Building representative real world drive cycles is an important component in the modelling of emissions, battery health of electric vehicles and autonomous vehicles. All these applications are sensitive to the transients and diversity present in real world driving patterns, which are not adequately captured by current approaches. To address this lacuna, we use clustering techniques involving time-series (shape) based distances on the raw data directly to obtain representative sets of real world drive cycles. We demonstrate the efficacy of our approach using experimental data from a fleet of eight motorcycles run across five locations in India. Dynamic Time Warping (DTW) distance based clustering gives optimal results. We give theoretical and experimental justification for our constructions. We believe that the constructed drive cycles using the proposed approach would help in assessing the impact of various policies aimed at building eco-friendly transportation systems.

构建具有代表性的现实世界驾驶循环是电动汽车和自动驾驶汽车的排放、电池健康状况建模的重要组成部分。所有这些应用程序都对现实世界驾驶模式中存在的瞬态和多样性敏感，而当前的方法无法充分捕捉到这些瞬态和多样性。为了解决这个问题，我们使用聚类技术直接在原始数据上使用基于时间序列（形状）的距离来获得真实世界驾驶周期的代表性集合。我们使用来自印度五个地点的八辆摩托车车队的实验数据证明了我们的方法的有效性。基于动态时间扭曲 (DTW) 距离的聚类给出了最佳结果。我们为我们的结构提供理论和实验证明。