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Aalto University 2011
HangzhouOctober 2016
http://www.megevh.org/
«« EENERGY NERGY MMANAGEMENT ANAGEMENT SSTRATEGIES TRATEGIES
OF OF HHYBRID YBRID EELECTRIC LECTRIC VVEHICLES (EHICLES (HEVHEVss)) »»
Prof. Alain BOUSCAYROL, Dr. Ali CASTAINGSL2EP, Université Lille1, MEGEVH network,
Dr. Rochdi TRIGUI,LTE, IFSTTAR Bron, MEGEVH network,
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MEGEVHVPPC’16
Hangzhou, 2016
- MEGEVH network -
Coordination:Prof. A. Bouscayrol
6 projects4 PhDs in progress11 PhDs defended
8 industrial partners10 academic Labs
(Energy management ofHybrid and Electric Vehicles)
http://www.megevh.org/
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MEGEVH-macro
MEGEVH-strategy
MEGEVH-optim
theoretical developments
MEGEVH-storeMEGEVH-FC
Development of modeling and energy management
methods
independentlyof the kind of vehicle
- MEGEVH philosophy -
Paper Prize Award of IEEE-VPPC’08
Paper Prize Award of IEEE-VPPC’12
Paper Award EPE’14 ECCE Europe
Best paper AwardIET-EST journal
2015
experimental plate-forms
Reference vehicles
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- Common formalism: EMR -
DCM1
DCM2
EP1
EP2
Bat. RoadME1
ME2
Energetic MacroscopicRepresentation
=organizationof models of
complex systems
Systematic deductionof organization ofcontrol schemes
[Bouscayrol 00, 12]
vBat road
Strategy
vref
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• Non-profit professional organizationfor 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 - Institute of Electrical & Electronics Engineers
• Technical topics– land, airborne and maritime services– mobile communication, vehicle electro-technology
• 2 publications and 4 annual conferences• Distinguished Lecturer Program
IEEE – Vehicular Technology Society (VTS)
Prof. A. Bouscayrol• HIL simulation• EMR formalism• EVs and HEVs
- VTS Distinguished Lecturer Program -
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- Outline -
1.1. CCONTEXT OF ONTEXT OF EVsEVs AND AND HEVsHEVs
22. . DDIFFERENT IFFERENT KKINDS OF INDS OF EVsEVs AND AND HEVsHEVs
33. . KKEY EY IISSUES OF SSUES OF EVsEVs AND AND HEVsHEVs
4. E4. ENERGY NERGY MMANAGEMENT OF ANAGEMENT OF EVsEVs AND AND HEVsHEVs
5. E5. EXAMPLES OF XAMPLES OF IINNOVATIVES NNOVATIVES VVEHICLESEHICLES
RREFERENCESEFERENCES
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Aalto University 2011
HangzhouOctober 2016
http://www.megevh.org/
1. Context of 1. Context of EVsEVs & & HEVsHEVs
• Global warming• Petroleum resources• Thermal Vehicle
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0% 5% 10% 15% 20% 25%
waste(incl. deforestation)
Energy production
Residential / trade
Agriculture
Industrial process
Transport
http://www.statistiques.developpement-durable.gouv.fr/
Road transport 80%
- Green House Gases -France 2012
[CGDD 15]
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0% 5% 10% 15% 20% 25%
Energy production
Residential/trade
Agricultural
Industrial Process
Transport
- Green House Gases (2) -
http://www.citepa.org/(Centre Interprofessionnel Techniques d’Etudes de la Pollution Atmosphérique)
Waste(incl. deforestation)
All GHG, World 2010
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clutch &gearbox
Fuel tank road
ICE gearbox differential wheels chassis
accelerator steeringwheel
clutchgear ratio
- Thermal Vehicle -
IC engine
fueltanklow efficiency
pollutant emission
no energy recovery
great autonomy
fast energy charge
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Iso specific consumption (g/kWh )
Pmax=60 ch (45 kW) @ 3750 rpm Tmax=119 Nm @ 3400 rpm
( 1700 cm3 )
Speed (rpm)
Torq
ue (
Nm
)
- Gasoline engine -
Efficiency map
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- 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(increase of prize? hydrogen production? etc)
• Etc.
but also• A more reasonable mobility!
(reduction of travels? Increase of common transport? Etc.)
No ideal and unique
solution
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Example of an urban drive cycleICE Power (kW)
t (s)
Pmean= 15 kW
Pmax= 60 kW
oversizing
- Power of a thermal vehicle -
P < 0
Energy loss
Interest of a system which:• delivers peak power at high efficiency• enables energy recovery
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speed (m/s)
78% of pollutant emissions during 14 % of the cycle
Example of a highway drive cycleCO (g/s)
t (s)
- Pollution of a thermal vehicle -
P < 0
Interest of a system which:• enables transients at high efficiency and low emission
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Speed (rpm)
Torque (Nm)
- Operation of an ICE -Urban drive cycle / iso-consumption map
mean efficiency 12%(88% of losses!!)
