Location Optimization Charging Station of Three-Wheel Electric Vehicle: A Case Study

Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management
Singapore, March 7-11, 2021

 Location Optimization Charging Station of Three-Wheel
 Electric Vehicle: A Case Study
 Nida An Khofiyah
 Master Program of Industrial Engineering Department, Faculty of Engineering
 Universitas Sebelas Maret, Jl. Ir. Sutami, 36 A, Surakarta, Indonesia
 nidaankhofiyah2@student.uns.ac.id

 Ananda Vania Arisa Putri
 Bachelor Program of Industrial Engineering Department, Faculty of Engineering
 Universitas Diponegoro, Jl. Prof. Soedarto, SH Tembalang, Semarang, Indonesia
 anandavaniaap@students.undip.ac.id

 Wahyudi Sutopo
 University Centre of Excellence for Electrical Energy Storage Technology
 Research Group Industrial Engineering and Techno-Economic, Industrial Engineering
 Department, Faculty of Engineering
 Universitas Sebelas Maret, Jl. Ir. Sutami, 36 A, Surakarta, Indonesia
 wahyudisutopo@staff.uns.ac.id

 Abstract
To supporting the downstream and commercialization of the battery electric vehicle (BEV), Universitas Sebelas Maret
is developing a battery-based three-wheeled electric vehicle. This 3-wheeled electric vehicle has the advantage of
being more stable in use and can carry passengers or goods. However, in the application of the use of 3-wheeled
electric vehicles, there is no sufficient charging station to support mass use, so people are still reluctant to use it. The
supply chain network design is needed to analyze the optimum location of the charging station. This study proposes
the location of a 3-wheeled electric vehicle charging station to achieve the widest possible coverage with the least
number of charging stations and minimal investment costs with a case study in the city of Surakarta. Potential public
facilities that are used as locations for charging stations are markets and malls, both of which are frequently visited
and easily found by the public. The method to be used is a modification of the backup coverage model and the distance-
based model run with IBM ILOG® CPLEX® Optimization. The results obtained are a recommendation to the city
government to support the acceleration of electric vehicles, especially 3-wheeled vehicles in Surakarta.

Keywords
Charging Station, Location Optimization, Supply Chain Network Design, and Three Wheel Electric Vehicle

1. Introduction
Air quality in Indonesia is getting worse, especially in big cities in Indonesia. The decline in air quality is caused by
various sectors with the biggest pollution coming from motor vehicles. In the last 10 years, there has been a very rapid
increase in the number of motorized vehicles, especially the increase in motorbikes which has reached 30% (Ismiyati
et. al., 2014; Habibie and Sutopo, 2020). This has an impact not only on the health and environmental sectors but also
on the economic and social sectors (Sutopo et. al., 2013). For this reason, the government makes regulations for the
acceleration of the battery-based electric motor vehicle program as an effort to increase energy efficiency, energy
security, and energy conservation in the transportation sector, and the realization of clean energy, clean air quality,
and environmentally friendly in Indonesia according to articles according to presidential regulation No.55 in 2019.

In realizing this, the government is working with industry and academia to make battery-based electric vehicle
products. Universitas Sebelas Maret has succeeded in building a lithium battery factory to support electric vehicles.

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Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management
Singapore, March 7-11, 2021

Currently, Universitas Sebelas Maret is developing a Three-Wheel Electric Vehicle which has the advantage of being
more stable than two-wheeled bicycles, can be used for goods distribution in urban areas, and can also be used to carry
passengers (Syafiq, 2015; Arifurrahman et. al., 2018). However, the problem in using this Three-Wheel Electric
Vehicle is that no charging station can be accessed in public facilities in urban areas. So that people are still reluctant
to use this Three-Wheel Electric Vehicle. The charging station is an important component of an electric vehicle, so it
is important to build a charging station before the Three-Wheel Electric Vehicle is used en masse by the public (Sutopo
et. al., 2019; Istiqomah and Sutopo, 2020). Figure 1 shown Three-Wheel Electric Vehicle produced by Universitas
Sebelas Maret.

