Prof. Alain BOUSCAYROL, Dr. Ali CASTAINGS L2EP, Université Lille1, MEGEVH network,

http://www.megevh.org/ «ENERGY MANAGEMENT STRATEGIES HEVs)» OF HYBRID ELECTRIC VEHICLES (HEV( Prof. Alain BOUSCAYROL, Dr. Ali CASTAINGS L2EP, Université Lille1, MEGEVH network, Alain.Bouscayrol@univ-lille1.fr Dr. Rochdi TRIGUI, LTE, IFSTTAR Bron, MEGEVH network, rochdi.trigui@ifsttar.fr Hangzhou October 2016 1 Aalto University 2011

- MEGEVH network - (Energy management of Hybrid and Electric Vehicles) Coordination: Prof. A. Bouscayrol 6 projects 4 PhDs in progress 11 PhDs defended 8 industrial partners 10 academic Labs http://www.megevh.org/ 2

- MEGEVH philosophy - theoretical developments MEGEVH-macro MEGEVH-strategy MEGEVH-optim Development of modeling and energy management methods independently of the kind of vehicle MEGEVH-FC MEGEVH-store experimental plate-forms Paper Prize Award of IEEE-VPPC 08 Reference vehicles Paper Prize Award of IEEE-VPPC 12 Paper Award EPE 14 ECCE Europe Best paper Award IET-EST journal 2015 3

- Common formalism: EMR - Energetic Macroscopic Representation = organization Bat of models of complex systems v road Systematic deduction of organization of control schemes v ref Strategy Bat. DCM EP 11 ME 1 Road DCM EP 22 ME 2 [Bouscayrol 00, 12] 4

- VTS Distinguished Lecturer Program - IEEE - Institute of Electrical & Electronics Engineers Non-profit professional organization for advancing technological innovation and excellence 400,000 members from 160 countries (30 % students) 38 societies on technical interest Activities scientific workshop, conferences, publications, standards database IEEE Xplore, 3.5 millions documents, etc IEEE Vehicular Technology Society (VTS) Technical topics land, airborne and maritime services mobile communication, vehicle electro-technology 2 publications and 4 annual conferences Distinguished Lecturer Program Prof. A. Bouscayrol HIL simulation EMR formalism 5 EVs and HEVs

- Outline - 1. CONTEXT OF EVs AND HEVs 2. DIFFERENT KINDS OF EVs AND HEVs 3. KEY ISSUES OF EVs AND HEVs 4. ENERGY E MANAGEMENT OF EVs AND HEVs 5. EXAMPLES E OF INNOVATIVES VEHICLES REFERENCES 6

http://www.megevh.org/ 1. Context of EVs & HEVs Global warming Petroleum resources Thermal Vehicle Hangzhou October 2016 7 Aalto University 2011

- Green House Gases - Transport Road transport 80% France 2012 Industrial process Agriculture Residential / trade Energy production waste (incl. deforestation) 0% 5% 10% 15% 20% 25% http://www.statistiques.developpement-durable.gouv.fr/ [CGDD 15] 8

- Green House Gases (2) - All GHG, World 2010 Transport Industrial Process Agricultural Residential/trade Energy production Waste (incl. deforestation) 0% 5% 10% 15% 20% 25% http://www.citepa.org/ (Centre Interprofessionnel Techniques d Etudes de la Pollution Atmosphérique) 9

- Thermal Vehicle - low efficiency pollutant emission no energy recovery fuel tank IC engine clutch & gearbox great autonomy fast energy charge ICE gearbox differential wheels chassis Fuel tank road accelerator clutch gear ratio steering wheel 10

- Gasoline engine - P max =60 ch (45 kw) @ 3750 rpm T max =119 Nm @ 3400 rpm ( 1700 cm 3 ) Iso specific consumption (g/kwh ) Efficiency map Torque (Nm) Speed (rpm) 11