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- Operation of an ICE -Extra-urban drive cycle / iso-consumption map
Speed (rpm)
Torque (Nm)
mean efficiency 20%
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Aalto University 2011
HangzhouOctober 2016
http://www.megevh.org/
2. Different kinds of 2. Different kinds of EVsEVs & & HEVsHEVs
• Electric Vehicles• Hybrid Electric Vehicles• Fuel Cell Vehicles
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1830 : first mini-electric-train
1890 : 3 kinds of vehicles on the automotive market thermal / electric / steam
1899 : « La Jamais contente » first vehicleto reach 100 km/h
1930 : last productions of electric vehicles “La jamais contente”
First EV?
EV in 1914
• technological reasons (autonomy, charging time)• economical reasons (reduction of the gasoline cost)• societal reasons (extension of the required range)
- EV history -
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nogearbox
Battery road
powerelectronics
electricmachine
differential wheels chassis
switch orders steeringwheel
- Electric Vehicle -
battery
powerelectronics
electricmachine
energy recovery
high efficiency
no local emission low autonomy
long energy charge
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thermalengine
fuel TM
Thermal Vehicle:- pollution- low efficiency
PE electricmachineBattery
Electric Vehicle:- long charge- low autonomy
- Thermal and Electric Vehicles -
http://www.nissan.com/
Nissan leaf
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- Hybrid Electric Vehicles -
fuelHybrid vehicle:- advantage of each technology- higher cost- complex control
Battery PE
thermalengine
TMelectric
machine
Various configurations:• different power ratios PICE/PEM• different component organization
Toyota Prius 3
http://www.toyota.com/
http://www.mpsa.com
Peugeot 3008 HY4
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fuel
Battery
ICE
electricalmachine
EM
PE
- HEVs or EVs? -
www.bmw.com/
Range extender EV= EV + ICE for
higher mileage range
BMW i3
fuelPlug-in HEV:= HEV + charger
+ plugBattery
ICengine
MTelectricalmachine
http://www.chevrolet.com//PE
Chevrolet Volt
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H2 PEFC electricalmachine
Fuel cell vehicle := EV with battery
replaced by a fuel cell and a H2 tank
- Fuel Cell vehicles? -
http://www.honda.com/
Honda Clarity FX
FC vehicle with hybrid storage= another kind
of RE-EV
electricalmachine
Battery
H2
PE
FC
http://www.toyota.com/
Toyota Mirai
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Nuclear
Coal
Biomass
Renewablesources
Fossil fuel
Hydrogen
ReformerCH4 +H20 C0 + 3 H2
Example (Natural Gaz)
H20
C0
Electricity Electrolysis
H20
H20H20+heat 1/202 + H2
02
02
H20+electricity 1/202 + H2
solar
4%
96%
experimental
- H2 production -
Thermo-chemistry
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HydrogenElectricity FuelCell
H20
or Air
H20 + electricity ½ 02 + H2
Fuel cell = inverse of the electrolysis water
½ 02 + H2 H20 + electricity + heat
Electrolysis :
Fuel cell :
heat
- Fuel Cell -
Pros: no local pollutionCons: high cost, low lifetime, H2 production
… and 50% efficiency, heat production
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- Other Electric Vehicles -
New technologies are also used in various vehicles in order to reduce the ecological footprint of transportation systems!
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Aalto University 2011
HangzhouOctober 2016
http://www.megevh.org/
3. Key issues of 3. Key issues of EVsEVs & & HEVsHEVs
• Energy Storage Subsystems• Energy Management• Societal changes
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Battery Ni-MHHigh energy density
High efficiencyPower electronics
Complex control
Permanent MagnetSynchronous Machines
Power split
GeneratorBattery
Motor
Mechanical power path
Electrical power path
Engine
Inverter Boost
Véhicule PRIUS IIhttp://www.toyota.com/
ECU
- Toyota Prius, a success story -
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ICE
EM
BAT
EG
Series Parallel HEV
Fuel
- Architecture bases -
ICE
EM
BAT
Fuel
??
ICE
EM
BAT
Fuel
EG
Series HEV electrical node
ICE
EM
BAT
Fuel
Parallel HEV
mechanical node
power flows
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• thermal traction
• internal charge of battery
• Stop & Go
• regenerative braking
• electrical boost
• electrical traction
• external charge (Plug-in HEV)
EMICE
ICE EM
ICE EM
ICE EM
ICE
EM
- Hybridization rate -
TV
EV
HEV
mild HEV
full HEV
(power ratio associated with functionalities)
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SuperC
batteries
petrol+
ICE
H2+
FuelCell
Flywheel
- Energy and Power -
[Chan 2007]
?
unidirectionalHybridation ?
