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.

翻译：本文提出evS2CP，一种基于优化的速度与充电协同规划框架，专为网联电动汽车设计。随着电动汽车逐渐成为内燃机汽车的有力替代品，克服充电基础设施有限等挑战至关重要。evS2CP通过最小化行驶时间、充电时间和能耗来解决这些问题，为人工驾驶和自动驾驶车辆提供实用解决方案。该框架利用V2X通信整合关键的电动汽车规划数据，包括路线几何结构、实时交通状况和充电站可用性，同时通过开放网络API服务模拟动态驾驶环境。速度与充电规划问题最初被构建为非线性规划模型，随后在不考虑充电停站约束的情况下凸化为二次规划模型。此外，采用混合整数规划方法优化充电站选择并最小化充电事件频率。混合整数二次规划的实现展现出卓越的计算效率和可扩展性，能在数秒内有效求解超过700公里的行程规划。使用开源和商业求解器进行的仿真验证了该框架的近似全局最优性，证明了其在网联电动汽车生态系统中实际应用的鲁棒性和可行性。