polytopic approach • Conclusion & future prospects

12. Energy Management : Optimal approachWhyhybrid cars? • Internal Combustion Engine • Maximal efficiency: just close to full load ¾ of vehicle use atlow/part load  Downsizing: smaller engine + supercharging (turbo) • Non-reversible thermodynamic cycle Kinetic energy is lost during braking  Hybridizing: storage + reuse • Hybrid Vehicle • Fuel economy (Stop&Start, downsizing, recuperation …) • Cost, weight, recycling

13. Energy Management : Optimal approach • Evaluation of the strategies • Driving cycles : standard, Artemis and creation with Markov chains! • Objective : find u* that minimizes fuel consumption over the cycle w.r.t Final constraint Fuel consumption State evolution (often state of charge) Initial condition

14. Energy Management : Optimal approach • Which approaches? • Optimal strategies : often offline, but necessary to benchmark! • Dynamic programming (DP) based on Bellman’s principle of optimality • Pontryagin Maximum Principle (PMP) based on dual problem x Difficult to find the optimal co-state! time Backwardreasoning tf t r

15. Energy Management : Optimal approach • Which approaches and why? • Sub-optimal strategies : compatible and relevant for the application • Heuristic • Equivalent Consumption Minimization Strategy (ECMS) is based on the Hamiltonian and the PMP • Adaptive-ECMS • Driving Pattern Recognition • Variable Penalty coefficient s(x) • Model Predictive control (MPC) • Telemetric-ECMS : use of GPS to adapt s(t) • On-line Dynamic Programming • Black Box : learning the off-line strategy and apply it on-line Fuel Power Electrochemical Power

16. Energy Management : Optimal approachHybrid Pneumatic Engine PhD A. Ivanco • Hybrid Pneumatic Engine (HPE) • Combustion chamber • Air tank • Charging valve 2 3 1

17. Energy Management : Optimal approachHybrid Pneumatic Engine PhD A. Ivanco • Discrete time backward vehicle model • Vehicle cycle speed  Acceleration  Desired Engine Torque  Fuel and Air consumption • Four driving modes • Two propulsive modes umode: pneumatic µp and conventional µc • One recuperative: pneumatic pump • One alternative: engine stop

18. Energy Management : Optimal approachHybrid Pneumatic Engine PhD A. Ivanco • Comparison of the fuel consumption w.r.t. Dynamic Programming • Comparison of the fuel consumption w.r.t Conventional Suboptimality [%] Fuel consumption gain [%]

19. Energy Management : Optimal approachHybrid Pneumatic Engine PhD A. Ivanco • Real-time sub-optimal generic approaches with minimal sub-optimality for Hybrid pneumatic vehicle • Pneumatic is a credible alternative to electric this project continue in our lab, at ETH, with industrial partnerships • And for Hybrid Electric Vehicle?

20. Energy Management : Optimal approachHybrid Electric Vehicle PhD M. Debert • Powertrain example • Series-parallel hybrid • High efficiency transmission • 2 manipulated variables u • ICE torque (Ti) • Electrical power (Pe) • Quasi-static modeling • Consider only one dynamic = State of Charge

21. Energy Management : Optimal approachHybrid Electric Vehicle PhD M. Debert CO2 emission • Control problem = optimal control • Off-line optimization (DP) knowing the whole cycle subject to constraint on SOC State of Charge Ti [Nm] SOC [%] 21 Discharge Pe [W] Charge • Losses comes from ICE part

22. Energy Management : Optimal approachHybrid Electric Vehicle PhD M. Debert • Possibility to consider only thermal path for a given demanded power • Best operating line • System more constrained • Remove one control • Computation time is reduced • Suboptimality (DP) NEDC Traffic-Jam Urban Road Highway FTP

23. Energy Management : Optimal approachPredictive EMS for HEV PhD M. Debert • Knowing the future improves the efficiency • Driving conditions influences CO2 emissions • Available information about future driving conditions (LIDAR, GPS…) • Sub-optimal DP is suitable for real-time on finite horizon • Results • Influence of possible prediction horizon • Influence of prediction uncertainty : prediction is not perfect • Representative (in vehicle acceleration) • Predictive Energy Management • In real-time with dynamic programming • Fuel consumption decreases exponentially with prediction horizon • A certain robustness is observed  Relevant energy management to improve fuel consumption • Reduce fuel consumption is not sufficient: constraints!

24. Pollution constraint into EMS ? PhD P. Michel • Principle • Global objective • Dual problemwithHamiltonian : Fuel consumption Pollutantemissions Additionaldynamics!

