Optimal network electrification plan for operation of battery-electric
multiple unit regional trains
M. Kapetanovića,*, N. Bešinovića, A. Núñez b, N. van Oorta, R.M.P. GoverdeaaDepartment of Transport and Planning, Delft University of Technology, Delft, The Netherlands
M.Kapetanovic@tudelft.nl, N.Besinovic@tudelft.nl, N.vanOort@tudelft.nl, R.M.P.Goverde@tudelft.nlbSection of Railway Engineering, Delft University of Technology, Delft, The Netherlands
A.A.NunezVicencio@tudelft.nl* Corresponding author

Extended abstract submitted for presentation at the 11th Triennial Symposium on
Transportation Analysis conference (TRISTAN XI)
June 19-25, 2022, Mauritius Island

April 1, 2022
Keywords: battery-electric multiple unit, regional railway transport, partial electrification

1INTRODUCTION

The Netherlands have one of the highest rail electrification rates in the EU with over 75% of the railway
network electrified (European Comission, 2018), offering environment-friendly trains operation.
However, in order to achieve carbon neutral railway sector by 2050, significant investments are required
to further improve environmental performance from trains operation, especially in regional nonelectrified networks with passenger services typically provided by diesel multiple unit (DMU) vehicles.
Due to their low utilization, full electrification of such networks is often not economically viable, thus
solutions are mainly sought in alternative propulsion system technologies, such as hydrogen fuel-cell
multiple unit (FCMU) and battery-electric multiple unit (BEMU) vehicles (Klebsch et al., 2019). Oneof the main challenges in introducing BEMU trains is determining the electrification plan for the railway
network, while satisfying requirements related to quality of service, maintaining current timetable, andvehicle-specific constraints. Previous research on BEMUs operation is mainly focused on continuous
partial lines electrification, or eventually limited scenario analysis on intermittent electrification(Abdurahman et al., 2021), with the optimization-based methods still lacking in the literature. This study
aims to fill this gap by proposing a method for developing an optimal electrification plan, while
minimizing total costs and considering several electrification alternatives for each track section.

2METHODOLOGY

The proposed methodology is based on a simulation-based optimization of railway network
electrification plan, with the objective to minimize the overall monetary costs. Three alternativeelectrification options are considered for each track section: (i) complete section electrification (between
two consecutive stops) (Network Rail, 2015), (ii) acceleration catenary (Mwambeleko et al., 2020), and(iii) rapid ESS charging facilities during stops (Ahmad et al., 2021). The overall costs include the partial
track electrification costs proportional to the length of overhead catenary installed, and the costs for a
traction electricity provision infrastructure, proportional to a number of separated electrified sections,
i.e., number of power substations.

TRISTAN XI Symposium

Original abstract submittal

M. Kapetanović, N. Bešinović, A. Núñez, N. van Oort and R.M.P. Goverde

2.1

2

Modeling of battery-electric multiple unit

The propulsion system architecture for the BEMU vehicle is shown in Figure 1. On electrified track
sections electricity is supplied from the catenary via pantograph, connected to the DC link via a power
converter. BEMU is propelled entirely by AC electric traction motors located at the drive shaft, andconnected to the DC link via bi-directional DC/AC converters. Additional on-board consumers include
auxiliary systems such as lighting, compressors and HVAC components. Lithium-ion battery-basedenergy storage system (ESS) is being charged from the traction grid, where available, and from theregenerative braking energy. In non-electrified track sections, entire power demand for traction andauxiliary systems is provided from the ESS. To achieve realistic estimations on BEMU’s powertraindynamics, a detailed simulation model based on a backward looking quasi-static simulation approach(Pröhl, 2017) is adopted. The dynamics of each component is modeled in MATLAB®/Simulink© using
the OPEUS Simulink library - a result of the built up knowledge from several European projects(Shift2Rail, 2021), and validated in a number of studies, including authors’ previous work (Kapetanovićet al., 2021a; Kapetanović et al., 2021b). The simulation model requires technical specifications forvariety of system components and infrastructure related characteristics as inputs, while the main output
is given with the ESS state-of-charge (SOC). For a particular solution to be feasible, ESS SOC mustremain within the permissible limits defined by a manufacturer.

Figure 1 – Schematic representation of a propulsion system for battery-electric multiple unit
vehicle2.2

Optimization framework for the network electrification plan

The optimization algorithm is presented in Figure 2. Since transport services can stretch over multiple
branches, the first step is to map all railway services to a physical railway network. For each railway
service, a matrix containing all possible electrification configurations is constructed, based on the
introduced three alternative electrification options for each track segment covered by the service. Using
nested search architecture (Silvas et al., 2016), each electrification configuration for each service isevaluated using the previously described simulation model, and the corresponding costs are calculated.
For unfeasible solutions where ESS SOC drops below the lower limit (e.g. 10%), costs are assigned aninfinite value. Once all possible solutions are evaluated, the cost-optimal electrification plan for thewhole network is derived by solving the mixed integer programming (MIP) problem using binary
decision variables linked to each of the three electrification options, while satisfying condition that only
one electrification option can be assigned to each track segment.

TRISTAN XI Symposium

Original abstract submittal

M. Kapetanović, N. Bešinović, A. Núñez, N. van Oort and R.M.P. Goverde

3

Figure 2 – Flowchart for the proposed railway network optimization algorithm

3

DUTCH CASE STUDY

3.1

Regional railway network in the northern Netherlands

Figure 3 – Schematic representation of regional railway network in the northern Netherlands, withindicated different passenger transport services

TRISTAN XI Symposium

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M. Kapetanović, N. Bešinović, A. Núñez, N. van Oort and R.M.P. Goverde

4

Presented methodology will be applied in a case study of regional non-electrified railway network in thenorthern Netherlands. The network encompass seven railway lines in provinces Friesland andGroningen, with provided sixteen different transport services shown in Figure 3. Detailed infrastructure
characteristics including track geometry, speed limits and timetable are provided by the railway
undertaking Arriva.

3.2

Reference battery-electric multiple unit

A bi-mode hybrid two-coach DEMU, based on a new platform WINK (abb. for Wandelbarer
Innovativer Nahverkehrs-Kurzzug, in English, convertible innovative commuter short train) will be used
as a reference vehicle in this study. Introduced in the network in 2021, these hybrid DEMUs are aimed
to be converted to their BEMUs counterparts by replacing the diesel generator and fuel tank withadditional battery power-packs (Stadler, 2020). Detailed vehicle characteristics and parameters used in
the analysis are provided by the vehicle manufacturer Stadler.

References
Abdurahman, B. M., Harrison, T., Ward, C. P., & Midgley, W. J. B. (2021). An investigation into
intermittent electrification strategies and an analysis of resulting CO2 emissions using a high-fidelity
train model. Railway Engineering Science, 29(3), 314–326.
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positioning of wayside charging stations for hybrid locomotive consists in heavy haul train
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Acknowledgment
This work is supported by Arriva Personenvervoer Nederland B.V. within the PhD project “Improvingsustainability of regional railway services” of Delft University of Technology.

TRISTAN XI Symposium

Original abstract submittal