« EMR for a Multi-objective Benchmark for Energy ... · « EMR for a Multi-objective Benchmark for Energy Management of ... Inverter Electrical machine ... representation energetique

EMR’17

University Lille 1

June 2017

Summer School EMR’17

“Energetic Macroscopic Representation”

« EMR for a Multi-objective Benchmark for

Energy Management of Hybrid Energy

Storage in Electric Vehicles »

Bảo-Huy NGUYỄN 1,2,3, João P. TROVÃO1,

Ronan GERMAN2,3, Alain BOUSCAYROL2,3, 1 e-TESC Lab., Université de Sherbrooke, Canada

2 L2EP, Université Lille 1, France3 MEGEVH, France

EMR’17, University Lille 1, June 20172

« A multi-objective benchmark for EMS of H-ESS for EVs »

- Global context -

Tiny, tiny amount

Badly

misunderstood

our research

Am departing

presidential

councils

Climate change is real.

Even though someone tries to ignore it

Electric and hybrid vehicles are one of the keys to solve the problem

Make our planet

great again

… and EMR ?



- Outline -

1. Context and objective

2. EMR for problem formulation

3. Multi-objective energy management

4. Results and discussion

5. Conclusion and perspective

EMR’17

University Lille 1

June 2017



« Context and objective »



- Context -

Studied system: battery/SC H-ESS

tracibati

chi

SCi

Batteries

Supercapacitors

batu

SCu chu

Chopper

Parallel

connection

Inductor

Inverter Electrical

machine

Mechanical

part

Traction subsystem

batu

traci

Traditionally: mono-objective EMS to extend battery life-time

What about a multi-objective approach considering SCs system ?

What kind of optimal benchmark for multi-objective EMS ?



- Objective of the study -

1) Develop a methodology for

multi-objective EMS

2) Generate a Pareto benchmark

Jmain

Jaux.

Pareto

front

Dynamic programming (DP)EMR formalism

+

multi-objective approach

(Pareto optimality)

This is the

optimal

Try to

reach it

How to do?

Which method

at which step?

EMR’17

University Lille 1

June 2017



« EMR for problem formulation »



- Modeling and local control -

Bat.

SC

batu

bati

chm

traciTract.

chiSCu

SCi chu

Parallel connection

ChopperInductor

batu

batu

SCi

ch refu

ch refiSC refi

Strategy

bat refi

chm

ch refu

ch refiSC refi

chiSCu

SCi chu batu

SCi

ch refi

chiSCu

SCi batu

Reduction of local control loop



- Reduced model for problem formulation-

Bat. Tract.

Strategy

batu

bati

SCu

SCi

traci

chi

batu

batu

ch refi

bat refi

SC

• Control variable ibat ref

• State variable uSC

• Disturbance ubat & itrac

Variable definition

EMR’17

University Lille 1

June 2017



« Multi-objective energy management »



- Benchmark generation-

0

ws bat/SCJ

bat cal SC cal

TJ J J

*

1J

*

nJ

*

iJ

batJ

SCJ

10

1

n

Weighting factor

Pareto front

benchmark

Multi-objective scalarization

DP DP DP

0 1

i

ws bat/SC

i

J 1

ws bat/SCJ

sum bat/SC bat SC1J J J



- Multi-objective scalarization -

bat peakbat rms batbat 0 0 0

bat rms bat peak bat

1

3

IIJ

I I

f

o

2

bat cal bat

t

tJ i dt

SC peakSC lossSC 1 1

SC loss SC peak

1

2

IEJ

E I

f

o

2

SC cal SC

t

tJ i dt

f f

o o

2 2

bat SCsum cal bat/SC 0 1

bat rms SC rms

1t t

t t

i iJ dt dt

I I

Battery stresses cost function SCs efficiency cost function

Battery calculating cost function SCs calculating cost function

Multi-objective calculating cost function

Simplification for calculation:

For performance evaluation:



