BESS Sizing in an Isolated Microgrid Including PHEVs and RERs

Fouladi, Ehsan; Baghaee, Hamid Reza; Bagheri, Mehdi; Lu, Maxim; Gharehpetian, Gevork B.

doi:10.1109/eeeic/icpseurope49358.2020.9160640

Cited by 3 publications

(2 citation statements)

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“…In [25] the authors propose an intelligent scheme for charging PHEVs in ac MGs that can simultaneously reduce the energy derived from UUG to charge PHEVs and increase the life of BESSs based on a multi-purpose optimization algorithm. In [26] the authors suggest a scheme for sizing BESSs in isolated MGs, including PHEVs and renewable energy resources (RERs), where sizing of BESSs is critical because MGs do not have a Supported primary utility network in isolated mode. The goal is to feed all MG loads continuously without any load separation.…”

Section: Introductionmentioning

confidence: 99%

“…Where the load profile of the MG unit and the output power of the distributed generation (DG) units are calculated for four different seasons, then the rated maximum power of BESS is determined based on the load profile plus the output power of the DGs. A strategy based on fuzzy logic control and adaptive neural fuzzy inference system (ANFIS) was suggested [26,27]. It is very suitable for dealing with nonlinear problems, thanks to its strength and adaptability.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Saving of Battery Electric Vehicle Powertrain and Efficiency Improvement during Different Standard Driving Cycles

et al. 2020

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This article focuses on the energy-saving of each driving distance for battery electric vehicle (BEV) applications, by developing a more effective energy management strategy (EMS), under different driving cycles. Fuzzy logic control (FLC) is suggested to control the power management unit (PMU) for the battery management system (BMS) for BEV applications. The adaptive neural fuzzy inference system (ANFIS) is a modeling technique that is mainly based on data. Membership functions and FLC rules can be improved by simply training the ANFIS with real driving cycle data gathered from the MATLAB/SIMULINK program. Then, FLC console blocks are rewritten by enhanced membership functions by ANFIS traineeship. Two different driving cycles are chosen to check the improvement in the efficiency of this proposed system. The suggested control system is validated by simulation and comparison with the traditional proportional-integral (PI) control. The optimized FLC shows better energy-saving.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%