Access to this full-text is provided by MDPI.
Content available from Energies
This content is subject to copyright.
energies
Article
Analysis of Hybrid and Plug-In Hybrid Alternative Propulsion
Systems for Regional Diesel-Electric Multiple Unit Trains
Marko Kapetanovi´c 1, *, Mohammad Vajihi 2and Rob M. P. Goverde 1
Citation: Kapetanovi´c, M.; Vajihi, M.;
Goverde, R.M.P. Analysis of Hybrid
and Plug-In Hybrid Alternative
Propulsion Systems for Regional
Diesel-Electric Multiple Unit Trains.
Energies 2021,14, 5920. https://
doi.org/10.3390/en14185920
Academic Editors: Tomáš Skrúcaný,
Borna Abramovi´c, Ondrej Stopka,
Csaba Csiszár and Jereb Borut
Received: 22 July 2021
Accepted: 14 September 2021
Published: 17 September 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Department of Transport and Planning, Delft University of Technology, P.O. Box 5048, 2600 GA Delft,
The Netherlands; R.M.P.Goverde@tudelft.nl
2
Department of Civil, Constructional and Environmental Engineering (DICEA), Sapienza University of Rome,
Via Eudossiana 18, 00184 Rome, Italy; mohammad.vajihi@uniroma1.it
*Correspondence: M.Kapetanovic@tudelft.nl
Abstract:
This paper presents a simulation-based analysis of hybrid and plug-in hybrid propulsion
system concepts for diesel-electric multiple unit regional railway vehicles. These alternative concepts
primarily aim to remove emissions in terminal stops with longer stabling periods, with additional
benefits reflected in the reduction of overall fuel consumption, produced emissions, and monetary
costs. The alternative systems behavior is modeled using a backward-looking quasi-static simulation
approach, with the implemented energy management strategy based on a finite state machine
control. A comparative assessment of alternative propulsion systems is carried out in a case study
of a selected regional railway line operated by Arriva, the largest regional railway undertaking
in the Netherlands. The conversion of a standard diesel-electric multiple unit vehicle, currently
operating on the network, demonstrated a potential GHG reduction of 9.43–56.92% and an energy
cost reduction of 9.69–55.46%, depending on the type of service (express or stopping), energy storage
technology selection (lithium-ion battery or double-layer capacitor), electricity production (green or
grey electricity), and charging facilities configuration (charging in terminal stations with or without
additional charging possibility during short intermediate stops) used. As part of a bigger project
aiming to identify optimal transitional solutions towards emissions-free trains, the outcomes of this
study will help in the future fleet planning.
Keywords:
regional railways; diesel-electric multiple units; hybrid propulsion systems; plug-in
hybrid propulsion systems; energy management strategy; GHG emissions; energy costs
1. Introduction
The transport sector is facing numerous challenges in meeting the greenhouse gas
(GHG) emissions reduction targets defined in various international treaties [
1
,
2
] and im-
proving energy efficiency and reducing operational costs [
3
]. Achieving carbon-neutral
railway operation by 2050 [
4
] is being mainly sought through the synergetic electrifica-
tion of railway lines and production of traction electricity from renewables. While this
instrument is economically viable for the highly utilized main corridors, regional railway
lines, which is the main subject in this study, require identification of alternative options for
the predominantly diesel traction. Replacing the typically employed diesel multiple units
(DMU) with battery-electric multiple unit (BEMU) [
5
–
8
] and/or fuel-cell multiple unit
(FCMU) vehicles [
9
–
11
] offers a potentially carbon-neutral final solution for catenary-free
operation. However, a “zero-one” transition such as this is hindered by numerous aspects
related primarily to the vehicle range, technology maturity and availability, relatively
high hydrogen and accompanying infrastructure costs, as well as the long lifecycle of
the existing diesel-driven rolling stock. Thus, this dynamic transition process requires
further exploitation of DMUs, while constantly improving their energy and environmental
performance by implementing novel technological solutions in order to meet increasingly
stringent emission reduction requirements.
Energies 2021,14, 5920. https://doi.org/10.3390/en14185920 https://www.mdpi.com/journal/energies
Energies 2021,14, 5920 2 of 29
Vehicle hybridization, achieved by adding an energy storage system (ESS), enables the
storing of braking energy and support to the internal combustion engine (ICE), resulting in
a significant reduction in fuel consumption and related emissions [
12
]. Hybrid and plug-in
hybrid propulsion systems are increasingly being developed and used in road transport
with the aim to improve vehicle fuel economy [
13
] and reduce emissions [
14
]. A number of
hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles (PHEV) became commer-
cially available over the last two decades [
15
,
16
], which is likewise reflected in the extensive
research efforts on their development as reported in the literature [
17
]. Despite potentially
great benefits from DMU hybridization, as confirmed in several research projects [
18
–
21
],
hybridization of railway powertrains is still in the early development stages. Due to a
comparably smaller market for railway vehicles, only a small number of hybrid DMUs
exist [
22
–
26
], mainly as prototypes. Plug-in hybrid systems offer further exploitation of
the benefits offered by the ESS using an external electric power source for their charging
during stabling periods. However, practical implementation of a plug-in hybrid concept
in the railway sector is limited to shunting locomotives thus far [27–30], with no reported
applications nor literature concerning commercial passenger transport. Utilization of fast
charging facilities in stations is considered mainly for BEMUs operation, as a comple-
ment to partially electrified regional railway lines [
31
–
35
] or in tram networks [
36
], which
represent other use cases than the main subject of the present study.
Energy management strategies (EMS) are the main driver of the fuel economy in
hybrid vehicles. Consequently, the reported literature on hybrid DMUs focusses primarily
on their development and implementation. Their aim is to minimize energy consumption
by managing the power flows from different energy sources in the system. Dynamic
programming (DP), as a global optimization method, is widely used in EMS optimization
for hybrid railway vehicles [
37
–
39
]. It was also used in deriving a fuel-optimal combined
driving and energy management strategy [
40
]. Although DP allows for deriving a glob-
ally optimal EMS, it is mainly employed for off-line controller optimization, with several
drawbacks hindering its real-time applications. These include its requirements for perfect
information on the future duty cycle, the extensive calculation time, frequent switches in
power distribution, and the inability to deal with variables that include counters due to its
non-causal nature, i.e., propagation backward in time. Therefore, the EMS obtained from
the DP is mainly used in defining other causal controllers [
41
], or as a benchmark in evalu-
ating real-time algorithms [
42
,
43
]. The equivalent consumption minimization strategies
(ECMS) [
44
] and Pontryagin’s minimum principle (PMP) optimal control strategies [
45
,
46
]
belong to a group of instantaneous optimization methods that can be used in defining
causal controllers. The effectiveness of these methods depends on how the future driving
conditions and critical parameters, namely the equivalent coefficient in ECMS and the
initial value of the co-state in PMP, are estimated [
47
]. Additionally, whether a certain
EMS can be used online is decided by computation cost and storage memory require-
ment [
48
], posing additional challenges in practical applications of such causal controllers.
Compared to the previous optimization-based methods, rule-based (RB) algorithms use
event-triggered Boolean rules in determining the power ratio between different power
sources in the system. These rules can be derived from heuristics or fuzzy rules based on
experts’ knowledge [
49
]. Although RB algorithms cannot guarantee optimality, they were
widely used in defining real-time EMSs [
50
,
51
], mainly due to their low computation time
and easy implementation, while also showing promising benefits in terms of fuel savings
and emissions reduction.
The present paper contributes to a bigger project realized in cooperation with Arriva,
the largest regional railway undertaking (RU) in the Netherlands, aiming to specify and
assess potential innovations in reducing total GHG emissions on a regional non-electrified
network in the provinces of Friesland and Groningen. Additionally, requirements of
emission-free and noise-free operation in terminal station areas with longer stabling periods
(above 5 min) are imposed for the current DMU fleet, with foreseen operation until 2035.
The development of detailed simulation models is required to incorporate numerous
Energies 2021,14, 5920 3 of 29
factors and case-specific constraints affecting trains’ performance, and to capture their
technological and operational characteristics. With this in mind, and considering previously
discussed aspects and identified knowledge gaps, the main contributions of this paper
are twofold:
1.
A method to support a hypothetical conversion of a conventional regional DMU
vehicle to its hybrid and plug-in hybrid counterparts, equipped with the prominent
ESS technologies and newly developed causal and easy-to-implement real-time power
control, allowing for a realistic estimation of fuel savings;
2.
A comparative analysis of alternative propulsion systems in a case study of a selected
benchmark vehicle and railway line in the northern Netherlands, providing the
railway undertaking with an assessment of potential benefits in terms of reduction of
produced GHG emissions and energy costs.
The remainder of the paper is organized as follows. Section 2presents a description of
standard, hybrid, and plug-in hybrid propulsion systems. A detailed simulation model
and the real-time power control are presented in Section 3. A Dutch case study comprising
of different systems, railway services, and charging scenarios is given in Section 4, followed
by a discussion in Section 5. The concluding remarks and future work efforts are outlined
in Section 6.
2. Configuration of Standard, Hybrid, and Plug-In Hybrid Propulsion Systems
Various propulsion system configurations can be found in regional DMU vehicles
based on their type of power transmission from the ICE to the wheels, i.e., an electrical,
hydraulic, or mechanical transmission [
52
]. We limit our analysis to electrical transmission,
namely to diesel-electric multiple units (DEMU), as the only traction option present in
the northern Netherlands. The power-plant of a standard DEMU (Figure 1a) consists of
an ICE powering an AC electric generator (G). The diesel generator (ICE-G) set powers
an AC electric motor (EM) via the rectifier and inverter. With EM acting as a generator
during braking, the regenerated energy is, in this case, dissipated through a braking resistor
(rheostat), connected to the DC link via a DC/DC converter. We assume total electrification
of mechanical auxiliaries, such as hydraulic pump and compressor, with auxiliary systems
connected to the DC link via a DC/AC inverter.
Hybridization of a DEMU can be accomplished with a properly sized and imple-
mented ESS. Numerous ESS technologies have emerged in the transport sector [
53
]. In
order to assess the influence of the ESS technology selection for a hybrid diesel-electric
multiple unit (HDEMU), we considered the two alternative ESSs that are especially suited
for onboard railway applications: Lithium-ion batteries (LB) and double-layer capacitors
(DLC) [
54
]. Compared to LBs, which are characterized by a high energy density, limited
power density, and relatively short lifetime, DLCs feature a high number of duty cycles,
low energy density, and a high-power density that allows the ESS to store all the energy
coming from regenerative braking in a short time period, and to release it to the EM during
acceleration [
55
]. There are different approaches to ESS implementation into the system,
i.e., by a direct connection to the DC link [
56
,
57
] or via bidirectional DC/DC converters [
51
].
