|
Scientific Paper / Artículo Científico |
|
|
|
|
https://doi.org/10.17163/ings.n29.2023.04 |
|
|
|
pISSN: 1390-650X / eISSN: 1390-860X |
|
|
STUDY OF THE ENERGY EFFICIENCY OF AN URBAN E-BIKE CHARGED WITH A STANDALONE PHOTOVOLTAIC SOLAR CHARGING STATION AND ITS COMPLIANCE WITH THE ECUADORIAN GRID CODE NO. ARCERNNR – 002/20 |
||
|
ESTUDIO DE EFICIENCIA ENERGÉTICA DE UNA BICICLETA ELÉCTRICA URBANA CARGADA CON UNA ESTACIÓN DE CARGA SOLAR FOTOVOLTÁICA AUTÓNOMA Y SU CUMPLIMIENTO CON LA REGULACIÓN ECUATORIANA ARCERNNR – 002/20 |
||
|
Vinicio Iñiguez- Morán 1,* Danny Ochoa- Correa 2 |
|
Received: 02-08-2022, Received after review:
20-10-2022, Accepted: 10-11-2022, Published: 01-01-2023 |
|
Abstract |
Resumen |
|
E-bikes are an emerging sustainable means of
transportation, if adopted massively, they can help
face the challenges of human mobility in urban centers worldwide. In Cuenca,
Ecuador, the local government built cycle routes (13.47km) connecting
strategic points to facilitate and encourage sustainable mobility. However,
the effective implementation of the electromobility
strategies at a large scale entails impacts on the power grid, like the
increase in the energy demand and the possible decrease of the energy quality
due to the harmonic distortion that characterizes the battery’s
charging current. This research aims to obtain a primary input to evaluate
such impacts through an energy efficiency study of an urban e-bike charged by
a standalone solar photovoltaic charging station implemented in the Microgrid Laboratory of Universidad de Cuenca. The
methodology includes the experimental characterization of the battery’s
charging regime, the vehicle’s energy efficiency calculation, and the
evaluation of its compliance with Ecuadorian grid code No. ARCERNNR – 002/20.
Results show that the battery’s charger performs a charging regime
standardized by German regulations, delivering 92% of charge in 4.82 hours.
The e-bike’s calculated average energy efficiency is
2.18 kWh/100 miles or 73.77 m/Wh, and a fuel
economy of 1545.1 MPGe. Finally, the magnitude of
the first four odd harmonic components and the total harmonic distortion of
the charging current exceeds the limits established by the grid code in
force. |
Las bicicletas eléctricas (e-bikes) son un medio de transporte sostenible emergente. Si se adoptan masivamente, ayudarían a enfrentar los desafíos de movilidad humana en las ciudades del mundo. En Cuenca, Ecuador, el gobierno local construyó ciclovías (13,47 km) que conectan puntos estratégicos, para facilitar y fomentar la movilidad sostenible. Sin embargo, la implementación efectiva de las estrategias de electromovilidad a gran escala conlleva impactos en la red eléctrica, como el aumento de la demanda de energía y la posible disminución de su calidad debido a la distorsión armónica de la corriente de carga de la batería. El propósito de esta investigación es hacer una evaluación preliminar de dichos impactos, mediante el estudio de eficiencia energética de una e-bike urbana cargada con una estación solar fotovoltaica aislada, implementada en el Laboratorio de Micro-Red de la Universidad de Cuenca. La metodología incluye la caracterización experimental del régimen de carga de la batería, el cálculo de la eficiencia energética del vehículo y la evaluación del cumplimiento de la normativa ecuatoriana No. ARCERNNR–002/20. Los resultados muestran que el cargador de la batería implementa un régimen de carga estandarizado por normas alemanas, entregando 92% de carga en 4,82 horas. La eficiencia energética promedio de la e-bike es 2,18 kWh/100 millas o 73,77 m/Wh, y una economía de combustible de 1545,1 MPGe. Finalmente, la magnitud de las primeras cuatro componentes armónicas impares y la distorsión armónica total de la corriente de carga supera los límites establecidos por la normativa. |
|
Keywords: e-Bike, Electromobility, Power Quality, Lithium-Ion
Batteries, Grid code No. ARCERNNR 002/20, Std.
DIN 41772 |
Palabras clave: bicicleta eléctrica, electromovilidad, calidad de energía, batería de iones de litio, resolución N.° ARCERNNR 002/20, Est. DIN 41772 |
|
1,* Laboratorio de Micro-Red, Universidad de Cuenca, Ecuador. Corresponding autor ✉: vinicio.iniguezm@ucuenca.edu.ec 2 Facultad de Ingeniería, Universidad de Cuenca, Ecuador.
Suggested citation: Iñiguez- Morán, V.; Villa- Ávila, E.; Ochoa- Correa, D.; Larco- Barros, C. and Sempertegui-Álvarez, R. “Study of the Energy Efficiency of an Urban E- Bike Charged with a Standalone Photovoltaic Solar Charging Station and its Compliance with the Ecuadorian Grid Code No. ARCERNNR- 002/20”. Ingenius, Revista de Ciencia y Tecnología. N.° 29, (january-june). pp. 46-57.23. DOI: https//doi.org/10.17163/ings.n29.2023.04 |
|
1.
