Adaptive online energy management controls for fuel cell and battery hybrid vehicles:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Düren
Shaker Verlag
2023
|
| Ausgabe: | 1. Auflage |
| Schriftenreihe: | Aachener Schriftenreihe zur Elektromagnetischen Energiewandlung
51 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xii, 148 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 237 g |
| ISBN: | 9783844092042 3844092048 |
Internformat
MARC
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| 100 | 1 | |a Deng, Kai |d 1991- |e Verfasser |0 (DE-588)1303005581 |4 aut | |
| 245 | 1 | 0 | |a Adaptive online energy management controls for fuel cell and battery hybrid vehicles |c Kai Deng |
| 264 | 1 | |a Düren |b Shaker Verlag |c 2023 | |
| 300 | |a xii, 148 Seiten |b Illustrationen, Diagramme |c 21 cm x 14.8 cm, 237 g | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Aachener Schriftenreihe zur Elektromagnetischen Energiewandlung |v 51 | |
| 502 | |b Dissertation |c RWTH Aachen University |d 2023 | ||
| 650 | 0 | 7 | |a Hybridfahrzeug |0 (DE-588)7524499-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Wasserstofferzeugung |0 (DE-588)4189271-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Brennstoffzelle |0 (DE-588)4008195-3 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Deep Learning |0 (DE-588)1135597375 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Akkumulator |0 (DE-588)4068497-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Energieeffizienz |0 (DE-588)7660153-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Modellprädiktive Regelung |0 (DE-588)1135937567 |2 gnd |9 rswk-swf |
| 653 | |a hydrogen powertrain | ||
| 653 | |a energy management | ||
| 653 | |a model predictive control | ||
| 653 | |a deep reinforcement learning | ||
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
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Datensatz im Suchindex
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| adam_text |
CONTENTS
LIST
OF
ABBREVIATIONS
XI
1
INTRODUCTION
1
1.1
BASICS
TO
HYBRID
VEHICLES
.
2
1.2
LITERATURE
REVIEW
.
4
1.3
MOTIVATION
AND
CONTRIBUTIONS
.
9
1.4
OUTLINE
OF
THE
THESIS
.
11
2
THEORETICAL
BACKGROUNDS
OF
MODEL
PREDICTIVE
CONTROL
AND
DEEP
REINFORCEMENT
LEARNING
13
2.1
MODEL
PREDICTIVE
CONTROL
.
13
2.1.1
PRINCIPLE
OF
MODEL
PREDICTIVE
CONTROL
.
13
2.1.2
FORMULATION
OF
THE
OPTIMIZATION
PROBLEM
.
15
2.1.3
CALCULATION
AND
APPLICATION
OF
CONTROL
COMMAND
.
20
2.1.4
AC
ADO
TOOLKIT
.
24
2.2
DEEP
REINFORCEMENT
LEARNING
.
26
2.2.1
PERCEPTRONS
.
26
2.2.2
BACK-PROPAGATION
ALGORITHM
.
29
2.2.3
REINFORCEMENT
LEARNING
.
31
2.2.4
REINFORCEMENT
LEARNING
WITH
NEURAL
NETWORKS
.
33
2.2.5
TD3
ALGORITHM
.
34
2.3
CHAPTER
SUMMARY
.
35
3
SYSTEM
MODELING
OF
FUEL
CELL
AND
BATTERY
POWERED
HYBRID
RAILWAY
VEHICLES
39
3.1
ENVIRONMENT
AND
VEHICLE
DYNAMICS
.
39
3.2
DRIVELINE
COMPONENTS
.
41
3.2.1
ELECTRIC
MACHINE,
INVERTER
AND
GEARBOX
.
41
3.2.2
AUXILIARY
SYSTEM
.
43
3.2.3
DC/DC
CONVERTER
.
44
3.3
ENERGY
SOURCE
SYSTEM
.
46
3.3.1
BATTERY
SYSTEM
.
46
3.3.2
FUEL
CELL
SYSTEM
.
48
3.4
ENERGY
MANAGEMENT
SYSTEM
.
52
3.5
CHAPTER
SUMMARY
.
53
4
MODEL
PREDICTIVE
CONTROL
BASED
ENERGY
MANAGEMENT
55
4.1
HIERARCHICAL
CONTROL
STRUCTURE
.
55
4.2
ECO-DRIVING
CONTROL
.
56
4.2.1
STATES,
CONTROL
VARIABLES
AND
OUTPUTS
.
57
4.2.2
CONSTRAINTS
.
58
4.2.3
COST
FUNCTION
.
58
4.2.4
PRECISION
MODE
.
60
4.3
PMP-MPC
BASED
ENERGY
MANAGEMENT
CONTROLLER
.
61
4.3.1
PMP-BASED
MPC
.