As the application of the DC/DC converter provides the ability to achieve an active control
of each power source and match its voltage to the DC bus voltage [
47
], we adopted the
latter approach (Figure 1b).
Typically, PHEVs use an electric vehicle supply equipment (EVSE) port and correspond-
ing connector for charging the ESS. For further conversion to a plug-in hybrid diesel-electric
multiple unit (PHDEMU), we considered adding a pantograph (or a contact shoe) connected
to the DC link via a line inductor in case of a DC external power grid, or via a transformer
and AC/DC converter in case of an AC external power source (Figure 1c).
Energies 2021,14, 5920 4 of 29
Figure 1.
Simplified schematic representation of (
a
) standard, (
b
) hybrid, and (
c
) plug-in hybrid
system architectures for a diesel-electric multiple unit vehicle.
3. Modeling and Control of Alternative Propulsion Systems
3.1. Simulation Model
A backward-looking quasi-static simulation approach [43,58] was adopted in model-
ing the dynamics of the previously described system architectures. The simulation model
was developed in the MATLAB
®
/Simulink
©
environment using the OPEUS Simulink
toolbox [
59
]. The model of a hybrid DEMU [
37
] was extended to include different power
sources (i.e., ICE, pantograph, LB, and DLC) and to capture the dynamics of ESSs using
typically available parameters published by the manufacturers. The simulation model
(Figure 2) allowed for the simulation of different configurations by disconnecting com-
ponents not included in the respective system. According to the backward orientation
of the model, the inputs encompass the train velocity and geometry profiles of the track,
and the main outputs are cumulative fuel and electricity demand. The arrows designate
the numerical evaluation sequence, opposite to the physical power flow. Due to the high
efficiencies of the power converters, their dynamics were omitted in the model, with their
efficiencies assumed to be ~100%. However, they were considered in the physical system
for controlling the power flows and dispatching different system components according to
the implemented energy management strategy (see Section 3.2). The braking rheostat was
Energies 2021,14, 5920 5 of 29
used only for assessing the balance of power flows in the system. The description of the
low-order models for the system components is provided in the remainder of this section.
Figure 2.
Layout of the simulation model for the assessment of the alternative diesel-electric multiple-unit propulsion
system configurations.
3.1.1. Vehicle
With the given velocity and track geometry profiles as input signals, the tractive or
braking effort at the wheel Fw[N]is determined by
Fw(v(t)) =mv·a(t)+Rv(v(t)) +Rg(γ(s(t))) +Rc(φ(s(t))) (1)
with
Rv(v(t)) =r0+r1·v(t)+r2·v(t)2(2)
Rg(γ(s(t))) =mv·g·sin(γ(s(t))) (3)
Rc(φ(s(t))) = (mv·4.91
φ−30 if φ<300 m
mv·6.3
φ−55 if φ≥300 m (4)
where
t[s]
is the time;
v[m/s]
is the vehicle velocity;
s=Rt
0v(τ)dτ[m]
is the distance
travelled;
a=dv/dt [m/s2]
is the acceleration;
mv[kg]
is the total mass of the vehicle,
i.e.,
mv=(1+λ)·mtare +mpax
, where
λ
denotes the factor accounting for rotating masses,
mtare [kg]
the vehicle tare weight, and
mpax [kg]
the cumulative passengers weight. The ve-
hicle resistance
Rv[N]
includes roll resistance and air resistance, modelled using the Davis
equation [
60
], with vehicle-specific coefficients
r0[N]
,
r1[N/(m/s)]
, and
r2[N/(m/s)2]
;
Rg[N]
is the grade resistance, with
g=
9.81
m/s2
denoting the gravitational acceleration,
and
γ[rad]
the angle of the slope [
61
]; and the curve resistance
Rc[N]
is calculated using
Roeckl’s formula [
62
], with
φ[m]
denoting the curve radius. With the given wheel diameter,
Energies 2021,14, 5920 6 of 29
dw[m]
, and the vehicle velocity,
v
, the torque at the wheel,
Tw[Nm]
, and its rotational
speed, ωw[rad/s], can be calculated by [37,43].
Tw=Fw·dw
2(5)
ωw=2·v
dw. (6)
3.1.2. Axle Gear
The power from the EM shaft to the wheels is transmitted via the axle gear, with
the constant gear ratio
iag
and the constant efficiency of the gearbox
ηag
. The torque
TEM [Nm]
and the rotational speed
ωEM [rad/s]
at the mechanical input of the axle gear
result from [37,43].
TEM =
Tw
iag·ηag if Tw≥0
Tw·ηag
iag if Tw<0(7)
ωEM =ωw·iag. (8)
3.1.3. Electric Motor
Based on the operation mode (motor or generator), and with the EM efficiency
ηEM =
fEM(TEM,ωEM )
determined by a linear 2D-interpolation in the efficiency map, the electric
power of the electric motor PEM [W]can be determined by [37,43].
PEM =(TEM·ωEM
ηEM if TEM ≥0
TEM·ωEM·ηEM if TEM <0. (9)
3.1.4. Auxiliaries
The total auxiliaries power
Paux [W]
is modelled as the sum of the constant term
Paux,const [W]
, representing constant consumers, such as lighting and the heating, ventilation
and air conditioning (HVAC) system, and the variable term, which accounts for the cooling
power [
58
], where we introduce the coefficient
pcool
, representing the proportion of the
total traction power required for cooling the main traction components, i.e.:
Paux(t)=Paux,const +pcool·|PEM(t)|. (10)
3.1.5. Diesel Generator Set
The diesel generator (ICE-G) set is the prime mover in all the propulsion system
configurations considered. Given the requested power from the ICE-G set (electrical output
power of the generator)
PG[W]
, the mechanical output power of the ICE
PICE [W]
is
calculated by:
PICE =PG
ηG, (11)
where the efficiency
ηG=fG(TG,ωICE)
is determined by a linear 2D-interpolation in
the efficiency map of the generator. The existence of a DC link between the ICE-G and
the EM allows for the independent rotational speed of the EM and ICE-G set, with the
optimal ICE-G set rotational speed
ωICE [rad/s]
pre-calculated using the Nelder-Mead
simplex method [
63
] for different possible levels of requested power, while accounting
for the efficiency of the generator and ICE-specific fuel consumption. With the specific
fuel consumption,
ψ=fICE(PICE,ωICE) [kg/Ws]
, determined by a 2D-interpolation of the
Energies 2021,14, 5920 7 of 29
static ICE map, and the density of the fuel,
ρ[kg/l]
, the cumulative ICE fuel consumption
BICE [l]follows from [37,43].
BICE(t)=
t
Z
0
PICE(τ)·ψ(τ)
ρdτ. (12)
3.1.6. Pantograph
A pantograph is introduced in PHDEMU configurations for connecting to the grid
and charging the ESS during stops. With the power received via pantograph
Ppan [W]
, the
total electrical energy consumed Epan [Ws]at time instant tresults from:
Epan(t)=
t
Z
0
Ppan(τ)dτ. (13)
3.1.7. Lithium-Ion Battery
The simplified simulation model of a lithium-ion battery (LB) reflects the equivalent
electrical circuit presented in Figure 3. It comprises of a state-of-charge (SoC)-dependent
voltage source,
UOC [V]
, and a constant internal resistance,
RLB [Ω]
, which account for
ohmic losses and depend on the direction of the battery current
ILB [A]
, i.e., charging or
discharging phase.
Figure 3. Equivalent electrical circuit for the lithium-ion battery-based energy storage system.
Given the power provided from the battery
PLB [W]
, battery SoC
σLB ∈[0, 1]
, open
circuit voltage
UOC
, and an internal resistance
RLB
, the battery current and terminal voltage
ULB [V]are defined by [64]:
ILB(t)=UOC(σLB(t)) −qUOC(σLB(t))2−4·PLB(t)·RLB(ILB(t))
2·RLB(ILB (t)) (14)
ULB(t)=UOC(σLB(t)) −RLB(ILB(t))·ILB(t). (15)
With the initial SoC
σLB(0)
and nominal battery capacity
QLB [As]
, the battery SoC at
time instant tresults from:
σLB(t)=σLB(0)−1
QLB ·
t
Z
0
ILB(τ)dτ. (16)
We limited the maximum (discharging) power
Pmax
LB [W]
and minimum (charging)
power
Pmin
LB [W]
by the maximum and minimum current,
Imax
LB [A]
and
Imin
LB [A]
, respectively,
while keeping the limits of the SoC
σ∈σmin
LB ,σmax
LB
, battery voltage
ULB ∈Umin
LB ,Umax
LB
,
and satisfying the limitations defined by the manufacturer, i.e.:
Energies 2021,14, 5920 8 of 29
Pmax
LB (t)=UOC(σLB(t)) −Rdch
LB ·Imax
LB (t)·Imax
LB (t)(17)
Pmin
LB (t)=UOC(σLB(t)) −Rch
LB·Imin
LB (t)·Imin
LB (t)(18)
with
Imax
LB (t)=min( UOC (σLB(t)) −Umin
LB
Rdch
LB !, σLB(t)−σmin
LB ·QLB
∆t!,Imax,dch
LB (t))(19)
Imin
LB (t)=max( UOC (σLB(t)) −Umax
LB
Rch
LB !,(σLB(t)−σmax
LB )·QLB
∆t,Imax,ch
LB (t)), (20)
where
·t[s]
is the simulation (integration) time step, and
Imax,dch
LB
and
Imax,ch
LB
are the
maximum discharging and charging current defined by the manufacturer, respectively.
Typically, peak (pulse) current values exceeding a defined threshold are allowed for a short
amount of time, preventing the damaging of LB. Therefore, we define the last term in (19)
and (20) by:
Imax,dch
LB (t)=(Ipeak,dch
LB if tdch
cnt (t)<tdch
peak
Icont,dch
LB if tdch
cnt (t)≥tdch
peak
(21)
Imax,ch
LB (t)=(Ipeak,ch
LB if tch
cnt(t)<tch
peak
Icont,ch
LB if tch
cnt(t)≥tch
peak,(22)
where
Icont,dch
LB [A]
and
Icont,ch
LB [A]
are the allowed maximum continuous discharging/
charging current values given by the manufacturer;
Ipeak,dch
LB [A]
and
Ipeak,ch
LB [A]
are the
peak (pulse) discharging/charging current values provided by the manufacturer, allowed
for the limited time period
tdch
peak [s]
and
tch
peak [s]
;
tdch
cnt [s]
and
tch
cnt [s]
are the introduced
discharging/charging counters increased in every time step by the sample time as long as
the current value exceeds the allowed maximum continuous values, which are reset in case
of a switch between discharging and charging phases. We did not consider the thermal
dynamics of the LB, as these characteristics are hardly available, and we assumed that the
thermal limitations on the LB were satisfied with the previously defined constraints on the
maximum power.