Introduction Nowadays, many urban population centers
worldwide are experiencing serious problems related to vehicular congestion,
negatively impacting people’s quality of life. Its main manifestation is the
progressive reduction in traffic speeds, observed in increases in travel
times, consumption of fossil fuels, other operating costs, and atmospheric
pollution, concerning a traffic flow free of traffic jams [1]. Therefore, the
authorities, the different social actors and citizems
have used some strategies aimed at mitigating these impacts. These strategies
follow various paths, such as improving the mass
public transport offer (in terms of fares and routes), restricting the access
of private transport in urban centers, promoting modes of transportation of
low environmental impact [2]. Among the initiatives that are part of this
last strategy, micromobility has been gaining
relevance as an increasingly widespread transport solution [3]. Micromobility refers to using a wide variety of
private or shared light vehicles that operate at low speeds and for short
trips; it includes electric bicycles (e-bikes), electric scooters
(e-scooters) and electric mopeds. The way people travel in urban areas is
rapidly changing as this concept is quickly adopted and
promoted to achieve a more sustainable transportation system [4]. The growing
popularity of electric micromobility means has
gained the interest of scientists around the world, a fact reflected in a
considerable number of research works. In the literature, many papers address
legislative issues related to micromobility,
perhaps motivated mainly by the report "Global EV Outlook 2020 -
Entering the decade of electric drive?" published by the International
Energy Agency (IEA) in 2020 [5]. For example, the
authors in [6] showed that European Union (EU) countries and the United
Kingdom (UK) have different approaches to electromobility
means in legal terms, considering them as a means of micro transportation or
personal transport. In addition, this work shows that in some countries, electromobility means such as e-scooters are not defined
in regulations, but other rules apply (e.g., regulations for bicycles). The
fact that such regulations consider users of micromobility
means as pedestrians or cyclists massifies them in
urban environments, generating the need to develop detailed legislation to
condition the safe movement of its users and manage them properly in cities
to avoid chaos, as mentioned by researchers in [7]. In this sense, [8]
presents a comparative study conducted in thirty European cities aimed at
assessing the management problems of e-scooters in urban spaces. The results
show that the shared use of micromobility means is
becoming more and more attractive, forcing public electric energy service
providers to assume significant increases in the loadability
of their networks. |
In the United States
of America (USA) context, researchers in [9] show that
people often prefer electric micromobility modes to
cars, especially in many USA cities. Moreover, in the same work, the authors
claim that electric micromobility modes could
complement public transport, emphasizing the modal integration and social
benefits of introducing a shared- use model. On the other hand,
researchers in [10] studied the impact of time variables, such
as weather data and time- invariant variables on the expected demand for micromobility means (taking e-scooters as a case) in
Chicago (USA). The authors focused on determining the location of
e-scooter charging stations and found that economic factors were decisive in
this task. Similarly, authors in [11] considered a multi-objective stochastic
location assignment model for e-scooter battery swapping stations. In [12],
the study focused on finding an optimization model to define the location of
multiple charging stations of different technical characteristics for
electric micromobility vehicles. Then, in the Latin
American context, interesting works in [13, 14] report electromobility
experiences in countries such as Brazil and Uruguay, respectively, while in
[15], the Inter-American Development Bank (IDB) presents a more general Latin
American perspective on this matter. In the Ecuadorian
context, since 2020, the most populated cities have experienced a significant
increase in the demand and use of electric vehicles to meet their mobility
needs [16]. According to [17, 18], the COVID-19 pandemic boosted this growth
in electromobility, which affected conventional
urban transport (buses and taxis) both in terms of availability and increased
risk of contagion in these mass media of human mobility. A reality perceived
on a global scale as well. Although government
authorities substantially eased the health restrictions established during
the pandemic and reactivated public and private transport services,
statistics show that the preference for using electric bicycles and scooters
has remained in the main cities. This fact points to an imminent paradigm
shift in mobility that neither local governments, companies, nor businesses
specialized in mobility have ignored. In the city of Cuenca, the third most
populated in Ecuador, the Decentralized Autonomous Government (GAD-Cuenca)
has undertaken actions to facilitate and encourage sustainable mobility
through a Mobility Plan [19], three of which are worth mentioning. The
construction of 13.47 km of cycle routes connecting the most strategic points
of the city [20]; the operation of "Tranvía de
Cuenca", the mass electric transport service with 21.4 km of tracks
[21]; the implementation of charging stations in one of the city’s emblematic
parks [22]. |
|
Universidad de Cuenca, a public institution
of higher education based in the city, launched its institutional program named MoverU,
whose purpose is to develop a sustainable mobility system based on scientific
evidence that contributes to proposals and actions to solve urban
transportation problems, aiming to improve people’s quality of life. Within
the program’s initiatives, it is possible to identify micromobility
projects build around using light means of transport with electric
assistance. In addition, theCentro Científico Tecnológico y de Investigación Balzay (CCTI-B)
of Universidad de Cuenca has an electrical micro-grid laboratory that constitutesa test bench for carrying out studies on using
and managing renewable energy sources to meet human energy necessities
cleanly and sustainably. This laboratory has different electrical
