Environomic design of hybrid electric vehicles - Cnamturbo-moteurs.cnam.fr/publications/pdf/conference3_2017.pdf · • Energy recovery systems • On board storage technologies

Paris, 21/03/2017

Environomic design of hybrid electric vehicles

Dr. Zlatina Dimitrova

Plan of the presentation

Context: Environomics, energy and mobility

State of the art of vehicle energy systems

Methodology for environomic design

Applications and Results:


Conclusions

Perspectives

2







Conclusions

Perspectives

3

Context – Environomics

Energy systems design and operation

4

Energy systemEnergy

services

Resources and energy

Wastes (emissions & heat)

Design &

production

Design &

productionOperation

Sustainably exploited

energy resources from

Life cycle perspective

Efficient energy

system and service

Resilient systems with

Sustainable impacts

Definition: The systematic consideration of thermodynamic, economic and

environmental aspects for a properly designed and operated energy system is

called environomics.

5

En

vir

on

me

nt

Enviro-

nomics

Context – Environomics

Source: F. Maréchal,

Modelling and optimization, 2012

Context – Energy & Mobility

General trends of personal mobility

Environmental regulation (�� and others emissions, natural resources …)

• g ��/ km, NOx, particles

Uncertainty of the fuel prices

Changing behavior of the personal mobility

6

* T-t-W – Tank to Wheels

Future requirements for efficient vehicles *T-t-W CO2 emission , PSA

Low CO2 emissions

CAFE, Europe

154g

130g

95g

Context- Vehicle energy system

What are the energy services delivered from a vehicle ?

Conceptual energy conversion system design criteria for a passenger's car:

What is the evolution direction of the vehicle energy systems?

Integrated and systems engineering approach

Fuel diversification integration for markets adaptation

Consideration of the environomic criteria on a holistic way in the design stage

7

High Economical

competitiveness

High Conversion

efficiency

Low

environmental

impacts

MobilityComfort

Security

Ta

nk

ba

sed

syst

em

Gri

d r

ela

ted

sy

ste

m







Conclusions

Perspectives

8

State of the art – Vehicle energy systems design and methodologies

9

Simulation

Tools

• Matlab/Simulink

• Modelica

• gProms

Use phase operation

Converters� tank to vehicle

Powertrain (system approach)

�Vehicle to Miles

Technical options:

• Energy recovery systems

• On board storage technologies

• HY Architectures

Simulation and energy Management

Strategies�heuristic

Estimation of fuel consumption

Optimization methods for

prediction of fuel consumption�heuristic

• Genetic Algorithms

• Dynamic Programming

LCA�assessment

Economics�assessment

FuelsFuel upstream � Well to Tank

Methods for vehicle system design :

Heuristic approach based on several iterations

• Preliminary design

• Economic evaluation

• Environmental evaluation

Fuels Gasoline Diesel Electricity

Converters SI ICE Diesel ICE EM

Efficiency of the converters

(average on NEDC) [Guzzella, 2013]0,17 0,2 0,9 (-)

Storage efficiency [Guzzella, 2013] 1 1 0,8 (-)

Density of the energy vector

[Guzzella, 2013]42,7 42,5 0,648* MJ/kg

10

α

vUrban

Highway

State of the art – Efficiency and energy density

Vehicle propulsion systems performances

100 km 200 km 300 km 400 km 500 km 600 km 700 km 800 km 1000 km

15 hours 10 hours 5 hours 2 hours 1 hour 30 mins 15 mins 10 mins 5 mins

Ra

ng

eR

efu

eli

ng

Tim

e

EVsConventional Vehicles

Plugin Hybrid

Fuel Cell vehicles

EVsConventional

Vehicles

Conventional

Vehicles

Plugin Hybrid

FCVs

GoodPoor Excellent

11

EVs

EVs

12

Applications:

• District heating systems

• Polygeneration systems

• Geothermal systems

• Biomass and biofuels

processing

• �� mitigation in Hydrogen

production processes

Environmental

analysis

Process systems

engineering

✓ Decision variables

✓ Superstructure

✓ Optimization framework

✓ Trade-offs

✓ Energy integration

How to consider the thermo-economic and environmental objectives on a

holistic way in a design tool for energy conversion systems?

