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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.
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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