The Route Not Taken: Driver-Centric Estimation of Electric Vehicle Range
The Route Not Taken: Driver-Centric Estimation of Electric Vehicle Range
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DOI：
10.1609/icaps.v24i1.13663
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发表时间：
2014-05
期刊：
影响因子：
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通讯作者：
Peter Ondruska;I. Posner
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文献类型：
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作者：
Peter Ondruska;I. Posner
本文解决了有效和准确地预测电动汽车的可达范围的挑战。具体而言，我们的方法考虑了驾驶员的一般路线偏好，以根据到达地图中每个可能目的地所需的能量估计提供最新的个性化信息。我们框架的顺序决策的背景下，这一任务，并表明，在到达一个特定的目的地的能源消耗可以制定为政策评估的马尔可夫决策过程。特别是，我们利用模型的属性，用于预测可能的能源消耗到每个可能的目的地在一个现实大小的地图实时。要评估的策略是学习的，并且随着时间的推移，使用反向强化学习进行优化，以提供终身自适应系统。我们的方法是使用一个公开的数据集，提供真实的轨迹数据的50个人跨越约10,000英里的旅行进行评估。我们表明，通过考虑驾驶员特定的路线偏好，我们的系统显着降低了能源预测的相对误差相比，更常见的，驾驶员不可知的最短路径或最短时间的路线。
This paper addresses the challenge of efficiently and accurately predicting an electric vehicle's attainable range. Specifically, our approach accounts for a driver's generalised route preferences to provide up-to-date, personalised information based on estimates of the energy required to reach every possible destination in a map. We frame this task in the context of sequential decision making and show that energy consumption in reaching a particular destination can be formulated as policy evaluation in a Markov Decision Process. In particular, we exploit the properties of the model adopted for predicting likely energy consumption to every possible destination in a realistically sized map in real-time. The policy to be evaluated is learned and, over time, refined using Inverse Reinforcement Learning to provide for a life-long adaptive system. Our approach is evaluated using a publicly available dataset providing real trajectory data of 50 individuals spanning approximately 10,000 miles of travel. We show that by accounting for driver specific route preferences our system significantly reduces the relative error in energy prediction compared to more common, driver-agnostic heuristics such as shortest-path or shortest-time routes.