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This study is aimed at designing offline and online controllers for energy management of series hybrid electric vehicles (SHEV). where beside decreasing fuel consumption and keeping battery state of charge within acceptable range, reduces air pollution .In this paper two energy management strategies of SHEV are designed. In first strategy based on known driving cycle, a fuzzy logic controller (FLC) is designed to manage energy and power between Electric battery (EB) and internal combustion engine (ICE). In Second control strategy widths of Gaussian membership functions in designed FLC of first strategy are optimized. Preliminarily optimization of Gaussian membership functions widths is done by ant colony optimization (ACO) algorithm, then according to four representative driving cycles, four optimized FLCs are designed. In contrast with first strategy these four FLCs can manage energy and power between EB and ICM for an unknown driving cycle.
Recognition of deriving cycle is based on main features extraction of each driving cycle. A learning vector quantization (LVQ) neural network is used to recognized pattern of unknown driving cycle. Finally after recognition of driving cycle, a Driving cycle recognition (DCR) network algorithm is used to manage switching between optimized FLCs. To verify performance and efficiency of proposed method simulation performed due to Matlab/Advisor environment

Keywords: Energy Management, Fuzzy Logic controller (FLC), ant colony optimization, Driving cycle, pattern recognition

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