generation systems: solar photovoltaic, wind, hydraulic, fuel cells,
etcetera; energy storage systems: electrochemical batteries, vanadium flow
batteries, hydrogen production by electrolysis, among others; and different
final uses of the energy produced/stored: the electrical installations of the
building that houses the laboratory, programmable electrical loads (for the
emulation of consumption profiles), and electric vehicle charging stations
[23]. All
the characteristics listed previously make the CCTI-B’s electrical Microgrid Laboratory an energy self-sustaining entity
with a wide range of opportunities for research, technological development
and innovation. According to the conclusions reported in [24], the laboratory
is a Latino-American benchmark since it is the most well-equipped
in the region. Among the technical equipment
available stands out three electric vehicles (a car and two vans), five urban
electric bicycles, one electric mountain bicycle, and two electric mopeds
that have motivated a line of research focused on electromobility,
the object of this research work. From
a technical point of view, the effective implementation of the electromobility strategies mentioned entails a series of
impacts on the electrical network. The most evident is the increase in the
demand for electrical energy caused by the massive connection of vehicles at
different points of the network to charge their batteries, which is a problem
for electricity distribution companies. Particularly in the case of electric micromobility users scattered throughout the electric
coverage area of a city, it is essential to characterize their short-term
power consumption profile. This information enables assessing the possible
effects on the energy quality presented by battery charging systems,
especially when there are a lot of users. A
characterization of this energy consumption will provide the distribution
company with invaluable input for planning and carrying out studies of the
impact on the network of residential consumers with loads intended for electromobility. This work presents a detailed study to
calculate the energy efficiency of a commercial model of an urban e-bike
charged with an isolated charging station |
based on a photovoltaic solar source,
implemented with equipment from the inventory of the electrical Microgrid Laboratory of the CCTI-B. The electric autonomy
tests of this electric means of transportation are carried
out through actual trips in the city of Cuenca, taking care of using
the existing cycle routes in most cases. Besides presenting
the methodology used to estimate the autonomy of the electric bicycle, the
paper describes a procedure leading to the characterization of the electrical
profile of energy consumption during the charging hours of its battery. Also, it contains an evaluation of the affectation on the
quality of energy supplied by the grid assessed under the Ecuadorian grid
code ARCERNNR 002/20. The study aims to obtain a primary input to evaluate
the impact that the massification of these new
actors would have on the electricity distribution network and to face this
new paradigm of urban mobility. 2.
Materials and Methods 2.1. E-bike under study The electric vehicle under study
is an e-bike of the brand ECOMOVE, model TIV, shown
in Figure 1. It belongs to the Microgrid
Laboratory, a part of the CCTI-B, Universidad de Cuenca. The electrical tests
performed on the e-bike, which are described below, were
non-invasive. In other words, the vehicle’s internal components were never accessed or tampered with. This urban e-bike
weighs 25 kg and is made of 6061 aluminum alloy. It has a 36 V/10 Ah
Lithium-Ion (Li-ion) battery and a 36 V/250 W Brushless Gearless Hub Motor in
the back or rear wheel. It has a maximum speed of 28 km/h and an autonomy of
30 km when driven in electric assist mode.
Figure 1. E-
bike ECOMOVE model TIV under study |
|
The battery’s
nominal electrical energy storage capacity does not appear in the manufacturer’s
datasheet. However, using the charging capacity (10 Ah) and average battery
voltage (36 VDC) for calculation resulted in 0.360 kWh. The battery charger
is the model KYLC084V42J – Class II, manufactured by WuXi
KeYu Electronic Technology Co., Ltd., and designed
for 42V lithium-ion rechargeable batteries. The device’s AC input supports a
nominal voltage of 100 to 240 VAC, at a frequency of 50 to 60 Hz, and a
maximum RMS current of 1.8 A. The DC output offers 42 VDC and a maximum
average current of 2 A (with protection fuse T/3.15 A/250 V, slow action). 2.2. Route planned to discharge the e-bike´s
battery A 34 km route (approx.) was
planned, allowing the use of the cycle routes in Cuenca. The Microgrid Laboratory, located in Campus Balzay, is the point of departure and arrival. A GPS
watch is used to calculate the actual distance
traveled. This device records geolocation data and other parameters of
interest to the user, such as the linear speed of movement (km/h) and heart
rate (bpm). Figure 2 shows the entire route, with five reference points
identified.
Figure 2. Route traveled with the e-bike. White bubbles with
numbers identify each Kilometer traveled. The geolocation data was recorded
with the GPS device The departure where the entire
battery’s state of charge (SOC) is available (SOC = 100%); the arrival where
the battery has been fully discharged (SOC = 0%); it is no longer possible to
use the electric assist mode of the e-bike; and three additional reference
points so the reader can easily recognize the route traveled. 2.3. Standalone solar photovoltaic charging
station A solar photovoltaic charging station
isolated from the power grid is implemented in the Microgrid
Laboratory to recharge the e-bike’s battery, using an Ampere Square Pro (ASP)
energy storage and management system based on lithium batteries. The ASP has
five main components starting with a 6-kWh lithium-ion battery (BT). A 3-kW
hybrid |
bidirectional
inverter (INV) to carry out the DC-AC and AC-DC power conversions. An energy
management system (EMS) manages the energy to regulate the charge/discharge
cycles of the device. It has a bidirectional energy meter to register
generation and consumption; and electrical protections (B1, B2, . . ., and B5) to look after the main components and to
perform command actions. Also, five polycrystalline solar panels of 38 V
at a maximum power of 335 W, series-connected, supply energy to the ASP. The
panels are installed on the roof of the laboratory
building, as can be seen in Figure 3.