 Figure 1. Three-Wheel Electric Vehicle

Supply chain network design is a strategic activity that must be carried out in Supply Chain Management and includes
decisions about the location, number, and capacity of production and distribution facilities in a supply chain. The
purpose of the existence of a supply chain network is to meet customer needs which of course can change dynamically
from time to time (Klibi et al, 2010, Pujawan and Er, 2017). The supply chain network is the result of several strategic
decisions, one of which is a decision about the location of production facilities and warehouses as well as decisions
about purchasing raw materials (Pujawan and Er, 20117). However, in every decision, many considerations need to
be taken into accounts, such as considerations of cost, location, environment, economy, and others. So that in designing
a supply chain network, a quantitative model is needed to solve existing problems. In this study, the problem of the
location of the charging station is a problem that needs to be resolved with a supply chain network design model.

In research, Jing et. al. (2017) examined the effect of the location of public facilities for charging a Battery Electrical
Vehicle (BEV) by placing several charging stations using a mixed nonlinear programming method, with an
equilibrium-based heuristic algorithm. Then in the research of Zhang, et. al. (2017) examined the optimization of the
location of electric ship charging stations based on the backup coverage model by analyzing the feasibility, reserve
location models, and optimization methods. Research by Akbari et. al. (2018) discussed the optimization of the EV
charging station location by applying a Genetic Algorithm and the best position to place the charging station is
obtained to meet customer demands. Likewise in the research of Pagany et. al. (2019) and Cui et. al. (2018) discusses
the same thing but different methods and objects of research. From this previous research, it can be seen that
optimizing the location of the charging station on the electric vehicle is an interesting theme reviewed in the last 5
years.

From previous research, the model used in this study is the backup coverage model. Hogan and ReVelle (1986)
introduced the notion of reserve coverage into the location model. These models are developed in the context of
determining the emergency location of the facilities needed to serve a specific population. Then, Araz (2007) proposes
a multi-purpose maximum covering the location model, three objectives are the maximization of the population
covered by one vehicle, maximizing the population with coverage reserves, and minimizing the total travel distance
from locations at a distance that is greater than the predetermined distance standard for all zones. Zhu (2016) proposes

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Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management
Singapore, March 7-11, 2021

a new model for the location of a plug-in electric vehicle problem charging station. Intending to minimize the total
cost, the proposed model simultaneously addresses the problem of where to find a charging station and how many
chargers should be installed at each charging station. So that the Backup coverage model can be used in the solution
to determine the optimal location of the Three-Wheel Electric Vehicle charging station.

The application of charging stations is the most significant problem for the electrical vehicle (EV) industry. For this
reason, by optimizing the distance between charging stations, costs can be minimized (Akbari et al, 2018). In
optimizing the distance between charger stations so that they can cover all areas, it is necessary to determine public
facilities that can be used as locations for charging stations. According to Nugrahadi et al (2020) markets and malls
that are public facilities and are often found in almost all regions can be used as charging stations.

In designing a supply chain network, things that need to be considered are the number of entities, and the flow of the
supply chain. So it is necessary to create a supply chain network for the location of filling stations. Figure 2 below
describes the supply chain network design process.

 Figure 2. Supply chain network design process

The problem described in this study is to find the best number of potential facilities built to minimize the total weighted
distance from the facility to the customer, assuming that all facilities can meet all customer demands and all requests
are met.

This study aims to optimize the location selection for the charging station for the Three Wheel Electric Vehicle public
facilities with a case study in the city of Surakarta. Potential public facilities that are used as locations for Three-Wheel
Electric Vehicle charging stations are markets and malls, where these two facilities are often and easily found by the
public. Then there are 4 (four) involved entities, namely charging station providers, market owners (government),
mall owners (private), and Three-Wheel Electric Vehicle users. Of these four entities, the battery charging activity is
carried out at the charging station, in which the swapped battery is moved from the charging station to the Three-
Wheel Electric Vehicle user.

2. Methods
The model in this research is the development of the model in Zhang et. al. (2017) and Akbari et. al. (2018). From
these two studies, a model for determining the optimization of the charging location of the Three Wheel Electric
Vehicle was made. This research considers the coverage demand maximization and cost minimization. The following
table 1 describes the comparison between the two models.