3.1.8. Double-Layer Capacitor
The DLC can be represented with the equivalent electrical circuit shown in Figure 4.
It is comprised of an internal resistance
RDLC [Ω]
in series with a capacitance
CDLC [F]
,
both in parallel to a self-discharging resistance
Rdch [Ω]
. Due to the large value of
Rdch
and a duty cycle characterized by short steady-state times, the losses caused by the self-
discharging resistance can be neglected [
43
], thus preventing the necessity of additional
filtering capacitance for braking the algebraic loop [65].
Figure 4. Equivalent electrical circuit for the double-layer capacitor-based energy storage system.
Energies 2021,14, 5920 9 of 29
Compared to the LB, the DLC has a unique electrostatic energy storage characteristic
with its SoC
σDLC
being linearly related to its terminal voltage
UDLC [V]
[
48
], which then
can be determined by:
UDLC(σDLC(t)) =σDLC(t)·Umax
DLC −Umin
DLC+Umin
DLC, (23)
where
Umin
DLC [V]
and
Umax
DLC [V]
are the maximum and minimum voltage of DLC, respectively.
Similar to the LB model, the DLC current IDLC [A]results from:
IDLC(t)=UDLC(σDLC(t)) −qUDLC(σDLC(t))2−4·PDLC(t)·RDLC
2·RDLC . (24)
With the initial SoC σDLC(0), and using (23) and (24), the resulting SoC follows from:
σDLC(t)=σDLC(0)−1
CDLC·Umax
DLC −Umin
DLC·
t
Z
0
IDLC(τ)dτ. (25)
The maximum and minimum power of the DLC (
Pmax
DLC [W]
and
Pmin
DLC [W]
, respectively)
are limited by the current of the DLC. Either the maximum (minimum) current is reached in
order to keep the voltage constrained
UDLC ∈Umin
DLC,Umax
DLC
, or the maximum (minimum)
permitted current for the DLC is reached, i.e.:
Pmax
DLC(t)=UDLC (σDLC(t))·Imax
DLC(t)(26)
Pmin
DLC(t)=UDLC (σDLC(t))·Imin
DLC(t)(27)
with
Imax
DLC(t)=min(UDLC (σDLC(t)) −Umin
DLC·CDLC
∆t,Imax,dch
DLC )(28)
Imin
DLC(t)=max(UDLC (σDLC(t)) −Umax
DLC·CDLC
∆t,Imax,ch
DLC ), (29)
where
Imax,dch
DLC [A]
and
Imax,ch
DLC [A]
are the maximum discharging and charging current
values provided by the manufacturer, respectively.
3.2. Energy Management Strategy
The aim of the EMS implemented in the control unit (see Figure 2) is to distribute
total demanded power for traction and auxiliaries between different power sources in the
system, while satisfying the following requirements, according to the level of priority:
1.
Removing emissions and noise in terminal stops by switching off the ICE and supply-
ing auxiliary systems from an ESS or electric power grid;
2.
Improving fuel economy by maximizing regenerative braking energy and its later use
in powering traction and auxiliary systems;
3. Increasing overall ICE-G efficiency by avoiding low load operation;
4. Supporting ICE-G by an ESS during high power demand phases (acceleration).
In order to fulfill these requirements, a real-time control based on a finite state machine
(FSM) was proposed for both HDEMU and PHDEMU configurations, which is applicable
to any of the two considered ESS technologies, i.e.,
ESS ∈{LB, DLC}
. FSM controls can
provide effective and implementable management of complex systems, such as hybrid
railway vehicles [
66
,
67
]. They can be easily programmed in microcontrollers [
68
], which
are then used for dispatching different power sources in the system by controlling their
unidirectional or bi-directional converters. The presented EMS thus allows for realistic and
Energies 2021,14, 5920 10 of 29
achievable estimations of potential fuel savings for the different configurations considered
in this paper.
3.2.1. FSM Control for HDEMU Vehicle
The FSM control for HDEMU is shown in Figure 5. It consists of five states (
S1–S5
)
representing typical operation modes of a propulsion system, and corresponding triggers
(T1–T5) covering all theoretically possible transitions between states, irrespective of the
degree of hybridization, i.e., relative ICE-G set to ESS power ratio. A line-specific critical
track section between the defined critical position,
scr [m]
, and the position of the terminal
stop,
sts [m]
, was introduced to ensure a maximally charged ESS when reaching the
terminal stop. ESS discharge processes were disabled in this section and ESS was being
charged from regenerative braking energy and/or ICE-G set. Additionally, a SoC limit
σlim
ESS ∈σmin
ESS ,σmax
ESS
was defined to prevent excessive ESS charge from ICE-G set and the
dissipation of braking energy. Both,
scr
and
σlim
ESS
were calibrated from an estimated duty
cycle for a particular railway line and vehicle configuration. To avoid frequent switches
between ESS charging and discharging operation modes that might cause damage and
degradation, a hysteresis cycle for the SoC,
σhyst
ESS ∈σmin
ESS ,σlim
ESS
, was implemented by
introducing a dynamic binary indicator
Flag(t)∈{0, 1}
, with
Flag(0)=
0. An optimal
level of electrical power from the ICE-G set
Popt
G[W]
corresponds to its optimal efficiency
region. Power flows corresponding to the different states and the triggers for the transition
to each particular state were defined as follows.
Figure 5. Finite state machine control for hybrid propulsion system.
Under the pure ICE state (S1), total demanded power
Pdem(t)=PEM(t)+Paux(t)
is
provided by ICE-G set, and the ESS converter is switched off. Depending on the requested
power level and ESS characteristics (maximum power), this state is active if ESS reaches its
SoC limiting values and/or the vehicle is located within the critical track section, i.e.:
T1 : Pdem(t)≥Popt
G∧σESS(t)=σmin
ESS ∨scr ≤s(t)<sts
∨0≤Pdem(t)<Popt
G∧Pdem(t)≤Pmax
ESS (t)∧σESS(t)≥σlim
ESS ∧scr ≤s(t)<sts
∨Pdem(t)<Popt
G∧Pdem(t)>Pmax
ESS (t)∧σESS(t)≥σlim
ESS
(30)
Energies 2021,14, 5920 11 of 29
S1 :
PESS(t)=0
PG(t)=Pdem(t)
Flag(t)=Flag(t−∆t).
(31)
In the pure ESS state (S2), the ESS provides the total requested power, with ICE running
with no load on idling speed, or switched off if the terminal stop is reached. This state is
enabled outside of the critical track section and its activation depends on the SoC value
and the implemented hysteresis, defined by:
T2 : 0≤Pdem(t)≤Pmax
ESS (t)∧(s(t)<scr ∨s(t)=sts)
∧Flag(t−∆t)=0∨Flag(t−∆t)=1∧σESS(t)≥σmin
ESS +σhyst
ESS (32)
S2 :
PESS(t)=Pdem(t)
PG(t)=0
Flag(t)=0.
(33)
Similar as in the previous state, the boost state (S3) is enabled outside of the critical track
section, and for particular SoC values and implemented hysteresis cycle. In this state, ESS
provides support for the ICE-G set by providing a portion of high requested power, i.e.:
T3 : Pdem(t)>Popt
G∧Pdem(t)>Pmax
ESS (t)∧σESS(t)>σmin
ESS ∧(s(t)<scr ∨s(t)=sts)
∧Flag(t−∆t)=0∨Flag(t−∆t)=1∧σESS(t)≥σmin
ESS +σhyst
ESS (34)
S3 :
PESS(t)=minnPmax
ESS (t),Paux(t),Pdem(t)−Popt
ICEo
PG(t)=Pdem(t)−PESS(t)
Flag(t)=0.
(35)
Under the load level increase state (S4), which features a low power demand, the ICE-G
set provides the excess power that is used for recharging the ESS, defined by:
T4 : Pdem(t)<Popt
G∧Pdem(t)>Pmax
ESS (t)∧σESS(t)<σlim
ESS
∨0≤Pdem(t)<Popt
G∧Pdem(t)≤Pmax
ESS (t)
∧σESS(t)<σlim
ESS ∧scr ≤s(t)<sts∨Flag(t−∆t)=1∧σESS(t)<σmin
ESS +σhyst
ESS
(36)
S4 :
PESS(t)=maxnPmin
ESS (t),Pdem(t)−Popt
Go
PG(t)=Pdem(t)−PESS(t)
Flag(t)=1.
(37)
The recuperation state (S5) is active during braking, with the negative power values
at the DC link, which is used for recharging the ESS. The power distributed to the ESS is
limited with its maximum charging power, with the excess power dissipated at the braking
rheostat, and ICE running with no load at idling speed, i.e.:
T5 : Pdem(t)<0 (38)
S5 :
PESS(t)=maxPmin
ESS (t),Pdem(t)
PG(t)=0
Flag(t)=Flag(t−∆t).
(39)
3.2.2. FSM Control for PHDEMU Vehicle
The FSM control for PHDEMU is shown in Figure 6. The previously defined FSM
control was extended with the additional state (S6) for the operational mode in stations
equipped with charging facilities, together with the corresponding transition conditions.
Energies 2021,14, 5920 12 of 29
Figure 6. Finite state machine control for plug-in hybrid propulsion system.
The EMS is defined by introducing a binary indicator
bel(s(t)) ∈{0, 1}
, to represent
the track electrification status. Operational characteristics related to the critical track section
were removed due to the existence of external power sources in terminal stops, resulting in
the following transition triggers:
T1 : bel(s(t)) =0∧Pdem(t)≥Popt
G∧σESS(t)=σmin
ESS
∨Pdem(t)<Popt
G∧Pdem(t)>Pmax
ESS (t)∧σESS(t)≥σlim
ESS (40)
T2 : bel(s(t)) =0∧0≤Pdem(t)≤Pmax
ESS (t)
∧Flag(t−∆t)=0∨Flag(t−∆t)=1∧σESS(t)≥σmin
ESS +σhyst
ESS (41)
T3 : bel(s(t)) =0∧Pdem(t)>Popt
G∧Pdem(t)>Pmax
ESS (t)∧σESS(t)>σmin
ESS
∧Flag(t−∆t)=0∨Flag(t−∆t)=1∧σESS(t)≥σmin
ESS +σhyst
ESS (42)
T4 : bel(s(t)) =0∧Pdem(t)<Popt
G∧Pdem(t)>Pmax
ESS (t)∧σESS(t)<σlim
ESS
∨0≤Pdem(t)<Popt
G∧Pdem(t)≤Pmax
ESS (t)∧Flag(t−∆t)=1∧σESS(t)<σmin
ESS +σhyst
ESS (43)
T5 : Pdem(t)<0 (44)
T6 : bel(s(t)) =1. (45)
The power distribution for the states S1–S5 remained the same as in the previous
case. Under the newly added pure electric state (S6), the ICE is switched off in case of a
stop duration longer than 5 min, or switched to idle operation with no load otherwise.
