Quantifying Repeatability of Real-World On-Road Driving Using Dynamic Time Warping

There are numerous activities in the automotive industry in which a vehicle drives a pre-defined route multiple times such as portable emissions measurement systems testing or real-world electric vehicle range testing. The speed profile is not the same for each drive cycle due to uncontrollable real-world variables such as traffic, stoplights, stalled vehicles, or weather conditions. It can be difficult to compare each run accurately. To this end, this paper presents a method to compare and quantify the repeatability of real-world on-road vehicle driving schedules using dynamic time warping (DTW). DTW is a well-developed computational algorithm which compares two different time-series signals describing the same underlying phenomenon but occurring at different time scales. DTW is applied to real-world, on-road drive cycles, and metrics are developed to quantify similarities between these drive cycles. This methodology is vehicle-agnostic and can be applied to conventional light-duty, hybrid, fully electric or heavy-duty on-road vehicles.