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Optimal Fuzzy Logic Energy Management Strategy of Hybrid Electric Locomotives J. Baert*, S. Jemei*, D. Chamagne*, D. Hissel*, D. Hegy** and S. Hibon**

* University of Franche-Comte, FEMTO-ST (Energy Department), UMR CNRS 6174,

90010 Belfort, France. ** Alstom Transport, 3 Avenue des Trois Chênes, 90000 Belfort, France.

jerome.baert@univ-fcomte.fr - samuel.hibon@transport.alstom.com

Summary  

1.  Introduction

2.  Modeling of the Hybrid Electric Locomotive

3.  Optimal fuzzy logic Energy Management Strategy

4.  Conclusion and outlooks

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Partners of the project

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Introduc1on  

Pr Didier Chamagne Pr Daniel Hissel Dr Samir Jemeï

Dominique Hegy Samuel Hibon

Partners of the project

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Introduc1on  

Context and problematic

Introduc1on  

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Adopted solution

•  60% less particles •  40% less NO •  15 dB less noise •  15% less maintenance

Summary  

1.  Introduction

2.  Modeling of the Hybrid Electric Locomotive

3.  Optimal fuzzy logic Energy Management Strategy

4.  Conclusion and outlooks

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Modeling  of  the  Hybrid  Electric  Locomo1ve  

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Energetic Macroscopic Representation approach

Advantages: •  Physical causality •  Highlight measures and sensors •  Control structure identification •  Implementation under Matlab / Simulink

Modeling  of  the  Hybrid  Electric  Locomo1ve  

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(1)  Diesel driven generator set (2) Batteries’ pack (3) Ultra-capacitors’ pack (4) Rheostat (5) Bus capacity (6) Energy Management Strategy

(1)  

(2)   (3)  

(4)  

(5)  

(6)  

Global structure [1]

[1] J. Baert, S. Jemei, D. Chamagne, D. Hissel, S. Hibon, and D. Hegy, “Practical Control Structure and Simulation of a Hybrid Electric Locomotive” IEEE Vehicle Power and Propulsion Conference, 2012. VPPC ’12.  

Summary  

1.  Introduction

2.  Modeling of the Hybrid Electric Locomotive

3.  Optimal fuzzy logic Energy Management Strategy a.  Structure of the EMS b.  Fuzzy Logic Controller design c.  Genetic algorithm

4.  Conclusion and outlooks

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Structure of the EMS

Op1mal  fuzzy  logic  Energy  Management  Strategy  

Goal: To share the power required by the driving cycle performed by the locomotive between the different on-board sources, taking into account their own specifications.

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Structure of the EMS

Ultra-capacitors: •  Limitation of the State Of Charge (SOC) between 50% and 100%, •  control of the SOC according to the speed of the vehicle, •  supply the high frequencies of the power mission,

α↓SOC↓uc  ={█■min (1, max (0, SOC↓uc↓max  − SOC↓uc↓est  /SOC↓uc↓max  − SOC↓uc↓high   ) ) if   P↓uc↓ref  >0@min (1, max (0, SOC↓uc↓est  − SOC↓uc↓min  /SOC↓uc↓low  − SOC↓uc↓min   ) )   if   P↓uc↓ref  <0  

SOC↓uc↓ref  = SOC↓uc↓max  − v↓veh /v↓max  (SOC↓uc↓max  − SOC↓uc↓min  )  

Op1mal  fuzzy  logic  Energy  Management  Strategy  

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Structure of the EMS

Batteries: •  Limitation of the SOC between 70% and 90%, •  control of the SOC according to the acceleration of the vehicle, •  supply the low frequencies of the power mission with the diesel driven generator set.

α↓SOC↓batt  ={█■min (1, max (0, SOC↓batt↓max  − SOC↓batt↓est  /SOC↓batt↓max  − SOC↓batt↓high   ) ) if   P↓batt↓ref  >0@min (1, max (0, SOC↓batt↓est  − SOC↓batt↓min  /SOC↓batt↓low  − SOC↓batt↓min   ) )   if   P↓batt↓ref  <0  

*+,↓-.//↓012  = *+,↓-.//↓3.4  − 1/5↓3.4  |7/7/ (5↓51ℎ (/))|(*+,↓-.//↓3.4  − *+,↓-.//↓39:  )  

Op1mal  fuzzy  logic  Energy  Management  Strategy  

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Structure of the EMS

Diesel driven generator set: •  Use of a Fuzzy Logic Controller to determine the power delivered by this source, •  supply the low frequencies of the power mission with the batteries.

