# Fuel Saving Effect and Performance of Velocity Control for Modern
Combustion-Powered Scooters

Jannis Kreß<EMAIL_ADDRESS>Jens Rau  Hektor Hebert  Fernando
Perez-Peña  Karsten Schmidt  Arturo Morgado-Estévez

###### Abstract

This paper investigates the performance and fuel-saving effect of a velocity
control algorithm on modern 50 cc scooters (Euro 5). The European Parliament
has adopted major CO2 emission reductions by 2030. But modern combustion-
powered scooters are inefficiently restricted and emit unnecessary amounts of
CO2. Replacing the original restriction method with the system presented in
this paper, the engine’s operating point is being improved significantly.
Therefore, a Throttle-by-Wire-System senses the rider’s throttle command and
manipulates the throttle valve. A redundant wheel speed sensor measures the
precise vehicle velocity using the magneto-resistive principle. The entire
system is managed by a central ECU, executing the actual velocity control,
fail-safe functions, power supply and handling inputs/outputs. For velocity
control, an adaptive PI-controller has been simulated, virtually tuned and
implemented, limiting the max. velocity regulated by legal constraints (45
km/h). In this way, the environmentally harmful restrictors used today can be
bypassed. By implementing a human-machine interface, including a virtual
dashboard, the system is capable of interfacing with the rider. For evaluation
purposes a measurement box has been developed, logging vehicle orientation,
system/control variables and engine parameters. A Peugeot Kisbee 50 4T (Euro
5) is serving as test vehicle. Finally, the system has been evaluated
regarding performance and fuel efficiency both through simulation and road
testing. Fuel savings of 13.6 % in real-world test scenarios were achieved
while maintaining vehicle performance.

###### keywords:

Velocity control , Throttle-by-Wire , Fuel saving , Motorcycle powertrain ,
Alternative restricting

††journal: arXiv

[1]organization=Department of Computing and Engineering, Frankfurt University
of Applied Sciences, city=Frankfurt, postcode=60318, state=Hessen,
country=Germany [2]organization=Department of Automation, Electronics and
Computing Architecture and Networks, University of Cadiz, city=Puerto Real,
postcode=11519, state=Andalusia, country=Spain

##  1 Introduction

According to the climate targets of the European Climate Change Act, at least
55 % of greenhouse gases are to be saved by 2030 compared to 1990 levels
(European Parliament, 2021). Considering the average distance traveled per
inhabitant in the EU, the values vary between 5 and 20 km a day. Depending on
the country of origin, 57 % to 81 % is covered by car. Most often, the
distance traveled by two-wheelers corresponds to less than 1 % (Statistical
office of the European Union, 2022). However, for urban mobility, scooters are
an excellent alternative. The potential for saving fuel and thus minimizing
CO2 emissions is enormous and can make a significant contribution to EU
climate targets. Harmful two-stroke engines are banned and the Euro 5 standard
also applies to 50 cc scooters (European Parliament, 2007). Comparing modern
four-stroke combustion and electrically powered scooters of Peugeot, Piaggio
and Vespa, an average price for electric models of almost 160 % can be
noticed. The difference in range is even more striking, as the combustion
engine exceeds that of the electric drive by a factor of 6. Users require
longer ranges because recharging is often difficult in cities.

To fulfill Euro 5 requirements, modern combustion-powered scooters are
equipped with gasoline injection and a catalytic converter (Peugeot Motocycle,
2023; Kymco, 2022). To ensure optimal operation of the catalyst, the air/fuel
mixture ($\lambda$) must be approx. 1 (Schreiner, 2020). Legal requirements
state that 50 cc scooters cannot exceed a maximum speed of 45 km/h. By
delaying the ignition timing, the engine is restricted in power, since
reducing the fuel injected would cause $\lambda$ to become larger 1 (lean
mixture). This can be solved by deactivating the original restriction of the
test vehicle and applying a Velocity Control (VC) algorithm. The basis is
provided by a low-cost Throttle-by-Wire-System (TbWS) (Kreß et al., 2023),
which enables electrical control of the throttle valve. For this purpose, the
throttle position is sensed and the throttle valve is manipulated by an
actuator. As a consequence, instead of the ignition timing, the airflow is
regulated by means of the throttle valve position, which also results in a
reduction of the fuel injection volume. This system can contribute
substantially to reaching EU climate targets. To the best of our knowledge,
this is the first approach of a VC application on a modern four-stroke 50 cc
(Euro 5) scooter as an alternative restriction. The VC system investigated
here is intended to optimize fuel economy and improve exhaust emissions.

The following novelties will make a significant scientific contribution in
this class of vehicles:

  * 1.

By suppressing ignition timing manipulation, the efficiency is increased and
the throttle valve opening can be reduced by almost 50 % at top speed.

  * 2.

