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energies
Article
Influence of Powertrain Topology and Electric Machine Design
on Efficiency of Battery Electric Trucks—A Simulative
Case-Study
Sebastian Wolff * , Svenja Kalt , Manuel Bstieler and Markus Lienkamp
Citation: Wolff, S.; Kalt, S.; Bstieler,
M.; Lienkamp, M. Influence of
Powertrain Topology and Electric
Machine Design on Efficiency of
Battery Electric Trucks—A Simulative
Case-Study. Energies 2021,14, 328.
https://doi.org/10.3390/en14020328
Received: 21 December 2020
Accepted: 7 January 2021
Published: 8 January 2021
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tribution (CC BY) license (https://
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4.0/).
Institute of Automotive Technology, Technical University of Munich, 85748 Garching b. München, Germany;
kalt@ftm.mw.tum.de (S.K.); manuel.bstieler@tum.de (M.B.); lienkamp@ftm.mw.tum.de (M.L.)
*Correspondence: sebastian.wolff@tum.de
Abstract:
The advancement of electric mobility as a measure to comply with international climate
targets and sustain renewable resources in the future has led to an electrification of the mobility
sector in recent years. This trend has not been spared in the logistics and commercial vehicle
sector. Emerging electric powertrain concepts for long-haul vehicles have since been developed and
adapted to different use cases and axle concepts. In this paper, the authors show the influence of the
powertrain topology and the associated design of the electric machine on the efficiency and energy
consumption of commercial vehicles. For this, existing series or prototype long-haul axle topologies
are analyzed regarding their efficiency and operating points within four driving cycles. Additionally,
a sensitivity analysis on the influence of the total gearbox ratio tests the assumed designs. We find that
single-machine topologies offer efficiency advantages over multiple-machine topologies. However,
this study highlights a joint consideration of application-specific machine design and topology to
realize the full technological potential.
Keywords:
battery electric; heavy-duty trucks; electricmachines; powertrain design; topology;efficiency
1. Introduction
With the Paris Agreement, not only the European Union (EU) but 189 countries in total,
pledged to reduce greenhouse gases and limit global warming to 1.5–2
◦
C [
1
]. Therefore,
the EU set goals to reduce total greenhouse gas emissions by 40% until 2030 and by at least
80% by 2050 compared to the levels of 1990.
Transportation, and road transportation in particular, is the backbone of our economy.
However, transportation also accounts for 27% of European greenhouse emissions, of
which road transport comprises 72% [
2
]. Road transportation consequently provides large
leverage to achieve the ambitious climate goals of the Paris Agreement. Consequently,
the EU introduced CO
2
limits for medium- and heavy-duty vehicles in 2019 to reduce
emissions by 15% in 2025 and 30% in 2030 compared to 2019 levels [3].
Rising environmental responsibility is one among other push factors towards elec-
tromobility. Rising energy as well as the dependency of fossil oil and gas promote the
transformation of today’s transportation systems [
4
,
5
]. However, electromobility refers
to different vehicle concepts such as hybrid (HEV), battery electric (BEV), and fuel-cell-
electric vehicles (FCEV), whereby the latter two enable locally emission-free driving. In
addition, the electric powertrains offer superior start-up torque and efficiency compared to
conventional internal combustion engine vehicles (ICEV) [4–6].
Regulations and technical development have created the potential for the application
of battery electric trucks in long-distance transportation. Currently, different concepts for
the electrification of heavy-duty trucks exist. However, it is not yet decided which vehicle
concept provides an optimal solution fulfilling long-haul requirements [
7
]. In this paper,
we show the influence of the topology choice and the associated design of the electric
Energies 2021,14, 328. https://doi.org/10.3390/en14020328 https://www.mdpi.com/journal/energies
Energies 2021,14, 328 2 of 15
machine (EM) on the efficiency and energy consumption of commercial vehicles. We use
simulation tools to obtain the machine designs and efficiencies for five powertrain concepts
in series or prototype state.
The essential components of a powertrain are the electric machine, the inverter, and
the battery. However, the optimization of the electric machine efficiency promises improve-
ments because its efficiency is currently lower than the remaining components.
In particular, the machine efficiency depends on its loads. Thus the application in a
wide range of operating points requires further optimization of the machine design (power,
weight, and size) to maximize the efficiency [
8
]. Therefore, this study neglects the potential
of battery or additional component optimization and focuses on the effect of the powertrain
topology combined with electric machine design.
