Avdevicius, Edvard

The process of electrification of the public transportation sector is resulting in a growing number of electric buses on the streets. Modelling and simulating the electric bus fleets can not only identify possible issues in time but can also provide valuable inputs for the optimal integration of these buses into existing operational plans and management systems. One of the important requirements for accurate modelling is knowledge of the energy consumption of the buses. This paper uses a data-driven approach to analyze the factors impacting energy consumption. The considered factors are: average daily temperature, trip length, total trip time, state of charge at the beginning of the trip, and average vehicle speed during the trip. Additionally, the impact of different buses and routes is analyzed by considering their ID numbers. The data from 96 different electric buses were collected in the city of Hamburg for 13 months. The analysis of individual parameters provides an insight into the actual operation of electric bus fleets. Additionally, using correlation analysis, it is possible to understand the relationship among all mentioned parameters. The analysis of the energy consumption of electric buses provided in this paper offers valuable inputs for future studies and the successful electrification of further bus fleets.

The electrification of public transportation fleets worldwide can pose a challenge to multiple stakeholders, such as the fleet operator or the operator of the local electrical grid. One of the important prerequisites for the successful integration of these fleets into the existing system is the knowledge of the energy consumption of the buses during their trips. The energy consumption varies depending on multiple factors such as the vehicle or route-related parameters, operational, and environmental parameters. This paper gives an overview of the latest research regarding these influential factors. Another essential prerequisite for the implementation of intelligent management systems for electric bus fleets is the forecasting of energy consumption. Researchers take different approaches to tackle this issue. A review of the latest research considering empirical approaches, physical models, regression, and machine learning is also provided in this paper. The findings of this paper provide a quick overview of different aspects of the energy consumption of electric buses and can therefore support other researchers or decision-makers in their work.

The emergence of new energy optimisation and control technologies with the concept of power system flexibility is a promising way to achieve the desired optimum, secure management within the smart grid and green energy transition. In this context, demand response is available through flexible demand management, taking into account various technical and time constraints. Accordingly, the aim of this paper is to address existing constraints in the field of electric mobility, in particular the operation of the charging infrastructure of bus depots, in order to actively and effectively participate in demand response events by forecasting day-ahead charging costs and load profiles of public transport infrastructure. In line with the development of a methodology for forecasting more accurately, this paper develops a prediction model based on machine learning (ML). A charging schedule is then produced based on Mixed-Integer Linear Programming (MILP) with various objective function scenarios, taking into consideration the electricity price forecast and load distribution. As a result, calculating new provisional load profiles involves assessing the flexibility potential of the bus fleet and preparing solutions in advance based on the electricity market situation.