Thermo-economics, environomics and Multi Objective Optimization

L. Gerber, 2012, Integration of life cycle assessment in the conceptual design of renewable energy conversion systems, Ph.D. thesis, Ecole

Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

L. Tock, 2013, Thermo-environomic optimization of fuel decarbonization alternative processes for hydrogen and power production, Ph.D.

thesis, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

S. Fazlollahi, 2014, Decomposition optimization strategy for the design and operation of district energy systems, Ph.D. thesis, Ecole

Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

13

Optimization frameworkDecision variables

Superstructure

Multi objectives

Integration of energy flows

for efficiency

Integration of Fuel upstream

Larger range of

environnemental impacts

Life Cycle Approach

Adapted Economic Model

Contribution of the

environimics

System engineering methodology for vehicle energy system design

Heuristic simulationsFuel consumption minimization

Parameters

Detailed models

Environnemental indicator

(�� T-t-W)Does not respect LCA principles

in the design

Use phase

Economic KPI: Complex and

experience based

Vehicle energy system (propulsion)

Heuristic design

State of the art - Synthesis

Scientific questions of the research work

Is it possible to apply the environomic approach for design on vehicle energy

systems?

What are the advantages of the environomic method for design in comparison

of the scenario based iterations approaches?

1414

Multi Objective

Optimization

Thermo-economic design

Environomic design

Multi Objective

Optimization

Process Design

Energy flow

model

Energy

Integration

model

LCA

Economic

Slave structure for computation

Master Superstructure







Conclusions

Perspectives

15

Methodology for environomic design of vehicle energy systems

16

Multi Objective Optimization

Evolutionary Genetic

Algorithm

(MOO)

Environmental (LCA) Model

Economic Model

Energy Integration Model

Utilities

Energy Flow model

(dynamic vehicle model -

Simulink®)

State Variables State Variables

State Variables

Decisions Variables

(thermo-dynamic targets)

Decisions Variables

(thermo-dynamic targets)

Performances

(OSMOSE)

Thermo-environomic model

Optimization and simulation structure with the following requirements: flexible to simulate a wide range of conversion technologies with different level of details

Integrate a dynamic profile simulation

include alternative fuels options

define the size of the equipment

estimate the cost of the equipment

estimate the environmental impacts

adapted from : L. Gerber, 2012, Integration of life cycle assessment in the conceptual design of renewable energy conversion systems,

Ph.D. thesis, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

17

Energy Integration model:

Branch& bound algorithm

Master Multi- objective Optimization

Evolutionary genetic algorithm - MINLP

Set of Master decision variables:

• Design:

Type and size of the equipment

• Operation strategy

Thermo-economic

Simulation models:

vehicle dynamic model

cost model

Thermo-economic & ENV

States (P(kW), T(°C), E(kWh))

of the selected equipment

Environmental indicators

Slave problem: MILP

Environomic evaluation – objective functions computation

Minimize energy

(fuel) consumptionMinimize

Cost

Minimize

Environmental impacts

Pe

rfo

rma

nce

s in

dic

ato

rs –

Ne

xt

ite

rati

onLife Cycle Assessment

model

State variables

State variables

Decision

variables

State variables

Optimal system

configuration and operation

for each master set of

decision variables

Available Equipment

Data base of technological

options : processes &

utilities

Energy

demand

profiles –

comfort

Start i=i+1

Pareto optimal curve

Dynamic

profiles


Generic computational structure


18

Flow sheeting model: vehicles simulation models

Backwards approach for the energy consumption estimation

• The energy flow is computed from the wheels to the energy sources.

• Hybrid electric vehicles:

– Parallel: HVB and supercapacitors

Economic model: KPI in €

��_� � � � �� !"#$

adapted from QSS Toolbox

Environmental model:

Based on LCA and the decision variables :

• based on the LCI of serial hybrid electric vehicle

• mass and materials balance

• sub-systems identifications

• adapted for iterations

• simplified: retail, end of life- neglected, second life for the HV battery

• GWP impact category

Functional unit: 150000 km and 10 years

19

Supplier

Range n+1

Supplier

Range n+1

Supplier

Range n+1

Supplier

Range n+1

Supplier

Range n+1

Supplier

Range n+1

Supplier

Range n+1

Sub-system

Plants

Production phase:

