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...
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| Published in: | IEEE transactions on power electronics Vol. 28; no. 8; pp. 3882 - 3895 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York, NY
IEEE
01.08.2013
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0885-8993, 1941-0107 |
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| Abstract | 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. |
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| AbstractList | 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. [PUBLICATION ABSTRACT] 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. |
| Author | Trescases, O. Laldin, Omar Moshirvaziri, M. |
| Author_xml | – sequence: 1 givenname: Omar surname: Laldin fullname: Laldin, Omar email: olaldin@purdue.edu organization: Purdue Univ., West Lafayette, IN, USA – sequence: 2 givenname: M. surname: Moshirvaziri fullname: Moshirvaziri, M. email: mazhar@ele.utoronto.ca organization: Motive, York, ON, Canada – sequence: 3 givenname: O. surname: Trescases fullname: Trescases, O. email: ot@ele.utoronto.ca organization: Dept. of Electr. & Comput. Eng., Univ. of Toronto, Toronto, ON, Canada |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | Predictive Algorithm for Optimizing Power Flow in Hybrid Ultracapacitor/Battery Storage Systems for Light Electric Vehicles |
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