- Future Vehicles? - Thermal vehicle with bio-fuels (coupling energy & food? water requirement? etc) Electric Vehicles (production of electricity? autonomy reduction? etc) Hybrid Electric vehicles (increase of prize? need of fossil fuel? etc) Fuel Cell Vehicle Etc. (increase of prize? hydrogen production? etc) No ideal and unique solution but also A more reasonable mobility! (reduction of travels? Increase of common transport? Etc.) 12

- Power of a thermal vehicle - ICE Power (kw) Example of an urban drive cycle P max = 60 kw oversizing P mean = 15 kw t (s) P < 0 Interest of a system which: delivers peak power at high efficiency enables energy recovery Energy loss 13

CO (g/s) - Pollution of a thermal vehicle - Example of a highway drive cycle speed (m/s) 78% of pollutant emissions during 14 % of the cycle P < 0 t (s) Interest of a system which: enables transients at high efficiency and low emission 14

- Operation of an ICE - Torque (Nm) Urban drive cycle / iso-consumption map Speed (rpm) mean efficiency 12% (88% of losses!!) 15

- Operation of an ICE - Torque (Nm) Extra-urban drive cycle / iso-consumption map Speed (rpm) mean efficiency 20% 16

http://www.megevh.org/ 2. Different kinds of EVs & HEVs Electric Vehicles Hybrid Electric Vehicles Fuel Cell Vehicles Hangzhou October 2016 17 Aalto University 2011

- EV history - 1830 : first mini-electric-train 1890 : 3 kinds of vehicles on the automotive market thermal / electric / steam First EV? 1899 : «La Jamais contente» first vehicle to reach 100 km/h 1930 : last productions of electric vehicles La jamais contente technological reasons (autonomy, charging time) economical reasons (reduction of the gasoline cost) societal reasons (extension of the required range) EV in 1914 18

- Electric Vehicle - high efficiency power electronics electric machine no local emission battery no low autonomy gearbox energy recovery long energy charge power electronics electric machine differential wheels chassis Battery road switch orders steering wheel 19

- Thermal and Electric Vehicles - fuel thermal engine TM Thermal Vehicle: - pollution - low efficiency Nissan leaf Battery PE electric machine Electric Vehicle: - long charge - low autonomy http://www.nissan.com/ 20

- Hybrid Electric Vehicles - Battery fuel PE thermal engine electric machine TM Hybrid vehicle: - advantage of each technology - higher cost - complex control Toyota Prius 3 Various configurations: different power ratios P ICE /P EM different component organization Peugeot 3008 HY4 http://www.toyota.com/ http://www.mpsa.com 21

- HEVs or EVs? - fuel Battery ICE EM PE electrical machine Range extender EV = EV + ICE for higher mileage range BMW i3 www.bmw.com/ Chevrolet Volt Battery fuel PE IC engine electrical machine MT Plug-in HEV: = HEV + charger + plug http://www.chevrolet.com// 22

- Fuel Cell vehicles? - H 2 FC PE electrical machine Fuel cell vehicle : = EV with battery replaced by a fuel cell and a H2 tank Honda Clarity FX http://www.honda.com/ Toyota Mirai H2 FC Battery PE electrical machine FC vehicle with hybrid storage = another kind of RE-EV http://www.toyota.com/ 23

- H2 production - Fossil fuel Coal Nuclear Electricity C0 Reformer H 2 0 Example (Natural Gaz) CH 4 +H 2 0 C0 + 3 H 2 0 2 Electrolysis 4% 96% Hydrogen H 2 0 H 2 0+electricity 1/20 2 + H 2 experimental Biomass 0 2 Renewable sources solar Thermo-chemistry H 2 0 H 2 0+heat 1/20 2 + H 2 24

- Fuel Cell - Fuel cell = inverse of the electrolysis water or Air Electricity Fuel Cell Hydrogen heat H 2 0 Fuel cell : ½0 2 + H 2 H 2 0 + electricity + heat Electrolysis : H 2 0 + electricity ½ 0 2 + H 2 Pros: no local pollution Cons: high cost, low lifetime, H2 production and 50% efficiency, heat production 25