Depending on the maximum power from the grid
Pmax
pan [W]
and the maximum charging
Energies 2021,14, 5920 13 of 29
power of ESS, electric power from the grid is used for supplying the auxiliaries and
recharging the ESS, i.e.:
S6 :
PESS(t)=maxnPmin
ESS (t),Pdem(t)−Pmax
pan o
PG(t)=0
Ppan(t)=Pdem(t)−PESS(t)
Flag(t)=Flag(t−∆t).
(46)
4. Case Study of the Dutch Northern Regional Railway Lines
The simulation methodology proposed in the previous section was applied in esti-
mating the energy consumption for each of the considered alternative propulsion systems,
followed by the calculation of related GHG emissions and energy costs. The following
sub-sections provide the description of the selected benchmark DEMU and railway line,
followed by a detailed comparative analysis of the different scenarios.
4.1. Benchmark Railway Vehicle
A two-coach DEMU of the type Gelenktriebwagen (GTW) 2/6 from the Swiss man-
ufacturer Stadler, currently employed on the network by the RU Arriva Nederland, was
selected as the benchmark vehicle for this study. The power-module of GTW 2/6 is located
between the two passenger coaches and contains two identical propulsion systems, shown
in Figure 1a. Simulation parameters for a standard GTW 2/6 DEMU are given in Table 1.
The EM, G, and ICE characteristic maps for the GTW 2/6 were reconstructed using data
provided in [
69
], with the available efficiency map of EM linearly scaled in order to comply
with the maximum requested power for traction and auxiliaries, the maximum available
power from ICE-G set at the DC link (Figure 7a), and an ICE-specific fuel consumption
map (Figure 7b) reconstructed using similarly sized ICE and Willan’s lines technique [
70
].
Table 1. Standard GTW 2/6 DEMU simulation parameters.
Parameter Unit Value Description
mtare t 70.4 Tare weight 1
λ- 0.05 Rotating mass factor 2
mpax t 7 Total passengers weight 3
r0N 1001 Davis equation coefficient (constant term) 2
r1N/(km/h) 22.3 Davis equation coefficient (linear term) 2
r2N/(km/h)20.1 Davis equation coefficient (quadratic term) 2
dwm 0.86 Powered wheel diameter 4
iag - 1.7218 Axle gear ratio 5
ηag - 0.97 Axle gear efficiency 6
vmax km/h 140 Maximum velocity 4
amax m/s21.05 Maximum acceleration 2
amin m/s2−1Maximum deceleration 2
Fmax
wkN 80 Maximum (starting) tractive effort at the wheel 4
Pmax
wkW 600 Maximum power at the wheel 4
Prated
EM kW 2 ×400 EM rated power 1
Prated
ICE kW 2 ×390 ICE rated power 1
Paux,const kW 50 Constant auxiliaries power 3
pcool - 0.01 Cooling power coefficient 3
ρg/L 825 Fuel density (diesel) 6
Source:
1
Giro Batalla and Feenstra [
71
];
2
Personal communication with Arriva;
3
Assumed values;
4
Stadler Bussnang AG [
72
];
5
Calculated
as the ratio between the maximum rotational speed of the GTW’s EM provided in [
71
] and the maximum rotational speed of the wheel
derived from the maximum vehicle speed; 6Adopted from Prohl [59].
Energies 2021,14, 5920 14 of 29
Figure 7.
(
a
) Efficiency map of an electric motor; (
b
) specific fuel consumption of an internal consumption engine; and
(c) lithium-ion battery module open circuit voltage as a function of state-of-charge.
Commercially available LB or DLC modules with proven railway applications were
considered for DEMU hybridization in order to obtain as realistic estimations as possible. A
Toshiba SCiB
™
module, type 1–23, contains 24 Li-ion cells, arranged in 2 parallel branches
with 12 cells in series. The cells are based on a Li nickel manganese cobalt oxide (NMC)
chemistry with a Li titanium oxide (LTO) anode, which offers a good compromise between
energy density, power density, and achievable lifetime [
73
,
74
]. Due to the unavailability
of the open-circuit voltage characteristic as a function of SoC, the function from [
75
] was
adopted and scaled according to voltage limits for the SCiB
™
module (Figure 7c). A
BMOD0063 module from the manufacturer Maxwell Technologies was selected as the DLC
technology. It contains 48 cells, with 6 parallel series of 8 cells each, and it is especially
suited for heavy-duty transport applications, such as trains and buses [
76
]. Detailed
characteristics of the selected LB and DLC modules are given in Table 2.
Table 2. Parameters of the selected lithium-ion battery and double-layer capacitor modules.
Parameter Unit Value Description
LB module 1
QLB Ah 45 Nominal capacity
Icont,ch
LB /Icont,dch
LB A−160/160 Minimum/maximum continuous current
Ipeak,ch
LB /Ipeak,dch
LB A−350/350 Minimum/maximum pulse current
tdch
peak/tdch
peak s 10 Allowed time for pulse current
Umin
LB /Umax
LB V 18/32.4 Minimum/maximum voltage
Rch
LB/Rdch
LB Ω0.006 Internal resistance charge/discharge
σmin
LB /σmax
LB % 10/90 Minimum/maximum SoC 2
Emax
LB kWh 1.24 Energy content
Euse
LB kWh 0.922 Usable energy content 3
mLB kg 15 Weight
DLC module 4
CDLC F 63 Rated capacitance
Imax,ch
DLC /Imax,dch
DLC A−240/240 Minimum/maximum continuous current
Umin
DLC/Umax
DLC V 12.5/125 Minimum/maximum voltage
RDLC Ω0.018 Internal resistance
EDLC kWh 0.14 Energy content
mDLC kg 61 Weight
Source:
1
Extracted value from specifications and data sheets in [
74
] unless otherwise indicated;
2
Adopted values for simulation purposes;
3Based on allowed SoC range; 4Extracted values from specifications and data sheets in [76].
The total required number of modules was derived from the energy requirement
of supplying the auxiliaries in terminal stops according to the extended layover time in
terminal stops of 30 min, resulting in 28 LB modules and 179 DLC modules. Train weight
was adjusted to account for the added ESSs. An additional weight of 1000 kg was assumed
for the converters and other equipment and 150 kg for the pantograph. Since the additional
Energies 2021,14, 5920 15 of 29
mass affects both acceleration and braking performance, it was accounted for in the velocity
profile calculation and simulations for each of the alternative vehicle configurations.
4.2. Benchmark Railway Line Selection
The main railway line on the network between the cities Leeuwarden and Groningen
was selected for the train simulations (Figure 8). Compared to the rest of the network,
the provision of the two different services on this line (stopping and express) allowed for
an impact assessment of the stopping frequency on the total energy consumption. Two
different scenarios were considered for the plug-in hybrid concepts regarding the charging
facilities location:
1. Charging facilities located only in terminal stations with long layover times;
2.
Charging facilities located in terminal stations and an additional fast charging facility
located in Buitenpost, a common short stop for the two services.
Figure 8.
(
a
) Position and (
b
) schematic representation of the Northern lines in the Netherlands; and
(
c
) track layout for the railway line Leeuwarden-Groningen with indicated locations for charging
facilities, stops for stopping and express service, track geometry, and maximum allowed speed.
Energies 2021,14, 5920 16 of 29
The vehicle round trip, based on the actual periodic timetable and rolling stock
circulation plan (Table 3), was analyzed to account for the difference in line resistances and
maximum speed limits for the two opposite directions. A dwell time of 30 s was presumed
for all intermediate stops. For the scenarios including the additional charging location in
Buitenpost, this time was extended to 2 min at this particular stop.
Table 3.
Distance between stops and departure times for the line Leeuwarden (Lw) to Groningen
(Gn).
Station Distance (km)
Departure Time (hh:mm)
Stopping Service 1Express Service
Lw →Gn Gn →Lw Lw →Gn Gn →Lw
Leeuwarden 0 hh: 51 hh + 2:40
(arrival) hh: 44 hh + 2:16
(arrival)
Leeuwarden C.
3.34 hh: 54 hh + 2:35 - -
Hurdegaryp 9.83 hh + 1:01 hh + 2:30 - -
Feanwalden 14.00 hh + 1:05 hh + 2:25 - -
De Westereen 17.24 hh + 1:08 hh + 2:20 - -
Buitenpost 24.74 hh + 1:16 hh + 2:15 hh + 1:00 hh + 2:00
Grijskerk 35.71 hh + 1:23 hh + 2:06 - -
Zuidhorn 42.35 hh + 1:30 hh + 2:01 - -
Groningen 54.05 hh + 1:39
(arrival) hh + 1:51 hh + 1:18
(arrival) hh + 1:42
Source: 1Stopping service departure times also reported in [37].
4.3. Comparative Assessment Results
Energy consumption for each of the alternative scenarios was estimated using the
MATLAB
®
/Simulink
©
simulation model described in Section 3, with the adopted fixed
time step
∆t=
0.1
s
, the ode3 (Bogacki-Shampine) solver used for numerical integration,
and implemented hysteresis cycles of
σhyst
LB =
5% and
σhyst
DLC =
20% for LB and DLC,
respectively. Due to its causal nature, the proposed FSM control cannot guarantee the SoC
sustenance. Therefore, each HDEMU and PHDEMU configuration was simulated twice,
with the initial SoC set to
σESS =
50%, and then replaced with the final value obtained in the
first simulation run. This allowed for a fair comparison between different configurations.
The maximum power from the grid
Pmax
pan
was determined from the national railway traction
grid characteristics, namely 1500 V DC voltage and current limitation of 2000 A [
77
]. To
account for a difference in weight due to additional components, optimized vehicle speed
profiles that comply with the timetable, vehicle, and track parameters were pre-calculated
using a bi-section algorithm [
78
] for each vehicle configuration. For the sake of brevity,
detailed simulation results are given in Appendix A(Figures A1–A3), with the main results
summarized in Table 4.
The obtained energy consumption was used afterwards in quantifying the total GHG
emissions and energy costs, using a consumption-based approach [
79
], by multiplying the
amount of fuel or electricity consumed with the corresponding emission factor and unit
cost, respectively. A well-to-wheel approach [
80
] was adopted in deriving the emission
factors to allow for a credible comparison between GHG emissions of different energy
carriers, namely diesel fuel and electricity in our case, and to comply with the international
norms [
81
]. Emission factors and energy prices representative for the Netherlands and the
year 2020 were used to reflect the analyzed case study and to account for the most recent
trends. An emission factor for diesel with 2.6% biofuel content of 3.23 kgCO
2
e/l and for
grey electricity reflecting a national power mix of 0.556 kgCO
2
e/kWh [
82
] were assumed.