Figure 3.
Solar panels installed on the terrace of the Microgrid
Laboratory. The five series-connected panels indicated and numbered in the
picture are series- connected to the PV String 1 input of the Ampere Square
Pro energy storage system Figure
4 shows the complete standalone solar photovoltaic charging station, pointing
out its main components. The photograph shows the measurement equipment
installed at the outlet of the charging station.
Figure 4. Standalone solar photovoltaic charging station for e-bikes implemented in the Microgrid Laboratory. The e-bike under study is being recharged |
|
Figure 5 shows the
electrical diagram of the standalone charging station. An energy & power
quality analyzer that fulfills the international standards IEC 61000-4-7, IEC
61000-4-30, and IEEE Std 519-2014 measures the
energy consumption of the e-bike and records the variables of interest.
Figure 5. Electrical
diagram of the standalone solar photovoltaic charging station implemented in
the Microgrid Laboratory [25] The
only connected load is the e-bike under study. The analyzer works in logger
mode to take measurements from an AC single-phase system with line and
neutral 127 VRMS nominal, using two voltage terminals and two current probes
(one for neutral) with a recording interval of one second. The electrical
variables recorded are in line to neutral (L-N) voltage, phase current,
frequency, distortion power factor, displacement power factor, current and
voltage total harmonic distortion, current and voltage harmonic components
(H2, H3, . . ., H11), crest factor of current and voltage, flicker, active
power, apparent power, and active energy consumed (Wh). 2.4. Ecuadorian
grid code ARCERNNR 002/20 The Ecuadorian grid
code ARCERNNR 002/20 entitled Quality of the Electricity Distribution and
Commercialization Service has the purpose of: "Establishing the
indicators, indices, and limits of quality of the service of distribution and
commercialization of electrical energy; and defining the measurement,
registration and evaluation procedures to be fulfilled by the electricity
distribution companies and consumers, as appropriate" [26]. For
this research, the quality attribute of interest is at the consumer’s side,
stated in section 5.2 of [26] as Consumer Quality Aspect and evaluated
through the current harmonic distortion. According to
section 29 of [26], the indexes to evaluate the current’s individual
harmonic |
distortion and its total demand
distortion (also known as the current’s total harmonic distortion, THD) are
calculated as follows:
Where: Ih,k
is the h th current’s harmonic in the kth
10-minute range as required by the standard IEEE Std
519-2014; Ih,i is the effective value
(RMS) of the h th current’s harmonic (for h = 2,
3,. . . , 50) measured every three seconds (for i =
1, 2, 3,. . . , 200); DIh,k is
the individual distortion factor of the h th
current’s harmonic (for h = 2, 3, . . . , 50) in the kth
10-minute range; T HDk is the
current’s total harmonic distortion in the kth 10-minute
range; and Ih,1 is the effective value (RMS) of
the fundamental component of the current (60 Hz). Table
1 shows the maximum levels for the current’s individual and total harmonic
distortion, taken from section 29.2 of [26], only up to the 17th harmonic
component for this study. Table 1. Comparison of monitoring articles similar to this
Section 29.4 of the grid code establishes
that consumers fulfill at the measurement point the current’s individual
harmonic distortion factor DIh,k
and the current’s total harmonic distortion factor THDk
,both calculated using equations (1), (2), and (3) when 95% or more
of the data registered during an evaluation period of at least seven days are
between the limits listed in Chart 1. This
research uses the indexes and limits specified by the Ecuadorian grid code
[26] to analyze the impacts on the power grid of charging the e-bike by
processing the data of the lithium-ion battery’s
charging entire regime, which takes a few hours to complete. |
|
3. Results
and discussion One of the technicians of the Microgrid Laboratory (male, 36 years old, 73 kg of weight and 1.73 m of height) used the e-bike
under study to ride through the previously planned route, only using the electric
assist mode of the vehicle. All the electrical energy stored in the battery
drained over 30.91 km of distance in two hours, ten minutes and thirty-five
seconds (02:10:35), with an average linear speed of 14.2 km/h. So, the experiment achieved a full discharge in ordinary
battery operation. The battery charging
process started in the Microgrid Laboratory,
connecting the charger to the standalone solar photovoltaic charging station
as its only load. It is necessary to mention that the charger stayed connected
even after it turned on the light signal of fully charged to register data of
the last charging stage of the battery, known as the float charge state. The
following is an analysis of the data, acquired by the energy & power
quality analyzer. 3.1. Energy quality analysis The lithium-ion battery’s
charging process was 6.77 hours long (06:46:06), and the analyzer took 24366
measurements of the electrical variables of interest. The average measured
values of the AC RMS phase to neutral voltage (VF-N) connected to the input
of the battery charger are 127.24 V at a frequency (f) of 59.95 Hz. The
equipment registered an average crest factor (CF-V) of 1.42, with a maximum
value of 1.43 over the entire measurement range. Regarding the AC RMS
phase current (IF) the analysis of the average measurements registered shows
that the charger performs a charging regime named IUoI
specified by the German standards DIN 41772 [27], Supplement 1 to DIN 41772
[28], and DIN 41773-1 [29]. The goal of this standardized regime is to charge
the battery in a relatively short time without affecting its useful life, and
to keep the charge in the battery while the charger remains connected.