1.1. Battery Electric Heavy-Duty Trucks
Long-haul trucks have three major requirements: high efficiency, high payload and
long range. Due to current developments in the automotive industry, commercial vehicles
also benefit from advances in battery and powertrain technology. This enables battery
electric vehicles to potentially fulfill the requirements of long-haul transportation [
4
,
6
,
9
,
10
].
Due to their high mileage, efficiency is a major requirement for trucks. The limitation
of range and the reduction of payload, however, require BEV trucks to rate efficiency
even higher.
Long-haul vehicles typically have one (4
×
2) or two (6
×
4) driven axles, although the
majority of European vehicles are single-axle [
11
,
12
]. Current internal combustion engine
(ICE)-powered vehicles have transmissions of up to 16 gears and a rear axle differential
(final drive) to achieve the best possible utilization of the tractive force [12,13].
Battery electric vehicles offer new possibilities to design heavy-duty vehicle concepts
and powertrains. Because electric machines are smaller than equivalent ICE, their position
in the vehicle package is more flexible. Additionally, the EM torque characteristics allow
for reduced gears or even a fixed gear ratio.
Verbruggen et al. [
14
] categorize electric powertrain topologies by three design choices:
central or distributed drive, fixed or multiple gears, and the number of electric machines.
However, for a precise topology description, a further differentiation must be made regard-
ing the mounting of the electrical machine. If the machine is mounted on the vehicle chassis,
and thus part of the sprung mass, we refer to the topology as (dual) central. If the machine
is integrated into the axle, and thus part of the unsprung mass, the topology is marked as
eAxle. This definition is independent of the number of machines because eAxle topologies
with one (central) or two (dual central) electric machines exist. Because distributed or hub
motors are unsprung mass by design, they are also referred to as eAxles [
15
]. Consequently,
our case study defines six topologies for the powered axles (Figure 1). Although technically
possible, to our knowledge, no multiple gear eAxle exists, and thus this study focuses on
the remaining five.
To extend the knowledge from Verbruggen et al. generic approach, we evaluate the
topologies from the perspective of currently available or announced vehicles or driven
axles. On the one hand, this shows current development trends in the industry and, on the
other hand, provides insights on future optimization potential.
All topologies could also be utilized in 6
×
4 vehicles. However, in this paper, we
focus on European trucks and thus 4
×
2 configurations. Due to the different number of
EMs, it is technically possible to use differently sized motors and consequently influence
the operating points of the EM to optimize vehicle performance such as the Tesla Model
3 [
16
]. Furthermore, this allows for lateral torque distribution found to be beneficial for
energy consumption [
17
] without sacrificing safety and stability with the correct control
strategies [
18
]. However, to the authors’ knowledge, there is currently no heavy-duty
vehicle concept utilizing this topology.
Energies 2021,14, 328 3 of 15
Figure 1. Topologies for electric drive axles for heavy-duty trucks.
Verbruggen at al. [
14
] performed a discrete topology optimization to assess the total
cost of ownership as well as energy consumption of feasible topologies. Expanding on
their work, we perform a simulative case study with existing vehicles. In addition, we use
detailed models to calculate electric machine maps. In contrast to Verbruggen et al., we
perform an electric machine design process for each studied configuration. Although this
increases the number of input parameters, we assume that we increase overall accuracy
with our approach. Furthermore, we test the results’ robustness with multiple driving
cycles. To test the sensitivity regarding the gearbox design and the associated assumptions,
we perform a variation of the overall gearbox ratio and its effect on efficiency and driving
performance. In the following, we describe the simulative analysis of the presented
topologies and compare them regarding their efficiency.
1.2. Electric Machine Efficiency
As an electro-mechanical energy converter, an electric machine is subject to losses [
19
].
Kremser differentiates three types of losses: (1) load-dependent (e.g., copper), (2) load-
independent (e.g., iron or mechanical) losses [
20
], and (3) additional losses, which can be
categorized either as load-dependent or independent [21].
Every machine has operating points with higher and lower efficiency that depend
on numerous machine parameters. Determined by the machine type, this choice strongly
correlates with the intended application. For example, applications close to the rated speed
and higher torque perform better with permanent-magnet synchronous machines (PMSM),
whereas induction machines (IM) have better efficiency at higher speeds [16,22].
