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      A Prediction Model for Battery Electric Bus Energy Consumption in Transit

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      Energies
      MDPI AG

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          Abstract

          This study investigates the impacts of vehicular, operational, topological, and external parameters on the energy consumption (EC) of battery-electric buses (BEBs) in transit operation. Furthermore, the study develops a data-driven prediction model for BEB energy consumption in transit operation that considers these four parameters. A Simulink energy model is developed to estimate the EC rates and validated using the Altoona’s test real-world data. A full-factorial experiment is used to generate 907,199 scenarios for BEB operation informed by 120 real-world drive cycles. A multivariate multiple regression model was developed to predict BEB’s EC. The regression model explained more than 96% of the variation in the EC of the BEBs. The results show the significant impacts of road grade, the initial state of charge, road condition, passenger loading, driver aggressiveness, average speed, HVAC, and stop density on BEB’s energy consumption, each with a different magnitude. The study concluded that the optimal transit profile for BEB operation is associated with rolling grade and relatively lower stop density (one to two stops/km).

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          Identification and review of sensitivity analysis methods.

          Identification and qualitative comparison of sensitivity analysis methods that have been used across various disciplines, and that merit consideration for application to food-safety risk assessment models, are presented in this article. Sensitivity analysis can help in identifying critical control points, prioritizing additional data collection or research, and verifying and validating a model. Ten sensitivity analysis methods, including four mathematical methods, five statistical methods, and one graphical method, are identified. The selected methods are compared on the basis of their applicability to different types of models, computational issues such as initial data requirement and complexity of their application, representation of the sensitivity, and the specific uses of these methods. Applications of these methods are illustrated with examples from various fields. No one method is clearly best for food-safety risk models. In general, use of two or more methods, preferably with dissimilar theoretical foundations, may be needed to increase confidence in the ranking of key inputs.
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            ADVISOR: a systems analysis tool for advanced vehicle modeling

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              Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                ENERGA
                Energies
                Energies
                MDPI AG
                1996-1073
                May 2021
                May 14 2021
                : 14
                : 10
                : 2824
                Article
                10.3390/en14102824
                cd35e23a-351a-4043-b7e4-52edc06f7f91
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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