- Dynamic programming for problem solving -

Bat. Tract.

batu

bati

SCu

SCi

traci

chi

batu

batubat refi

SC

Driving cycle

known in advance

Multi-objective scalarization

Dynamic programming

ws bat/SCJ

Tactical

layer

Strategic

layer

EMR-based

backward model

Strategy

SC measu

Backward representation: [Horrein 2015]

Multi-level hierarchical structure: [Trovão 2013]

EMR’17

University Lille 1

June 2017



« Results and discussion »



- Simulation -

Variable Value Step size No. of steps

Time t 1028 [s] 0.1 [s] 10280

State variable uSC 22.5 – 45 [V] 0.25 [V] 90

Control variable ibat ref 0 – 250 [A] 2 [A] 125

ARTEMIS total urban driving cycle

Tazzari Zero



- Results: Pareto front as a benchmark -



- Examples of 2 cases -

= 0.75 (trade-off) = 1 (mono-objective)

EMR’17

University Lille 1

June 2017



« Conclusion and perspectives »



- Conclusion and perspectives -

Perspectives

Near future works:

• Real–time strategies development

• HIL experimental validation

1) Develop a methodology for

multi-objective EMS

2) Generate a Pareto benchmark

This work has achieved the objectives:

• Systematically

• Can be extended for the

other system

• Global optimal benchmark

• Can be used to evaluate the

on-going real-time EMS

EMR’17

University Lille 1

June 2017



« Biographies and references »



- Authors -

Bảo-Huy NGUYỄN

PhD student since 2015

Université Lille 1, L2EP, MEGEVH, France

Université de Sherbrooke, Sherbrooke, QC, Canada

MSc in Electrical Engineering at Hanoi Univ. Sci. Tech., Vietnam (2015)

Research topics: EVs and HEVs, control in power electronics and electrical drives

Prof. João P. TROVÃO

Université de Sherbrooke, Sherbrooke, QC, Canada

PhD in Electrical Engineering at University of Coimbra, Portugal (2012)

Research topics: EVs, renewable energy, energy management, power quality,

and rotating electrical machines



- Authors -

Prof. Alain BOUSCAYROL


Coordinator of MEGEVH, French network on HEVs

PhD in Electrical Engineering at University of Toulouse (1995)

Research topics: EMR, HIL simulation, tractions systems, EVs and HEVs

Dr. Ronan GERMAN


PhD in Electrical Engineering at University of Lyon, France (2013)

Research topics: Energy Storage Systems, EMR, HIL simulation, EVs and HEVs



- References -

L. Horrein, “Gestion d’energie decomposee d’un vehicule hybride integrant les aspects thermiques via larepresentation energetique macroscopique,” PhD thesis, Université Lille 1, 2015.

J. P. Trovão, P. G. Pereirinha, H. M. Jorge, and C. H. Antunes, “A multi-level energy management system formulti-source electric vehicles - An integrated rule-based meta-heuristic approach,” Appl. Energy, vol.105, pp. 304–318, 2013.

and

the missing book on EMR by Prof. Alain Bouscayrol !

EMR’17

University Lille 1

June 2017



« Appendix »



- Dynamic programming solving procedure -

Final

state

constraint

k 1k 0 N

t

( )x t

0x

1ix

ix

Mx

minx

maxx

( )ix k ( 1)ix k

1( 1)ix k

1( 1)ix k

2 ( 1)ix k

(0)x ( )x N

Discretization

Qu

an

tiza

tio

n

Limitation (constraint)

Infeasible

Feasible

Backward computation

Bellman equation

* *

, 1,

Cost-to-go from the current stage Optimal cost-to-go from the next stage 1to the next stage 1 to the final stage

min , ,k N D k Nu k

k kk N

J x k g x k u k J f x k u k

Documents

« EMR for a Multi-objective Benchmark for Energy ... · « EMR for a Multi-objective Benchmark for Energy Management of ... Inverter Electrical machine ... representation energetique