This research introduces a novel hybrid sliding mode controller integrated with the artificial neural network (SMC-ANN) used for purpose of real power exchange using Intelligence Power Management System (IPMS). This paper's purpose is to explore the transient and steady state voltage, transient current, transient power of EV, along with the EV torque and EV speed. In giving a nonlinear signal that simulates the network to "glide" across a cross-section of its normal behavior, sliding mode control is a nonlinear control strategy that affects a dynamic of a nonlinear system. The setup consists of utility grid, integrated Photovoltaic source (PV) energy with Battery Storage System (BSS). A high gain DC/DC boost converter is implemented to interface BSS to the DC-bus. The Power Conditioning System receives the DC output voltage from the PV panel (PCS). These converters' bidirectional nature offers the benefit of power transfer between storage systems, loads, and PV systems. The results obtained using newly developed algorithm is compared with the conventional sliding mode controller (SMC) and it is seen that the a newly developed algorithm produced better outcomes than the conventional one. Simulation is performed using MATLAB software.