Browsing by Subject "Vehicle Modeling"

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    A Machine Learning Model for Average Fuel Consumption in Heavy Vehicles
    (IEEE, 2019) Schoen, Alexander; Byerly, Andy; Hendrix, Brent; Bagwe, Rishikesh Mahesh; dos Santos, Euzeli C., Jr.; Ben Miled, Zina; Electrical and Computer Engineering, School of Engineering and Technology
    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.
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    Modeling and Energy Management of Hybrid Electric Vehicles
    (2019-12) Bagwe, Rishikesh Mahesh; dos Santos Jr, Euzeli; Ben Miled, Zina; King, Brian
    This thesis proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (P-HEV). The strategy can effciently be deployed online without the need for complete knowledge of the entire duty cycle in order to optimize fuel consumption. ARBS improves upon the established Preliminary Rule-Based Strategy (PRBS) which has been adopted in commercial vehicles. When compared to PRBS, the aim of ARBS is to maintain the battery State of Charge (SOC) which ensures the availability of the battery over extended distances. The proposed strategy prevents the engine from operating in highly ineffcient regions and reduces 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 across eight short duty cycles and one long duty cycle with urban and highway characteristics. Compared to PRBS, the results show that, on average, a 1.19% improvement in the miles per gallon equivalent (MPGe) is obtained with ARBS when the battery initial SOC is 63% for short duty cycles. However, as opposed to PRBS, ARBS has the advantage of not requiring any prior knowledge of the engine effciency maps in order to achieve optimal performance. This characteristics can help in the systematic aftermarket hybridization of heavy duty vehicles.