全电动赛车在热约束下的最佳耐力赛策略
Optimal Endurance Race Strategies for a Fully Electric Race Car under Thermal Constraints
AI总结：
本文提出一个双层优化框架，通过扩展凸圈速优化并构建混合整数二阶锥规划，联合优化赛段、充电时间与进站次数，在热约束下最大化电动耐力赛车的行驶距离。
摘要：
本文提出了一个双层优化框架，用于计算全电动耐力赛车的最大行驶距离比赛策略，同时考虑了低级别车辆动力学和动力总成部件的热限制。其中，低级别计算给定充电时间和赛段长度下的最短赛段时间，而高级别利用该信息联合优化赛段长度、充电时间和进站次数，以在固定时间的耐力赛中最大化行驶距离。具体而言，我们首先扩展了一个凸圈速优化框架，以捕捉低级别车辆动力学和热模型，并利用它创建一个将充电时间和赛段长度与可实现赛段时间相关联的映射。其次，我们利用该映射将最大比赛距离问题构建为一个混合整数二阶锥规划，该问题可以在几秒钟内通过现成的优化算法高效求解到全局最优。最后，我们展示了在Zandvoort赛道进行的模拟6小时比赛中该框架的应用。结果表明，最佳比赛策略可能涉及对电池进行部分充电，并且与为固定进站次数优化赛段的情况相比，联合优化赛段和进站次数可以显著增加行驶距离，从而通过几圈提升比赛性能。
英文摘要：
This paper presents a bi-level optimization framework to compute the maximum-distance race strategies for a fully electric endurance race car, whilst accounting for the low-level vehicle dynamics and the thermal limitations of the powertrain components. Thereby, the lower level computes the minimum-stint-time for a given charge time and stint length, whilst the upper level leverages that information to jointly optimize the stint length, charge time and number of pit stops, in order to maximize the driven distance in the course of a fixed-time endurance race. Specifically, we first extend a convex lap time optimization framework to capture low-level vehicle dynamics and thermal models, and use it to create a map linking the charge time and stint length to the achievable stint time. Second, we leverage the map to frame the maximum-race-distance problem as a mixed-integer second order conic program that can be efficiently solved in a few seconds to the global optimum with off-the-shelf optimization algorithms. Finally, we showcase our framework for a simulated 6h race around the Zandvoort circuit. Our results show that the optimal race strategy can involve partially charging the battery, and that, compared to the case where the stints are optimized for a fixed number of pit stops, jointly optimizing the stints and number of pit stops can significantly increase the driven distance and hence race performance by several laps.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