Figure 6. The average RMS phase
current (IF) data registered throughout the charging period show the three
stages of the charging regime IUoI |
Data shows that the
charging regime carried out by the charger of the e-bike under study consists
of 3 well-defined stages (refer to Figure 6). The following is a brief
description of these stages. I-phase. Also called the bulk charge stage.
The charger provides a constant charging current to the battery when there is
a deep discharge. The voltage increases without exceeding Umax
(which can be a fixed value or dependent on temperature), and the battery
absorbs the load. When the battery terminals reach Umax,
the charger goes to phase UO. The charger operated
in the I-phase stage for 4 hours and 49 minutes; the average RMS phase
current IF applied was 1 A; the average active power delivered was 81.30 W;
and the charging station supplied 385 Wh of active
energy to the e-bike’s charger. UO-Phase. Known as the constant-voltage
boost stage or absorption stage. Here the charger maintains a constant
overvoltage at the battery terminals while the charging current decreases.
This voltage value is not safe for an indefinite application, but it allows
charging the battery in less time. According to [29], the maximum voltage
should be between 2.33 V and 2.40 V per cell, depending on the battery’s
design and operating conditions. This phase ends when the charging current
reaches a minimum threshold Imin, and the charger
passes to phase U. In this stage, the
average RMS phase current IF applied decreases from 1.1 A to 0.2 A (Imin) in 1 hour and 25 minutes. Figure 6 shows that the
decrease of the charging current occurs in 0.1 A steps. Throughout the
UO-phase, only 34 Wh were
supplied to charge the e-bike’s battery. As a result, at the end of
the stage, the total active energy provided by the charging station to the
charger is 419 Wh in 6 hours and 14 minutes. At
this point, the battery is fully charged (SOC = 100%). U-phase. This last stage is
called the float charge stage. The charger applies a voltage safe to
maintain for longer periods without significantly affecting battery´s life.
In this stage, the current decreases to a residual value, making it possible
to compensate for the battery’s self-discharge. Since
the charger was connected even after it turned the light signal of fully
charged on to register data of the U-phase stage, it was possible to
determine that the residual value of the charging current is 0.1 A. Figure 6
shows that in this stage, the charging current stays at 0.1 A and commutes to
0.2 A for a few seconds to compensate the battery’s self-discharge. This behavior keeps happening
while the charger is connected to the station. In 32
minutes, the station supplied only 3 Wh to the
charger, for the compensation. A well-known fact
about lithium-ion batteries is that they do not need charging up to 100% to
prolong the battery´s life by avoiding the overvoltage of the second stage of
the charging regime shown in Figure 6 |
|
Because measurements
prove that 91.89% of the total electrical energy supplied by the charging
station to the charger (385 Wh out of 419 Wh, see Figure 7) occurred in the I-phase, the
forthcoming analysis focuses on the electrical parameters of interest
registered during the I-phase.
Figure 7. Active energy measured in
Wh delivered to the charger during the charging
process Regarding total
harmonic distortion, THD-V for the voltage VF-N and THD-A for the charging
current IF, the measured values were 4.71% and 106.01%, respectively. The
total power factor of the charger is 0.62, calculated by multiplying the
average registered values of displacement power factor (PF) and distortion
power factor (DPF), 0.63 and 0.99, respectively, since the charger is a
non-linear load. The odd harmonic
components of the phase current IF are responsible for the high values of
THD-A and the low total power factor, especially the components H3, H5 and
H7, whose average magnitudes are 80.71%, 57.14% and 32.41% of the
fundamental, respectively. Figure 8 shows the behavior of the first five odd
harmonic components of IF for the entire charging regime.
Figure 8. Measured average values
of the odd harmonic components (H3, H5, H7, H9 and H11) of the charging
current IF. The measuring equipment calculates these and other data in a time
window of 200ms according to the IEC 61000-4-7 standard for FTT fast Fourier
transform algorithms At 3 hours and 51 minutes of
charging during the I-phase, the waveform of the charging current IF shown in
Figure 9 |
results from the
effect of its odd harmonic components. The high value of THD-A makes sense by
only looking at the waveform. Regarding the waveform AC RMS phase to neutral
voltage VF-N, also presented in Figure 9, it is possible to obtained its closeness to a perfect sinusoidal. However,
the absolute instant values of VF-N slightly decrease when IF goes up to its
maximum positive value and negative minimum (highlighted in blue circles in
figure 8), which justifies the 4.71% of the measured THD-V.
