Predictive Algorithm for Optimizing Power Flow in Hybrid Ultracapacitor/Battery Storage Systems for Light Electric Vehicles

This study deals with the optimal control of hybrid energy storage systems for electric vehicle applications. These storage systems can capitalize on the high specific energy of Lithium-Ion batteries and the high specific power of modern ultracapacitors. The new predictive algorithm uses a state-bas...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on power electronics Jg. 28; H. 8; S. 3882 - 3895
Hauptverfasser: Laldin, Omar, Moshirvaziri, M., Trescases, O.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.08.2013
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Applied sciences
Batteries
Battery
Battery management
Capacitance
Capacitors. Resistors. Filters
Computer simulation
dc-dc converters
Dielectric, amorphous and glass solid devices
digital control
Electric power generation
Electric power plants
Electric vehicles
electric vehicles (EVs)
Electrical engineering. Electrical power engineering
Electrical power engineering
Electronics
energy management
Energy storage
Exact sciences and technology
hybrid energy storage
Hybrid systems
Markov analysis
Miscellaneous
Operation. Load control. Reliability
Optimization algorithms
Power networks and lines
Power supply
Prediction algorithms
Real-time systems
Regenerative
Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices
Simulation
Storage systems
Supercapacitors
System-on-a-chip
ultracapacitors (u-caps)
Various equipment and components
Markov process
Lithium ion batteries
Electric vehicle
Energy storage system
electric vehicles (EVs)
Battery management
Optimization
Load flow
Hybrid system
ultracapacitors (u-caps)
dc-dc converters
Power flow
Supercapacitor
energy management
Trajectory
hybrid energy storage
Battery storage plants
Direct current convertor
digital control
Storage system
Electrolytic capacitor
Real time
Algorithm
Forecasting
High power
Electrical network
High energy
Optimal control
System simulation
Energy demand
ISSN:0885-8993, 1941-0107
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This study deals with the optimal control of hybrid energy storage systems for electric vehicle applications. These storage systems can capitalize on the high specific energy of Lithium-Ion batteries and the high specific power of modern ultracapacitors. The new predictive algorithm uses a state-based approach inspired by power systems optimization, organized as a probability-weighted Markov process to predict future load demands. Decisions on power sharing are made in real time, based on the predictions and probabilities of state trajectories along with associated system losses. Detailed simulations comparing various power sharing algorithms are presented, along with converter-level simulations presenting the response characteristics of power sharing scenarios. The full hybrid storage system along with the mechanical drivetrain is implemented and validated experimentally on a 500 W, 50 V system with a programmable drive cycle having a strong regenerative component. It is experimentally shown that the hybrid energy storage system runs more efficiently and captures the excess regenerative energy that is otherwise dissipated in the mechanical brakes due to the battery's limited charge current capability.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:0885-8993
1941-0107
DOI:10.1109/TPEL.2012.2226474