Identifying the type of drive cycle is crucial for analyzing customer usage, optimizing vehicle performance and emission control. Methods that rely on geographical location for drive cycle identification are limited by varying driving conditions at the same location (e.g. heavy traffic during peak hours vs. free-flowing traffic at night). This paper proposes a methodology to identify the type of drive cycle (city, interurban, highway or hybrid) using drive characteristics derived from vehicle data rather than geographical location.
Real-world vehicle data from testing trucks is taken, whose drive profiles are already known. Initially, multiple characteristic features of the drive cycle are identified from literature surveys and domain experience. These features, which can be extracted from basic signal data, include gear shifts, time spent in different driving modes (acceleration, cruise, standstill), velocity distributions, and an 'aggressiveness factor' representing overall driving style.
Using ML based feature selection techniques, the most important features are selected for this cause. With these finalized parameters, a data-driven classification model is developed. This model is trained, validated, and tested using the identified real-world vehicle data. It classifies drive cycles into four major types: city, interurban, highway, and hybrid with a high degree of accuracy.
This classification enables accurate identification of drive cycles, addressing the limitations of location-based methods. The developed model is employed to determine the type of drive cycle driven by customers, thereby aiding in the analysis of the influence of drive cycles on vehicle performance and emissions.