Figure 9. Waveforms of the charging
current IF and phase to neutral voltage VF-N taken by the oscilloscope of the
energy & power quality analyzer at 3 hours and 51 minutes of charging
during the I-phase Concerning
the Ecuadorian grid code ARCERNNR 002/20, the limits for the magnitude of the
individual charging current’s odd harmonic components as a percentage of the
fundamental and its total harmonic differ in different ranges of the
relationship between the maximum short circuit current at the point of common
coupling and the maximum load current of fundamental frequency (H1), named
ICC/IL (refer to Chart 1). The grid code mandates that the magnitude of the first four odd
harmonics cannot exceed 15% in all the ranges. Likewise, the current’s total
harmonic distortion (THD-A) should never exceed 20%. In this context, the
measurements of the parameters above mentioned (H3 = 80.71%, H5 = 57.14%, H7
= 32.41%, H9 = 14.51%, and THD-A = 106.01%) exceed the limits established by
the grid code regarding the consumer quality aspect, which has to be
accounted to evaluate the impact of a future large-scale utilization of
e-bikes (brand and model under study, and others) in Ecuador. 3.2. Energy efficiency analysis According to [30], the user’s
habits determine an e- bike’s energy efficiency. It also mentions that no
unique metric exists to study this critical parameter. However, it suggests
that manufacturers often use the number of kWh per 100
miles of kWh per 100 miles to inform their
products’ energy efficiency ranges. Thus, the charger’s 419 Wh (0.419 kWh) |
|
absorbed in 6 hours
and 14 minutes at the end of the UO-phase is the battery’s actual electrical
energy storage capacity (SOC = 100%), assuming the losses in the charger are neglectable. Since this amount of electrical energy
drained over the 30.91 km ride (19.21 miles) during the experiment, then the
average energy efficiency of the e-bike under study is:
Where
ηE is the average energy efficiency measured
in kWh/100miles, d is the distance in miles, and E is the battery’s
electrical energy stored at SOC = 100% in kWh. The result of substituting
terms in (4) is 2.18 kWh/100miles. With the Ecuadorian national average rate
of 0.092 USD per kWh, the cost of riding 100 miles with the e-bike under
study is 0.2 USD (20 cents). The
number of meters the e-bike can go per watthour of electrical energy stored
in its battery is also helpful in evaluating energy efficiency, which is
calculated as follows:
With
d = 30910 m and E = 419 Wh, the calculation
results in 73.77 m/Wh for the vehicle under study
The USA’s Environmental Protection Agency (EPA) defines the term miles per
gallon equivalent or MPGe to rate the efficiency or
fuel economy of electric vehicles (EVs). It compares the amount of electric
energy required to charge EVs to the energy provided by a gallon of gas.
According to EPA estimations, the energy provided by one gallon of gas is
33.7 kWh. So, the 0.419 kWh required to charge the
e-bike entirely is equivalent to 0.0124 gallons of gas. Since the e-bike
covered 19.21 miles with that amount of energy, its MPGe
is calculated as follows:
Where
d is the distance covered by the e-bike measured
in miles, and G is the gallons of gas equivalent previously
calculated. The substitution of terms results in 1545.1 MPGe,
which is 12 times fold the fuel economy of a 2022 Tesla Model 3 RWD (132 MPGe), the highest-ranked EV in the Fuel Economy Guide
Model Year 2022 of the US Department of Energy [31]. The last statement
exemplifies the potential benefits of micromobility
vehicle usage. 4. Conclusions The research carried out in the Microgrid Laboratory of Universidad de Cuenca analyzes
the energy behavior of an |
urban e-bike of the brand ECOMOVE,
model TIV, on a route of 30.91 km in the urban area of Cuenca, in electric
assistance mode only. A solar photovoltaic charging station isolated from the
power grid was implemented in the laboratory. The
station has five polycrystalline solar panels of 335 W each,
series-connected; an energy storage and management system; and electrical
protections. To estimate the energy consumption of the e-bike and record the
variables of interest, a power quality and energy analyzer was
used. The average AC RMS
phase current data analysis shows that the e-bike’s battery charger applies
an IUoI charging regime standardized by the German
regulations DIN41772, Supplement 1 to DIN 41772, and DIN 41773-1, with three
well-defined stages. First, the bulk charge stage (I-phase), in which the
charger receives a constant current of 1 A for about 4.82 hours, delivers
91.89% of the charge to the battery, and raises the voltage at its terminals.
Then, the constant-voltage boost stage (UO-phase), where the charging current
decreases in discrete steps of 0.1 A, from 1.1 A to a minimum value of 0.2 A
in about 1.42 hours, and only delivers the 8.11% of charge remaining.
