Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning

Teresa Pamuła and Danuta Pamuła
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Teresa Pamuła: Faculty of Transport and Aviation Engineering, Silesian University of Technology, 40-019 Katowice, Poland
Danuta Pamuła: Rockwell Automation, 40-382 Katowice, Poland

Energies, 2022, vol. 15, issue 5, 1-19

Abstract: The energy demand of electric buses (EBs) is a very important parameter that should be considered by transport companies when introducing electric buses into the urban bus fleet. This article proposes a novel deep-learning-based model for predicting energy consumption of an electric bus traveling in an urban area. The model addresses two important issues: accuracy and cost of prediction. The aim of the research was to develop the deep-learning-based prediction model, which requires only the data readily available to bus fleet operators, such as location of the bus stops (coordinates, altitude), route traveled, schedule, travel time between stops, and to find the most suitable type and configuration of neural network to evaluate the model. The developed prediction model was assessed with different types of deep neural networks using real data collected for several bus lines in a medium-sized city in Poland. Conducted research has shown that the deep learning network with autoencoders (DLNA) neural network allows for the most accurate energy consumption estimation of 93%. The proposed model can be used by public transport companies to plan driving schedules and energy management when introducing electric buses.

Keywords: energy prediction; electric buses; deep learning; urban bus network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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