- Other Electric Vehicles - New technologies are also used in various vehicles in order to reduce the ecological footprint of transportation systems! 26

http://www.megevh.org/ 3. Key issues of EVs & HEVs Energy Storage Subsystems Energy Management Societal changes Hangzhou October 2016 27 Aalto University 2011

- Toyota Prius, a success story - Complex control Battery Ni-MH High energy density Mechanical power path Electrical power path Generator Inverter ECU Boost Battery Engine Motor Power split High efficiency Power electronics Véhicule PRIUS II http://www.toyota.com/ Permanent Magnet Synchronous Machines 28

- Architecture bases - BAT BAT Parallel HEV power flows EM? EM ICE Fuel ICE Fuel mechanical node Series HEV BAT electrical node BAT Series Parallel HEV EM EM EG ICE EG ICE Fuel Fuel 29

- Hybridization rate - (power ratio associated with functionalities) TV thermal traction ICE HEV mild HEV internal charge of battery Stop & Go regenerative braking electrical boost ICE ICE ICE EM EM EM full HEV electrical traction ICE EM EV EM external charge (Plug-in HEV) 30

- Energy and Power - SuperC Hybridation? unidirectional Power density (W/kg) ~ acceleration, charge time Fly wheel batteries? H2 + petrol + ICE Fuel Cell [Chan 2007] Energy density (Wh/kg) ~ mileage range 31

- Different sources - non reversible reversible fossil fuel thermal engine compressed air hydraulic machine mechanical interface Fly wheel electrical interface H2 Fuel Cell Electrochemical batteries supercapacitors 32

- Series hybrid topologies - fuel thermal engine + electrical machine power electronics electrical coupling compressed air hydraulic machine + DC bus fly wheel + H2 Battery Fuel Cell + electric traction superc + + 33

- Parallel hybrid topologies - fuel compressed air thermal engine hydraulic machine + + CVT / gearbox (manual, automated ) PSA HybridAir mechanical coupling belts Planetary trains etc. fly wheel + H2 Fuel Cell + Battery + superc + 34

fuel Battery thermal engine DC bus - Split hybrid topologies - various solutions electrical AND mechanical coupling ICE EM1 + + EM2 + + optimal point for ICE operation point pequested by the vehicle more fuel optimization more complex control 35

- Well to Wheel analysis - HEV Coal? Coal Natural Gas wood EV Nuclear Wind g CO 2 / km 36

- Evolution of batteries - 11 000 Wh/kg Most promising Technology for EVs and HEVs but Lead acid in commerce in development in research Fossil fuel 37

- Energy charge - slow charge at home / at work (4-8h?) (plug or induction) ultra-fast charge at specific station (1/2h?) battery swap station (5-10 min?) http://france.betterplace.com/ New technologies and developments? Smart charge? but also A new way to manage our energy charge? 38

- Impact on the grid - G2V http://my.epri.com V2G (Vehicle to Grid) New concepts for grid management? but also A new way to manage our energy prize? 39

- Day trip analysis - Average values of daily trips in Europe in 2007 daily trip > 60 km 20% 30% 50% 20 km < daily trip < 60 km daily trip < 20 km Mileage range of a classical EV = 100 to 150 km Possible uses of EVs? but A new way to manage our mobility? 40

- Challenge EVs and HEVs - EVs battery cost and lifetime charging time, range cabin thermal management HEVs ZEV mode topology and design energy management FCs topology and design energy management FC cost and lifetime H2 production and distribution Driving conditions Energy Storage Susbsystem balanced design complex control 41

- Control of TVs and EVs - ICE TV Fuel clucth GB TV control pedals EV Bat. power electronics Electric machine Trans. EV control pedals Controls of TVs and EVs: mono-objective (no optimization) ensure the driving cycle 42

- Challenge of HEV Control - Bat. parallel HEV power electronics Fuel Electric machine ICE Power coupling Trans. HEV control pedals Controls of HEVs: multi-objective: ensure the driving cycle AND reduce the fuel consumption various modes: pure electric, pure thermal, hybrid, etc. How to achieve the objectives? Which mode and when? How to switch between modes? Etc. 43