Energy density (Wh/kg)~ mileage range
Power density (W/kg)
~ acceleration,charge time
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- Different sources -
thermal engine
fossilfuel
hydraulic machine
compressed air
FuelCellH2
Electrochemical batteries
superCapacitors
Fly wheel
non reversible reversible
mechanical interface
electrical interface
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superC
- Series hybrid topologies -electrical coupling
thermal enginefuel
hydraulicmachine
compressedair
Fuel CellH2
Battery
electrictraction
flywheel
DC bus
+
electrical machine
power electronics
+
+
+
+
+
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superC
thermal enginefuel
hydraulicmachine
compressedair
Fuel CellH2
Battery
flywheel
- Parallel hybrid topologies -mechanical
couplingbelts
Planetary trainsetc.
+
+
+
+
+
+
CVT / gearbox(manual, automated…)
PSAHybridAir
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- Split hybrid topologies -
electrical AND mechanical
coupling
thermal enginefuel
Battery
DC bus
various solutions…
+
+ICE
EM1
EM2
++ optimal point for ICE
operation point pequested by the vehicle
more fuel optimization…… more complex control
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- Well to Wheel analysis -
HEV
EV
g CO2 / km
?Coal
Coal
Natural Gas
wood
Nuclear
Wind
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- Evolution of batteries -
but
Fossil fuel
11 000 Wh/kg
in development in researchin commerce
Most promisingTechnology forEVs and HEVs
Lead acid
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- Energy charge -
New technologies and developments? “Smart” charge?but alsoA new way to manage our 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/
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- Impact on the grid -
http://my.epri.com
V2G
G2V
New concepts for grid management?but alsoA new way to manage our energy prize?
(Vehicle to Grid)
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- Day trip analysis -
Mileage range of a classical EV = 100 to 150 km
50%30%20%
Average values ofdaily trips inEurope in 2007
daily trip < 20 km
daily trip > 60 km
20 km < daily trip < 60 km
Possible uses of EVs?butA new way to manage our mobility?
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- 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
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- Control of TVs and EVs -
EV
GB
TV control
Electricmachine
TV
Trans.power
electronicsBat.
EV control
Controls of TVs and EVs: mono-objective (no optimization)ensure the driving cycle
pedals
clucth
pedals
FuelICE
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- Challenge of HEV Control -
parallel HEV
Electricmachine
Trans.
powerelectronicsBat.
HEV control
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.
pedals
FuelICE
Powercoupling
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Electricmachine
Trans.
powerelectronicsBat.
EMcontrol
FuelICE
Powercoupling
- Organization of HEV control -
ICEcontrol
Transcontrol
Energy Management Strategy EMS driver
requests
fast subsystem
controls
slow systemsupervision(subsystem
coordination)
How to split the control?How to develop efficient EMS?
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Aalto University 2011
HangzhouOctober 2016
http://www.megevh.org/
4. Energy Management 4. Energy Management StategiesStategies (EMS)(EMS)of of EVsEVs and and HEVsHEVs
• EMS classification• ruled-based EMS• optimization-based EMS
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- Example of a multi-sources EV-
SCs
electricalmachinePE
PE
Battery
Basic example: Battery / Supercapacitors (SCs) EVMain objective: to increase the battery lifetime
How to manage such a system?
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Trans.
powerelectronicsSCS.
PEcontrol
- Organization of vehicle control -
PEcontrol
EMcontrol
Energy Management Strategy (EMS) driver
requests
Bat. Electricmachine
powerelectronics
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Trans.
powerelectronicsSCS.
PEcontrol
- Organization of vehicle control -
PEcontrol
EMcontrol
Energy sources EMS Measur.
Bat. Electricmachine
powerelectronics
Traction EMSMeasur.
Energy sources power split
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MesuresEMS
idc-ref
iL-ref udc-ref
- Organization of vehicle control -
ub
it
ub
Bat.
SCs.
ib
ub
idc
udc
m
iLusc
iL
Tract.
it-meas
ib-ref
Using EMR approach
Battery currentreference as control
variable
Focus on EMSs
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- Classification -[Sciaretta 2007], [Salmasi 2007], [Trigui 2011]
Rule-based
Optimization-based
Deterministic rules
Fuzzyrules
Real-time optimization -
based
Global optimization
Filtering, Thermostat control …
Fuzzy logic, Neural networks …
Dynamic programming, Pontryagin’s minimum
λ-control, Predictivecontrol ,…
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- Classification -
Rule-based
Deterministic rules
Fuzzyrules
Filtering, Thermostat control …
Fuzzy logic, Neural networks …
No optimal performances
Real-time implementation
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- Classification -
Optimization-based
Real-time optimization -
based
Global optimization
Optimal performancesReal-time implementation
Benchmarking use
Compromise between performances and real-
time implementation
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-Application: Filtering –(Rule-based)
0 100 200 300 400-0.5
0
0.5
1
1.5
t(s)
Courant batterie VE (p.u)
Traction current
Current (p.u)
(t)
Low dynamics for the battery
High dynamic for the SCs
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-Application: Filtering –(Rule-based)
Energy sources EMS
Traction current
ib-ref
it-meas
f0
Low-pass filter
ib-ref
Real-time Driving cycle not required
No optimal
Performance criterion:
Battery current RMS value
(lifetime)
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- Application: Dynamic programming –(global optimization)
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.