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Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management
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 Table 1. Illustrate the comparison between the two models
 Aspect Zhang et. al. (2017) Akbari et. al. (2018)
 Object of research Electric ship Charging Stations Electric Vehicle Charging Stations
 Methods Backup Coverage Model Genetic Algorithm
 Performance criteria • Number of ship in demand • Minimize the total charging cost
 • The construction cost of charging station • Distance between settlements and the
 closets charging station
 Constraint • The ships in the demand zone should go • The average cost per kilometer for the
 to the same charging station of a fixed user
 grade at a certain time • Cost of electricity generated per kWh
 • The ship charging requirement must be • Cost contamination controlling for
 satisfied production
 • One charging station of a grade can be • Power utilization of an EV for every
 constructed in the candidate site kilometer (kWh/km)
 Alternative solution 5 charging station sites are selected as Mode 2: Slow charging mode
 candidate charging sites Mode 3: Medium to fast speed charging
 mode
 Mode 4: Ultra-fast charging mode
 Application/software Matlab 7.0 Genetic Algorithm (GA)
 Output The location problem is overall, from initial Charging mode of the most precise
 possible station sites chosen to the capacity charging station and suitable location
 determination for the final location sites.

 2.1 Influence Diagram
The influence diagram of optimizing the location of the three-wheel electric vehicle charging station can be seen in
Figure 3. The purpose of this research is to optimize the location of the Three-Wheel Electric Vehicle charging station
in the city of Surakarta by maximizing coverage and minimizing costs. The constraint is to fulfill all requests and limit
the number of facilities to p to keep costs minimal. And the criteria are minimizing the total distance from facilities to
customers, minimizing the number of facilities, and minimizing costs.

 Figure 3. Influence diagram this research

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Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management
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 2.2 Mathematical Modeling
The method used is the comparison between the backup coverage models by Zhang et al. (2017) and Akbari et. al.
(2018). This model is developed in the context of site facilities required to serve certain requests. In such a situation,
the unavailability of facilities within a certain time or standard distance will jeopardize the performance of the system
(Zhang et. al., 2017). This method is proposed to make the overall location decision from the initial possible location
of the selected station for the determination of the final site capacity. A model that can easily be generalized to other
problems regarding user-requested allocation facilities. The objective functions in this model are maximizing coverage
and minimizing costs. The decision variables are the location of the charging station and investment costs. The fixed
variable it is the location of the charging station, the type of vehicle (in this case, the Three-Wheel Electric Vehicle).
Then the variable that changes is the Three-Wheel Electric Vehicle user request. There is one supply chain involved,
namely the supply chain network design location for the charging station Three-Wheel Electric Vehicle.
Objective function:
 = ∑ ∈ ∑ ∈ + ∑ ∈ ∑ ∈ (1)

Constraint:
∑ ∈ ∑ ∈ , ≥ 1; ∀ ∈ (2)
 ≤ ∑ ∈ ; ∀ ∈ , ∈ (3)
∑ ∈ ≤ ∑ ∈ , ∀ ∈ (4)
∑ ∈ ≤ 1 , ∀ ∈ (5)
∑ ∈ ∑ ∈ = , ∀ ∈ (6)
 ∈ {0,1}, ∀ ∈ , ∈ (7)
 ∈ {0,1}, ∀ ∈ , ∈ (8)

The descriptions for each notation are as follows:
 = The set of charging station candidate sites
 = The set of demand points
 = Set of charging station levels
 = Investment cost to build charging station i level k
 = The distance between charging station i to demand point j
 = Transportation cost per kilometer
 = demand at node j
 = The service capacity of charging station level k
 1, if we choose the candidate site and transform it into charging station level 
 = �
 0, otherwise
 1, if demand point is covered by a charging station located at 
 = �
 0, otherwise

The objective function (1) is to minimize the transformation cost. Constraint (2) states that each demand point should
be covered by at least one charging station. Constraint (3) means that the service can be offered only if the charging
station is located in this candidate site. Constraint (4) restricts the service capacity of the charging station. Constraint
(5) means one candidate site can only be transformed into one-grade charging station. Constraint (6) means we want
to locate exactly P facilities. Finally, constraints (7) and (8) are the integrality constraint.

