Drive Cycle Analysis for the Electric Powertrain in Terminal Tractors: A Data-Driven Approach to Performance Evaluation

Abstract

The transition toward electrified powertrains in industrial vehicles places increased demands on understanding real world operational behavior. Terminal tractors operate under highly variable and transient conditions, making traditional standardized drive cycles insufficient for accurate performance evaluation and optimization. This thesis presents a data driven framework for analyzing, categorizing, and simulating operational drive cycles of electric terminal tractors based on real world field data. Multivariate time series data collected from electric terminal tractors were preprocessed and segmented into individual drive cycles using application specific operational signals. A set of interpretable features capturing both steady state and dynamic behavior was extracted for each cycle. Dimensionality reduction and feature selection were performed using Principal Component Analysis and Principal Feature Analysis to retain the most informative characteristics while maintaining interpretability. The results were that only 17 principal components out of the original 24 were needed to describe 95% of the explained variance. Multiple clustering techniques, including Hierarchical Agglomerative Clustering, K-Means, K-Medoids, and a convolutional autoencoder based clustering, were applied and evaluated using internal validation metrics. The resulting clusters revealed two distinct operational regimes, representative usage patterns, and outlying behaviors across the fleet. These two operational regimes were defined as low load and high load, where the low load cluster is defined by its lower variance and more stable values, while the high load cluster is defined by higher variance and a broader range of values in torque for example. Representative and atypical drive cycles from each cluster were subsequently integrated into a simulation model of the electric power train to evaluate component behavior, energy usage, and engine efficiency operation under different load configurations. The results demonstrate that data driven cycle analysis can effectively characterize real world usage patterns and provide valuable insights for how powertrain components, such as the engine and battery, are affected during operation. The proposed methodology offers a scalable framework for leveraging operational data to identify dominant operating regimes and guide structured evaluation of the electrical powertrain in terminal tractors.