Due to the required high start-up torque but low-speed requirements in heavy-duty
trucks, the electric machine design needs to be adapted compared to passenger electric
vehicles [
23
]. In regard to the dimensions of the machine, this results in a large stator outer
diameter and small active length, giving the machine a “disc-like” shape, [
24
,
25
]. Due to
the incurred high magnetizing currents in an IM and the high resulting excitations losses for
synchronous machines but high-efficiency areas of PMSM for high-torque and low-speed
applications, a PMSM is usually preferred. High-torque machines are usually established
using a lower pole count of p= 2–5 than in regular passenger electric vehicles [
24
,
26
]. The
ratio between the maximum and rated rotational speed is 1.75–4.4 [27].
2. Methodology
For the analysis in this paper, existing electric machines in heavy-duty trucks (40 t
gross vehicle weight) in production or prototype stage were summarized. Besides publicly
available vehicle parameters, we present our assumptions regarding the gearbox design
and the resulting operating speed of the electrical machine. Based on the vehicle parameters,
the machine efficiency maps are calculated. To determine average vehicle efficiency, energy
Energies 2021,14, 328 4 of 15
consumption, and driving performance, the efficiency maps are fed into a longitudinal
simulation model. Depending on the driving cycle, the resulting operating points and thus
efficiency can be derived. The download links to the machine design tool MEAPA and the
longitudinal simulation LOTUS are given as Supplementary Material.
2.1. Vehicle Parameters
Existing heavy-duty electric trucks and respective electric powertrains currently on
the market are summarized in Table 1regarding their powertrain topology parameters. The
topology, number of gears and machines, as well as the machine type, power, and torque,
are taken from the respective sources, whereas the rotational velocity of the machine
n85, rated
for a maximum vehicle velocity of 85 km/h is calculated using Equations (1)
and (2).
n85, rated =85 km
h·60
3.6
2·π·rdyn
·igear . (1)
igear =35000 Nm
(No.o f ma chines)·(max torque). (2)
where
rdyn
represents the dynamic rolling radius of the tires and
igear
is the gear ratio. The
35,000 Nm represents the startup torque necessary for 40 t heavy-duty trucks. This allows
for typical road acceleration (>0.646 m/s
2
) and provides sufficient torque reserves enabling
starting on roads with a gradient of 15–17% [11,28,29].
Table 1.
Summary of heavy-duty trucks or axles of various manufacturers and their respective powertrain topology
parameters.
Bold
concepts are simulated in this study. Empty fields indicate unknown information. (Note: EM: electrical
machine; PMSM: permanent magnet synchronous machine; IM: induction machine).
Concept Topology Gear No. of
Machines
Machine
Type
Power
(Rated)
Rot.
Velocity
(Rated) 1
EM Torque
(Max) Source
Axletech
EPS785 central motor 1 1 PMSM2350 - 3500 [30]
DAF CF
Electric central motor 1 1 PMSM 210 8837 2000 [31,32]
Scania L central motor 1 2 - 230 - 1300 [33]
Hyundai
XCIENT central motor 1 6 - 350 - 3400 [34]
Meritor 17xe central motor,
eAxle 2–3 1 - 410 - 2000 [35]
ZF AxTrax distributed 1 2 IM 60 11,362 485 [13,36,37]
Nikola Two distributed 1 4 - 186.25 677.75 [38]
Tesla Semi distributed 1 4 PMSM 223 5700 380 [39]
E-Force EF18
SZM
dual central
motor 3 2 PMSM 150 2525 2025 [40–42]
Alisson AXE dual central
motor 2 2 - 400 - - [43,44]
Ansorge
Elias
dual central
motor 12 2 PMSM 140 1250 [45]
Nikola Tre dual central
motor, eAxle 1 2 PMSM 240 9819 900 [46–48]
1Calculated values using Equations (1) and (2).
For the results in this paper, the electric trucks indicated in bold type in Table 1
were utilized. They were chosen as examples since they represent a reasonable variety
of topologies using 1, 2, and 4 electric machines, as well as a combination of fixed and
multiple gears. Here, the powertrain topologies in Figure 1were considered. In order
to represent the central fixed gear, the DAF CF electric powertrain was chosen with an
implemented PMSM as an electric machine. For the dual central, multiple gear topology,
Energies 2021,14, 328 5 of 15
the E-Force EF18 SZM was chosen, also using a PMSM. The Ansorge Elias vehicle concept
has the same topology but is not commercially available and consequently not included.