The controlled air supply minimizes the amount of fuel injected in line with
the optimum stoichiometric ratio by up to approx. 20 % at top speed.

  * 3.

Based on the frequently maxed-out vehicle velocity, this alternative
restriction significantly lowers fuel consumption/CO2 emissions by 14 %.

  * 4.

Despite the minimized injection quantity, consistent performance can be
demonstrated because the amount of energy supplied is used more effectively.

###  1.1 Background on restricting methods

Scooters are defined by EU law. In addition to the max. speed of 45 km/h and
the max. engine capacity of 50 cc, the power is also limited to 4 kW,
regardless of the type of drive (European Parliament, 2002). In most cases,
vehicle performance is sufficient to reach higher speeds, so the surplus power
above 45 km/h must be compensated. The surplus is still needed for heavy loads
or inclines. In the past, mechanical restrictors were often used, which
manipulated the flow behavior either through throttle orifices in the air
intake or exhaust (Stoffregen, 2018a). Limitation of the Continuously Variable
Transmission (CVT) was particularly common, resulting in an unnecessarily high
engine speed. Electronic throttling was opened with the arrival of electronic
ignition. When the top speed was reached, the ignition was interrupted,
resulting in the emission of unburned fuel. Modern powertrains have further
capabilities to influence engine control due to gasoline injection. Injection
volume and ignition timing are decisive here. Reducing the fuel supplied would
be obvious, but leads to high combustion temperatures and interferes with the
function of the catalytic converter (Pischinger and Seiffert, 2021). As a
consequence, engine damage and unacceptable exhaust emissions would be
expected. Typically, ignition occurs 6° to 40° before the Top Dead Center
(TDC), depending on the engine speed. For restriction, the ignition timing is
delayed towards the TDC or even behind as shown in Figure 1 (efficient left,
restricted right).

Figure 1: Ignition timing

To achieve optimum efficiency, the maximum pressure must be reached shortly
after TDC. This ensures that the max. combustion chamber pressure can act on
the piston from the start of expansion. By retarding the ignition timing,
expansion is already taking place during combustion, thus reducing both the
peak pressure and the usable expansion distance. The efficiency drops
(Stoffregen, 2018b).

###  1.2 Background on TbWSs & velocity control

TbWSs have been researched and integrated industrially to motorcycles (Corno
et al., 2008; Miritsch et al., 2021; Poschner et al., 2010). A TbWS has also
been applied successfully to small-volume engines, coming with the minimum
number of low-cost components, combined in the throttle body. The system is
based on a throttle servo, throttle position sensor, throttle demand and speed
sensor. To limit and adapt the engine speed in various load situations, the
speed can be adjusted by an integral electronic control and resonant
frequencies can be avoided. Besides the performance limitation, the system is
capable of managing speed and load reductions during overheating. For cold
starts, the idle speed can be adjusted on the outside temperature (Smither et
al., 2008). Also, an electronic throttle control for a 50 cc two-stroke
scooter application has been developed, improving fuel economy, idle-stability
and implementing an electronic vehicle velocity control. By use of an
electronic throttle control, the conventional CVT restriction can be replaced,
which leads to fuel savings of 22 % caused by the lowered engine speed.
Additionally, the system allows controlling the idle engine speed after the
start-up. The system is based on a carburetor-driven two-stroke scooter with a
power of 4.2 kW and a twin variator pulley CVT (Smither et al., 2011). Due to
the mentioned powertrain, the system is not compliant with the Euro 5
standard. Nowadays systems use an originally optimal-designed CVT. In
addition, modern air-cooled four-strokes have a power output of max. 2.5 kW
due to the significantly lower power density. The potential within power
surplus and transmission thus no longer exists. In addition, the fuel
consumption of modern non-optimized scooter drives is considerably lower than
that of an optimized two-stroke (Zhang and Zhao, 2014).

##  2 System development

The TbW-based VC uses the set velocity (throttle grip position) specified by
the rider as the command variable/ input. A Wheel Speed Sensor (WSS) measures
the vehicle’s dynamics (velocity), used as control reference variable. Driving
mode and settings serve as additional rider input. The desired vehicle
velocity is manipulated through an electronically set throttle valve position
(output). For safety reasons, the system is capable of interrupting the
engine’s ignition line. Lastly, the system requires power supply, outputs
diagnose data and rider-intended system information for evaluation purposes.
Figure 2 shows a general black box schematic.