61
4.3.2
STATES,
CONTROL
VARIABLES
AND
OUTPUTS
.
62
4.3.3
CONSTRAINTS
.
62
4.3.4
PREDICTION
MODEL
IN
MPC
.
63
4.3.5
PROBLEM
FORMULATION
.
64
4.3.6
ESTIMATION
OF
THE
CO-STATE
IN
PMP
.
66
4.4
CHAPTER
SUMMARY
.
71
5
DEEP
REINFORCEMENT
LEARNING
BASED
ENERGY
MANAGEMENT
73
5.1
TD3-BASED
ENERGY
MANAGEMENT
STRATEGY
.
73
5.1.1
TD3-BASED
AGENT
ACTIONS
.
74
5.1.2
ENVIRONMENT
STATES
.
74
5.1.3
REWARDS
.
75
5.2
IMPLEMENTATION
OF
TD3
ALGORITHM
.
77
5.2.1
ENVIRONMENT
SETTINGS
.
77
5.2.2
HYPERPARAMETERS
AND
TRAINING
SETTINGS
.
77
5.2.3
TRAINING
PROCESS
.
80
5.3
IMPACT
OF
THE
REWARD
SETTINGS
ON
CONVERGENCE
.
83
5.4
CHAPTER
SUMMARY
.
85
6
SIMULATION
RESULTS
AND
MEASUREMENT
VALIDATION
87
6.1
SIMULATION
RESULTS
OF
MPC-BASED
ENERGY
MANAGEMENT
.
87
6.1.1
ECO-DRIVING
AND
DRIVING
PERFORMANCE
.
88
6.1.2
BATTERY
'
S
CHARGE-SUSTAINING
.
91
6.1.3
FUEL
ECONOMY
.
94
6.1.4
FUEL
CELL
AGING
.
95
6.1.5
SENSITIVITY
ANALYSIS
.
97
6.2
SIMULATION
RESULTS
OF
DRL-BASED
ENERGY
MANAGEMENT
.
98
6.2.1
BATTERY
'
S
CHARGE-SUSTAINING
.
100
6.2.2
FUEL
CELL
AGING
.
102
6.2.3
OPERATIONAL
COST
AND
FUEL
ECONOMY
.
103
6.3
COMPARISON
AND
DISCUSSION
OF
THE
TWO
STRATEGIES
.
105
6.3.1
ENERGY
DISTRIBUTION
PERFORMANCE
.
106
6.3.2
OPERATIONAL
COST
.
107
6.4
HARDWARE-IN-THE-LOOP
VALIDATION
.
109
6.4.1
HIL
SETUP
.
109
6.4.2
HIL
RESULTS
.
112
6.5
CHAPTER
SUMMARY
.
115
7
CONCLUSIONS
AND
PERSPECTIVES
119
7.1
SUMMARY
.
119
7.2
FUTURE
WORK
.
122
LIST
OF
SYMBOLS
125
REFERENCES
131
OWN
PUBLICATIONS
141
SUPERVISED
THESES
145
CURRICULUM
VITAE
147 |
| adam_txt |
CONTENTS
LIST
OF
ABBREVIATIONS
XI
1
INTRODUCTION
1
1.1
BASICS
TO
HYBRID
VEHICLES
.
2
1.2
LITERATURE
REVIEW
.
4
1.3
MOTIVATION
AND
CONTRIBUTIONS
.
9
1.4
OUTLINE
OF
THE
THESIS
.
11
2
THEORETICAL
BACKGROUNDS
OF
MODEL
PREDICTIVE
CONTROL
AND
DEEP
REINFORCEMENT
LEARNING
13
2.1
MODEL
PREDICTIVE
CONTROL
.
13
2.1.1
PRINCIPLE
OF
MODEL
PREDICTIVE
CONTROL
.
13
2.1.2
FORMULATION
OF
THE
OPTIMIZATION
PROBLEM
.
15
2.1.3
CALCULATION
AND
APPLICATION
OF
CONTROL
COMMAND
.
20
2.1.4
AC
ADO
TOOLKIT
.
24
2.2
DEEP
REINFORCEMENT
LEARNING
.
26
2.2.1
PERCEPTRONS
.
26
2.2.2
BACK-PROPAGATION
ALGORITHM
.
29
2.2.3
REINFORCEMENT
LEARNING
.
31
2.2.4
REINFORCEMENT
LEARNING
WITH
NEURAL
NETWORKS
.
33
2.2.5
TD3
ALGORITHM
.
34
2.3
CHAPTER
SUMMARY
.
35
3
SYSTEM
MODELING
OF
FUEL
CELL
AND
BATTERY
POWERED
HYBRID
RAILWAY
VEHICLES
39
3.1
ENVIRONMENT
AND
VEHICLE
DYNAMICS
.
