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.

I. Introduction

Several countries are phasing out the use of gasoline and diesel vehicles and replacing them with electric vehicles (EVs) or vehicles that use alternate sources of energy. Governments are providing tax rebates and credits to incentivize users willing to switch to EVs at an early stage. In August, California, the nation's largest state and economy, announced rules that would ban all new gas-powered cars by 2035. New York plans to follow [1].