Operating Analysis and Driving Cycle Construction of Electric Passenger Vehicles Based on Real-World Operational Data

Constructing the driving cycle of pure electric passenger vehicles (PEPVs) can provide a comprehensive understanding of the operational characteristics, thereby offering fundamental support for the continuous improvement of their performance. This study utilized the actual operational data of PEPVs to fully analyze and construct reliable drive cycles, aiming to mitigate deviations between standard cycles and real-world conditions and provide support for vehicle energy management and range assessment. First, the data were preprocessed, divided into short operational segments, and characterized by selecting typical feature values that sufficiently represent these segments. Second, principal component analysis (PCA) was employed to reduce the dimensionality of the features of each short segment, followed by clustering analysis using the k-means++ algorithm. Finally, the driving cycles were constructed based on the information from the short, clustered segments. Compared with the original data, the average relative error of the constructed driving cycles is 3.35%, indicating that the constructed driving cycles can effectively reflect the current operational characteristics of the PEPVs.