Since all national trains on the electrified lines run on the electricity produced from wind
power since 2017 [
83
], an alternative scenario considered the utilization of green electricity
coming from the same source, with the emission factor equal to zero. For the calculation of
Energies 2021,14, 5920 17 of 29
energy costs, an average diesel price of 1.237 EUR/l [
84
] and a railway traction electricity
price of 0.024137 EUR/kWh [77] were adopted.
Table 4.
Energy consumption, GHG emissions, and energy costs for standard, hybrid, and plug-in hybrid vehicle configurations.
Service Configuration ESS Charging Option 1
Energy Consumption GHG
Emissions 2
[kgCO2e]
Energy Costs
[EUR]
Fuel
[L]
Electricity
[kWh]
Stopping
DEMU - - 106.31 - 343.38 131.51
HDEMU LB - 92.01 - 297.19 113.82
DLC - 72.43 - 233.95 89.60
PHDEMU
LB
TSs 75.77 41.01 267.54
(244.74) 94.72
TSs + IS 75.84 47.44 271.34
(244.96) 94.96
DLC
TSs 50.38 63.43 197.99
(162.73) 63.85
TSs + IS 46.04 100.55 204.62
(148.71) 59.38
Express
DEMU - - 140.40 - 453.49 173.67
HDEMU LB - 126.80 - 409.56 156.85
DLC - 87.11 - 281.37 107.76
PHDEMU
LB
TSs 106.61 49.61 371.93
(344.35) 133.07
TSs + IS 118.58 49.84 410.72
(383.01) 147.89
DLC
TSs 61.98 83.48 246.61
(200.20) 78.68
TSs + IS 60.49 104.81 253.66
(195.38) 77.36
Note:
1
TS: Terminal stop, IS: Intermediate stop;
2
The values in brackets were calculated for the scenarios that consider green electricity for
ESS charging.
The estimated GHG emissions (Table 4) showed significant benefits from hybridization,
primarily as a consequence of reduced diesel consumption. Both total GHG emissions
for each alternative scenario and estimated relative emissions reduction compared to the
standard DEMU are shown in Figure 9. Emission reductions compared to a standard
DEMU vehicle range between 9.43% and 56.92%, depending on the type of service and
vehicle/charging configuration. The results indicated the stopping pattern, ESS technology
selection, and the charging facilities location had a considerable influence. In general, a
positive effect from further conversion of a particular hybrid vehicle to its plug-in hybrid
counterpart was observed. The DLC ESS demonstrated better performance compared to
the LB ESS, both in hybrid and plug-in hybrid alternatives, mainly due to its higher power
density and the ability to recuperate total available regenerative braking energy. While the
additional charging location at the intermediate stop resulted in further emission reductions
for the DLC-based ESS, it showed negative effects for the LB ESS. Finally, utilization of
green instead of grey electricity contributed to a further emission reduction of ~6–8% and
~10–16% for PHDEMUs with LB and DLC-based ESS, respectively.
Energies 2021,14, 5920 18 of 29
Figure 9.
Total GHG emissions depending on the propulsion system, charging location, and electricity production configu-
rations; and estimated potential reduction compared to a standard diesel-electric multiple unit.
Similar to the GHG emissions, results on energy costs (Table 4and Figure 10) indicated
higher benefits from DLC-based configurations, with cost reductions of 31.87–55.46%
compared to 9.69–27.97% savings for vehicles with LB ESS, and with plug-in hybrid vehicles
showing better performance than their hybrid counterparts for each scenario. The same
negative effect from an additional charging facility in the intermediate stop for PHDEMU
with LB ESS was observed. In general, energy cost savings resulted predominantly from
the reduction in diesel consumption and a high diesel-to-electricity price ratio.
Figure 10.
Estimated fuel costs for different propulsion system and charging location configuration, and potential reduction
compared to a standard diesel-electric multiple unit.
Energies 2021,14, 5920 19 of 29
5. Discussion
The results of the comparative analysis indicate promising potential benefits from the
hybridization of a DEMU. A further conversion to its plug-in hybrid counterpart allowed
for significantly greater energy savings and the reduction in GHG emissions and costs in all
scenarios. The results also provided insight into numerous interrelated factors influencing
the vehicle performance, which are further elaborated in this section.
The comparison of estimated energy consumption for the stopping and express service
showed considerable impact on the stopping frequency and applied timetable. While
frequent stops in the first case offered a higher amount of braking energy, they also
required more energy for the high-power acceleration phases. Even though these energy
levels were much lower for the considered express service with only one intermediate stop,
the obtained total energy demand was higher in all analyzed scenarios. This was mainly
due to the short running times defined by the timetable, requiring vehicles running at
the maximum speed and preventing them from using the benefits of coasting operation
(see speed diagrams in Appendix A). Energy-efficient timetabling approaches [
85
] could
potentially contribute to revising the existing timetable and reducing the overall energy
demand for train operation.
The selection of ESS technology plays an important role in defining future powertrain
solutions, as identified in the results for HDEMU and PHDEMU vehicles. The DLC-based
ESS demonstrated significantly better performance compared to the LB, mainly as a conse-
quence of the differences in their physical characteristics. Due to the low power density, the
LB ESS could not cover high power fluctuations, both during traction and braking phases,
causing a lack of support to the ICE and significant dissipation of braking energy. On the
other hand, DLC allowed for recuperation of total regenerative braking energy and ICE
operation in the most-efficient region. However, due to its low energy density, and consid-
ering the main criteria in sizing the ESS, it comes at the price of a high total weight, reaching
almost 11 tonnes in this case. This raises the question of the feasibility of such a solution,
requiring further investigation into the physical constraints [
86
], including the available
volumetric space on the vehicle and maximum axle load as defined by EN 15528 [
87
],
which, in our case, was 20 tonnes corresponding to the track category C for the Northern
lines [
77
]. Combining the individual benefits of LBs and DLCs into a hybrid ESS [
88
] could
be an effective approach in overcoming the limitation of a single-technology ESS. However,
this raises significant challenges in terms of the optimal sizing, the complexity of energy
management, and the integration of such a solution into the system.
The identified impact of infrastructure and vehicle characteristics, applied timetable,
and technology selection imply the need for a comprehensive line-by-line and vehicle-
by-vehicle analysis in the case of heterogeneous rolling stock fleets operating on multiple
lines. Additionally, external factors and their variability, such as ambient temperature and
number of passengers, should be considered. Variations in the number of passengers during
the day, and the ambient temperature depending on the season, could potentially have a
significant influence on the auxiliaries power load and the overall energy consumption.
Emissions from train operation not only arise due to the fuel or electricity consumption,
but also result from a number of direct and indirect sources, including vehicle produc-
tion and infrastructure construction [
89
]. Although international standards on emissions
calculation and declaration [
81
] stipulate consideration of only well-to-wheel emissions,
the emissions resulting from the production and disposal/replacement of additional sys-
tem components, including ESSs and stationary charging facilities in our case, should be
identified. For instance, recent studies estimated GHG emissions from battery production
for electric cars to be in the span of 150–200 kgCO
2
e per kWh of battery capacity [
90
],
contributing 31–46% to the total GHG impact from vehicle production [
91
]. Even though
these relative contributions would be significantly lower for railway vehicles due to their
much higher utilization and longer life cycle, further investigation in terms of detailed life
cycle assessment (LCA) [
92
] is needed in order to assess the overall environmental impact
of a particular solution.
Energies 2021,14, 5920 20 of 29
Similar to the GHG emissions, next to the fuel/energy-related costs, other investment
costs will occur when rolling out a new propulsion system concept. These monetary costs
are related to a particular technology and its lifetime, and include initial, maintenance,
and replacement costs. Considering the obtained fuel savings for different solutions, high
vehicle utilization, and foreseen operation for the next 15 years, it can be assumed that
the investment costs would be compensated with the energy savings in a relatively short
period of time. However, a comprehensive life cycle costs (LCC) analysis [
93
] would allow
for identification of overall costs and benefits in this investment decision process.
6. Conclusions
This paper presented a comparative assessment of standard, hybrid, and plug-in
hybrid propulsion system alternatives for regional diesel-electric multiple unit vehicles.
The analysis encompassed the development of a detailed simulation model, which consid-
ered different energy storage technologies, namely lithium-ion battery and double-layer
capacitor, and the real-time energy management strategies based on finite state machines.
Focusing on the regional railway services in the Netherlands, we investigated the hy-
pothetical conversion of a conventional benchmark vehicle found on the network, and
provided a simulation-based assessment in terms of overall energy consumption, related
greenhouse gas emissions, and monetary costs. With the energy storage systems sized
to ensure emission-free and noise-free train operation in terminal stations, the results
indicated higher potential benefits from implementing the double-layer capacitor instead
of the lithium-ion battery, with an identified need for further investigation on its practical
implementation due to the high associated weight. Compared to the standard vehicle,
these benefits are reflected in emissions and cost reduction that exceeded 55% for certain
scenarios. Positive effects from further conversion of a hybrid to a plug-in hybrid system
were observed, with significant impacts of the stopping patterns (type of service), timetable,
and the charging facilities configuration.
The presented research is part of a larger project aiming to identify optimal solutions
for reducing the total well-to-wheel and life cycle emissions on the regional non-electrified
network in the northern Netherlands by analyzing different technical, operational, and
policy measures. In this context, extensions of the present work will consider remaining
rolling stock and lines, as well as testing and validation of the proposed method using
field test data. Further extensions to the current research will include investigation of
hydrogen-powered propulsion systems and upstream processes related to the production
of alternative fuels, such as biofuels and hydrogen, through a detailed well-to-wheel
analysis. The overall impact of vehicle production or refurbishment will be evaluated
through LCA and LCC approaches.
Author Contributions:
Conceptualization, M.K. and M.V.; methodology, M.K.; software, M.K.;
validation, M.K.; writing—original draft preparation, M.K., M.V. and R.M.P.G.; writing—review and
editing, M.K., M.V. and R.M.P.G.; visualization, M.K.; supervision, R.M.P.G. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: All data is contained within the article.
Acknowledgments:
This work is supported by Arriva Personenvervoer Nederland B.V. within the
Ph.D. project “Improving sustainability of regional railway services”. The second author would like
to thank the Sapienza University of Rome for the support from the PhD student mobility program
no. 1879, and the Delft University of Technology for hosting the research visit.