 Figure 4. Flowchart for solving the model

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Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management
Singapore, March 7-11, 2021

Figure 4 above explains the flowchart of this research, which begins with determining the decision variable, then the
initial input parameters, then running the model, if the optimal solution has been obtained, the work is finished. But if
not, it will be repeated for the input or mathematical model. The following table 2 describes the IEC 61851-1 charging
mode which will be used as a limiting level in this study. Charging mode is divided into 4 modes with different
specifications and different charging times.

 Table 2. IEC 61851-1 charging mode
 Charging Mode Max Current per Phase Charging Time Vehicle Battery Charger
 Mode 1 16 A 4–8h On Board
 Mode 2 32 A 2–4h On Board
 Mode 3 63 A 1–2h On Board
 Mode 4 400 A DC 5 – 30 min Off-Board

Based on IEC 61851-1 charging mode, the following 3 modes are selected which will be the type of charging station
to be built (Raboaca, et. al., 2020; Akbari et. al., 2018).
 a. Mode 2: This is the slow charging mode applied by household type receptacles with the inner wire protection
 component in the air conditioner.
 b. Mode 3: This is a medium to fast speed charging mode that uses a specific EV outlet with built-in AC control
 and protection functions.
 c. Mode 4: This is a very fast charging mode with an external charger in DC.

3. Results and Analysis
 3.1. Case Study in Surakarta City
This research was conducted with a case study in the city of Surakarta. Surakarta City is a city located in the province
of Central Java. With a coordinate point of 7 ° 34′0 ″ S 110 ° 49′0 ″ E and with a city area of 44.04 km2.
There are 5 districts in this city with the largest Banjarsari sub-district. The daily transportation used by the people of
Solo is various, some are using motorbikes, cars, or public transportation. However, not many people use electric
vehicles as private vehicles, because there are no public charging station facilities in the city of Surakarta.

 Figure 5. Candidate Three-Wheel Electric Vehicle charging station sites

Figure 5 explains the Candidate Three-Wheel Electric Vehicle charging station sites, in each sub-district in the city of
Surakarta. Candidate locations for charging stations use public market facilities and malls. There are 45 markets and

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Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management
Singapore, March 7-11, 2021

malls in the city of Surakarta, 4 of which are malls. The distance between the charging station candidates and the sub-
districts in Surakarta is explained in table 3 below.

 Table 3. Distance charging station i to demand point j (in kilometer)
 No. Market Name of District
 (CS location) Laweyan Serengan Pasar Kliwon Jebres Banjarsari
 1 3.4 3.9 3.8 5.5 1.7
 2 3.2 4.2 4.2 5.8 1.4
 3 3.4 2.8 3.1 5.5 2.6
 4 2.4 3.8 4.4 6.5 1.4
 5 1.9 4.2 5.0 7.0 1.2
 6 3.3 2.9 3.2 5.5 2.5
 7 3.6 3.6 3.4 5.2 2.2
 8 2.2 4.4 5.0 6.8 0.9
 9 1.4 4.0 5.1 7.4 1.8
 10 3.3 4.6 4.5 5.9 1.1
 11 3.5 4.4 4.1 5.5 1.5
 12 3.2 2.5 3.1 5.8 2.8
 13 3.2 2.4 3.2 5.9 2.9
 14 3.6 2.5 2.8 5.4 3.0
 15 3.3 5.0 5.0 6.2 0.8
 16 3.4 4.5 4.4 5.8 1.2
 17 4.0 4.5 4.0 5.1 1.9
 18 4.4 4.4 3.7 4.7 2.3
 19 2.0 3.5 4.5 6.9 1.9
 20 5.5 3.0 1.3 3.5 4.4
 21 5.1 3.1 1.8 3.8 3.8
 22 5.4 3.7 2.1 3.4 3.9
 23 5.0 4.2 2.9 3.9 3.1
 24 7.0 5.3 3.0 1.9 5.1
 25 6.6 5.9 4.1 3.1 4.3
 26 5.0 5.0 3.9 4.4 2.7
 27 5.1 4.3 3.0 3.8 3.2
 28 6.7 5.4 3.4 2.4 4.7
 29 5.1 4.3 3.0 3.8 3.2
 30 4.5 3.3 2.5 4.3 3.1
 31 6.3 5.6 3.8 3.2 4.0
 32 1.0 5.2 7.0 9.7 3.6

