随机环境下的电动汽车充电站搜索
Electric Vehicle Charging Station Search in Stochastic Environments
摘要：
电动汽车是未来移动系统的核心组成部分，因为如果使用清洁能源，它们有望减少当地的有毒和粉尘排放以及二氧化碳排放。然而，尽管政府采取了重大激励措施，但迄今为止电动汽车的采用仍低于预期。这种缓慢采用的一个原因是司机的感知范围焦虑，特别是对个人拥有的车辆。这里，糟糕的用户体验，例如，传统汽车阻塞充电站或不一致的实时可用性数据，表明了驾驶员的里程焦虑。在此背景下，我们研究随机搜索算法，可以很容易地部署在今天的导航系统，以尽量减少弯路，以达到一个可用的充电站。我们模拟这样的搜索作为一个有限的地平线马尔可夫决策过程，并提出了一个全面的框架，考虑不同的问题变量，加速技术，和三个解决方案的算法：一个精确的标签算法，一个启发式标签算法，推出算法。大量的数值研究表明，我们的算法显着减少了预期的时间找到一个免费的充电站，同时提高了解决方案的质量鲁棒性和搜索成功的可能性相比，近视的方法。
英文摘要：
Electric vehicles are a central component of future mobility systems as they promise to reduce local noxious and fine dust emissions and CO2 emissions, if fed by clean energy sources. However, the adoption of electric vehicles so far fell short of expectations despite significant governmental incentives. One reason for this slow adoption is the drivers' perceived range anxiety, especially for individually owned vehicles. Here, bad user-experiences, e.g., conventional cars blocking charging stations or inconsistent real-time availability data, manifest the drivers' range anxiety. Against this background, we study stochastic search algorithms, that can be readily deployed in today's navigation systems in order to minimize detours to reach an available charging station. We model such a search as a finite horizon Markov decision process and present a comprehensive framework that considers different problem variants, speed-up techniques, and three solution algorithms: an exact labeling algorithm, a heuristic labeling algorithm, and a rollout algorithm. Extensive numerical studies show that our algorithms significantly decrease the expected time to find a free charging station while increasing the solution quality robustness and the likelihood that a search is successful compared to myopic approaches.
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