The Meritor 17xe is an example of a central motor with multiple gears, and it is currently
not implemented in a series truck. However, this concept could not be considered because
the machine type is unknown. The dual central fixed gear topology is represented by the
Nikola Tre using a PMSM for traction and the distributed fixed gear topology by the Tesla
Semi with a PMSM. The ZF AxTrax shares the topology with the Tesla Semi, but it is the
only concept integrating an IM.
2.2. MEAPA Tool
In previous works, the authors presented a holistic, automated design model for IM
and PMSM [
49
,
50
]. After selecting the machine type, the rated power, rated rotational
velocity, rated voltage, and the number of pole pairs need to be defined. In a further step,
assumptions in regard to the power factor, number of phases, magnet arrangement, circuit
connection, cooling, material selection, and winding layout need to be made. After the
machine dimensions are determined, the stator and rotor design continue. Consequently,
based on the design parameters, the tool computes valid currents and voltages with
separate motor and generator loss models, resulting in the efficiency diagram [49].
In the conducted analysis, the calculated efficiency diagrams were created using the
following assumptions in Table 2. The voltage level of 800 V was implemented, since this
voltage level is used in the electric trucks from DAF and Nikola [46]. Since the number of
pole pairs is not known for the chosen trucks, a value of 4 was chosen since lower pole
pair numbers are usually applied for low-speed and high-torque applications [
26
]. The
maximum rotational velocity was set to approximate 1.75 nN[27].
Table 2.
Assumptions of input parameters for the machine design tool MEAPA (Note: rpm: revolu-
tions per minute).
Parameter Symbol Value Unit
Voltage (rated) U 800 V
Number of pole pairs p 4 -
Rot. velocity (max) nmax 1.75 nNrpm
Power factor cos ϕ1 -
Number of phases m 3 -
Magnet arrangements - internal, embedded -
Circuit-wiring - Star -
Wire-type - Round-wire -
Cooling type - Liquid -
Iron material - VACOFLUX50 -
Conductor material - Copper -
Winding type - Single-layer, integral-slot -
Since the power factor and number of phases, as well as circuit-wiring and wire type,
were not known from the manufacturers, they were set to the respective values. The magnet
arrangement was set to an interior permanent-magnet synchronous machine (IPMSM)
with an embedded rotor magnet arrangement, due to the low-speed application [
19
,
25
].
A limitation of the machine design tool was reached for the DAF powertrain, since this is
usually operated using 9 phases. In order to compare the results to the remaining trucks, a
3-phase topology was applied to all regarded electric machines.
2.3. LOTUS
The longitudinal simulation (LOTUS) [
51
] calculates the energy consumption and
the efficiency for each vehicle configuration. The tool allows different powertrain designs
and user-defined vehicle parameters. The input parameters and the vehicle weight for
this study are based on the values by Fries et al. [
52
,
53
] and summarized in Table 3. In
accordance with the European commercial vehicle certification tool VECTO, the payload
Energies 2021,14, 328 6 of 15
is set to 19.3 t [
54
]. All vehicles are equipped with a net battery capacity of 640 kWh
(depth-of-discharge of 80%), which is sufficient for most long-haul applications [
12
]. In
addition, the simulation constrains the maximum possible current per machine to 500 A to
exclude impractically large diameters of the cables due to cost and weight reduction.
Table 3. Assumptions of input parameters for vehicle simulation [52,53].
Parameter Symbol Value Unit
Payload mpayl oad 19,300 kg
Frontal area A10.2 m2
Drag coefficient cW0.53 -
Tire radius rdyn 0.4465 m
Rolling drag coefficient cR0.0043 -
Auxiliary consumers Paux 3.5 kW
The electric machine weight is calculated based on a linear regression model [
55
] and
shown in Table 4. Because the weight only depends on the machine type and the power,
no correlation between weight and topology can be seen. While the battery dominates the
total vehicle weight [
12
], the machine weight comprises 2–6% of the total gross vehicle
weight if 40 t payload is assumed.
Table 4. Weight of the electrical machines based on a linear regression model [55].
