Summary: Integrating network expansion planning into electric vehicle (EV) smart charging solutions involves designing scalable infrastructure to accommodate the growing demand for electric mobility while considering grid capacity and energy distribution efficiency. This paper proposes a hybrid approach for EV smart charging with network expansion planning. The hybrid technique is the joint execution of the coati optimization algorithm (COA) and cascade-correlation-growing deep learning neural network, commonly known as the COA-CCG-DLNN technique. The objective of the proposed method is to minimize the cost of charging EVs, and it forecasts the best course of action. EV charging with network expansion is based on vehicle-to-building (V2B), vehicle-to-grid (V2G), and grid-to-vehicle (G2V). The COA approach is used to minimize the cost of EV charging and the CCG-DLNN approach is used to predict the optimal solution for the system. The proposed method is executed on the MATLAB platform and is compared with existing techniques like particle swarm optimization (PSO), heap-based optimization (HBO), and wild horse optimization (WHO). The proposed method achieves a low cost of $1.33 and a high accuracy of 99.5% compared with other existing techniques. The performance metrics for the proposed method include 2996.348 as the best result, 3000.100 as the mean, 3001.261 as the worst, and a standard deviation of 1.160348, along with a median of 2998.816, all of which outperform other existing methods.
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