- Organization of HEV control - Bat. power electronics Fuel Electric machine ICE Power coupling Trans. fast subsystem controls EM control ICE control Trans control slow system supervision (subsystem coordination) Energy Management Strategy EMS How to split the control? How to develop efficient EMS? driver requests 44

http://www.megevh.org/ 4. Energy Management Stategies (EMS) of EVs and HEVs EMS classification ruled-based EMS optimization-based EMS Hangzhou October 2016 45 Aalto University 2011

- Example of a multi-sources EV- SCs PE Battery PE electrical machine Basic example: Battery / Supercapacitors (SCs) EV Main objective: to increase the battery lifetime How to manage such a system? 46

- Organization of vehicle control - SCS. power electronics Bat. power electronics Electric machine Trans. PE control PE control EM control Energy Management Strategy (EMS) driver requests 47

- Organization of vehicle control - SCS. power electronics Bat. power electronics Electric machine Trans. PE control PE control EM control Energy sources EMS Measur. Traction EMS Measur. Energy sources power split 48

- Organization of vehicle control - Bat. u b i b u b Tract. i t SCs. u sc i L i L u dc m i dc u b Using EMR approach i L-ref u dc-ref i dc-ref i t-meas Battery current reference as control variable i b-ref EMS Mesures Focus on EMSs 49

- Classification - [Sciaretta 2007], [Salmasi 2007], [Trigui 2011] Deterministic rules Filtering, Thermostat control Rule-based Fuzzy rules Fuzzy logic, Neural networks Global optimization Dynamic programming, Pontryagin s minimum Optimizationbased Real-time optimization - based λ-control, Predictive control, 50

- Classification - Deterministic rules Filtering, Thermostat control Rule-based Fuzzy rules Fuzzy logic, Neural networks Real-time implementation No optimal performances 51

- Classification - Optimizationbased Global optimization Real-time optimization - based Optimal performances Real-time implementation Benchmarking use Compromise between performances and realtime implementation 52

-Application: Filtering (Rule-based) 1.5 Current (p.u) Courant batterie VE (p.u) Traction current 1 0.5 0-0.5 0 100 200 300 400 t(s) (t) Low dynamics for the battery High dynamic for the SCs 53

-Application: Filtering (Rule-based) i b-ref Low-pass filter i t-meas Performance criterion: Battery current RMS value (lifetime) f 0 Real-time Driving cycle not required i b-ref No optimal Energy sources EMS Traction current 54

- Application: Dynamic programming (global optimization) [Kirk 2004] Bellman s optimality principle If a-b-e is an optimal way from a to e, then b-e is the optimal way from b to e. b c a Dynamic programming e Recurrence relationship Max limit, min, 1 1, To find the optimal trajectory Min limit k 55

- Application: Dynamic programming (global optimization) Backward simplfied model DP algorithm Optimal Off-line i b-ref Driving cycle requested Energy sources EMS Driving cycle SCs voltage 56

- Application: Calculus of variations- (Real-time optimisation-based) Minimization of a Hamiltonian funtion: Criterion System to optimize State variables constraints Control variable expression by solving : 57

- Application: Calculus of Variations- (Real-time optimization-based) - Control variable expression Optimal Real-time i b-ref Driving cycle requested Energy sources EMS Driving cycle SCs voltage 58

- Application: λ-control - (Real-time optimisation-based) Minimization of a Hamiltonian funtion: Control variable expression by solving : Criterion System to optimize State variables constraints Based on Calculus of Variations Feedback control to face driving conditions vrariations 59

- Application: Calculus of Variations- (Real-time optimization-based) Limitations of u sc + + Controller + u sc-meas 0 u sc-ref i b-ref i b-ref i t-meas r b η g OCV Suboptimal Real-time i b-ref Driving cycle not requested Energy sources EMS SCs voltage 60