25. Pollution constraint into EMS ? PhD P. Michel • Simulation on Worldwide harmonized Light vehicles Test Cycle • A good trade-off between pollutant and fuel consumption permits to be under the standards • 3WCC model under validation & strategy will be implemented

26. Battery ageing into EMS ? • Battery : key stone of electrified vehicles • Ageing is a technological lock due to • Use (depth of Charge/Decharge) • Time • State of Charge • High Temperature : negative impact of first order anode (-) cathode (+) Collector(aluminium) Collector(copper) Active material(oxyde "LMO blend" LiMn2O4, LiCoO2, LiNiO2) Active material(graphite C6) charge carrier (lithium) electrolyte(lithium salt LiPF6 & solvent) Séparator

27. Battery constraint into EMS ? PhD T. Miro-Padovani • Principle • Idea : take into account ageing through thermal management • Dual problem with Hamiltonian • Existing tradeoff between fuel consumption and final cell temperature • Propose to estimatecell temperature!

28. Drivabilityconstraintinto EMS ? PhD T. Miro-Padovani Weight on kinematic modes changes • Uncomfort • Hamiltonian • Influence of k on comfort • Trade-off between fuel consumption and uncomfort withoutthisconstraint in the EMS, the vehicleisundriveable • Prototype vehicleunder validation Optimal control is erratic uncomfortable with k=0

29. Energy Management : Optimal approachConclusion • Main contributions • Real-time sub-optimal energy management strategies for hybrid vehicles with minimal sub-optimality • Demonstration of potential of Hybrid vehicles (pneumatic or electric) and maximization of this potential by using prediction/recognition • Strategies that takes into account in the Energy Management constraints such as pollutant emissions, comfort or battery • And also … • Implementation of sub-optimal strategies on real vehicles and on high fidelity models • State of Charge has been redefined into State of Energy • Strategies for Plug-in hybrid • Realistic Dynamic Programming(non-erratic control)

30. Outline • Extended curriculum Vitae • General Context • ResearchActivities • Supervisory control : Optimal approach • Control : Robust or Predictiveapproach • Observer : Polytopicapproach • Conclusion & future prospects 30

31. wastegate VWG Air Path Air Path control Supervisor Air Set point generation Control : Robust or Predictiveapproach Throttle valve Air mass set point pamb VTH manifold compressor Torque Set point pman pboost • Diesel or Gasoline : quite the same control problematic • General Torque Control Scheme Fuel mass Set point turbine Gasoline Diesel lambda sensor Qegr Gasoline engine air path Diesel engine air path

32. Control : Robust or Predictiveapproach • Engine control research meets industrial constraints • Need quasi-systematic approaches with tools • Two model based control approaches • Robust control : classical and Crone  frequency domain • Non linear Predictive control  physical or generic model in time domain • Complete methodology from specifications to real implementation for robust control • Choice of excitation signals (sinusoid-based) • Frequency response computation (FFT) • System analysis • Robust Control design (classical or crone) • Experimental validation identification

33. EGR Qair Th pboost Robust control of the air path PhD C. Deng and A. Lamara 1 & 2. Identification 3. System analysis • Condition Number • Relative Gain Array • Column Diagonal Dominant Degree 73 operating point RGA elements of G13 RGA elements of G11 RGA elements of G12 WG EGR EGR Th Th WG WG RGA elements of G23 RGA elements of G22 RGA elements of G21 Qair Qair 73 operating point 73 operating point pboost pboost

34. Robust control of the air path Actuators [%] PhD C. Deng and A. Lamara 4. Robust control synthesis • Analysis  decentralizedMIMO • Here, crone synthesis (collaboration IMS) • Open loop optimization • Resonance peak optimization w.r.t. sensibility functions constraints • Take into account every processes 5. Experimental validation • Comparison square/non square • 4% NOx reduction • Throttle (more EGR) Boost pressure [bar] Set point Measure Air flow [kg/h] Set point Measure Non-square Square Torque set point [Nm] Engine speed [rpm] Time [s]

35. Quasi-systematic predictive control design PhD J. El Hadef Physics-basedmodel • Application to gasoline engine air path • Step 1: Physics-based model Nonlinear MPC Explicit solution

36. Quasi-systematic predictive control design PhD J. El Hadef Physics-basedmodel • Step 2: Non linear model predictive Control • Find u that minimizes a “thermodynamics” criteria Cylinder pressure Nonlinear MPC + _ pexh pman Cylinder volume Explicit solution

37. Quasi-systematic predictive control design PhD J. El Hadef Physics-basedmodel • Step 3: Explicit solution (collaboration Supelec) • Approximate the control law by a piecewise affine function • Store the control into binary search tree • Add a slow integrator to ensure zero-steady state error Nonlinear MPC Explicit solution