Conflicts of Interest: The authors declare no conflict of interest.
Energies 2021,14, 5920 21 of 29
Nomenclature
Abbreviations
AC Alternating current
BEMU Battery-electric multiple unit
DC Direct current
DEMU Diesel-electric multiple unit
DLC Double-layer capacitor
DMU Diesel multiple unit
DP Dynamic programming
ECMS Equivalent consumption minimization strategy
EM Electric motor
EMS Energy management strategy
ESS Energy storage system
EVSE Electric vehicle supply equipment
FCMU Fuel-cell multiple unit
FSM Finite state machine
G Generator
GHG Greenhouse gasses
GTW Gelenktriebwagen
HDEMU Hybrid diesel-electric multiple unit
HEV Hybrid electric vehicle
HVAC Heating, ventilation, and air conditioning
ICE Internal combustion engine
IS Intermediate stop
LB Lithium-ion battery
LCA Life cycle assessment
LCC Life cycle costs
LTO Li titanium oxide
NMC Nickel manganese cobalt
PHDEMU Plug-in hybrid diesel-electric multiple unit
PHEV Plug-in hybrid electric vehicle
PMP Pontryagin’s minimum principle
RB Rule-based
RU Railway undertaking
SoC State-of-charge
TS Terminal stop
Parameters
amax Maximum acceleration m/s2
amin Maximum deceleration m/s2
CDLC Capacitance of the double-layer capacitor [F]
dwWheel diameter [m]
EDLC Energy content of the double-layer capacitor [Kwh]
Emax
LB Energy content of the battery [Kwh]
Euse
LB Usable energy content of the battery [Kwh]
Fmax
wMaximum (starting) tractive effort at the wheel [N]
gGravitational acceleration m/s2
iag Constant gear ratio [−]
Imax,ch
DLC Allowed maximum charging current for double-layer capacitor [A]
Imax,dch
DLC Allowed maximum discharging current for double-layer capacitor [A]
Icont,ch
LB Allowed maximum continuous charging current of the battery [A]
Icont,dch
LB Allowed maximum continuous discharging current of the battery [A]
Ipeak,ch
LB Allowed peak (pulse) charging current of the battery [A]
Ipeak,dch
LB Allowed peak (pulse) discharging current of the battery [A]
mDLC Weight of the double-layer capacitor [kg]
Energies 2021,14, 5920 22 of 29
mLB Weight of the battery [kg]
mpax Total weight of passengers [kg]
mtare Empty vehicle mass [kg]
mvTotal vehicle mass [kg]
Paux,const Constant auxiliaries power [W]
pcool Cooling power coefficient [−]
Prated
EM Rated power of the electric motor [W]
Popt
GOptimal level of electrical power from the diesel generator [W]
Prated
ICE Rated power of the internal combustion engine [W]
Pmax
pan Maximum power from the charging grid [W]
QLB Nominal capacity of the battery [As]
Rdch Self-discharging resistance of the double-layer capacitor [Ω]
RDLC Internal resistance of the double-layer capacitor [Ω]
Rch
LB Battery internal resistance during charging [Ω]
Rdch
LB Battery internal resistance during discharging [Ω]
r0Davis equation coefficient (constant term) [N]
r1Davis equation coefficient (linear term) [N/(M/S)]
r2Davis equation coefficient (quadratic term) [N/(M/S)2]
scr Line-specific critical track section [m]
sts Position of the terminal stop [m]
tch
peak Time limit for the allowed battery pulse charging current [s]
tdch
peak Time limit for the allowed battery pulse discharging current [s]
Umax
DLC Maximum voltage of the double-layer capacitor [V]
Umin
DLC Minimum voltage of the double-layer capacitor [V]
Umax
LB Maximum battery voltage [V]
Umin
LB Minimum battery voltage [V]
vmax Maximum velocity [m/s]
∆tSimulation (integration) time step [s]
ηag Constant efficiency of the gearbox [−]
λRotating mass factor [−]
ρFuel density [kg/L]
σhyst
ESS Energy storage system hysteresis cycle for the state-of-charge [−]
σlim
ESS State-of-charge limit for the energy storage system [−]
σmin
ESS Minimum state-of-charge for the energy storage system [−]
σmax
LB Maximum battery state-of-charge [−]
σmin
LB Minimum battery state-of-charge [−]
Dynamic variables
aVehicle acceleration [m/s2]
bel Binary indicator for the track electrification status [−]
BICE Total fuel consumption [L]
Epan Total electrical energy consumption [Ws]
Flag Binary indicator for the state-of-charge hysteresis cycle [−]
FwTractive/braking effort at the wheel [N]
IDLC Current of the double-layer capacitor [A]
Imax
DLC Maximum current of the double-layer capacitor [A]
Imin
DLC Minimum current of the double-layer capacitor [A]
ILB Battery current [A]
Imax
LB Maximum battery current [A]
Imax,ch
LB Maximum battery charging current defined by the manufacturer [A]
Imax,dch
LB Maximum battery discharging current defined by the manufacturer [A]
Imin
LB Minimum battery current [A]
Energies 2021,14, 5920 23 of 29
Paux Total auxiliaries power [W]
Pdem Total requested power for traction and auxiliaries [W]
PDLC Power of the double-layer capacitor [W]
Pmax
DLC Maximum power of the double-layer capacitor [W]
Pmin
DLC Minimum power of the double-layer capacitor [W]
PEM Electric power of the electric motor [W]
Pmax
ESS Maximum power of the energy storage system [W]
Pmin
ESS Minimum power of the energy storage system [W]
PGElectrical output power of the generator [W]
PICE Mechanical output power of the internal combustion engine [W]
PLB Power of the battery [W]
Pmax
LB Maximum power of the battery [W]
Pmin
LB Minimum power of the battery [W]
Ppan Electric power received via pantograph [W]
RcCurve resistances [N]
RgGrade resistances [N]
RLB Battery internal resistance [Ω]
RvVehicle resistances [N]
sDistance traveled [m]
tTime [s]
tch
cnt Battery pulse charging time counter [s]
tdch
cnt Battery pulse discharging time counter [s]
TEM Torque at the mechanical output of the electric motor [Nm]
TwTorque at the wheel [Nm]
UDLC Terminal voltage of the double-layer capacitor [V]
ULB Battery terminal voltage [V]
UOC Battery open circuit voltage [V]
vVehicle velocity [m/s]
γAngle of the slope [rad]
ηEM Efficiency of the electric motor [−]
ηGEfficiency of the generator [−]
σDLC State-of-charge of the double-layer capacitor [−]
σLB Battery state-of-charge [−]
φCurve radius [m]
ψSpecific fuel consumption [Kg/Ws]
ωEM Rotational speed of the electric motor [rad/s]
ωICE Rotational speed of the internal combustion engine [rad/s]
ωwRotational speed of the wheel [rad/s]
Energies 2021,14, 5920 24 of 29
Appendix A
Figure A1. Simulation results for a standard DEMU vehicle on (a) stopping service and (b) express service.
Figure A2.
Simulation results for a HDEMU vehicle on stopping and express service, respectively: (
a
,
b
) with LB ESS; and
(c,d) with DLC ESS.
Energies 2021,14, 5920 25 of 29
Figure A3.
Simulation results for a PHDEMU vehicle on stopping and express service, respectively: (
a
,
b
) LB ESS with
charging at TSs; (
c
,
d
) LB ESS with charging at TSs and IS; (
e
,
f
) DLC ESS with charging at TSs; and (
g
,
h
) DLC ESS with
charging at TSs and IS.
Energies 2021,14, 5920 26 of 29
References
1.
UN. Kyoto Protocol to the United Nations Framework Convention on Climate Change, Kyoto. 1998. Available online: https:
//unfccc.int/resource/docs/convkp/kpeng.pdf (accessed on 22 January 2021).
2.
UN. Paris Agreement, Paris. 2015. Available online: https://unfccc.int/sites/default/files/english_paris_agreement.pdf
(accessed on 22 January 2021).
3.
Di Domenico, G.C.; Dick, C.T. Methods of Analyzing and Comparing Energy Efficiency of Passenger Rail Systems. Transp. Res.
Rec. 2015,2475, 54–62. [CrossRef]
4.
UIC; CER. Moving Towards Sustainable Mobility: A Strategy for 2030 and Beyond for the European Railway Sector, Paris. 2012.
Available online: http://www.cer.be/sites/default/files/publication/CER-UIC_Sustainable_Mobility_Strategy_-_SUMMARY.pdf
(accessed on 28 January 2021).
5.
RailwayTechnology. Bombardier Talent 3 Battery Train. Available online: https://www.railway-technology.com/projects/
bombardier-talent-3-battery-train/ (accessed on 5 April 2021).
6.
Siemens. Desiro ML ÖBB Cityjet eco for ÖBB Personenverkehr AG. Available online: https://assets.new.siemens.com/siemens/
assets/api/uuid:b26911b1-2b0e-48b4-b593-81adbf032d75/db-desiro-ml-oebb-cityjet-eco-e.pdf (accessed on 5 April 2021).
7.
Siemens. Mireo Plus B Ortenau for SFBW, Baden-Württemberg, Germany. Available online: https://assets.new.siemens.com/
siemens/assets/api/uuid:636c61e0-bd2d-4f97-a2c7-78e690792a44/mors-b10022-00dbmireoplusortenauenus-72_original.pdf (ac-
cessed on 5 April 2021).
8.
RailTech. Stadler Gets First Order for Akku Battery-Powered Trains. Available online: https://www.railtech.com/rolling-stock/
2019/06/20/stadler-gets-first-order-for-akku-battery-powered-trains/?gdpr=accept (accessed on 5 April 2021).
9.
Alstom. Alstom’s Hydrogen Train Enters Regular Passenger Service in Austria. Available online: https://www.alstom.com/
press-releases-news/2020/9/alstoms-hydrogen-train-enters-regular-passenger-service-austria (accessed on 5 April 2021).
10.
FuelCellWorks. Siemens Mireo Plus H Fuel Cell Hydrogen Train—The Middle Distance Champion. Available online: https:
//fuelcellsworks.com/news/siemens-mireo-plus-h-fuel-cell-hydrogen-train-the-middle-distance-champion/ (accessed on 5
April 2021).
11.
IRJ. Zillertalbahn Hydrogen Train Design Revealed. Available online: https://www.railjournal.com/fleet/zillertalbahn-
hydrogen-train-design-revealed/ (accessed on 5 April 2021).
12.
Bai, S.; Liu, C. Overview of energy harvesting and emission reduction technologies in hybrid electric vehicles. Renew. Sustain.
Energy Rev. 2021,147, 111188. [CrossRef]
13. Fuhs, A. Hybrid Vehicles, 1st ed.; CRC Press: Boca Raton, FL, USA, 2008. [CrossRef]
14.
