Electric Vehicle Charging Network Optimization using Multi-Variable Linear Programming and Bayesian Principles

Electric vehicles charging stations are capital intensive and time consuming. It is necessary to optimize resources to make fast charging a viable and easily available option. To solve this problem, we use statistical approaches to identify a near-optimal solution in distributing an EV charging network over a large geographical region. Based on principles of linear programming and Bayesian theory, an improved EV charging distribution network is proposed that considers socio-economic parameters. Finally, the numerical results are presented, using these mathematical principles; it is shown that the impact on grid can be minimized while providing a safe and cost-effective EV Charging infrastructure.