EV-Planner: a machine learning approach to electric vehicle charging infrastructure planning

Electric Vehicles (EVs) offer pathways to lower emissions and increased energy efficiency. However, a broad and equitable adoption of electric vehicles can only be realized with well-planned growth in public charging infrastructure. We propose EV-Planner, a software tool that formulates EV charging station placement as a constrained multi-objective optimization problem and computes approximate solutions to these problems. In contrast to previous efforts, EV-Planner considers multiple criteria, including minimizing average user-to-station distance, maximizing fairness in coverage, and balancing load across stations. We propose a clustering-based approximation to solve the constrained multi-objective problem, along with a Suggest-Accept (SA) criterion that iterates over two steps: (i) generation of suitable clusters; and (ii) acceptance of a subset of clusters based on the specified cost function and constraints. To tune the hyperparameters in SA clustering, we develop a neural network model and an associated training procedure. Based on real-world data from 10 US states, EV-Planner reduces average distances to charging stations by 52.3%, the number of users who are more than three miles from a charging station by up to 10.7 times, and overloaded EV stations by 71.7% over current baselines, offering a promising solution to the problem of planning EV infrastructure. The code is available at https://github.com/bharathanand0/EV-Planner.