Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning

Teresa Pamuła,Wiesław Pamuła

doi:10.3390/en13092340

Abstract

The estimation of energy consumption is an important prerequisite for planning the required infrastructure for charging and optimising the schedules of battery electric buses used in public urban transport. This paper proposes a model using a reduced number of readily acquired bus trip parameters: arrival times at the bus stops, map positions of the bus stops and a parameter indicating the trip conditions. A deep learning network is developed for deriving the estimates of energy consumption stop by stop of bus lines. Deep learning networks belong to the important group of methods capable of the analysis of large datasets—“big data”. This property allows for the scaling of the method and application to different sized transport networks. Validation of the network is done using real-world data provided by bus authorities of the town of Jaworzno in Poland. The estimates of energy consumption are compared with the results obtained using a regression model that is based on the collected data. Estimation errors do not exceed 7.1% for the set of several thousand bus trips. The study results indicate spots in the public transport network of potential power deficiency which can be alleviated by introducing a charging station or correcting the bus trip schedules.

Highlights

Public transport is a key factor for the functionality of urban systems
In the case of countries with a domination of coal-fired thermal power stations (Poland), the impact of the introduction of battery electric buses may not contribute to the reduction of the carbon footprint as the electricity is sourced by high-carbon emission plants
A stochastic gradient descent is used to solve the problem of finding the coefficients (m) of the

Summary

Introduction

Public transport is a key factor for the functionality of urban systems. Much attention is given to limit urban pollution and reduce the total cost of ownership (TCO) of the transport fleet at the same time as improving the quality of the services. In this context, the electrification of bus transport gains favour as it does not emit polluting gases in the urban environment, it is highly power efficient and less noisy than conventional bus transport. Environmental pollution, first of all the carbon footprint, highly depends on the way the electricity is generated. In the case of countries with a domination of coal-fired thermal power stations (Poland), the impact of the introduction of battery electric buses may not contribute to the reduction of the carbon footprint as the electricity is sourced by high-carbon emission plants

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Conclusion