Doucette, T.T.; McCulloch, M.D. Modeling the prospects of plug-in hybrid electric vehicles to reduce CO
2
emissions. Appl. Energy
2011,88, 2315–2323. [CrossRef]
15.
Orecchini, F.; Santiangeli, A. Automakers’ powertrain options for hybrid and electric vehicles. In Electric and Hybrid Vehicles;
Elsevier: Amsterdam, The Netherlands, 2010; pp. 579–636.
16.
Orecchini, F.; Santiangeli, A.; Dell’Era, A. EVs and HEVs using lithium-ion batteries. In Lithium-Ion Batteries; Elsevier: Amsterdam,
The Netherlands, 2014; pp. 205–248.
17.
Williamson, S.S. Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles; Springer: New York, NY, USA, 2013.
18.
EC. Public Report: Ultra Low Emission Vehicle—Transport Using Advanced Propulsion 2 (ULEV-TAP II); EC: Erlangen, Germany, 2005.
19.
Marsilla, M. CleanER-D Deliverable 7.5.4: Future Scenarios and Recommendations for Implementation of Hybrid Solutions. 2013.
Available online: http://www.cleaner-d.eu/deliverables.htm (accessed on 9 February 2021).
20.
Hillmansen, S.; Roberts, C.; Mcgordon, A.; Jennings, P. Concept Validation for Hybrid Trains. Final Report DfTRG/0078/2007,
Birmingham. 2008. Available online: https://www.birmingham.ac.uk/Documents/college-eps/railway/Hybirdconceptfinal5.pdf
(accessed on 14 February 2021).
21.
Hillmansen, S.; Roberts, C.; Mcgordon, A.; Jennings, P. DMU Hybrid Concept Evaluation. Final Report DfTRG/0078/2007,
Birmingham. 2009. Available online: https://www.birmingham.ac.uk/Documents/college-eps/railway/HybridRailReportDMUV1
.pdf (accessed on 14 February 2021).
22.
Railway Gazette International. Hybrid Drive Demonstrates 15% Fuel Saving. Available online: https://www.railwaygazette.
com/news/traction-rolling-stock/single-view/view/hybrid-drive-demonstrates-15-fuel-saving.html (accessed on 17 May 2021).
23.
Fujii, T.; Teraya, N.; Osawa, M. Special edition paper Development of an NE train. JR EAST Tech. Rev.
2004
,4, 62–70. Available
online: https://www.jreast.co.jp/e/development/tech/pdf_4/Tech-no.4-62-70.pdf (accessed on 2 April 2021).
24.
Shiraki, N.; Satou, H.; Arai, S. A hybrid system for diesel railcar series Ki-Ha E200. In Proceedings of the 2010 International
Power Electronics Conference—ECCE ASIA, Sapporo, Japan, 21–24 June 2010; pp. 2853–2858. [CrossRef]
25.
Engel, B.; Soefker, C. The Innovative Traction System with the Flywheel of the LIREXTM. In Proceedings of the World Congress
on Railway Research, Cologne, Germany, 25–29 November 2001.
26.
Research and Technology Centre of Deutsche Bahn AG. Applications for energy storage flywheels in vehicles of Deutsche Bahn
AG. In Proceedings of the World Congress on Railway Research, Cologne, Germany, 25–29 November 2001.
27.
INSIDEEVs. Meet Audi’s Plug-In Hybrid Locomotive. Available online: https://insideevs.com/news/328130/meet-audis-plug-
in-hybrid-locomotive/ (accessed on 1 June 2021).
28.
Alstom. Alstom to Supply Two H3 Hybrid Shunting Locomotives for Audi. Available online: https://www.alstom.com/press-
releases-news/2015/2/alstom-to-supply-two-h3-hybrid-shunting-locomotives-for-audi (accessed on 1 June 2021).
Energies 2021,14, 5920 27 of 29
29.
Alstom. Alstom to Deliver Two Prima H3 Shunting Locomotives to Metrans. Available online: https://www.alstom.com/press-
releases-news/2016/2/alstom-to-deliver-two-prima-h3-shunting-locomotives-to-metrans (accessed on 1 June 2021).
30. Railcolor News. Romania’s First Hybrid Locomotive Design Plugs-in to Sustainability. Available online: https://railcolornews.
com/2018/02/28/ro-romanias-first-hybrid-locomotive-design-plugs-in-to-sustainability/ (accessed on 1 June 2021).
31.
Masatsuki, I. Development of the Battery Charging System for the New Hybrid Train that Combines Feeder Line and the Storage
Battery. In Proceedings of the 2010 International Power Electronics Conference—ECCE ASIA, Sapporo, Japan, 21–24 June 2010;
pp. 3128–3135. [CrossRef]
32.
Masatsuki, I. Development of Catenary and Battery powered hybrid railcar system. In Proceedings of the 9th World Congress on
Railwau Reearch, Lille, France, 22–26 May 2011.
33.
Hirose, H.; Yoshida, K.; Shibanuma, K. Development of catenary and storage battery hybrid train system. In Proceedings of the
2012 Electrical Systems for Aircraft, Railway and Ship Propulsion, Bologna, Italy, 16–18 October 2012; pp. 1–4. [CrossRef]
34.
Kono, Y.; Shiraki, N.; Yokoyama, H.; Furuta, R. Catenary and storage battery hybrid system for electric railcar series EV-E301. In
Proceedings of the 2014 International Power Electronics Conference (IPEC-Hiroshima 2014—ECCE ASIA), Hiroshima, Japan,
18–21 May 2014; pp. 2120–2125. [CrossRef]
35.
Shiraki, N.; Tokito, K.; Yokozutsumi, R. Propulsion system for catenary and storage battery hybrid electric railcar series EV-E301.
In Proceedings of the 2015 International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road
Vehicles (ESARS), Aachen, Germany, 3–5 March 2015; pp. 1–7. [CrossRef]
36.
Mwambeleko, J.J.; Kulworawanichpong, T. Battery electric multiple units to replace diesel commuter trains serving short and idle
routes. J. Energy Storage 2017,11, 7–15. [CrossRef]
37.
Kapetanovi´c, M.; Núñez, A.; van Oort, N.; Goverde, R.M.P. Reducing fuel consumption and related emissions through optimal
sizing of energy storage systems for diesel-electric trains. Appl. Energy 2021,294, 117018. [CrossRef]
38.
Sorrentino, M.; Serge Agbli, K.; Hissel, D.; Chauvet, F.; Letrouve, T. Application of dynamic programming to optimal energy
management of grid-independent hybrid railcars. Proc. Inst. Mech. Eng. Part F J. Rail Rapid Transit
2021
,235, 236–247. [CrossRef]
39.
Ogawa, T.; Yoshihara, H.; Wakao, S.; Kondo, K.; Kondo, M. Energy consumption analysis of FC-EDLC hybrid railway vehicle by
dynamic programming. In Proceedings of the European Conference on Power Electronics and Applications, Aalborg, Denamrk,
2–5 September 2007; pp. 1–8. [CrossRef]
40.
Leska, M.; Aschemann, H. Fuel-optimal combined driving strategy and energy management for a parallel hybrid electric railway
vehicle. In Proceedings of the 20th International Conference on Methods and Models in Automation and Robotics, MMAR 2015,
Miedzyzdroje, Poland, 24–27 August 2015; pp. 1127–1132. [CrossRef]
41.
Peng, H.; Li, J.; Thul, A.; Deng, K.; Ünlübayir, C.; Lowenstein, L.; Hameyer, K. A scalable, causal, adaptive rule-based energy
management for fuel cell hybrid railway vehicles learned from results of dynamic programming. eTransportation
2020
,4, 100057.
[CrossRef]
42.
Leska, M.; Prabel, R.; Aschemann, H.; Rauh, A. Optimal Operating Strategy for Hybrid Railway Vehicles based on a Sensitivity
Analysis. IFAC Proc. Vol. 2014,47, 942–947. [CrossRef]
43.
Leska, M.; Aschemann, H.; Melzer, M.; Meinert, M. Comparative Calculation of the Fuel-Optimal Operating Strategy for Diesel
Hybrid Railway Vehicles. Int. J. Appl. Math. Comput. Sci. 2017,27, 323–336. [CrossRef]
44.
Torreglosa, J.P.; Jurado, F.; García, P.; Fernández, L.M. Hybrid fuel cell and battery tramway control based on an equivalent
consumption minimization strategy. Control. Eng. Pract. 2011,19, 1182–1194. [CrossRef]
45.
Peng, H.; Li, J.; Löwenstein, L.; Hameyer, K. A scalable, causal, adaptive energy management strategy based on optimal control
theory for a fuel cell hybrid railway vehicle. Appl. Energy 2020,267, 114987. [CrossRef]
46.
Liu, J.; Wu, X.; Li, H.; Qi, L. An optimal method of the energy consumption for fuel cell hybrid tram. Int. J. Hydrogen Energy
2020
,
45, 20304–20311. [CrossRef]
47.
Zhang, W.; Li, J.; Xu, L.; Ouyang, M. Optimization for a fuel cell/battery/capacity tram with equivalent consumption minimiza-
tion strategy. Energy Convers. Manag. 2017,134, 59–69. [CrossRef]
48.
Li, H.; Ravey, A.; N’Diaye, A.; Djerdir, A. Online adaptive equivalent consumption minimization strategy for fuel cell hybrid
electric vehicle considering power sources degradation. Energy Convers. Manag. 2019,192, 133–149. [CrossRef]
49.
Lanneluc, C.; Pouget, J.; Poline, M.; Chauvet, F.; Gerbaud, L. Optimal Energy Management of a Hybrid Train: Focus on
Saving Braking Energy. In Proceedings of the 2017 IEEE Vehicle Power and Propulsion Conference (VPPC), Belfort, France,
11–14 December 2017; pp. 1–6. [CrossRef]
50.
Dittus, H.; Hülsebusch, D.; Ungethüm, J. Reducing DMU fuel consumption by means of hybrid energy storage. Eur. Transp. Res.
Rev. 2011,3, 149–159. [CrossRef]
51.
García-Garre, A.; Gabaldón, A. Analysis, Evaluation and Simulation of Railway Diesel-Electric and Hybrid Units as Distributed
Energy Resources. Appl. Sci. 2019,9, 3605. [CrossRef]
52.
Spiryagin, M.; Cole, C.; Sun, Y.Q.; McClanachan, M.; Spiryagin, V.; McSweeney, T. Design and Simulation of Rail Vehicles, 1st ed.;
Taylor & Francis Group, LLC: Boca Raton, FL, USA, 2014.