- Simulations results - B-EV case 50 Velocity Vitesse (km/h) 40 Real driving cycle 30 20 10 0 0 100 200 300 400 t(s) 1.5 Current (p.u) Courant batterie VE (p.u) (t) 1 Battery current 0.5 0-0.5 0 100 200 300 400 t(s) (t) 61

- Simulation results - Battery-SCs vehicle Battery current 1.5 1 Courants Current batterie (p.u) VE mixte (p.u) i b CDV λ-ctrl i b filtrage Filter. i b Prg DP. dyn 0.5 0 0 100 200 300 400 t(s) B-EV (t) 62

- Simulation results - Battery-SCs vehicle 0.4 RMS Valeur value efficace of battery de i b current Performance criterion 0.3 Criterion: 0.2 λ-control: -25 % 0.1 Filtering: -20 % 0 DP strategy (theoretical optimal i.e. benchmark) DP λ-ctrl -control strategy Filter B-EV filtering strategy Pure battery EV 63

- More complex system - Fuel Cell (FC) Battery Supercapacitors (SCs) vehicle Experimental implementation Front FC Battery SCs Rear 60 40 20 Vehicle velocity (km/h) WLTC driving cycle (low speed) Decomposed λ-control More details in Special session SS5 1 0.5 0 Currents (p.u) FC conv Traction t(s) -0.5 0 200 400 600 0 t(s) 0 200 400 600 Battery and SCs currents (p.u) 2 SCs 1 Bat 0-1 64-2 t(s) 0 200 400 600

http://www.megevh.org/ 5. Examples of Innovative Vehicles Non available on the pdf version Hangzhou October 2016 65 Aalto University 2011

http://www.megevh.org/ Conclusion HEVs and EVs could be valuable alternative vehicles but Design and Energy Management Strategies are key issues for their development Hangzhou October 2016 66 Aalto University 2011

- MEGEVH at IEEE- - tutorial «Energy Management of Evs and HEVs» Monday October 17, 14:00-16:00, room E Prof. Alain BOUSCAYROL, Dr. Ali CASTAINGS (Univ. Lille1, MEGEVH, France) Dr. Rochdi TRIGUI (IFSTTAR, MEGEVH, France) tutorial «Will Hydrogen Fuel Cells Power Next Vehicle Generation?» Monday October 17, 14:00-16:00, room F Prof. Daniel HISSEL, Prof. Marie-Cécile PERA (Univ. Bourgogne Franche-Comté, MEGEVH, France) SS «Energy Management of Electrical Hybrid Energy Sources» Wednesday October 19, 10:30-11:30, room D Dr. Ronan GERMAN, Dr. Walter LHOMME (Univ. Lille1, MEGEVH, France) Dr. Joao TROVAO (Univ. Sherbrooke, Canada) SS «Energetic Macroscopic Representation and other graphical descriptions» Wednesday October 19, 14:00-15:30, room D Dr. Clément MAYET (Univ. Lille1, MEGEVH, France) Prof. MINH C. TA (Hanoi Univ. Of Science and Tech., Vietnam) 67

