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International Journal of Automotive Technology > Volume 18(5); 2017 > Article
International Journal of Automotive Technology 2017;18(5): 891-900.
doi: https://doi.org/10.1007/s12239-017-0087-4
OPTIMIZATION OF POWER MANAGEMENT AMONG AN ENGINE, BATTERY AND ULTRA-CAPACITOR FOR A SERIES HEV: A DYNAMIC PROGRAMMING APPLICATION
Jongryeol Jeong1, Namwook Kim2, Wonsik Lim3, Yeong-Il Park3, Suk Won Cha1, Myeong Eon Jang6
1Seoul National University
2Hanyang University
3Seoul National University of Science and Technology
6Agency of Defense Development
ABSTRACT
In a hybrid electric vehicle (HEV) system, it is an important issue on how to distribute the output power from multiple power generating components to operate a vehicle more efficiently. Many studies have been conducted on how to manage multiple power sources of a vehicle based on various optimization theories. In this study, an algorithm to calculate the optimization of a series HEV that has three power generating components, engine, battery and ultra-capacitor, is developed based on dynamic programming. Normally dynamic programming is applied to the optimization of power management and components sizing by estimating potential fuel economy for electrified vehicle such as HEV, Plug-in HEV or Fuelcell HEV. In contrast with most objective systems that have only two power generating components, the system in this study has three power sources. Since the system has three power sources, the number of state and control variables of optimization problem increases. Therefore the number of calculations increases unreasonably. To decrease the number and time of calculations, a new electric model that contains the both characteristics of battery and ultra-capacitor is developed with some assumptions. In comparison with the optimization algorithm which follows the theory of DP with no assumptions, the results from the newly developed algorithm has 1.04 % discrepancy in terms of fuel economy, even though the calculation time decreases to 4400 times less.
Key Words: Power management optimization, Dynamic programming, Backward simulation, Fuel economy, Series hybrid electric vehicle
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