Dynamic programming –
Recurrence relationship
, min , 1 1,
[Kirk 2004]
To find the optimal trajectory
ab
e
c
Max limit
Min limit
k
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- Application: Dynamic programming –(global optimization)
Energy sources EMS
Driving cycle
ib-ref
Optimal Off-line
SCs voltage
Backward simplfied model
DP algorithm
Driving cycle requested
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- Application: Calculus of variations-(Real-time optimisation-based)
Criterion System to optimizeState variables constraints
Minimization of a Hamiltonian funtion:
Control variable expression by solving :
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- Application: Calculus of Variations-(Real-time optimization-based)
-
Energy sources EMS
Driving cycle
ib-ref
Optimal Real-time
SCs voltage
Driving cycle requested
Control variable expression
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- Application: λ-control -(Real-time optimisation-based)
Criterion System to optimizeState variables constraints
Minimization of a Hamiltonian funtion:
Control variable expression by solving :
Based on Calculus of Variations
Feedback control to face driving conditions
vrariations
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- Application: Calculus of Variations-(Real-time optimization-based)
Energy sources EMS
ib-ref
Suboptimal Real-time
SCs voltage
Driving cycle not requested
usc-meas
it-meas
Limitations of usc
ib-ref ib-ref
Controller +
+0
rb ηg
OCV
‐+
usc-ref
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0 100 200 300 4000
10
20
30
40
50
t(s)
Vitesse (km/h)
- Simulations results -B-EV case
0 100 200 300 400-0.5
0
0.5
1
1.5
t(s)
Courant batterie VE (p.u)
Real driving cycle
Battery current
Velocity (km/h)
(t)
Current (p.u)
(t)
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- Simulation results -Battery-SCs vehicle
Battery current
B-EV0 100 200 300 4000
0.5
1
1.5
t(s)
Courants batterie VE mixte (p.u)
ib CDVib filtrageib Prg dyn
Current (p.u)
(t)
λ-ctrl
Filter.
DP.
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- Simulation results -Battery-SCs vehicle
0
0.1
0.2
0.3
0.4Valeur efficace de ib
DPλ-ctrl Filter
B-EV
(theoretical optimal i.e. benchmark)
Performance criterion
Criterion:
λ-control: -25 %
Filtering: -20 %
Purebattery
EVDP
strategy-controlstrategy
filteringstrategy
RMS value of battery current
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- More complex system -
Battery
Front
Rear
FC
SCs
Fuel Cell (FC) – Battery –Supercapacitors (SCs) vehicle
Experimental implementation
0 200 400 6000
20
40
60
t(s)
Vehicle velocity (km/h)
WLTC driving cycle (low speed)
0 200 400 600-0.5
0
0.5
1
t(s)
Currents (p.u)
FC convTraction
0 200 400 600-2
-1
0
1
2
t(s)
Battery and SCs currents (p.u)
SCsBat
Decomposedλ-control
More details inSpecial session SS5
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Aalto University 2011
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http://www.megevh.org/
5. Examples of Innovative Vehicles5. Examples of Innovative Vehicles
Non available on the pdf version
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Aalto University 2011
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HEVs and EVs could be valuable alternative vehicles
butDesign and Energy Management Strategies
are key issues for their development…
ConclusionConclusion
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- MEGEVH at IEEE-VPPC’16 -tutorial « Energy Management of Evs and HEVs »Monday October 17, 14:00-16:00, room EProf. 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 FProf. 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 DDr. Ronan GERMAN, Dr. Walter LHOMME (Univ. Lille1, MEGEVH, France)Dr. Joao TROVAO (Univ. Sherbrooke, Canada)
SS « Energetic Macroscopic Representation and other graphicaldescriptions »Wednesday October 19, 14:00-15:30, room DDr. Clément MAYET (Univ. Lille1, MEGEVH, France)Prof. MINH C. TA (Hanoi Univ. Of Science and Tech., Vietnam)
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ReferencesReferences
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- 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 strategiesof 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 Representation and Inversion-Based control of a CVT-based HEV”, EVS’27, Barcelona (Spain), November 2013 (L2EP Lille and Tech. Univ. Eindhoven)
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