http://www.megevh.org/ References Hangzhou October 2016 68 Aalto University 2011

- References (1) - [Boulon 13] L. Boulon, A. Bouscayrol, D. Hissel, O. Pape, M-C Péra, Inversion-based control of a highly redundant military HEV", IEEE trans. on Vehicular Technology, vol. 62, no. 2, Feb. 2013, pp. 500-5010 (Univ Trois- Rivières, L2EP Lille, FEMTO-ST and Nexter within MEGEVH network) [Bouscayrol 00] A. Bouscayrol, B. Davat, B. de Fornel, B. François, J. P. Hautier, F. Meibody-Tabar, M. Pietrzak- David, "Multimachine Multiconverter System: application for electromechanical drives", European Physics Journal - Applied Physics, vol. 10, no. 2, pp. 131-147, May 2000. [Bouscayrol 12] A. Bouscayrol, J. P. Hautier, B. Lemaire-Semail, "Graphic Formalisms for the Control of Multi- Physical Energetic Systems", Systemic Design Methodologies for Electrical Energy, Chap. 3, ISTE Willey ed., Oct.2012, ISBN: 9781848213883. [Castaings 2016] A. Castaings, W. Lhomme, R. Trigui, A. Bouscayrol Comparison of energy management strategies of a battery/supercapacitors system for electric vehicle under real-time constraints, Applied Energy, vol. 163, p. 190-200, February 2016 (L2EP Lille and LTE-IFSTTAR, MEGEVH network). [Chan 09] C.C. Chan, Y. S. Wong, A. Bouscayrol, K. Chen, "Powering Sustainable Mobility: Roadmaps of Electric, Hybrid and Fuel Cell Vehicles", Proceedings of the IEEE, vol. 97, no. 4, April 2009, (Hong-Kong Univ. and L2EP). [Chan 10] C. C. Chan, A. Bouscayrol, K. Chen, Electric, Hybrid and Fuel Cell Vehicles: Architectures and Modeling", IEEE trans. on Vehicular Technology, vol. 59, no. 2, February 2010, pp. 589-598 (L2EP Lille and Honk-Kong Univ.). [Cheng 13] Y. Cheng, A. Bouscayrol, R. Trigui, C. Espanet, S. Cui, Field weakening control of a PM electric variable transmission for HEV", Journal of Electrical Engineering and Technology, Vol. 8, no. 5, September 2013, pp. 1096-1106 (L2EP Lille, LTE-IFSTTAR, FEMTO-ST and Harbin Inst. of Tech within MEGEVH network) [Chouhou 13] M. Chouhou, F. Gree, C. Jivan, A. Bouscayrol, T. Hofman, Energetic Macroscopic 69 Representation and Inversion-Based control of a CVT-based HEV, EVS 27, Barcelona (Spain), November 2013 (L2EP Lille and Tech. Univ. Eindhoven)

- References (2) - [Horrein 15] L. Horrein, A. Bouscayrol, Y. Cheng, M. El-Fassi, Dynamical and quasi-static multi-physical models of a diesel internal combustion engine using Energetic Macroscopic Representation", Energy Conversion and Management, Vol. 91, February 2015, pp. 280 291 (L2EP Lille and PSA Peugeot Citroen, within MEGEVH network). [Kirk 2004] D. E. Kirk, Optimal Control Theory: An Introduction. Dover Publications, 2004 [Letrouvé 13] T. Letrouvé, W. Lhomme, A. Bouscayrol, N. Dollinger, Control validation of Peugeot 3 8 Hybrid4 vehicle using a reduced-scale power HIL simulation", Journal of Electrical Engineering and Technology, Vol. 8, no. 5, September 2013, pp. 1227-1233 (L2EP Lille, and PSA Peugeot Citroën within MEGEVH network) [Mayet 12] C. Mayet, J. Pouget, A. Bouscayrol, W. Lhomme, Influence of an energy storage system on the energy consumption of a diesel-electric locomotive", IEEE trans. on Vehicular Technology, Vol. 63, no. 3, March 2014, pp. 1032-1040 (L2EP Lille and SNCF, within MEGEVH network). [Trigui 11 ] R. Trigui, Systelic appraoch for modeling, energy management and design of hybrid electric vehicles, HDR report, IFSTTAR-Université de Lille1, September 2011. [Salmasi 07] F. R. Salmasi, "Control strategies for Hybrid Electric Vehicles: evolution, classification, comparison and future trends", IEEE Trans. on Vehicular Technology, September 2007, Vol. 56, No. 3, pp. 2393-2404. [Sciaretta 2007] A. Sciarretta et L. Guzzella, «Control of hybrid electric vehicles», IEEE Control Syst., vol. 27, no 2, p. 60-70, April 2007. [Vinot 14] E. Vinnot, R. Trigui, Y. Cheng, C. Espanet, A. Bouscayrol, V. Reinbold, Improvement of an EVTbased HEV using dynamic programming", IEEE trans. on Vehicular Technology, Vol. 63, no. 1, January 2014, pp. 40-49 (LTE-IFSTTAR, FEMTO-ST and L2EP Lille, 70 within MEGEVH network)