Modeling and Performance Evaluation of ANFIS Controller-Based Bidirectional Power Management Scheme in Plug-In Electric Vehicles Integrated With Electric Grid

Abstract

A bi-directional power exchange between the plug-in electric vehicle (PEV) and the AC electrical grid is necessary to perform the Vehicle to Grid (V2G) and Grid to Vehicle (G2V) operations. While performing these operations, different power converters and controllers play an important role as mediators between the PEV and electric grid. Various works have demonstrated the utilization of controllers for PEV's battery power management. However, the existing conventional controllers have technical shortcomings about vulnerability to controller gain, accurate mathematical modelling, poor adaptability, sluggish response to a sudden outburst and lengthy interval execution processing. Therefore, this paper develops an adaptive neuro-fuzzy inference system (ANFIS) control strategy based bidirectional power management scheme to ensure the optimal electrical power flow exchange between the AC electric grid and battery storage system in PEVs. This paper aims to reduce the stress on the grid power side and utilize the unused power properly. The performance of the ANFIS model is varied using two PEVs based on real-life power consumptions by different loads at home based on five operational modes. Besides, a comparative analysis between the ANFIS controller and the PI controller is carried out to demonstrate the effectiveness of the proposed control scheme. The results illustrate that the proposed ANFIS controller delivers a smoother power injection from the PEV to the AC power grid with the least harmonics as well as achieves a smoother battery profile and less distortion when power is absorbed by PEV battery.INDEX TERMS ANFIS controller, plug-in electric vehicle, bidirectional power, state of charge, PI controller.

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“…• Layer 5: A single fixed node with the letter S is present in the final layer, and it sums up all the input signals to get the following final output total given in Eq. ( 6) [25],…”

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“…mainly manages non-fuzzy sets and depends on three parameters: m, n, and p.• Layer 2: The output of this layer is derived from the multiplication of all incoming signals at this node, designated as P. Eq. (3) represents the node function for this layer[25], Layer 3: The nodes shown in this layer are responsible for performing the normalization process. These nodes are fixed and shown as circles with the letter N in them.…”

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