Vehicle Weight in kg
E Force 1437
ZF 681
Nikola 2345
Tesla 1265
DAF 2118
Besides the electric machine, losses in the powertrain occur in all gearings. We assume
an efficiency of 99% for all fixed-gear configurations as well as the highest gear. Lower
gears and planetary gears have an efficiency of 96%, while the final drive has an efficiency
of 97% [
14
,
53
]. These losses account for all losses in the transmission caused by gearing,
friction, and oil [
13
]. The model neglects further losses such as battery charging as they are
not affected by the topology or the machine design.
To simulate topologies with a different number of electric machines, the total vehicle
torque is divided by the number of machines. The operating points are simulated using
efficiency diagrams, as described in Section 2.2. The output torque of a single machine
is then multiplied again by the number of EM and, together with the gear ratios, yields
the drive torque at the wheel. The required current is also multiplied by the number of
EMs and applied to the battery. Consequently, only one machine is simulated to reduce
complexity and computation time. However, this implies that all machines provide an
equal amount of torque and that lateral torque distribution is neglected.
The input for LOTUS is a driving cycle. For this study, one real-world-based and
three synthetic driving cycles are used. The cycles are shown in Figure 2. The first cycle,
Truckerrunde, is used by the technical journal Trucker to evaluate different heavy-duty
vehicles. The roundtrip in southern Germany includes highway sections (80 km/h) and
rural roads (60 km/h) [
56
]. The average velocity of the 400 km long test cycle is 75 km/h
with a maximum road gradient of 5.2%. Although this cycle is based on a real road profile,
the version used does not include any stops. The second and third cycles are driving
cycles included in the VECTO tool and used for emission certification by the European
Union [
57
]. The long haul (LH; Figure 2b) has a higher average velocity of 83 km/h
compared to the regional delivery driving cycle (RD; Figure 2c) with 66 km/h. In contrast
to the Truckerrunde, both VECTO cycles include stops of varying length. However, the
Energies 2021,14, 328 7 of 15
average road gradient is lower compared to the Truckerrunde cycle (LH: 6.6% and RD:
6.2%). With a total stopping time of 67 s over 2 stops, the long-haul cycle represents a
stationary load. The regional delivery cycle has a more dynamic profile with 11 stops and
a total stopping time of 746 s. Both VECTO cycles are synthetic cycles, based on real-world
driving data. The last assessed driving cycle also represents real-world data synthesized
with a Markov-chain approach by Fries et al. [
58
]. With an average velocity of 73 km/h
and no stops, this cycle is comparable to the Truckerrunde cycle, although it has a maximum
gradient of 6.8%. All synthetic cycles cover a distance of 100 km.
Figure 2.
Speed profile and road gradient of different driving cycles (
a
–
d
). Truckerrunde is based on a real road-profile, while
the other driving cycles are synthetic driving cycles based on collected and processed driving data.
3. Results
The results of the conducted simulation of the machine design are illustrated in form
of the maximum torque vector and the maximum power of each regarded electric machine
in Figure 3. The electric machine design for the DAF and E-Force powertrain show the
highest torque of the respective concepts, while the E-Force and Tesla provide the highest
power. Both the DAF and E-Force are equipped with one and two PMSMs, respectively. A
PMSM is implemented in this case in order to offer the necessary high torque.
The different maximum rotational velocities of the powertrain concepts show the
impact of the number of gear ratios. While the powertrains of the DAF, Nikola, ZF, and
Tesla truck are all equipped with fixed gears, the respective electric machine needs to be
designed with a higher rated rotational velocity in order to enable the necessary wheel
speed at a variety of driving speeds.
In order to illustrate the operating points of each of the regarded powertrain topolo-
gies, LOTUS was utilized as described in Section 2.3. The resulting efficiency diagram
displaying the respective operating points for propulsion (positive ordinate) and recupera-
tion (negative ordinate) can be seen in Figure 4. The given average efficiency of the vehicles
has a standard deviation of 3.5%.
Energies 2021,14, 328 8 of 15
Figure 3.
Curves of the maximum motor torque (
a
) and the power (
b
), measured at the output of the
electrical machines. The maximum power is limited to the rated power due to technical restriction of
the maximum possible current (500 A).
Figure 4.
Simulation results of operating points, maximum, and average efficiency for regarded electric truck concepts (
a
–
e
)
during the Truckerrunde driving cycle. The hyperbolic shapes are caused by the technical, maximum current of 500 A.