Figure 2: Blackbox schematic

Several bus-connected subsystems are required to implement VC and efficient
restriction. The sensing of the rider’s demand (throttle grip position) and
the vehicle velocity must be achieved. To be able to influence the scooter’s
longitudinal driving dynamics, the throttle valve position must be controlled
electronically. In parallel, emulation of the Engine Controller (EC) is
essential to prevent ignition timing manipulation. The remaining drive-train,
consisting of the engine, gearbox and rear wheel, retains its original
condition. A central Electronic Control Unit (ECU) processes input variables,
performs VC, provides necessary data on the bus and ensures safe vehicle
operation. Furthermore, the ECU serves as powerbox, providing electric energy
to all subsystems. The overall system shall be designed in such way, as to
allow switching between restrictions by ignition timing and throttle valve
manipulation. By means of a virtual dashboard, system quantities and an eco-
score are to be displayed to the rider. For evaluation, this Human Machine
Interface (HMI) will be used to switch between restrictions and to start
measurements. These are to be recorded by a measurement box accessing the
vehicle bus. Now that the foundation has been laid for assistance systems,
cruise control is to be implemented. This section presents the architecture
developed, including the sensors and systems needed. Figure 3 gives the system
layout which has been outlined.

Figure 3: System layout

###  2.1 Throttle-by-Wire-System

The basis for the VC implementation is provided by the previously developed
low-cost TbWS. A redundant, contactless AMR sensor is used to sense the
throttle position with a measuring accuracy of 99.84 %. The measured and
plausibility-checked position is sent via CAN by the Throttle Position Sensor
(TPS). To enable the system’s influence onto longitudinal vehicle dynamics, a
Throttle Valve Actuator (TVA) was developed. By determining the position using
a Hall sensor in combination with a stepper motor, the throttle valve can be
precisely position-controlled. This allows positioning with an accuracy of
99.63 % within max. 60 ms. TPS and TVA make use of embedded fail-safe
functions, monitoring measurement and position quality. Design, function,
reliability and performance have already been evaluated (Kreß et al., 2023).

###  2.2 Wheel speed sensor

The scooter’s velocity is required as a control variable for VC. Since 50 cc
scooters are not legally obligated to be equipped with an anti-lock braking
system, they do not have WSSs (European Parliament, 2013). In the test
vehicle, the rear wheel speed is only measured with a passive inductive
Transmission Speed Sensor (TSS) on the clutch bell for restriction purposes.
Due to its design, it is not suitable for measuring low speeds. Therefore, a
redundant WSS was developed, which enables the precise sensing of the wheel
speed. Two automotive magneto-resistive WSSs independently measure the motion
of a 48-step encoder disc by determining the width of successive pulses. A
sampling rate of 1 MHz results in a resolution of 0.02 km/h. The WSS assembly
was tested within a hardware-in-the-loop environment (Kreß et al., 2021). The
assembly is to be integrated, whereby a constant distance ($<$0.5 mm) between
sensors and the encoder disc is to be ensured. Preferably, the controlled rear
wheel speed is sensed to intervene in case of wheel slip. Adaptations were
developed in several iterations. Unlike chain-driven motorcycles, there is no
decoupling between the driven wheel and the engine/gearbox. Vibrations from
strong low to high frequencies are directly transferred to the WSS resulting
in extreme measurement inaccuracies. Decoupling, damping and software filters
for signal processing could not achieve sufficient signal quality. For that
reason, the WSS is adapted to the front wheel. In addition, the TSS is used to
detect wheel slip/lock at the rear. With a bracket, both sensors are precisely
placed, as shown in Figure 4.

Figure 4: WSS adaption

###  2.3 Main ECU

The Main ECU (MECU) (STM32G431KB) (STMicroelectronics, 2021) is intended to
act as a higher-level system master. On the input side, this includes
processing the wheel speed, receiving the TPS measured value and the rider
command from the HMI. On the output side, the TVA position, HMI system states,
emulated TSS signal and evaluation data must be transmitted. At the hardware
level, the MECU must supply the entire system with power, provide the
measurement electronics of the WSSs and enable the amplification of the
emulated speed signal. At software level, the VC needs to be implemented,
adjusting the vehicle’s velocity to the rider’s demand. Here, the continuous
functional safety of the overall system must be ensured by means of
appropriate fail-safe functions.

Power box: The MECU provides individually fused voltages to all subsystems.
Especially the TVA motor is to be supplied with an independent power line to
prevent system errors in case of damage. Three 12 V DC/DC step-down converters
provide stabilized voltages of 3.3 V (MECU internal), 6 V (TVA motor) and 7.5
V (common system voltage). In addition, the 12 V battery voltage is also
provided. A wiring harness originates from the MECU that connects all
subsystems, consisting of 6 wires: GND, VCCstep, VCCsys, VCCbat, CANhigh and
$CAN_{low}$.

