This study proposes a data-driven model for prediction of the energy consumption of fleet vehicles in various missions, by characterization as the linear combination of a small set of exemplar travel segments. The model was constructed with reference to a heterogenous study group of 29 light municipal fleet vehicles, each performing a single mission, and each equipped with a commercial OBD2/GPS logger. The logger data was cleaned and segmented into 3-minute periods, each with 10 derived kinetic features and a power feature. These segments were used to define three essential model components as follows: The segments were clustered into six exemplar travel types (called "eigentrips" for brevity) Each vehicle was defined by a vector of its average power in each eigentrip Each mission was defined by a vector of annual seconds spent in each eigentrip 10% of the eigentrip-labelled segments were selected into a training corpus (representing historical observations), with the remainder held back for testing (representing future operations to be predicted). A Light Gradient Boost Machine (LGBM) classifier was trained to predict the eigentrip labels with sole reference to the kinetic features, i.e., excluding the power observation. The classifier was applied to the held-back test data, and the vehicle's characteristic power values applied, resulting in an energy consumption prediction for each test segment. The predictions were then summed for each whole-study mission profile, and compared to the logger-derived estimate of actual energy consumption, exhibiting a mean absolute error of 9.4%. To show the technique's predictive value, this was compared to prediction with published L/100km figures, which had an error of 22%. To show the level of avoidable error, it was compared with an LGBM direct regression model (distinct from the LGBM classifier) which reduced prediction error to 3.7%. / Graduate