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Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management
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 Table 3. Distance charging station i to demand point j (in kilometer) continued
 No. Market Name of District
 (CS location) Laweyan Serengan Pasar Kliwon Jebres Banjarsari
 33 1.1 3.8 5.2 7.8 2.5
 34 2.3 2.8 4.1 6.8 2.7
 35 1.7 3.2 4.9 7.7 3.0
 36 1.3 3.8 5.7 8.5 3.4
 37 0.6 4.5 6.3 8.9 3.2
 38 2.0 3.1 4.4 7.0 2.4
 39 5.7 2.2 0.7 4.1 5.0
 40 5.0 2.0 1.4 4.5 4.3
 41 4.7 1.8 1.7 4.8 4.2
 42 4.3 2.7 2.3 4.6 3.3
 43 5.7 2.3 0.6 3.9 5.0
 44 5.7 2.3 0.7 4.0 4.9
 45 4.2 1.4 2.2 5.4 4.1

 3.2. Numerical Example
Due to the addition of Three Wheel Electric Vehicle / Three-Wheel Electric Vehicle users every month, in this study
researchers used a numerical example. For example, in running the location model for the Three-Wheel Electric
Vehicle charging station in the city of Surakarta. Table 4 explains the numerical example of the request for a Three-
Wheel Electric Vehicle in each sub-district in the city of Surakarta, which is obtained from multiplying the market
share of 5% with the population in the sub-district in the city of Surakarta.

 Table 4. A numerical example for demand Three-Wheel Electric Vehicle
 No District Total Demand
 1 Laweyan 101196 5060
 2 Serengan 53923 2696
 3 Pasar Kliwon 84126 4206
 4 Jebres 144175 7209
 5 Banjarsari 178849 8942
 Total 562269 28113

The roadmap for public electric vehicle battery exchange stations for two-wheeled or three-wheeled vehicles in the
presentation of the Opportunities and Challenges of National Electric Car Development states that Indonesia is
preparing investment funds for the manufacture of battery swap charging stations totaling 342 billion with a target of
800,000 electric motorbike users and 4000 charging stations (Hendra, 2020). So it can be said that the investment cost
in building 1 charging station is 85,500,000 with charging mode 2 specifications. And it is assumed that it will increase
by 10% for the faster charging mode. So that it can be seen the investment costs of the charging station in table 5
below.

 Table 5. Investment cost charging station Three-Wheel Electric Vehicle
 Max Current per Output Power Investment cost
 Charging Mode Charging Time
 Phase (kW) (IDR)
 Mode 2 63 AC 4h ≤ 22 kW 85.500.000
 Mode 3 100 AC/250DC 30 min ≤ 50 kW 94.050.000
 Mode 4 300 AC/500DC 15 min ≤ 150 kW 102.600.000

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Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management
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 3.3. Results and Discussion
Table 6 below describes the optimal location from the results of running using ILOG CPLEX at the location of the
Three-Wheel Electric Vehicle charging station by considering the optimal coverage area in the city of Surakarta. So
that the results are obtained in Figure 6, which illustrates the points of the selected market/mall as the charging station
location. The result has shown that all level 3 are selected to minimize costs.

 Table 6. Location of charging station Three-Wheel Electric Vehicle selected (Xij)
 Candidate P=3 P=4 P=5
 sites Lvl 1 Lvl 2 Lvl 3 Lvl 1 Lvl 2 Lvl 3 Lvl 1 Lvl 2 Lvl 3
 8 v
 15 v v v
 24 v v
 37 v v v
 44 v v
 45 v

Table 7 below shows the results for the Yij decision variable. This table shows which candidate locations are
supportive of the point of demand. Because there are 5 points of demand, the results must meet the demand. And the
following results are obtained, in the scenario of making 3 CS, candidate sites (8) will be created to supply Laweyan
and Banjarsari Demand points, to candidate sites (24) to supply Jebres demand points. And candidate sites (43) to
supply-demand points for Serengan and Pasar Kliwon. Then in the scenario of making 4 CS, the result is that at
candidate sites (24) to supply Jebres, candidate sites (15) supply Banjarsari, candidate sites (37) to supply Laweyan.
And candidate sites (43) to supply Serenggan and Pasar Kliwon. Then in the scenario of making 5 CS, 5 candidate
sites are selected to supply each of the existing demand points according to the district candidate sites, namely (15),
(24), (37), (43), and (45). Figure illustration can be seen in Figure 6.