39
3.2
DRIVELINE
COMPONENTS
.
41
3.2.1
ELECTRIC
MACHINE,
INVERTER
AND
GEARBOX
.
41
3.2.2
AUXILIARY
SYSTEM
.
43
3.2.3
DC/DC
CONVERTER
.
44
3.3
ENERGY
SOURCE
SYSTEM
.
46
3.3.1
BATTERY
SYSTEM
.
46
3.3.2
FUEL
CELL
SYSTEM
.
48
3.4
ENERGY
MANAGEMENT
SYSTEM
.
52
3.5
CHAPTER
SUMMARY
.
53
4
MODEL
PREDICTIVE
CONTROL
BASED
ENERGY
MANAGEMENT
55
4.1
HIERARCHICAL
CONTROL
STRUCTURE
.
55
4.2
ECO-DRIVING
CONTROL
.
56
4.2.1
STATES,
CONTROL
VARIABLES
AND
OUTPUTS
.
57
4.2.2
CONSTRAINTS
.
58
4.2.3
COST
FUNCTION
.
58
4.2.4
PRECISION
MODE
.
60
4.3
PMP-MPC
BASED
ENERGY
MANAGEMENT
CONTROLLER
.
61
4.3.1
PMP-BASED
MPC
.
61
4.3.2
STATES,
CONTROL
VARIABLES
AND
OUTPUTS
.
62
4.3.3
CONSTRAINTS
.
62
4.3.4
PREDICTION
MODEL
IN
MPC
.
63
4.3.5
PROBLEM
FORMULATION
.
64
4.3.6
ESTIMATION
OF
THE
CO-STATE
IN
PMP
.
66
4.4
CHAPTER
SUMMARY
.
71
5
DEEP
REINFORCEMENT
LEARNING
BASED
ENERGY
MANAGEMENT
73
5.1
TD3-BASED
ENERGY
MANAGEMENT
STRATEGY
.
73
5.1.1
TD3-BASED
AGENT
ACTIONS
.
74
5.1.2
ENVIRONMENT
STATES
.
74
5.1.3
REWARDS
.
75
5.2
IMPLEMENTATION
OF
TD3
ALGORITHM
.
77
5.2.1
ENVIRONMENT
SETTINGS
.
77
5.2.2
HYPERPARAMETERS
AND
TRAINING
SETTINGS
.
77
5.2.3
TRAINING
PROCESS
.
80
5.3
IMPACT
OF
THE
REWARD
SETTINGS
ON
CONVERGENCE
.
83
5.4
CHAPTER
SUMMARY
.
85
6
SIMULATION
RESULTS
AND
MEASUREMENT
VALIDATION
87
6.1
SIMULATION
RESULTS
OF
MPC-BASED
ENERGY
MANAGEMENT
.
87
6.1.1
ECO-DRIVING
AND
DRIVING
PERFORMANCE
.
88
6.1.2
BATTERY
'
S
CHARGE-SUSTAINING
.
91
6.1.3
FUEL
ECONOMY
.
94
6.1.4
FUEL
CELL
AGING
.
95
6.1.5
SENSITIVITY
ANALYSIS
.
97
6.2
SIMULATION
RESULTS
OF
DRL-BASED
ENERGY
MANAGEMENT
.
98
6.2.1
BATTERY
'
S
CHARGE-SUSTAINING
.
100
6.2.2
FUEL
CELL
AGING
.
102
6.2.3
OPERATIONAL
COST
AND
FUEL
ECONOMY
.
103
6.3
COMPARISON
AND
DISCUSSION
OF
THE
TWO
STRATEGIES
.
105
6.3.1
ENERGY
DISTRIBUTION
PERFORMANCE
.
106
6.3.2
OPERATIONAL
COST
.
107
6.4
HARDWARE-IN-THE-LOOP
VALIDATION
.
109
6.4.1
HIL
SETUP
.
109
6.4.2
HIL
RESULTS
.
112
6.5
CHAPTER
SUMMARY
.
115
7
CONCLUSIONS
AND
PERSPECTIVES
119
7.1
SUMMARY
.
119
7.2
FUTURE
WORK
.