Finally, the charger reaches the float charge stage (U-phase) to solely
compensate battery self-discharge by applying 0.2 A for short periods, when
required. At this point, the reader has a characteristic model of the load
profile of a micromobility transport, which will
allow carrying out electrical network studies related to massifying
this type of consumer. Moreover,
measurements show that the battery’s actual electrical energy storage
capacity is 419 Wh, approximately. This capacity
provides an autonomy of 30.91 km, measured through a previously planned urban
route and only using the electric assist mode of the vehicle. Therefore, the
estimated average energy efficiency of the e-bike under study is 2.18
kWh/100miles or 73.77 m/Wh, and a fuel economy of
1545.1 MPGe. Therefore, the cost of riding 100
miles is 0.2 USD (20 cents). About 92% of the
total electrical energy supplied by the charging station to the charger, 385 Wh out of 419Wh, occurred in the
first stage of the charging regime (I-phase). The charging current is
constant and presents its highest average value, 1 A. Hence, the I-phase data
are the most relevant for energy quality analysis. Data analysis proves
that the total power factor of the charger is 0.62, and the total harmonic
distortion of the charging current (THD-A) is 106.01%, primarily due to its
first three odd harmonic components, H3, H5, and H7, whose average magnitudes
are 80.71%, 57.14% and 32.41% of the fundamental, respectively. The voltage
waveform is close to a perfect sinusoidal, which is proven
by a total harmonic distortion (THD-V) of 4.71%. According to the technical
specifications for assessing the consumer quality aspect of the Ecuadorian
grid code ARCERNNR 002/20, the magnitudes of the individual charging
current’s odd harmonic components as a percentage of the fundamental (H3 =
80.71%, H5 = 57.14%, H7 = 32.41%, and H9 = 14.51%, and the charging current’s total harmonic distortion (THDA= 106.01%)
are well above the limits established by the grid code. |
|
For a future
large-scale use of e-bikes as a means of transportation in the urban centers
of Ecuador, the vehicles’ charger’s fulfillment of
the law in force must be checked beforehand. Acknowledgments The work documented
in this manuscript is part of the
activities carried out in the project
entitled: “Movilidad Eléctrica: retos, limitaciones
y plan de implementación en el régimen especial de la Provincia de Galápagos
enfocada en el desarrollo sostenible y su factibilidad en la Ciudad de
Cuenca”, II Concurso de Proyectos de Investigación – Vinculación,
Vicerrectorado de Investigación y la Dirección de Vinculación con la Sociedad
de la Universidad de Cuenca. The
authors thank Universidad de Cuenca for easing access to the facilities of
the Microgrid Laboratory of the Centro Científico Tecnológico y de Investigación Balzay (CCTI-B),
for allowing the use of its equipment, and for authorizing its staff the
provision of technical support necessary to carry out the experiments
described in this article. References [1] A. Bull, Congestión de tránsito: el problema y cómo enfrentarlo. Comisión Económica para América Latina y el Caribe (CEPAL), Naciones Unidas, 2003. [Online]. Available: https://bit.ly/3TW587t [2] I. Thomson and A. Bull, La congestión del tránsito urbano: causas
y consecuencias económicas y sociales. Comisión Económica para América
Latina y el Caribe (CEPAL), División de Recursos Naturales e Infraestructura,
Unidad de Transporte, Naciones Unidas, 2001. [Online]. Available: https://bit.ly/3TW587t [3] P. Felipe-Falgas, C. Madrid-Lopez, and O. Marquet,
“Assessing environmental performance of micromobility
using lca and self-reported modal change: The case
of shared e-bikes, e-scooters, and e-mopeds in barcelona,”
Sustainability, vol. 14, no. 7, p. 4139, 2022. [Online]. Available:
https://doi.org/10.3390/SU14074139 [4] M. Elhenawy, H. A. Rakha, Y. Bichiou, M. Masoud, S. Glaser,
J. Pinnow, and A. Stohy, |
[5] IEA,
Global EV Outlook 2020. IEA. Paris, 2020. [Online]. Available: https://bit.ly/3gne4EW [6] M.
M. Sokolowski, “Laws and policies on electric
scooters in the european union: A ride to the micromobility directive?” European Energy and
Environmental Law Review, vol. 29, pp. 127–140, 2020, place:
Alphen aan den Rijn, The Netherlands Publisher:
Kluwer Law International. [Online]. Available: https://doi.org/10.54648/EELR2020036 [7] K. Turoń and P. Czech, “The concept of rules and
recommendations for riding shared and private e-scooters in the road network
in the light of global problems,” in Modern Traffic Engineering in the
System Approach to the Development of Traffic Networks, E. Macioszek and G. Sierpiński,
Eds. Springer International Publishing, 2020, pp. 275–284. [Online].
Available: https://doi.org/10.1007/978-3-030-34069-8_21 [8] A.
Li, P. Zhao, X. Liu, A. Mansourian, K. W. Axhausen, and X. Qu, “Comprehensive comparison of
e-scooter sharing mobility: Evidence from 30 european
cities,” 2022, vol. 105, p. 103229, Transportation Research Part D:
Transport and Environment. [Online]. Available:
https://www.sciencedirect.com/ science/article/pii/S1361920922000591 [9] K.
Wang, X. Qian, D. T. Fitch, Y. Lee, J. Malik, and G. Circella,
“What travel modes do shared e-scooters displace? a
review of recent research findings,” Transport Reviews, pp. 1–27,
2022. [Online]. Available: https://doi.org/10.1080/01441647.2021.2015639 [10] E. Ayyildiz, “A novel pythagorean
fuzzy multi-criteria decision-making methodology for e-scooter charging
station location-selection,” Transportation Research Part D: Transport and
Environment, vol. 111, p. 103459, 2022. [Online]. Available: https://doi.org/10.1016/j.trd. 2022.103459 [11] M.-D. Lin, P.-Y. Liu, J.-H. Kuo, and Y.-H. Lin, “A multiobjective stochastic location-allocation model for scooter battery swapping stations,” Sustainable Energy Technologies and Assessments, vol. 52, p. 102079, 2022. [Online]. Available: https://doi.org/10.1016/j.seta.2022.102079 |
|
[12] Y.-W. Chen, C.-Y. Cheng, S.-F. Li, and C.-H. Yu, “Location optimization for multiple types of charging stations for electric scooters,” Applied Soft Computing, vol. 67, pp. 519–528, 2018. [Online]. Available: https://doi.org/10.1016/j.asoc.2018.02.038 [13]
