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

TL;DR

evS2CP introduces an optimization-based framework for real-time, connected EV trip planning that jointly optimizes speed and charging by fusing NLP, convex QP, and mixed-integer QP formulations. The approach leverages V2X data to incorporate route geometry, traffic, and charging-station availability, and it operates primarily in the spatial domain to enable rapid planning over long distances. A progression from NLP to MIQP demonstrates substantial gains in computational efficiency, with MIQP (and MIQP-greedy) achieving near-global optimality and solving long-range trips in seconds, suitable for real-time ADAS and connected-EV ecosystems. The work highlights trade-offs between solution quality and compute time, showing that MIQP variants can dramatically reduce charging stops while maintaining close trip-time and energy performance, albeit with caveats around charging-power profiles and station capacity that warrant further refinement. Overall, evS2CP offers a scalable, real-time planning framework that aligns with evolving V2X-enabled infrastructure and paves the way for practical eco-driving and charging optimization in connected EVs.

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.

Figures (8)

• Figure 1: Conceptual diagram of EV trip (vehicle speed and charging strategy) planning using V2X connectivity.
• Figure 2: Schematics of the longitudinal vehicle dynamics.
• Figure 3: Real-time data acquisition and visualization framework for route geometry, traffic conditions, and geographic information across three driving scenarios:(a) short-distance trips ($\leq 250\:\textrm{km}$) with frequent urban and suburban stops, (b) mid-distance trips ($250\sim500\:\textrm{km}$) emphasizing intercity travel with balanced energy management, and (c) long-distance trips ($\geq 500\:\textrm{km}$) focusing on efficient charging strategies and minimal travel interruptions.
• Figure 4: Motor efficiency and force limits obtained from real-world experiments and approximated by numerical regressors.
• Figure 5: State-of-Charge (SoC)-dependent charging power profiles utilized in optimization-based speed and charging planning strategies. In (MI)QP formulations, a piecewise constant charging power is assumed, while (MI)NLP formulations incorporate charging power profiles that are piecewise affine with respect to the battery SoC.