 Table 7. Result for Yij
 P=3 P=4 P=5
 Pasar Kliwon

 Pasar Kliwon

 Pasar Kliwon
 Banjarsari

 Banjarsari

 Banjarsari
 Candidate
 Serengan

 Serengan

 Serengan
 Laweyan

 Laweyan

 Laweyan
 Jebres

 Jebres

 Jebres
 sites

 8 v v
 15 v v
 24 v v v
 37 v v
 43 v v v v v
 45 v

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Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management
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 P=5 P=4

 P=3

 Figure 6. Optimal Locating Charging Station Three-Wheel Electric Vehicle

 4. Conclusion
In this research, the Three-Wheel Electric Vehicle charging station location is proposed to solve the problem of
optimum charging location to support the acceleration of electric vehicles, especially three-wheeled electric vehicles,
with a case study in the city of Surakarta. This model has been implemented by considering 3 levels/scenario types of
charging stations. This model can provide input to local governments regarding the optimal solution in determining
the location of the Three-Wheel Electric Vehicle charging station. However, the weakness of this model is that it still
uses numerical examples in determining the user demand for Three-Wheel Electric Vehicle, so that the results obtained
do not fully cover the demand in Surakarta. And also transportation costs and investment costs are still using the
assumptions of researchers. This study also has not considered additional parameters such as queues or traffic density
due to the limitations of the model used. So it is hoped that the next research can use real and primary data so that it
can answer problems real and accurately according to real conditions.

Acknowledgments
This research was supported by the Students Exchange of Merdeka Belajar Kampus Merdeka (MBKM) Program
between the Department of Industrial Engineering Universitas Sebelas Maret and Universitas Diponegoro.

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Biographies

Nida An Khofiyah is a student at the Master Program of Industrial Engineering Department, Universitas Sebelas
Maret, Surakarta, Indonesia. She is also a research assistant in the Laboratory of Logistics System and Business. She
received her Bachelor of Engineering degree from Universitas Sebelas Maret. Research interests are related to techno-
economics, logistics, commercialization technology, and drone technology. She has published research papers twice
during her final year in engineering associated with the commercialization of drone technology and drone batteries.
She has published 6 articles Scopus indexed.

Ananda Vania Arisa Putri is a student in the Bachelor Program of Industrial Engineering, Diponegoro University,
Semarang, Indonesia. She is still a graduate student of Industrial Engineering, Diponegoro University. Her research
interests are health safety and environment, logistics and supply chain management, and procurement. She already
has several certificates such as the Certificate of Young Expert in Construction Occupational Safety and Health,
Project Risk Management certificate, and Basic Logistics certificate.

Wahyudi Sutopo is a professor in industrial engineering and coordinator for the research group of industrial
engineering and techno-economy (RG-RITE) of Faculty Engineering, Universitas Sebelas Maret (UNS), Indonesia.
He earned his Ph.D. in Industrial Engineering & Management from Institut Teknologi Bandung in 2011. He has done
projects with Indonesia endowment fund for education (LPDP), sustainable higher education research alliances
(SHERA), MIT-Indonesia research alliance (MIRA), PT Pertamina (Persero), PT Toyota Motor Manufacturing
Indonesia, and various other companies. He has published more than 160 articles indexed Scopus, and his research
interests include logistics & supply chain management, engineering economy, cost analysis & estimation, and
technology commercialization. He is a member of the board of industrial engineering chapter - the institute of
Indonesian engineers (BKTI-PII), Indonesian Supply Chain & Logistics Institute (ISLI), Society of Industrial
Engineering, and Operations Management (IEOM), and Institute of Industrial & Systems Engineers (IISE).
His email address: wahyudisutopo@staff.uns.ac.id.

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