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.

In distributed data mining, secrecy of private data input of parties with similar background, is achieved by Secure Multi Party Computation (SMC). One of the mostly used tool of SMC is secure sum protocol which has been modified by researchers using many techniques to provide utmost security. In this paper, we propose another novel secure sum protocol to provide more data security in an efficient way named Double Random Partitioned Model (DRPM) protocol for multi-party computation that uses the collaboration of data segmentation, value randomization technique and trusted third party for ensuring zero data leakage among participating parties. Proposed method have reduced computational steps noticeably than all other existing protocols. The comparative study shows that the proposed protocol performs much better than the existing protocols in terms of communication complexity and computation complexity, e.g., proposed DRPM protocol improves 85% on computational complexity over the existing best one.

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