WSS processing: A square-wave signal voltage is generated via a measuring
resistor in the supply line of the WSSs. The signal conversion must be
performed by the MECU for the WSS (front) and TSS (rear). The WSS signal
acquisition can be done by external interrupts due to the constant measuring
voltage. For processing the TSS, a comparator is used to digitize the varying
voltage of the inductive sensor. Afterward, the given pulse widths are
measured by an input capture timer. The stored pulse widths ($T_{wss}$) are
smoothed by a moving average filter with the array size of ($s_{A}$) before
velocity conversion ($v_{scooter}$). For the determination, the wheel
circumference ($D_{wheel}$) and the resolution of the encoder disc
($n_{steps}$) are required. Equation (1) transfers the pulse widths into a
velocity. In order to prevent dead time behavior, the WSS’s array size
($s_{A}$) adapts to vehicle velocity. Due to the redundant signals, errors in
velocity measurement can be detected.

$v_{scooter}=\frac{d_{step}}{t_{step}}=\frac{D_{wheel}}{n_{steps}}\cdot{\frac{s_{A}}{\sum_{0}^{s_{A}}T_{wss}}}$
(1)

Sensor emulation: For bypassing the original restriction, the original EC
needs to be emulated with a manipulated TSS signal. Therefore, a signal is to
be emulated and amplified, depending on the current velocity ($v_{scooter}$).
The output frequency chosen is two times lower than the by the TSS provided
oscillating signal. That way, the EC is not interfering the optimal ignition
timing. For proper stimulation, the emulated signal has to be within the
original speed-dependent voltage range ($\pm 30~{}V$). The emulated signal is
generated by switching the 12 V battery voltage by a MOSFET, overcoming the
ECs threshold. Equation (2) is used to calculate the emulating frequency
($f_{emul}$) for the PWM timer, based on the second-stage transmission ratio
($i_{gear}$ = 13) and the number of poles ($n_{pole}$ = 4) attached to the
clutch bell. For a velocity of max. 50 km/h, a frequency of 3.64 kHz results.
To facilitate the comparison between the original and VC system, a switch can
be used to select between transferring the emulated or original TSS signal to
the EC.

$f_{emul}(v_{scooter})=v_{scooter}\cdot\frac{2\cdot i_{gear}\cdot
n_{pole}}{D_{wheel}}$ (2)

Fail-safe: Every system interfering with vehicle dynamics, is to be classified
as safety critical. The system manipulates longitudinal dynamics by throttle
intervention. Depending on the error detected, the MECU must react by
predefined fail-safe states to ensure safe vehicle operation. Table 1 shows
the error cases identified as possible. The listed fail-safe states are
classified into four stages. Class 1 describes warnings that require re-
calibration but do not danger safe operation. Class 2 can affect the operation
by malfunction of the cruise control, which is why it is disabled. Class 3 can
lead to total scooter misbehavior by loss of acceleration control. In this
case, the TVA is not accepting any throttle request. Class 4 represents the
worst case. The MECU can no longer control the powertrain and a contactor
interrupts the ignition line to shut down the engine. A relay is to be
integrated, controlling the ignition line. All error cases include monitoring
of CAN bus communication, using safety features and life detection.

Table 1: List of fail-safe states ID | C | Error | Type | Action
---|---|---|---|---
1 | 1 |  TPS calib. req. | Warning |  HMI notification
2 | 1 |  TVA calib. req. | Warning |  HMI notification
3 | 2 |  HMI CAN error | Error | Disable cruise contr.
4 | 3 |  WSS inaccuracy | Error |  Reset TVA & set vel.
5 | 3 |  TPS error det. | Error |  Reset TVA & set vel.
6 | 4 |  TVA error det. | Error | Engine shut down

###  2.4 Velocity control

Conventional vehicles require a throttle command by the rider that relates to
the required power needed. The rider controls the desired vehicle velocity.
The VC approach describes a continuous control of the velocity. Instead of any
required engine power, the throttle grip position represents a set velocity.
Basically, the system performs a continuously varying cruise control
algorithm. For this, a controller is designed, as shown in Figure 5. The TPS
set velocity (wθ) serves as input reference variable. After determining the
control deviation (eθ) by using the WSS measurement as control variable
feedback (yθ_meas), the PI-controller outputs the manipulated variable uω in
form of a throttle valve opening. Next, the TVA controls the airflow (uAθ)
within the air intake. Then, the powertrain reacts to the system’s throttle
demand and generates a propulsion force in relation to the throttle valve
opening. Windy conditions, heavy load or street gradients can result in a
disturbing/varying resistance force (Fresist). The vehicle velocity yθ is
measured by the WSS and fed back.

Figure 5: VC control loop

Modeling: For adequate controller design, the system is simulated. Considering
translatory ($F_{I_{trans}}$) and rotational ($F_{I_{rot}}$) mass inertia, air
($F_{Air}$) and rolling ($F_{Roll}$) resistance, the equilibrium of forces is
described in (3).

$F_{Drive}=F_{I_{trans}}+F_{I_{rot}}+F_{Air}+F_{Roll}$ (3)

In dependence on the vehicle position, a non-linear differential equation of
second-order results for $F_{Drive}$ in (4).

