TOOL FOR PLANNING THE CITY BUS TRANSPORT ELECTRIFICATION
Summary description of the key features of the tool
This project output relates to the developed software tool aimed at planning of city bus transport electrification. The tool consists of four modules:
- Driving cycle data post-processing module (DPPM),
- E-bus simulation module (EBSM),
- Charging optimization module COM) and
- Techno-economic analysis module(TEAM).
The tool is written in Python programming language, with computationally demanded routines coded in C language. It is designed in a user-friendly way (based on a graphical user interface (GUI) including windows, tabs, input/output data interfaces etc.), where different modules share the same database open for the user. The DPPM serves for postprocessing of on-line recorded driving cycle data, where the outputs include smooth trajectories of vehicle velocity and road slope, as well as statistical indices characterizing the city bus transport driving behaviour.
The EBSM provides computer simulation of different types of city buses (conventional ones and different types of electric buses: hybrid, plug-in hybrid and fully-electric buses) over the driving cycles generated in the DPPM. The module outputs include the energy consumption (fuel and/or electricity), the battery state-of-charge trajectory, engine and e-motors operating points plotted over their operating maps, and similar.
The COM utilises the outputs of DPPM and EBSM to simulate the overall city bus transport over the given driving routes and optimises the e-bus charging management for the cases of fast chargers placed at end stations and/or slow chargers installed in depot. This module provides the number of buses and chargers required to fulfil the driving schedules, as well as fuel and/or electricity consumption for the bus fleet and
electricity consumption for charging.
The TEAM uses the output data from the COM and EBSM modules, as well as the data on bus transport investment and exploitation/maintenance cost, in order to calculate the total cost of ownership related to city bus transport electrification.
Expected impact and benefits of the tool for the concerned territories and target groups
The developed software tool can be exploited by different users to deliver a number of benefits for the concerned territories and various target groups.
First, the city bus transport companies can use the tool for planning of future introduction of different types of electric buses and related charging infrastructure. As explained above, the tool is designed to use real/recorded driving cycles and techno-economic data, which the transport companies are at disposal of, to calculate the optimal type and number of e-buses and chargers, as well as prediction of total cost of ownership including investment and exploitation cost. The calculation also includes savings in energy consumption, and equally important reduction of pollutant gases and CO2 emissions in the concerned
The public administrators can use the tool to analyse the benefits of city bus transport electrification for different techno-economic scenarios, and shape the incentives for end users to proliferate such green technologies for the benefits of citizens.
The tool can also be used by research and development institutions (e.g. universities) for various projects aimed at greening the city transport of future, as well as for education purposes.
Sustainability of the tool and its transferability to other territories and stakeholders
The tool has originally been designed to have an open architecture.
The user is approached through a graphical user interface as well as through an open database shared by different tool modules, so that different input data characteristic for different users and territories can be properly inputted to obtain representative output data for the given scenario and city. As a part of subsequent project activities to be conducted in 2019, the tool will further be extended and refined to maximise its transferability and sustainability. For instance, currently the user can set the powertrain data of
different e-bus types of parallel architecture, while in the final version it would be beneficial to enable the user to specify other powertrain architectures such as series one. Also, different transport companies can record the driving cycles by using different bus tracking devices. Therefore, an input filter should be designed or specified, which would transfer the recorded driving cycle data into a unique format required by the tool.