The influence of driving cycle characteristics on the integrated optimization of hybrid energy storage system for electric city buses

This paper analyzes the influence of different driving cycles on the integrated optimization of hybrid energy storage system, including the optimization of supercapacitor size and energy management strategy for the electric vehicle application. The driving cycle is divided into micro-trips, and a fu...

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Vydáno v:Energy (Oxford) Ročník 135; s. 91 - 100
Hlavní autoři: Song, Ziyou, Hou, Jun, Xu, Shaobing, Ouyang, Minggao, Li, Jianqiu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 15.09.2017
Elsevier BV
Témata:
Algorithms
Buses
Component sizing
Computer simulation
Drains
Driving cycle characteristics
Electric vehicle
Electric vehicles
energy
Energy distribution
Energy management
Energy storage
Fuzzy pattern recognition
Hybrid energy storage system
Hybrid systems
Integrated optimization
Optimization
Pattern recognition
Probability density functions
probability distribution
Strategy
Studies
Supercapacitors
vehicles (equipment)
Integrated optimization
Hybrid energy storage system
Fuzzy pattern recognition
Electric vehicle
Driving cycle characteristics
Component sizing
ISSN:0360-5442, 1873-6785
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Shrnutí:This paper analyzes the influence of different driving cycles on the integrated optimization of hybrid energy storage system, including the optimization of supercapacitor size and energy management strategy for the electric vehicle application. The driving cycle is divided into micro-trips, and a fuzzy pattern recognition algorithm is proposed to distinguish different micro-trips within a driving cycle. The intensity factor indicates how intensely the micro-trip drains energy from the hybrid energy storage system. The distribution of each driving cycle is analyzed by the probability density function. The integrated optimization of the hybrid energy storage system is conducted based on four driving cycles. Simulation results show that for different driving cycles, the optimal supercapacitor size and the on-line energy management strategy are directly determined by the maximum intensity factor. The driving cycles with similar maximum intensity factors can use same amount of supercapacitor modules and employ the same on-line energy management strategy. Therefore, the optimization results can be easily generalized to practical bus lines. •Influence of different driving cycles on electric vehicle optimization is analyzed.•A fuzzy pattern recognition is proposed to distinguish different micro-trips.•The intensity factor distribution of each driving cycle is analyzed.•The optimal component size and energy management strategy are obtained.•The proposed criterion is convenient to find optimal solution fast and accurately.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2017.06.096