$\begin{split}F_{Drive}(x)=&\
(m_{sc}+m_{r})\cdot\ddot{x}+\frac{(J_{f}+J_{r})}{r^{2}}\cdot\ddot{x}\\\ \
&+\frac{A\cdot\rho\cdot
c_{w}}{2}\cdot\dot{x}^{2}+c_{r}\cdot(m_{sc}+m_{r})\cdot g\end{split}$ (4)

$F_{drive}$ depends on the operating point of the engine and gearbox. To
determine the power characteristics, performance measurements were taken on a
roller dynamometer at various operating points, shown in Figure 6. With the
clutch engagement, the power increases up to 15 km/h. The power declines with
growing velocity due to the poor CVT efficiency and the speed-dependent power
drop of the engine. Caused by the test setup, the $F_{Roll}$ is part of the
measured power and has to be compensated proportionally in case of partial
load conditions. A polynomial ($P(v)_{poly}$) is derived from the data set.
For a full throttle acceleration, the driving force can be determined by (5).

$F_{Drive}(\dot{x})=\frac{P(v)_{poly}}{\dot{x}}$ (5)

Figure 6: Engine characteristics

The constants given in Equation (4) were measured or determined experimentally
and are listed in Table 2.

Table 2: List of simulation related properties | Property | Symbol | Value
---|---|---|---
1 | Frontal area | $A$ |  0.78 m2
2 | Wheel radius | $r$ | 0.226 m
3 | Inertia front wheel | $J_{f}$ |  0.327 Nms2
4 | Inertia rear wheel | $J_{r}$ |  1.3228 Nms2
5 | Mass scooter | $m_{sc}$ | 99 kg
6 | Mass rider | $m_{r}$ | 80 kg
7 | Air resistance coeff. | $c_{w}$ | 0.64
8 | Rolling resis. coeff. | $c_{r}$ | 0.015
9 | Air density | $\rho$ |  1.225 kg/m3

To check on model plausibility, the simulated step response was compared to a
reference measurement, shown in Figure 7. The final velocities and the
acceleration behavior match. The maximum deviation is $\pm 1$ km/h and the
validity of the simulation is sufficient. In addition, the simulated forces
are shown as further simulation output.

Figure 7: Evaluation of simulation

Control: By applying the VC, the throttle grip position equals a set velocity.
Controlling the scooter’s velocity constitutes a non-linear system. With
increasing velocity, the control deviation rises due to higher driving
resistance. Typically, control algorithms for non-linear systems are optimized
for a certain operating point by linearization. Here, an adaptive PI-
controller is proposed to change its characteristics velocity-dependent. This
strategy achieves a balance between high controllability and control accuracy
over the whole velocity range. In theory, a P-controller could already lead to
adequate control behavior. If the gain is too high, not only the overshoot
behavior but also signal disturbances of the velocity feedback would
negatively affect the performance. Through adding an I-controller, the set
velocity can be reached by smaller P-gains, stabilizing the controller and
decreasing the susceptibility to disturbances. Therefore, the controller’s
parametrization needs to adapt continuously to the scooter’s operating point.
Proportional ($K_{P}$) and integral ($K_{I}$) gain adapts to the current
scooter velocity ($v_{sc}$) given in (6) and (7). For both the max./min. gain
is to be determined. In addition, a constant offset ($Os$) of the linear I
gain curve prevents instability at very low velocities.

$K_{P}=\frac{v_{sc}}{v_{max}}\cdot(K_{P_{max}}-K_{P_{min})}+K_{P_{min}}$ (6)
$K_{I}=\frac{v_{sc}-Os}{v_{max}}\cdot(K_{I_{max}}-K_{I_{min})}+K_{I_{min}}$
(7)

Caused by the continuously changing set velocity and the varying steps within
the throttle command, the I-controller can not perform precisely. Depending on
the operating point, there are different settling times for different
velocities and, as a result, varying integral values/overshoots. By
implementing a set velocity-dependent threshold ($T_{I}$), the enabling time
of the I-controller can be predefined related to the rising control deviation
($dev$) and set velocity ($v_{set}$). $T_{I}$ is described by (8).

$T_{I}=\frac{v_{set}}{v_{max}}\cdot(K_{T_{max}}-K_{T_{min}})+K_{T_{min}}$ (8)

If the control deviation overcomes the threshold in its current operating
point, the integrator is reset by (9). The I-controller is only enabled in a
certain error band in order to adjust the system as precisely as possible.
This error band is centered around the set point and changes its size relative
to the set point. This allows the control behavior to be kept consistent,
regardless of the operating point and abrupt throttle commands. Additionally,
the integrator is limited to prevent an integration wind-up when the actuator
is saturated.