53. Vazquez, S.; Lukic, S.M.; Galvan, E.; Franquelo, L.G.; Carrasco, J.M. Energy storage systems for transport and grid applications.
IEEE Trans. Ind. Electron. 2010,57, 3881–3895. [CrossRef]
54.
Ghaviha, N.; Campillo, J.; Bohlin, M.; Dahlquist, E. Review of Application of Energy Storage Devices in Railway Transportation.
Energy Procedia 2017,105, 4561–4568. [CrossRef]
Energies 2021,14, 5920 28 of 29
55.
Meinert, M.; Prenleloup, P.; Schmid, S.; Palacin, R. Energy storage technologies and hybrid architectures for specific diesel-driven
rail duty cycles: Design and system integration aspects. Appl. Energy 2015,157, 619–629. [CrossRef]
56.
Xu, L.; Mueller, C.D.; Li, J.; Ouyang, M.; Hu, Z. Multi-objective component sizing based on optimal energy management strategy
of fuel cell electric vehicles. Appl. Energy 2015,157, 664–674. [CrossRef]
57.
Cipek, M.; Pavkovi´c, D.; Kljai´c, Z.; Mlinari´c, T.J. Assessment of battery-hybrid diesel-electric locomotive fuel savings and emission
reduction potentials based on a realistic mountainous rail route. Energy 2019,173, 1154–1171. [CrossRef]
58.
Pröhl, L. OPEUS Deliverable DO2.1—OPEUS Simulation Methodology, EU-Project OPEUS (S2R-OC-CCA-02-2015); University of
Rostock: Rostock, Germany, 2017.
59.
Pröhl, L. OPEUS Deliverable DO2.2—OPEUS Simulation Tool, EU-Project OPEUS (S2R-OC-CCA-02-2015); University of Rostock:
Rostock, Germany, 2017.
60. Davis, W.J. The tractive resistance of electric locomotives and cars. Gen. Electr. Rev. 1926,29, 685–707.
61.
Brünger, O.; Dahlhaus, E. Running time estimation. In Railway Timetabling & Operations; Hansen, I.A., Pachl, J., Eds.; Eurailpress:
Hamburg, Germany, 2014; pp. 65–89.
62.
Huerlimann, D.; Nash, A. Opentrack-Simulation of Railway Networks, User Manual Version 1.3.; ETH Zurich, Institute for Transporta-
tion Planning and Systems: Zurich, Switzerland, 2003.
63.
Leska, M.; Gruning, T.; Aschemann, H.; Rauh, A. Optimization of the longitudinal dynamics of parallel hybrid railway vehicles.
In Proceedings of the 2012 IEEE International Conference on Control Applications, Dubrovnik, Croatia, 3–5 October 2012; pp.
202–207. [CrossRef]
64.
Prohl, L.; Aschemann, H. Grey Wolf optimisation of an operating strategy for energy storage systems in electrically driven railway
vehicles. In Proceedings of the 18th European Control Conference ECC, Naples, Italy, 25–28 June 2019; pp. 1908–1913. [CrossRef]
65.
Schmid, S.; Ebrahimi, K.; Pezouvanis, A.; Commerell, W. Model-based comparison of hybrid propulsion systems for railway
diesel multiple units. Int. J. Rail Transp. 2017,6, 16–37. [CrossRef]
66.
Han, Y.; Meng, X.; Zhang, G.; Li, Q.; Chen, W. An energy management system based on hierarchical control and state machine for
the PEMFC-battery hybrid tramway. In Proceedings of the IEEE Transportation Electrification Conference and Expo, Asia-Pacific
(ITEC Asia-Pacific), Harbin, China, 7–10 August 2017; pp. 1–5. [CrossRef]
67.
Yan, Y.; Huang, W.; Liu, J.; Li, Q.; Chen, W. The Control Strategy of Fuel Cell Hybrid Tram Based on State Machine Control. In
Proceedings of the IEEE Sustainable Power and Energy Conference (iSPEC), Beijing, China, 21–23 November 2019; pp. 699–703.
[CrossRef]
68.
Li, Q.; Yang, H.; Han, Y.; Li, M.; Chen, W. A state machine strategy based on droop control for an energy management system of
PEMFC-battery-supercapacitor hybrid tramway. Int. J. Hydrogen Energy 2016,41, 16148–16159. [CrossRef]
69.
Paukert, H. CleanER-D Deliverable 7.2.1: Detailed Specification: Parameters Definition. 2011. Available online: http://www.
cleaner-d.eu/deliverables.htm (accessed on 9 January 2021).
70.
Pourabdollah, M.; Murgovski, N.; Grauers, A.; Egardt, B. Optimal Sizing of a Parallel PHEV Powertrain. IEEE Trans. Veh. Technol.
2013,62, 2469–2480. [CrossRef]
71.
Giro Batalla, R.; Feenstra, M. Energy Consumption in GTW DMU Trains—ECO Driving; Project Statement; Arriva Nederland:
Sunderland, UK, 2012.
72.
Stadler Bussnang AG. GTW DMU-2 2/6 and GTW 2/8 Low-Floor for Arriva, Netherlands. Available online: https://www.
stadlerrail.com/media/pdf/garr1008e.pdf (accessed on 15 January 2021).
73.
Takami, N.; Inagaki, H.; Tatebayashi, Y.; Saruwatari, H.; Honda, K.; Egusa, S. High-power and long-life lithium-ion batteries using
lithium titanium oxide anode for automotive and stationary power applications. J. Power Sources 2013,244, 469–475. [CrossRef]
74.
Toshiba. SCiBTM Rechargeable Battery. Available online: https://www.global.toshiba/ww/products-solutions/battery/scib.
html (accessed on 16 February 2021).
75.
SAFT; UNEW. OPEUS Deliverable D6.1—Innovative Technologies Outlook Update. EUproject OPEUS (S2R-OC-CCA-02-2015).
2017. Available online: https://projects.shift2rail.org/s2r_ip4_n.aspx?p=OPEUS (accessed on 16 February 2021).
76.
Maxwell. 125 Volt Transportation Module. 2021. Available online: https://www.maxwell.com/products/ultracapacitors/125v-
tran-modules# (accessed on 16 February 2021).
77.
ProRail. Network Statement 2020. Available online: https://www.prorail.nl/siteassets/homepage/samenwerken/vervoerders/
documenten/network-statement-2020-prorail-version-1.6.pdf (accessed on 21 March 2021).
78.
Leska, M.; Gruning, T.; Aschemann, H.; Rauh, A. Optimal trajectory planning for standard and hybrid railway vehicles with a
hydro-mechanic transmission. In Proceedings of the European Control Conference ECC, Zurich, Switzerland, 17–19 July 2013; pp.
4550–4555. [CrossRef]
79.
Kirschstein, T.; Meisel, F. GHG-emission models for assessing the eco-friendliness of road and rail freight transports. Transp. Res.
Part B Methodol. 2015,73, 13–33. [CrossRef]
80.
Hoffrichter, A.; Miller, A.R.; Hillmansen, S.; Roberts, C. Well-to-wheel analysis for electric, diesel and hydrogen traction for
railways. Transp. Res. Part D Transp. Environ. 2012,17, 28–34. [CrossRef]
81.
CEN. Standard EN 16258: Methodology for Calculation and Declaration of Energy Consumption and GHG Emissions of Transport
Services (Freight and Passengers). Available online: https://standards.cen.eu/dyn/www/f?p=204:110:0::::FSP_PROJECT:
32935&cs=12646506D5D79DADCB74CA2C32A2DAA45 (accessed on 3 March 2021).
Energies 2021,14, 5920 29 of 29
82.
CO2emissiefactoren. 2020 List of CO2 Emission Factors [in Dutch]. Available online: https://www.co2emissiefactoren.nl/
wijzigingen-overzicht/ (accessed on 9 May 2021).
83.
EcoWatch. Dutch Trains Are World’s First to Run on 100% Wind Power. Available online: https://www.ecowatch.com/dutch-
trains-wind-energy-2187547588.html (accessed on 29 March 2021).
84.
CBS. Pump Prices for Motor Fuels; Location Gas Station, Fuel Type [in Dutch]. Available online: https://opendata.cbs.nl/
statline/?ts=1585295852166#/CBS/nl/dataset/81567NED/table (accessed on 1 May 2021).
85.
Scheepmaker, G.M.; Goverde, R.M.P.; Kroon, L.G. Review of energy-efficient train control and timetabling. Eur. J. Oper. Res.
2017
,
257, 355–376. [CrossRef]
86.
Hoffrichter, A.; Roberts, C.; Hillmansen, S. Conceptual propulsion system design for a hydrogen-powered regional train. IET
Electr. Syst. Transp. 2016,6, 56–66. [CrossRef]
87.
CEN. EN 15528: Railway Applications—Line Categories for Managing the Interface Between Load Limits of Vehicles and
Infrastructure. 2015. Available online: https://www.en-standard.eu/din-en-15528-railway-applications-line-categories-for-
managing-the-interface-between-load-limits-of-vehicles-and-infrastructure/ (accessed on 21 February 2021).
88.
Peng, F.; Zhao, Y.; Chen, T.; Zhang, X.; Chen, W.; Zhou, D.; Li, Q.F. Development of robust suboptimal real-time power sharing
strategy for modern fuel cell based hybrid tramways considering operational uncertainties and performance degradation. Appl.
Energy 2018,226, 503–521. [CrossRef]
89. Esters, T.; Marinov, M. An analysis of the methods used to calculate the emissions of rolling stock in the UK. Transp. Res. Part D
Transp. Environ. 2014,33, 1–16. [CrossRef]
90.
Romare, M.; Dahllöf, L. The Life Cycle Energy Consumption and Greenhouse Gas Emissions from Lithium-Ion Batteries.
2017. Available online: http://www.energimyndigheten.se/globalassets/forskning--innovation/transporter/c243-the-life-cycle-
energy-consumption-and-co2-emissions-from-lithium-ion-batteries-.pdf (accessed on 20 June 2021).
91.
Ellingsen, L.A.W.; Singh, B.; Strømman, A.H. The size and range effect: Lifecycle greenhouse gas emissions of electric vehicles.
Environ. Res. Lett. 2016,11, 054010. [CrossRef]
92.
Jones, H.; Moura, F.; Domingos, T. Life cycle assessment of high-speed rail: A case study in Portugal. Int. J. Life Cycle Assess.
2017
,
22, 410–422. [CrossRef]
93.
García Márquez, F.P.; Lewis, R.W.; Tobias, A.M.; Roberts, C. Life cycle costs for railway condition monitoring. Transp. Res. Part E
Logist. Transp. Rev. 2008,44, 1175–1187. [CrossRef]
Available via license: CC BY 4.0
Content may be subject to copyright.