38. Control : Robust or predictiveapproachConclusion • Main contributions • Two quasi-systematic methodologies of control synthesis from specifications to real application for non linear multivariable systems (square or not) function of desired performances • Maximizing engine efficiency and minimizing pollutant emissions using a good knowledge of the engine • And also … • Implementation of robust and predictive engine torque control on real engine test benches and on high fidelity models

39. Outline • Curriculum Vitae • General context • ResearchActivities • Supervisory control : Optimal approach • Control : Robust or Predictiveapproach • Observer : Polytopicapproach • Conclusion & future prospects 39

40. Estimation and observation : why? • Why estimate « on-line » of non-measured states ? • Need of variable estimation but no sensor (or too costly) • e.g. In-cylinder air mass, cell temperature, turbo-charger speed, … • How? • Model • Measurement feedback • Which model? • Discrete time Linear Parameter Varying (LPV) model Input Measured output Unknown states Estimated state Observer Measurement noise with and

41. Polytopic observers : principle • Observer • Objective : findLiof the variable feedback gain • Resolution? • Polyquadratic stability hypothesis • Linear Matrix Inequalities (LMI) • Take into account the noise • Single high level tuning parameter s • Link between error and noise through and with without

42. Batteryageing : Need to obtaincelltemperature Application to electrified vehicle PhD M. Debert Mass production sensor : toomuchfiltered! Thermal modeling

43. Obtained model : LinearParameterVarying Application to electrified vehicle PhD M. Debert LPV

44. Application to electrified vehicle PhD M. Debert • Results • Good estimation of cell temperature • Internal resistance estimation • Perspectives • Possibility of diagnosis through internal resistance • Permits to take into account internal battery parameters into vehicle energy management

45. Observer : polytopicapproachConclusion • Main results • Linear parameter varying observers using physical and generic models (e.g. neural networks) • Easy tuning between measurement confidence and model fiability through a single high level parameter • Application to • In-cylinder air-mass estimation • Battery temperature (patent) • And also for estimation … • Black box modeling (volumetric efficiency) • Turbocharger modeling and look-up-tables extrapolation using a good mix between physics and generic models

46. To conclude, in summary • General philosophy : a model based approach • Plant nonlinearities and specificities by gray box modeling (physical and generic) • Real time energy management strategies with minimal sub-optimality for Hybrid vehicles : credible & efficient • Reduction of the complexity of optimal strategies with real-time sub-optimal generic approaches and the choice of prediction to look-ahead system behavior • Demonstrated the potential of hybrid vehicles (pneumatic or electric), proposition of solutions to maximize this potential by using prediction/recognition and by taking into account constraints such as pollutant emissions, comfort or battery temperature • Quasi-systematic methodologies for internal combustion engine control • Methodology of robust or predictive control synthesis from specifications to real application for non linear multivariable systems compatible with industrial constraints • Maximizing engine efficiency and minimizing pollutant emissions using a good knowledge of the engine • Estimation of non-measured states • Definition of Linear Parameter Varying observer with an easy tuning between measurement confidence and model fiability through a single high level parameter • And experimental validations of nearly all of the proposed methods

47. Future prospects 1 • Multi-criteria global optimal control • Taking into account a maximum of constraints • Increase the number of states into strategy • Optimization on a dynamic trajectory between different modes • Today is static, tomorrow will be dynamics • In Real time! • Application to mobility and energy • Homogeneous combustion into hybrid electric vehicle  new possibilities for the vehicle • Vehicle environment (habitation, drivability, pollution, battery) • Energy Management with several sources, e.g. wind hybrid power system

48. Future prospects 2 • Robust Predictive control • Prove properties (robustness) to predictive control for non linear multi-variable systems and choose explicit to solve • Application to mobility and energy • Pollutant modeling and control (cold) • When delay are important, prediction will help to stabilize (low pressure EGR) • Robust Predictive Energy Management w.r.t dispersions (Flex-fuel, …) • Applications to energy and automotive are a great source of complex problem to solve • Encountered systems : nonlinear, constrained, multi-variable, multi-objectives, with limited computation time • Fundamental aspects = application independent : generic Energetics for Control Control for Energetics 

49. ManyThanks! • Lars and Janan for coming to my defense • Alain, Thierry-Marie and Olivier for their report • Yann and Gérard for orienting me and their friendship! • Alain, Domi, Benoit, Kristan, Sylvie and every laboratory colleagues for their helps • All the PhD students : Andrej, Maxime, Chao, Jamil, Pierre, Abderrahim, Thomas, and … • Ma famille : Emilie, Lucie, Timothée • Mescollègues de Polytech • Mesamis, mes parents et mes beaux parents • En bref, merci à tous !!!

50. Questions? Air tank