122
LIST
OF
SYMBOLS
125
REFERENCES
131
OWN
PUBLICATIONS
141
SUPERVISED
THESES
145
CURRICULUM
VITAE
147 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Deng, Kai 1991- |
| author_GND | (DE-588)1303005581 |
| author_facet | Deng, Kai 1991- |
| author_role | aut |
| author_sort | Deng, Kai 1991- |
| author_variant | k d kd |
| building | Verbundindex |
| bvnumber | BV049371768 |
| classification_rvk | ZN 8730 |
| ctrlnum | (OCoLC)1418848571 (DE-599)DNB1297988159 |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| edition | 1. Auflage |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV049371768 |
| illustrated | Illustrated |
| index_date | 2024-07-03T22:54:41Z |
| indexdate | 2025-02-13T09:00:48Z |
| institution | BVB |
| institution_GND | (DE-588)1064118135 |
| isbn | 9783844092042 3844092048 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034631706 |
| oclc_num | 1418848571 |
| open_access_boolean | |
| owner | DE-83 |
| owner_facet | DE-83 |
| physical | xii, 148 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 237 g |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Shaker Verlag |
| record_format | marc |
| series | Aachener Schriftenreihe zur Elektromagnetischen Energiewandlung |
| series2 | Aachener Schriftenreihe zur Elektromagnetischen Energiewandlung |
| spelling | Deng, Kai 1991- Verfasser (DE-588)1303005581 aut Adaptive online energy management controls for fuel cell and battery hybrid vehicles Kai Deng Düren Shaker Verlag 2023 xii, 148 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 237 g txt rdacontent n rdamedia nc rdacarrier Aachener Schriftenreihe zur Elektromagnetischen Energiewandlung 51 Dissertation RWTH Aachen University 2023 Hybridfahrzeug (DE-588)7524499-8 gnd rswk-swf Wasserstofferzeugung (DE-588)4189271-9 gnd rswk-swf Brennstoffzelle (DE-588)4008195-3 gnd rswk-swf Deep Learning (DE-588)1135597375 gnd rswk-swf Akkumulator (DE-588)4068497-0 gnd rswk-swf Energieeffizienz (DE-588)7660153-5 gnd rswk-swf Modellprädiktive Regelung (DE-588)1135937567 gnd rswk-swf hydrogen powertrain energy management model predictive control deep reinforcement learning (DE-588)4113937-9 Hochschulschrift gnd-content Hybridfahrzeug (DE-588)7524499-8 s Brennstoffzelle (DE-588)4008195-3 s Wasserstofferzeugung (DE-588)4189271-9 s Akkumulator (DE-588)4068497-0 s Energieeffizienz (DE-588)7660153-5 s Modellprädiktive Regelung (DE-588)1135937567 s Deep Learning (DE-588)1135597375 s DE-604 Aachener Schriftenreihe zur Elektromagnetischen Energiewandlung 51 (DE-604)BV021706262 51 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034631706&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Deng, Kai 1991- Adaptive online energy management controls for fuel cell and battery hybrid vehicles Aachener Schriftenreihe zur Elektromagnetischen Energiewandlung Hybridfahrzeug (DE-588)7524499-8 gnd Wasserstofferzeugung (DE-588)4189271-9 gnd Brennstoffzelle (DE-588)4008195-3 gnd Deep Learning (DE-588)1135597375 gnd Akkumulator (DE-588)4068497-0 gnd Energieeffizienz (DE-588)7660153-5 gnd Modellprädiktive Regelung (DE-588)1135937567 gnd |
| subject_GND | (DE-588)7524499-8 (DE-588)4189271-9 (DE-588)4008195-3 (DE-588)1135597375 (DE-588)4068497-0 (DE-588)7660153-5 (DE-588)1135937567 (DE-588)4113937-9 |
| title | Adaptive online energy management controls for fuel cell and battery hybrid vehicles |
| title_auth | Adaptive online energy management controls for fuel cell and battery hybrid vehicles |
| title_exact_search | Adaptive online energy management controls for fuel cell and battery hybrid vehicles |
| title_exact_search_txtP | Adaptive Online Energy Management Controls for Fuel Cell and Battery Hybrid Vehicles |
| title_full | Adaptive online energy management controls for fuel cell and battery hybrid vehicles Kai Deng |
| title_fullStr | Adaptive online energy management controls for fuel cell and battery hybrid vehicles Kai Deng |
| title_full_unstemmed | Adaptive online energy management controls for fuel cell and battery hybrid vehicles Kai Deng |
| title_short | Adaptive online energy management controls for fuel cell and battery hybrid vehicles |
| title_sort | adaptive online energy management controls for fuel cell and battery hybrid vehicles |
| topic | Hybridfahrzeug (DE-588)7524499-8 gnd Wasserstofferzeugung (DE-588)4189271-9 gnd Brennstoffzelle (DE-588)4008195-3 gnd Deep Learning (DE-588)1135597375 gnd Akkumulator (DE-588)4068497-0 gnd Energieeffizienz (DE-588)7660153-5 gnd Modellprädiktive Regelung (DE-588)1135937567 gnd |
| topic_facet | Hybridfahrzeug Wasserstofferzeugung Brennstoffzelle Deep Learning Akkumulator Energieeffizienz Modellprädiktive Regelung Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034631706&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV021706262 |
| work_keys_str_mv | AT dengkai adaptiveonlineenergymanagementcontrolsforfuelcellandbatteryhybridvehicles |