$\begin{split}(dev>T_{I})\ \ \ I_{enbl}=0\ \ \ ;\ \ \ (dev\leq T_{I})\ \ \
I_{enbl}=1\end{split}$ (9)

The throttle valve position ($Pos_{thr}$) is given by (10).

$Pos_{thr}=K_{P}(v_{sc})\cdot dev+K_{I}(v_{sc})\cdot I_{enbl}(T_{I})\cdot\int
dev$ (10)

Based on the longitudinal scooter model, a closed loop simulation was set up
for tuning the controller. A max. overshoot of 0.3 km/h (barely noticeable for
the rider) and robust control behavior, under consideration of sensor
disturbances, were required. In addition to a fast settling time, driveability
was also taken into account. At high gains, the powertrain reacts too
sensitively to small throttle deflections, despite constantly stable control
behavior. Therefore, a parameter set was chosen that combines the highest
possible performance with enabling smooth accelerations. Using simulation, it
was determined which parameter set was best with regard to control
performance. This was then optimized for driveability in a road test. Table 3
shows the resulting controller parameters. The algorithm is being executed
with a cycle time of 20 ms.

Table 3: List of controller parameters | $P$ | $I_{sim}$ | $I$ | $T_{I,sim}$ | $T_{I}$
---|---|---|---|---|---
$K_{min}$ | 2.1 | 0.02 | 0.01 | 1.44 | 1.3
$K_{max}$ | 16 | 0.07 | 0.075 | 6.84 | 6.3
$Os$ | / | 3.0 | 3.0 | 3.0 | 3.0
$Limit$ (%) | -100/100 | 0/100 | 0/100 | 0/100 | 0/100

###  2.5 HMI \- Virtual dashboard

Virtual dashboards are uncommon in this vehicle class. In context of the VC,
cruise control and driving modes, they offer plenty of opportunities for
adequate visualization and interaction. The HMI consists of a 4.3” LCD display
combined with a STM32 MCU (H7B3) (STMicroelectronics, 2019). Inputs were
realized using a micro joystick integrated into the throttle armature. In
addition to the original information, the following functions have been
enhanced, as shown in Figure 8.

Figure 8: HMI virtual dashboard

  1. 1.

Set/scooter velocity: Based on the VC strategy, the by the rider given
velocity demand (blue) needs to be displayed in addition to the scooter
velocity (green).

  2. 2.

Cruise control interface: For enabling/disabling the cruise control, the
selected velocity is set by a small arrow in combination with a cruise control
sign.

  3. 3.

Eco-score: To challenge eco-friendly driving, an eco-score (green leaf) is
built up with the fuel savings made.

  4. 4.

Evaluation information: The engine speed, amount of fuel injected and
recording state of the Measurement Box (MB) are to be displayed.

  5. 5.

Driving mode: By use of the HMI, the developer can switch between the original
restriction (TSS signal/no VC) and enabled VC-System (TSS emulated by MECU).

###  2.6 Measurement box

For system integration, VC parametrization and lastly functional verification,
system states and dynamic vehicle data must be measured. Therefore, a MB was
designed for logging CAN bus data, measuring engine speed and injection cycle,
storing files in real-time and offering data transfer via a mircoSD card. Set
point and process value of the velocity as well as the throttle valve position
are tapped directly from the vehicle’s CAN bus. For the measurement of the
current engine speed, the speed signal of the original crankshaft sensor was
processed and evaluated. To measure vehicle dynamics, a 6-axis inertial
measurement unit was used, fusing sensor values by complementary filters to
measure orientation and acceleration. All measured quantities are periodically
stored with a time stamp, provided by a real-time clock. As shown in Figure 3,
the MB (No. 5) is not part of the VC-system. Starting or stopping a recording
is done via the HMI. New files are automatically created and adjustments to
the recording rate or CAN message selection can be edited quickly.

##  3 Results

###  3.1 Controller performance

To achieve stable, stationary and precise control, an adaptive PI-controller
was tuned simulation-based (Table 3) under the criteria of max. 0.3 km/h
overshoot. A test cycle, consisting of a series of increasing velocity steps
(0, 10, 20,… km/h) and sudden accelerations from standstill (0, 10, 0, 20,…
km/h), was performed. That way, the proper function of the adaptive threshold
for larger steps in set point as well as small variations can be tested. By
comparing the set velocity with the resulting simulated/measured velocity and
the simulated closed-loop/measured TVA position, the simulation validity and
real-world control performance can be investigated. Figure 9 presents the test
results (top: incremental, bottom: initial). For both scenarios, the
controller reached the exact set velocity without any overshoot or
instability.