Welding

Painting

Assembly

Use phase End

Of

Life

Secondary data

Primary data

System boundaries – Vehicle


Parts production

Adapted from S. Richet, P. Tonnelier, 2013, PSA Peugeot Citroën,

internal unpublished report, PSA Peugeot Citroën, Vélizy, France

%�& � ' � , � ∈ *+��,��-��./�,







Conclusions

Perspectives

20

Application – Environomic design for hybrid electric vehicles

21

)),(),(min( xCostxpowertrainη− ∈ *+��,��-��./�,

Decision variables for design Range

ICE displacement volume [l] [0.8; 1; 1.4 ; 1.6; 2.2]

Electric motor rated power [kW] [1-150]

Battery energy capacity [kWh] [5-50]

Number of super capacitors [-] [0-10]

Multi-objective optimization to define the environomic design (D-Class vehicle)

2D objectives optimization- minimization of the energy consumption and

minimization of the cost


22

HEVP-HEV

REX

Results for techno-economic optimization, NEDC- Pareto curve

)(SCBTfuel

wheelpowertrain PPP

Pmean

++=η in [-]

shellcarpowertrainvehicle CostCostCost _+= in €

Powertrain efficiency [-]


Em

issi

on

s [g

CO

2/k

m]

Tota

l m

ass

[kg

]


23

HEV; P-HEV

REX

Results for techno-economic optimization, NEDC, decision variables

Powertrain efficiency [-] Powertrain efficiency [-]


Ba

tte

ry c

ap

aci

ty [

kW

h]

ICE

dis

pla

cem

en

t v

olu

me

[l]

Ele

ctri

c m

oto

r p

ow

er

[kW

]

3D multi objective environomic optimization:

Maximization of the powertrain efficiency

Minimization of the investment cost

Minimization of the GWP

Decisions variables same as the previous optimization

Functional unit – 150000 km


24

)),(),(_),(min( xGWPxCostInvestmentx totalpowertrainη− � ∈ *+��,��-��./�,

phaseuseproductiontotal GWPGWPGWP _+=


25

Results for environomic optimization, NEDC- Pareto curve

GWPproduction>GWPuse


26

1) Selection of the sensitivity parameters [1….n]

2) Application of distribution functions [type]

3) Generation of Pn economic scenarios

of the Pareto solutions

Input:

Pareto Solutions

4) Evaluation of the economic scenarios – Performances indicators

5) Selection criteria: 3 best configurations: lowest YAC and Investment cost

6) Evaluation of the dominance: the probability to be part of 3 best configurations

Output:

Most competitive configurations

economic scenarios

Decisions aid method: Monte-Carlo simulation for sensitivity of the Pareto

Solutions on the macro-economic uncertainties (investment and operating cost)

Adapted from L.Tock, 2013


27

Parameters Variation Distribution

Electricity price [€/kWh] [0.14-0.24] Uniform

Diesel price [€/l] [1.20-1.40] Uniform

Li-Ion battery cost coefficient [€/kWh] [300-600] Uniform

Decisions aid method: Monte-Carlo simulation for sensitivity of the Pareto

Solutions on the macro-economic uncertainties (investment and operating cost):

Economic inputs for the Monte Carlo simulation

Generation of the economic scenarios


28

ID 1091 Value

Probability 0.00038961

ICE displacement [l] 1.6

EM [kW] 51

Battery capacity [kWh] 7

Number of supercapacitors [-] 4

Vehicle mass [kg] 1808

Powertrain efficiency [-] 0.27

Fuel consumption [l/100 km] 4.66

CO2 emissions/km 124

Total investment cost with CO2 bonus [€] 33380

GWP [kg CO2 eq.] 32563

De

sig

nP

erf

orm

an

ces

Monte-Carlo simulation NEDC: D- Class vehicle

Evaluation of the economic scenario and selection of the most competitive ones

Monte-Carlo simulation: optimal configuration NEDC

Repartition of the design configurations on the best probability point

Plug in HEV is the optimal solution:

• Optimal investment and annualized cost

Configurations ID 878

Total number of configurations[-] 17

Part of Plug In HEV configurations [%] 59

Part of heavy Plug In HEV configurations [%] 17

Part of REX configurations [%] 24


29


30

Evolution of the total GWP and repartition of the contribution of each phase as

a function of the hybridation ratio, D –Class vehicles

203 g CO2 eq./ km 160 g CO2 eq./ km

141 g CO2 eq./ km 140 g CO2 eq./ km







Conclusions

Perspectives

31

32

Conclusions – Method for environomic design of vehicle

energy systems Modeling:

• Vehicle propulsion systems

• Energy technologies

• Cost model

• Environmental model

Energy integration:

• Combination of

energy technologies

• Cost evaluation

Integrated energy services

• efficiency benefit

Optimization:

• Definition of objectives

• Definition of decision variables

Convergence on a Pareto Front

• Optimal designs

• Trade-off

• Performance indicators:

• Energy consumption

• Cost

• Environmental impact

Monte Carlo Simulation:

• Definition of the parameters

• Definition of impact factors

Selection of an optimal

design:

• Decision or orientation

for environomic design

KPI:

Range , €,

kg CO2 eq.