J. G. Schmidt, F. J. De Faveri, J. C. De Bona, E.
A. Rosa, L. S. Dos Santos, C. Q. Pica, L. B. R. Morinico,
and I. França, “Forecasts and impact on the
electrical grid with the expansion of electric vehicles in northeast of
brazil,” in 2022 IEEE/PES Transmission and Distribution Conference and
Exposition (T&D), 2022, pp. 1–5. [Online]. Available: https://doi.org/10.1109/TD43745.2022.9816978 [14] L. Di Chiara, F. Ferres, and F. Bastarrica, “Impact of electromobility deployment scenarios in the power system of uruguay by 2028,” in 2021 IEEE URUCON, 2021, pp. 360–363. [Online]. Available: https: //doi.org/10.1109/URUCON53396.2021.9647426 [15] D. P. Jaramillo, M. C. Gutiérrez, and R. Mix, Electromovilidad: Panorama actual en América Latina y el Caribe: Versión infográfica. Banco Interamericano de Desarrollo, 2019. [Online]. Available: http://dx.doi.org/10.18235/0001654 [16] Primicias. (2020) Las ventas de vehículos híbridos y eléctricos
crecen casi 300 %. [Online].
Available: https://bit.ly/3EM9mZR [17] J. Figura and T. Gadek-Hawlena,
“The Impact of the COVID-19 Pandemic on the Development of Electromobility in Poland. The Perspective of Companies
in the Transport-Shipping-Logistics Sector: A Case Study,” Energies,
vol. 15, no. 4, pp. 1–18, 2022. [Online]. Available: https://bit.ly/3XkWfaj [18] T. Rokicki, P. Bórawski, A. Beldycka-Bórawska, A. Zak, and G. Koszela, “Development of electromobility in european union countries under covid-19 conditions,” Energies, vol. 15, no. 1, 2022. [Online]. Available: https://doi.org/10.3390/en15010009 [19] GAD Cuenca. (2022) Plan de movilidad. [Online]. Available: https://bit.ly/3EPQlqh [20] P. Ochoa, Ciclovía de los Ríos de Cuenca. GAD Municipal de Cuenca, 2018. [Online]. Available: https://bit.ly/3ETFQCw [21] C. A. Cruz Leal and M. Virviescas Arias, “Evaluación de la operación del tranvía de cuenca ecuador y del tranvía de medellín colombia,” 2018.
|
[22] Redacción El Mercurio. (2021) Nueva electrolinera en Cuenca.
[23] J. L. Espinoza,
L. G. González, and R. Sempértegui, “Micro grid
laboratory as a tool for research on non-conventional energy sources in ecuador,” in 2017 IEEE International Autumn Meeting on
Power, Electronics and Computing (ROPEC), 2017, pp. 1–7. [Online].
Available: https://doi.org/10.1109/ROPEC.2017.8261615 [24] J. M. Rey, G.
A. Vera, P. Acevedo-Rueda, J. Solano, M. A. Mantilla, J. Llanos, and D. Sáez, “A review of microgrids
in latin america:
Laboratories and test systems,” IEEE Latin America Transactions, vol.
20, no. 6, pp. 1000–1011, 2022. [Online]. Available: https://doi.org/10.1109/TLA.2022.9757743 [25] AMPERE ENERGY, AMPERE square PRO Manual del instalador. AMPERE POWER ENERGY S.L., 2021. [Online]. Available: https://bit.ly/3ABgxm4
[26] ARCERNNR, Calidad del servicio de distribución y
comercialización de energía eléctrica. Agencia de Regulación y Control de
Energía y Recursos Naturales no Renovables, 2020. [Online]. Available: https://bit.ly/3tRydGr [27] DIN, DIN 41772
Static power convertors; semiconductor rectifier equipment, shapes and letter
symbols of characteristic curves. German Institute for Standardisation (Deutsches Institut für Normung), 1979. [Online]. Available: https://bit.ly/3GCFD8b [28] ——, DIN
41772 Supplement 1 Static power convertors; semiconductor rectifier
equipment, examples of characteristic curves for battery chargers. German
Institute for Standardisation (Deutsches
Institut für Normung). [Online]. Available: https://bit.ly/3GCJTEp [29] ——, DIN
41773-1 Static power convertors; semiconductor
rectifier equipment with IU-characteristics for charging of lead-acid
batteries, guidelines. German Institute for Standardisation
(Deutsches Institut für Normung). [Online].
Available: https://bit.ly/3hSi28X [30] T. McCarran and
N. Carpenter, “Electric bikes: Survey and energy efficiency analysis,” Efficiency
Vermont, 2018. [Online]. Available: https://bit.ly/3ACmgYP [31] Department of
Energy. (2022) Fueleconomy.gov Top Ten. [Online]. Available: https:
//bit.ly/3VdjKjy |