The diagrams show that the operating points are located primarily at the vertical
lines that represent the rotational speed around 80 km/h and 60 km/h, respectively. All
concepts show most of the operating points near the rated rotational speed and thus at
85 km/h. This confirms the assumption made regarding the gear ratios.
Energies 2021,14, 328 9 of 15
The diagram of the E-Force One (Figure 4b) clearly shows that operating points with
lower speeds are present due to shifting. Consequently, the E-Force One powertrain offers
the greatest potential to utilize the entire efficiency map due to the multiple-gear topology,
and the overall efficiency during real operation is the best out of the regarded topologies.
Because the Nikola Tre’s high system torque and power are distributed over two
machines, the operating points are located in areas with low torque demand but also low
efficiency near the abscissa (Figure 4c). Therefore, the concept shows the lowest efficiency
of all vehicles.
The Tesla Semi (Figure 4d) makes full use of the diagram of the EM in both directions.
The load on the Tesla machines is relatively high. In contrast, the DAF (Figure 4a) only uses
a fraction of the diagram due to the limitation of the maximum current. Thus, the machine
could be downsized and designed with a lower-rated rotational speed. The fixed-gear
concept implemented in the ZF (Figure 4e) utilizes the entire efficiency diagram despite the
lack of shiftable gears, even in high-torque areas.
In general, the multiple-gear box concept of the E-Force offers an advantage of 1.3–
9.3% compared to the other topologies. However, the central fixed gear motor topology
offers a similar overall efficiency and should therefore not be neglected.
Besides the average efficiency, Figure 5shows the energy consumption of each vehicle
for the four driving cycles. This confirms that within the same driving cycle, the standard
deviation for consumption and efficiency is low (4%) for three driving cycles.
Figure 5.
Varying driving cycles with different characteristics have an influence on energy con-
sumption and average electric machine efficiency. While Truckerrunde and T2030 do not include
stops, both VECTO cycles include multiple stops with varying times (LH: 67 s and RD: 746 s). The
spreadsheet containing the results is provided as Supplementary Material.
As the average efficiency is determined by the position of the operating points in
the machine map, we investigate how different driving cycles affect the operating points
and consequently the efficiency. Thus, the mean value represents both recuperation and
propulsion. Consequently, the energy consumption must not necessarily be correlated with
the average efficiency, because the later does not consider the location of the operating point
above or below the ordinate. Furthermore, it must be noted that all vehicles recuperate
the maximum possible electrical energy. However, if the brake maneuver requires energy
beyond that limit, the conventional friction brakes decelerate the vehicle. This means that a
high efficiency can be superimposed by conventional braking due to electrical limitations
and resulting in higher overall consumption or vice versa. On the one hand, this explains
why the Tesla has a high consumption despite high efficiency during the T2030 and the
VECTO LH cycle, as both require extensive braking during downhill sections. On the other,
the ZF seems to exploit recuperating during the VECTO RD cycle, resulting in relatively
low consumption despite low efficiency.
Both central drive topologies have lower energy consumption compared to wheel-
independent topologies. The DAF CF has the lowest average consumption over all driving
cycles (100 kWh/100 km) followed by E-Force (101 kWh/100 km) and ZF (105 kWh/
100 km). The Tesla Semi (109 kWh/100 km) and the Nikola Tre (110 kWh/100 km) pow-
Energies 2021,14, 328 10 of 15
ertrain perform the worst. Although the driving cycles result in different dynamics, we
cannot conclude that multi-speed gearboxes have advantages within the selected driving
cycles and the different vehicles.
Although all distributed topologies perform worse than the central ones, the ZF results
in the regional-delivery cycle—being the most dynamic driving cycle—standing out. The
results suggest that the machine design is better suited for this application, which we can
assume valid given the machine’s usual application in passenger vehicles.
Comparing the different machine designs, the results show that high-torque machines
perform best regarding efficiency and consumption. The advantage regarding consumption
becomes less pronounced as the dynamics of the drive cycle increase, suggesting that
smaller machines are better suited for the application.
4. Sensitivity Analysis
Besides the driving cycle, the transmission design and foremost the overall gear ratio
have an influence on the operating points and thus the efficiency. To test the robustness
of our assumptions, we therefore varied the overall ratio in the range of
±
20% from
the calculation in Equation (2). Because the VECTO driving cycle showed a consistent
relation between high efficiency and low consumption, it was used for this computation.