Figure 9: Controller performance

Velocities lower than 11 km/h led to unsteady settling due to the centrifugal
clutch engagement/slip. For higher velocities, the max. error between
simulated and measured dynamics is 1.9 % for step-wise increments. Initial
accelerations from standstill lead to shifted velocity origins that can be
justified by the clutch engagement. The resulting error is max. 2.3 % which
proves a plausible controller simulation beyond the engagement process.
Deviations in TVA openings are due to the non-linear relationship between
throttle opening and engine power output. This was neglected in the
simulation, but has no measurable effect on the control behavior. Higher
controller gains are feasible but lead to oversensitive response to throttle
changes.

###  3.2 System behavior

After validation of the control strategy and performance, the effect on
vehicle dynamics and engine control is to be examined in real-world scenarios.
Three frequently occurring disturbance scenarios were analyzed: max.
acceleration (S1), a sudden change from a level road to an uphill/downhill
slope (S2) and an acceleration on a downward slope followed by a transition to
a level (S3). The comparison to the original restriction is shown in Figure 10
by measurements of the velocity, throttle valve position and the fuel
injected. Differences between scenarios result from small road gradient
variations.

Figure 10: System behavior

Scenario (S1) demonstrates the preservation of driving performance.
Acceleration is equally fast with both systems, whereby the original system,
contrary to the VC, shows a slight overshoot of 0.3 km/h (VC: 0 km/h) when
reaching the speed limit. Significant differences can be found in the throttle
valve position and injection quantity. When approaching the speed limit (orig.
48.7 km/h, dash-dotted line), the VC regulates the throttle valve at the
corresponding operating point (50 %). Consequently, the injection quantity
also drops by 25 %. Scenario (S2) is initiated at top speed, which is why the
VC has already settled to approx. 35 % throttle position. At time 4.75 s, a 8
% downhill slope suddenly occurs after a small bump. Despite aggressive
excitation, the system behavior remains stable and even resembles the response
of the control applied by Peugeot. In both cases, the overshoot behavior at
the entry of the gradient also shows max. 1 km/h. The VC closes the throttle
valve almost completely, whereas originally it’s fully open and three times
the fuel is injected. Scenario (S3) shows the max. acceleration on a 15 %
downhill slope followed by a transition to level ground. An improved overshoot
(1.1 km/h smaller) can be seen with the VC. While the speed limit can no
longer be maintained in the original condition, it can be successfully
controlled by the VC. It is again shown that the throttle valve is completely
closed and the injection is reduced to idle state. At time 16 s, the gradient
ends. The system response is stable with no discernible response in velocity.

###  3.3 Fuel savings

In order to demonstrate the fuel savings/CO2 reduction under real-world
conditions, a 50.4 km long test cycle was driven that depicts 61 % urban and
39 % rural environments. Three cycles each were driven for the original
throttle and the VC-system. Weather conditions, driving time and traffic
situation were monitored to create comparable test conditions. The tire
pressure was kept constant at the manufacturer’s specification of 2.1 bar and
the rider (85 kg) was not changed. Consumptions were determined by means of
calibrated measuring cylinders. Inaccuracies of max. 2.5 ml occur due to
measurement errors, which result in a max. total error of 0.3 %. The measured
volumes were referenced to a temperature of +15 °C. By applying the VC, a
saving of 13.6 % could be demonstrated compared to the original condition
(1.82/2.11 l/100 km). Since the combustion of one liter of gasoline releases
2280 g CO2, this corresponds to an estimated reduction of approx. 661 g/100
km. Table 4 shows the related results.

Table 4: List of Road testing results Mod. | Weath. | Dur. | Mile. | Vol. | Cons. | Aver.
---|---|---|---|---|---|---
/ | °C | h | km | ml | l/100km | l/100km
Org. | sun,dry,12 | 1:25 | 50.4 | 1060 | 2.10 |
Org. | sun,dry,16 | 1:26 | 50.4 | 1061 | 2.10 | 2.11
Org. | sun,dry,17 | 1:31 | 50.4 | 1065 | 2.11 |
VC | sun,dry,17 | 1:29 | 50.4 | 925 | 1.83 |
VC | sun,dry,12 | 1:32 | 50.4 | 885 | 1.76 | 1.82
VC | sun,dry,18 | 1:33 | 50.4 | 945 | 1.87 |

This saving can be explained by the optimization of the ignition timing. On
the flat, approx. 16 degrees after TDC can be measured with original
restriction. By applying the TbWS, the air supply is controlled instead of the
ignition timing. This allows the EC to set the optimum ignition timing and
reduce the injection quantity to maintain $\lambda$=1. Now the ignition timing
corresponds to -5 degrees (before TDC) at max. velocity and is thus in the
optimum range (compare Fig. 1). A significant improvement in exhaust gas
composition can also be expected.