IF is N AND is P THEN is P

Op1mal  fuzzy  logic  Energy  Management  Strategy  

Summary  

1.  Introduction

2.  Modeling of the Hybrid Electric Locomotive

3.  Optimal fuzzy logic Energy Management Strategy a.  Structure of the EMS b.  Fuzzy Logic Controller design c.  Genetic algorithm

4.  Conclusion and outlooks

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Fuzzy Logic Controller design

Primary  Membership  Func1on   Secondary  Membership  Func1on  

Type-­‐1      

Interval  Type-­‐2      

Improve  un

certain1

es  m

odeling  

Op1mal  fuzzy  logic  Energy  Management  Strategy  

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Fuzzy Logic Controller design

Fuzzy Logic Controller of the implemented EMS

IF is NH AND is PH THEN is PH

7 linguistic variables defined by trapezoid, triangular and Interval membership functions: NH, NM, NL, Z, PL, PM, PH

N - Negative P - Positive  

H - High M - Medium  

Z - Zero L - Low  

=> 2 inputs × 7 linguistic variables + output × 7 linguistic variables = 21 membership functions

Op1mal  fuzzy  logic  Energy  Management  Strategy  

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Fuzzy Logic Controller design

Fuzzy Logic Controller of the implemented EMS

How to define the parameters of the 21 membership functions???

Op1mal  fuzzy  logic  Energy  Management  Strategy  

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Fuzzy Logic Controller design

Fuzzy Logic Controller of the implemented EMS How to define the parameters of the 21 membership functions or the parameters??? •  Realization of a survey based on human behavior and knowledge, •  optimization of the parameters of the controller using a genetic algorithm in order to minimize the fuel

consumption of the diesel driven generator set.

PhD defended in February 2012 Energy management of a hybrid electric vehicle: an approach based

on type-2 fuzzy logic by Dr Solano Martinez

[2] Javier Solano Martínez, Robert I. John, Daniel Hissel, Marie-Cécile Péra, A survey-based type-2 fuzzy logic system for energy management in hybrid electrical vehicles, Information Sciences, Volume 190, 1 May 2012, Pages 192-207, ISSN 0020-0255, 10.1016/j.ins.2011.12.013.  

Computer Science and Electronic Engineering department of the University of Essex

Colchester - United Kingdom From April to July 2012

Op1mal  fuzzy  logic  Energy  Management  Strategy  

Summary  

1.  Introduction

2.  Modeling of the Hybrid Electric Locomotive

3.  Optimal fuzzy logic Energy Management Strategy a.  Structure of the EMS b.  Fuzzy Logic Controller design c.  Genetic algorithm

4.  Conclusion and outlooks

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Genetic algorithm

Satisfy constraints?

Selection

Crossover

Mutation

Initialize population

Evaluate fitness

New individual

yes

no

Op1mal  fuzzy  logic  Energy  Management  Strategy  

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Genetic algorithm - IT2 Fuzzy Logic

Satisfy constraints?

Selection

Crossover

Mutation

Initialize population

Evaluate fitness

New individual yes

no Satisfy constraints?

Selection

Crossover

Mutation

Initialize population

Evaluate fitness

New individual yes

no

⎟⎟⎠

⎞⎜⎜⎝

⎛+⎟⎟⎠

⎞⎜⎜⎝

⎛+⎟⎟⎠

⎞⎜⎜⎝

⎛= ∫∫∫

missionmissionmission t

ge

t

uc

t

batt dttPdttPdttPfitness000

)(min)(max)(max

Satisfy constraints?

Selection

Crossover

Mutation

Initialize population

Evaluate fitness

New individual yes

no

The batteries must be fully charged (90%) at the end of the driving cycle  

Satisfy constraints?

Selection

Crossover

Mutation

Initialize population

Evaluate fitness

New individual yes

no

( )( ) 1122

2111parentpparentpchildparentpparentpchild×−+×=

×−+×=

Satisfy constraints?

Selection

Crossover

Mutation

Initialize population

Evaluate fitness

New individual yes

no Satisfy constraints?

Selection

Crossover

Mutation

Initialize population

Evaluate fitness

New individual yes

no

if 0.001 ≤ mutation_probability ≤ 0.01 => The gene value of the considered chromosome is

randomly modified

Op1mal  fuzzy  logic  Energy  Management  Strategy  

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Results – Fuzzy maps

Op1mal  fuzzy  logic  Energy  Management  Strategy  

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Results – Powers distribution

Op1mal  fuzzy  logic  Energy  Management  Strategy  

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Results – Powers distribution (zoom)

High frequencies of the power are dedicated to the UCs Low frequencies of the power are dedicated to the batteries

Op1mal  fuzzy  logic  Energy  Management  Strategy  

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Results – States Of Charge and speed

UCs’ SOC is controlled using the speed of the locomotive and is limited between 50% and 100%

Batteries’ SOC is controlled using the acceleration of the locomotive and is limited between 70% and 90%

Op1mal  fuzzy  logic  Energy  Management  Strategy  

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Results – Bus voltage control

The implemented fuzzy EMS ensures the stability of the bus voltage

Op1mal  fuzzy  logic  Energy  Management  Strategy  

Summary  

1.  Introduction

2.  Modeling of the Hybrid Electric Locomotive

3.  Optimal fuzzy logic Energy Management Strategy

4.  Conclusion and outlooks

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Conclusion  and  outlooks  

•  Development of the on-board sources dynamical models with their control

•  Development of an intelligent Energy Management Strategy:   No prior knowledge of the driving cycle   Fuzzy Logic management of the diesel driven generator set   Minimization of the use of the internal combustion engine

Conclusion

Outlooks

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•  Improvement of the Type-2 membership functions definition •  Improvement of the GA taking into account batteries ageing for instance

Thanks for your attention

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