Furthermore, current European vehicle certification utilizes this cycle, which is why we
assume these results are easily comparable. The elasticity test was performed without any
gradient. The results (Figure 6a,b) show a linear relation between lower gear ratios and
higher average efficiency.
Figure 6.
The sensitivity of the average electric machine efficiency (
a
,
b
) to the change in gear ratio
shows a linear decrease in efficiency with higher gear ratios. As a measure for driving performance,
the elasticity (
c
) describes the acceleration time from 60–80 km/h. Its sensitivity to the change in gear
ratio shows that smaller ratios increase the elasticity except for the DAF concept. The VECTO LH cycle
was used for all simulations. The spreadsheet containing the results is provided as Supplementary
Material.
However, the improved efficiency with lower ratios comes at the cost of worse driving
performance. As a measure for driving performance, Figure 6c shows the non-linear
correlation of the elasticity—the time to accelerate from 60 to 80 km/h—and the gear
ratio change. A 20% reduced gear ratio worsens the elasticity up to 34% in the case of
Energies 2021,14, 328 11 of 15
the Tesla. However, an increase in gear ratio (meaning worse efficiency) results in smaller
or no improvements in elasticity. The DAF CF shows a different behavior, which can be
explained with the constrained maximum current of 500 A. Higher gear ratios cause this
limit to be reached earlier and thus reduce the acceleration performance. Although this
applies to all vehicles, the impact on the DAF is highest. Due to the oversized torque
and power reserves, the DAF and Nikola concept could be optimized regarding efficiency
without sacrificing the elasticity. Averaged over the four driving cycles, the low ratio DAF
performs best (95.4%), while Nikola (90.1%) outperforms the ZF. The same would hold true
for the ZF AxTrax; however, the gear ratio is known [
13
] and consequently not optimized
in this study.
The sensitivity analysis confirms that both central topologies perform better than the
concepts with hub or eAxle topologies. Altogether, we conclude that our assumptions
regarding the gearbox ratios and design are sufficiently accurate, as the efficiency cannot be
improved without worsening driving performance by 4–34%. Although lower gear ratios
optimize the DAF and Nikola concepts, they do not alter the general conclusions drawn.
5. Discussion
The sensitivity analysis is contradictory to the results presented by Verbruggen
et al. [
14
], who showed the superiority of distributed topologies and the additional advan-
tage of multi-speed gearboxes regarding energy consumption and thus efficiency. However,
we assume the vehicles in this study were not optimized in the same way as Vebruggen
et al. were. Including the machine design in our approach, we show that the choice of
machine design and topology can influence the overall efficiency in the same range as
the choice of topology, as Verbruggen et al. showed. Consequently, we conclude that an
optimized battery electric truck requires both the optimum topology and the optimum
machine design.
The longitudinal simulation was carried out for a single machine, whose output torque
was multiplied by the respective number of machines in the topology. Consequently, lateral
torque distribution as neglected. Finken et al. [16] showed the potential of optimizing the
lateral torque distribution to maximize the overall efficiency. A combination of different
types or sized machines could further optimize the efficiency. A combination of different
types or sizes of machines could further optimize efficiency. For example, Tesla offers
its Model 3 as a four-wheel-drive with a PMSM on the front and an IM on the rear axle.
Depending on the load, either one or both drive the car in order to provide maximum
efficiency in all situations. At higher speeds, the IM primarily propels the car, while at
lower speeds the PMSM takes over. As both machines have efficiency advantages in the
respective ranges, efficiency and range improve [
16
]. This could also be considered in future
studies for heavy-duty applications; however, none of the currently known prototypes
provide this technology.
A limitation of this study is that only the gear ratios of the ZF AxTrax system are
known. The other ratios were assumed such that rated rotational speed matches the
vehicle’s legal speed on European highways. If different ratios are chosen, the results could
differ significantly. Furthermore, the assumed shifting rules try to keep the rotational speed
in the range of 0.2
nN
and 0.8
nmax
. As the real shifting rules are unknown, this assumption
cannot be verified.
