evS2CP: Real-time Simultaneous Speed and Charging Planner for Connected Electric Vehicles

TL;DR

evS2CP is a real-time optimization framework that plans speed and charging for connected electric vehicles, reducing travel and charging times while considering traffic, route, and charging station data.

Contribution

The paper introduces a novel convexified and mixed-integer quadratic programming approach for simultaneous speed and charging planning in connected EVs, enabling efficient, scalable trip optimization.

Findings

Efficient trip planning over 700 km in seconds.

Validated near-global optimality through simulations.

Framework robustly integrates real-time traffic and charging data.

Abstract

This paper presents evS2CP, an optimization-based framework for simultaneous speed and charging planning designed for connected electric vehicles (EVs). With EVs emerging as competitive alternatives to internal combustion engine vehicles, overcoming challenges such as limited charging infrastructure is crucial. evS2CP addresses these issues by minimizing the travel time, charging time, and energy consumption, providing practical solutions for both human-operated and autonomous vehicles. This framework leverages V2X communication to integrate essential EV planning data, including route geometry, real-time traffic conditions, and charging station availability, while simulating dynamic driving environments using open-web API services. The speed and charging planning problem was initially formulated as a nonlinear programming model, which was then convexified into a quadratic programming model without charging-stop constraints. Additionally, a mixed-integer programming approach was employed to optimize charging station selection and minimize the frequency of charging events. A mixed-integer quadratic programming implementation exhibited exceptional computational efficiency and scalability, effectively solving trip plans over distances exceeding 700 km in a few seconds. Simulations conducted using open-source and commercial solvers validated the framework's near-global optimality, demonstrating its robustness and feasibility for real-world applications in connected EV ecosystems.