This paper advocates a data summarization approach based on distance rather than the traditional time period when developing individualized machine learning models for fuel consumption. This approach is used in conjunction with seven predictors derived from vehicle speed and road grade to produce a highly predictive neural network model for average fuel consumption in heavy vehicles. The proposed model can easily be developed and deployed for each individual vehicle in a fleet in order to optimize fuel consumption over the entire fleet. The predictors of the model are aggregated over fixed window sizes of distance traveled. Different window sizes are evaluated and the results show that a 1 km window is able to predict fuel consumption with a 0.91 coefficient of determination and mean absolute peak-to-peak percent error less than 4% for routes that include both city and highway duty cycle segments.

This paper proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (HEV). The aim of the strategy is to facilitate the aftermarket hybridization of medium-and heavy-duty vehicles. ARBS can be deployed online to optimize fuel consumption without any detailed knowledge of the engine efficiency map of the vehicle or the entire duty cycle. The proposed strategy improves upon the established Preliminary Rule-Based Strategy (PRBS), which has been adopted in commercial vehicles, by dynamically adjusting the regions of operations of the engine and the motor. It prevents the engine from operating in highly inefficient regions while reducing the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink ® , both the proposed ARBS and the established PRBS strategies are compared over an extended duty cycle consisting of both urban and highway segments. The results show that ARBS can achieve high MPGe with different thresholds for the boundary between the motor region and the engine region. In contrast, PRBS can achieve high MPGe only if this boundary is carefully established from the engine efficiency map. This difference between the two strategies makes the ARBS particularly suitable for aftermarket hybridization where full knowledge of the engine efficiency map may not be available.

This article investigates the ability of data-driven models to estimate instantaneous fuel consumption over 1 km road segments from different routes for different heavy-duty vehicles from the same fleet. Models are created using three different techniques: parametric, linear regression, and artificial neural networks. The proposed models use features derived from vehicle speed, mass, and road grade, which can be easily obtained from telematics devices, in addition to power take-off (PTO) active time, which is needed to capture the power requested by accessories in several heavy-duty vehicles. The robustness of these models with respect to the training data selection is improved by using k-fold cross-validation. Moreover, the inherent underestimation or overestimation bias of the model is calculated and used to offset the fuel consumption estimates for new routes. The study shows that the target application dictates the choice of model features. In fact, the results indicate that depending on the vocation the linear regression and neural network models, which use the same input features, are able to adequately differentiate between the fuel consumption of two vehicles from the same fleet as well as between the fuel consumption of a single vehicle over two different routes. However, the parametric model, which does not utilize PTO active time, is unable to differentiate between two vehicles from the same fleet. This latter model is more suitable for comparing fuel consumption across different fleets of vehicles. In summary, vocation-specific models should be used to optimize fuel consumption for a given fleet of vehicles, whereas general models can only provide insight into aggregated fuel consumption for entire fleets. Moreover, both the accuracy and the precision of the models as measured by their confidence interval should be taken into consideration when comparing fuel consumption estimates for two vehicles from the same fleet or the fuel consumption estimates of an individual vehicle for two different routes. This study shows that the artificial neural network models have narrow 95% confidence intervals and are therefore more precise than the equivalent linear regression models.

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