Computationally efficient model for energy demand prediction of electric city bus in varying operating conditions. (15th February 2019)

• Record Type: Journal Article
• Authors: Vepsäläinen, Jari; Otto, Kevin; Lajunen, Antti; Tammi, Kari
• Is Part Of: Energy. Volume 169(2019)
• Page Start: 433
• Page End: 443
• Publication Date: 2019-02-15
• Subjects: Surrogate modeling -- Simulation -- Energy demand -- Electric bus -- Sensitivity analysis -- Uncertainty; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2018.12.064
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The uncertainty of operating conditions such as weather and payload cause variations in the energy demand of electric city buses. Uncertain variation in energy demand is a challenge in the design of charging systems and on-board energy storages. To predict the energy demand, a computationally efficient model is required for real-time applications. We present a novel approach to predict energy demand variation with a wide range of uncertain factors. A factor identification is carried out to recognize the range of variation in the operating conditions. A computationally efficient surrogate model is generated based on a previously developed numerical simulation model. The surrogate model is shown to be 10 000 times faster than the numerical model. The surrogate model output corresponds with the numerical model with less than 1% error. The energy demand of the surrogate model varied from 0.43 to 2.30 kWh/km, which is realistic in comparison to previous studies. Successful sensitivity analysis of the surrogate model revealed the most crucial factors. Uncertainty in temperature, rolling resistance and payload contributed most to the variation in energy demand. Variation in these factors should be taken into account when predicting energy consumption and while planning schedules for a bus network. Highlights: Novel surrogate modeling approach for energy demand prediction. Uncertainty margins of 14 noise factors are identified. Performing a sensitivity analysis of electric bus energy demand and regeneration. Computational efficiency is improved significantly with high prediction accuracy. Consumption depends mainly on the ambient temperature, rolling resistance and mass.