In addition, the validation of the part models needs to be discussed. Since the electric
machine design tool used for the creation of the efficiency diagrams is mainly validated
for parameter ranges of passenger vehicles, high-torque machines pose a simulation area
that could not be validated to date. However, the resulting diagrams were verified by
comparing similar high-torque applications such as electric busses. In future studies,
the impact of the number of phases on the performance of the concept could be further
investigated, since the DAF electric machine has a nine-phase instead of the implemented
three-phase topology.
Energies 2021,14, 328 12 of 15
The examined concepts are not in series production yet. Thus, fleet data could not be
collected, nor were test data publicly accessible. However, LOTUS is validated for conven-
tional ICE-powered vehicles [
59
]. Since the vehicle model and remaining components are
unchanged, we assume the results to be plausible and valid within the given limitations.
However, as the presented study compares the topologies relatively to each other, the
general conclusions are plausible. Future studies should consider the optimization of gear
ratios and shifting rules depending on the topology and electric machine design.
6. Conclusions
In this paper, we investigated the influence of the powertrain topology in the com-
bination of electric with machine design on the vehicle efficiency operation. The results
show that due to the topology, averaged efficiencies range between 87% and 95%. Our
results show that the single central topology shows the highest overall efficiency and lowest
consumption. However, the applications’ effect (i.e., driving cycles) superimposes this
conclusion and yields consumptions in a range between 93–122 kWh/100 km. Furthermore,
the application can result in low consumption despite low efficiency, as the ZF concepts
show during the VECTO RD cycle.
On the one hand, we show that current concepts are far from the optimal solutions
presented by Verbruggen at al [
14
]. On the other hand, our results highlight that future
approaches must not only consider the powertrain topology. Only an application-specific
machine design combined with an optimized topology can realize the full potential of
electric trucks.
Furthermore, we expect our findings to become more relevant, considering future
energy sources. In particular, hydrogen-powered vehicles require the optimization of
the complete powertrain to counteract the lower fuel-cell efficiency. However, regardless
of the energy source, the cost-sensitive transportation sector requires all technical levers
to be pulled to optimize efficiency, energy consumption, and ultimately lower transport
emissions.
Supplementary Materials:
The following are available online at https://www.mdpi.com/1996-107
3/14/2/328/s1, Both the LOTUS vehicle consumption model and the electric machine design tool
MEAPA are available as open source Matlab/Simulink code on Github [
51
,
60
]. In addition, the data
presented in Table 1as well as the derived machine design parameters are available as supplementary
information. The spreadsheet is available online at INSERT LINK.
Author Contributions:
Conceptualization, S.W. and S.K.; methodology, S.W. and S.K.; software,
S.W., S.K., and M.B.; validation, S.W., S.K., and M.B.; formal analysis, S.W. and S.K.; investigation,
S.W., S.K., and M.B.; resources, M.L.; data curation, S.W., S.K., and M.B.; writing—original draft
preparation, S.W. and S.K; writing—review and editing, S.W, S.K., M.B., and M.L.; visualization, S.W.
and M.B.; supervision, M.L.; project administration, M.L.; funding acquisition, M.L. All authors have
read and agreed to the published version of the manuscript.
Funding:
This research received no external funding. The research of S. W. was founded by basic
research funds of the Institute of Automotive Technology. The research of S. K. was supported by the
organization Bayern Innovativ and the Bavarian Ministry of Economic Affairs, Regional Development
and Energy within the research project DeTailED—Design of Tailored Electrical Drivetrains.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Informed.
Data Availability Statement:
The data presented in this study are openly available in the repositories
LOTUS at 10.13140/RG.2.2.27745.53607 and MEAPA at https://github.com/TUMFTM/Electric_
Machine_Design. The data displayed is provided as supplementary material.
Conflicts of Interest: The authors declare no conflict of interest.
Energies 2021,14, 328 13 of 15
Abbreviations
The following abbreviations are used in this manuscript:
BEV Battery Electric Vehicle
EM Electric Machine
EU European Union
FCEV Fuel Cell Electric Vehicle
ICE(V) Internal Combustion Engine (Vehicle)
IM Induction Machine
IPMSM Interior Permanent-Magnet Synchronous Machine
LH Long Haul
LOTUS Long-Haul Truck Simulation
MEAPA Model for the design and analysis of a PMSM or ASM
(German: Modell für den Entwurf und die Analyse einer PMSM oder ASM)
PMSM Permanent-Magnet Synchronous Machine
RD Regional Delivery
VECTO Vehicle Energy Consumption Calculation Tool
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