##  4 Discussion

Modern four-stroke scooters already enable a comparatively environmentally
friendly type of individual mobility. The system developed increases
efficiency by an additional 14 %. More and more electric scooters are
available as alternatives, but they come with significantly smaller ranges,
heavy batteries and higher prices. If CO2 emissions are considered in
comparison, vehicle production and electricity generation must be considered.
One liter of gasoline emits about 2280 g (Kawamoto et al., 2019) of CO2 when
burned, while the European average for the production of one kilowatt-hour of
electrical energy is about 226 g of C02 (World Energy Council, 2021). The
average consumption of combustion-powered scooters from several leading
manufacturers in the EU (Peugeot, Vespa, Piaggio) equals an average of 2.22
l/100 km (5143 gCO2/100 km) for gasoline-powered and 4.11 kWh/100 km (928
gCO2/100 km) for elec-tric-powered scooters. Taking into account the average
battery capacity of the referred manufacturers, a capacity of 4.88 kWh
results. The production emissions of both vehicle types differ mainly through
battery production, which releases about 177 kg of CO2 per kWh of capacity
(Kawamoto et al., 2019). Consequently, the electric drive generates an
additional 864 kg offset of CO2 on average. With a life expectancy of
scooters/mopeds of 20000 km, the CO2 emission for the gasoline-powered scooter
is 1028.6 kg and 1049.6 kg for the electric-powered one. The test vehicle
equipped with the velocity-controlled TbWS achieves a CO2 footprint of 829.9
kg during its service life with an average consumption of 1.81 l/100 km,
making it 20.9 % more environmentally friendly than average electric scooters.
After 26800 km, the electric scooter would show a CO2 benefit. When charging
with electricity from renewable energy sources solely, the by battery
production caused emissions would still overcome the test vehicle’s footprint.
By using eco-fuels, the CO2 balance could even be neutral. Accordingly,
consumption optimization would serve for economic efficiency and effective
utilization of the eco-fuel resource (Agarwal and Valera, 2022).

Besides the eco-friendliness, the driving experience can be questioned. By VC
implementation, there is no direct command line to the engine. The system
reacts proportionally to the control deviation, which is similar to
conventional acceleration behavior. Like cruise control, the system can
accelerate independently on uphill or downhill grades to maintain speed. The
rider has to get used to this behavior, as it differs from conventional
scooters. Other control approaches could be considered. Furthermore, the
highest energy loss of 28 % (test vehicle) occurs in the variomatic
transmission. This design-related problem affects all scooters with CVT belt
drive (Zhu et al., 2010). By developing a more efficient torque converter, the
efficiency could be increased significantly. Consumption of less than 1.5
l/100 km could become feasible. Hydrodynamic torque converters could be
investigated (Lindemann et al., 2014).

##  5 Conclusion

In this article, the development of a velocity-controlled Throttle-by-Wire-
System is presented. The objective, to provide a more eco-friendly restriction
method, was successfully reached. Instead of shifting the ignition timing to
comply with the legal maximum velocity for scooters, this system regulates the
air supply to the engine. For this purpose, a wheel speed and throttle grip
sensor were designed to sense feedback and control variable. A throttle
actuator was designed to allow system intervention by engine power
manipulation. Power supply, data processing, motor control emulation, fail-
safe functions and actual control were implemented on a main ECU. By
simulating the vehicle’s longitudinal dynamics, an adaptive controller could
be designed to control the velocity stable, with stationary accuracy and
without overshoot. The driveability/operability of the vehicle was
prioritized. A virtual dashboard was used to interface between the system and
the rider, clarifying the functionality and promoting eco-friendly driving
with an eco-score. Additionally, it enables the activation and adjustment of
the cruise control. In order to be able to evaluate the system behavior, a
measurement box was integrated, logging all system parameters. Ultimately, a
fuel saving of 13.6 % was achieved and proven in the road test, while
maintaining the vehicle’s performance. As a result, the competitiveness
against electrically powered scooters/mopeds was underlined. As long as large
amounts of CO2 are emitted during battery production, the optimized four-
stroke powertrain presented here represents an excellent transitional
solution. Mechanical optimizations could further reduce consumption.

## Acknowledgements

The herein presented research was funded and supported by the University of
Cadiz and Frankfurt University of Applied Sciences. The authors acknowledge
the valuable support of Peugeot Motocycles Deutschland GmbH.

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  *[VC]: Velocity Control
  *[TbWS]: Throttle-by-Wire-System
  *[CVT]: Continuously Variable Transmission
  *[TDC]: Top Dead Center
  *[TbWSs]: Throttle-by-Wire-System
  *[WSS]: Wheel Speed Sensor
  *[EC]: Engine Controller
  *[ECU]: Electronic Control Unit
  *[HMI]: Human Machine Interface
  *[TPS]: Throttle Position Sensor
  *[TVA]: Throttle Valve Actuator
  *[WSSs]: Wheel Speed Sensor
  *[TSS]: Transmission Speed Sensor
  *[MECU]: Main ECU
  *[ECs]: Engine Controller
  *[MB]: Measurement Box