Simulation

Multi-objective

Optimization

Decision Aid

Perf. Indicators







Conclusions

Perspectives

33

Perspectives

Tool to compare systematically design options for energy systems under

different economic and environmental scenarios

Extension of the energy technologies options –

• Flowsheeting, economic and environmental models

Research of innovative concepts for increased energy efficiency

Variation of the economic models and use cases to explore the superstructure to

find competitive vehicles business models for the specified types of clients

Extension of the environomic computational superstructure for grid related

vehicles

• Research of the optimal macro level energy distribution strategies – V2G (vehicles to grid

) and G2V (grid to vehicles)

Hybrid electric Vehicles (Electric Vehicles): importance of the energy density

increase and cost decrease of the HV Battery for their future market penetration

34

Customers &

Mobilities

Final product

ENERGY TECHNOLOGIES

ENERGY VECTORS ENERGY RECOVERY

HIGH EFFICIENCY Grid, V2G, G2V

Perspectives – Environomic design

CONVERTERS

STOCKERS

Economic

modelsEnvironmental

models

Flowsheeting

models

Energy technologies library

Thank you for your attention!

36

Publications

Z. Dimitrova, F. Maréchal, Energy integration on multi-periods for vehicle thermal powertrains, Canadian Journal of Chemical

Engineering (2016)

Z. Dimitrova, F. Maréchal, Environomic design of hybrid electric vehicles, ECOS – efficiency, cost, optimization of the energy systems,

conference, Portoroz, Slovenia, June 2016

Z. Dimitrova, F. Maréchal, Efficiency improvement for vehicle powertrains using energy integration techniques, International Journal of

Thermodynamics (2016)

Z. Dimitrova, F. Maréchal, Techno-economic design of hybrid electric vehicles and possibilities of the multi-objective optimization

structure, Applied Energy Journal (2015)

Z. Dimitrova, F. Maréchal, Energy integration study on a hybrid electric vehicle energy system, using process integration techniques,

Applied Thermal Engineering Journal (2015)

Z. Dimitrova, F. Maréchal, Techno-economic design of hybrid electric vehicles, Energy Journal (2015)

Z. Dimitrova, F. Maréchal, Efficiency improvement for vehicle powertrains using energy integration techniques, ECOS – efficiency, cost,

optimization of the energy systems, conference, Pau, France, June 2015

Z. Dimitrova, F. Maréchal, Performance and economic optimization of an organic Rankine cycle for a gasoline hybrid pneumatic

powertrain, Energy Journal (2015)

Z. Dimitrova, F. Maréchal, Gasoline hybrid pneumatic engine for efficient vehicle powertrain hybridization, Applied Energy Journal

(2015)

Z. Dimitrova, F. Maréchal, Energy integration on multi-periods and multi-usages for hybrid electric and thermal powertrains, Energy

Journal (2015)

Z. Dimitrova, F. Maréchal, Environomic design of vehicle energy systems for optimal mobility service, Energy Journal, (2014)

Z. Dimitrova, F. Maréchal, Environomic design of vehicle integrated energy systems- application on a hybrid electric vehicle energy

system, CET, volume 39, 2014, p. 475-480, DOI:10.3303/CET1439080

Z. Dimitrova, F. Maréchal Environomic design of a vehicle integrated energy system – application on an electric vehicle, ECOS –

efficiency, cost, optimization of the energy systems, conference, Turku, Finland, June 2014

Z. Dimitrova, T. Alger , T. Chauvet, Synergies between high EGR operation and GDI Systems – SAE Paper, 2008-01-0134, pages 101-114

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Documents

Environomic design of hybrid electric vehicles - Cnamturbo-moteurs.cnam.fr/publications/pdf/conference3_2017.pdf · • Energy recovery systems • On board storage technologies