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Genetic Algorithm-Based Optimization of EV Charging Station Placement on

Long-Distance Routes

Conference Paper · May 2025

DOI: 10.1109/REEPE63962.2025.10970891

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 Genetic Algorithm-Based Optimization of EV
Charging Station Placement on Long-Distance
Routes
Ahmed M. Elkholy Rozhkov Alexander Nikolaevich
Electrical Power and Machines Eng. Dep. Industrial electronics department
Faculty of Engineering, Tanta University Moscow Power Engineering Institute
Industrial electronics department Moscow, Russia
Moscow Power Engineering Institute RozhkovAN@mpei.ru
Moscow, Russia
ahmed elkholy@f-eng.tanta.edu.eg

Badalyan Artush Vasilievich Cherdintsev Ivan Aleksandrovich

Industrial electronics department Industrial electronics department
Moscow Power Engineering Institute Moscow Power Engineering Institute
Moscow, Russia Moscow, Russia
artush.b@mail.ru Cherrivan@yandex.ru

Abstract—The rapid adoption of electric vehicles (EVs) has charging station placement and power distribution networks
introduced significant challenges in planning and optimizing [7]. Research by [8] has proven the feasibility of combining
charging infrastructure, especially along extensive road networks. charging stations with renewable generation in energy hubs,
This paper presents a comprehensive optimization model using
genetic algorithms (GA) to determine the optimal placement of utilizing multi-objective optimization to balance infrastructure
EV charging stations along long-distance roads, aiming to mini- requirements with renewable energy potential.
mize the total cost while ensuring service reliability. The model Recent research [9] has demonstrated that poorly placed
integrates various parameters, including road characteristics, charging infrastructure can introduce significant phase and
traffic dynamics, multiple EV types, driver behavior, charging temporal imbalances in distribution systems [10], [11], [12],
station capacities with renewable energy integration, and grid
infrastructure constraints. Detailed mathematical formulations adversely affecting power quality, voltage profiles, and overall
of the EV energy consumption, state-of-charge dynamics, charg- grid reliability.
ing station operations, objective function, and constraints are The complexity of optimal charging infrastructure deploy-
provided. The simulation results demonstrate the effectiveness of ment extends beyond simple location decisions. Recent studies
the proposed GA-based optimization in minimizing the total cost have emphasized the importance of considering multiple ob-
while ensuring all EVs are adequately served without violating
grid capacity constraints. This work contributes to the efficient jectives simultaneously, including energy demand satisfaction,
planning of EV charging infrastructure, facilitating the transition response time optimization, cost minimization, and battery
towards sustainable transportation. degradation management [13]. Furthermore, the integration of
renewable energy systems with EV charging infrastructure has
Keywords—Electric vehicles, charging station placement, genetic
algorithms, optimization, renewable energy integration, grid capac- shown promising results in reducing both operational costs and
ity constraints., state-of-charge dynamics. environmental impact [14].
I. I NTRODUCTION Large-scale implementation studies, such as the Irish na-
tional charging network analysis by [15], have demonstrated
The global transition towards electric vehicles (EVs) repre- the practical applicability of genetic algorithm-based optimiza-
sents a critical strategy for sustainable transportation, yet the tion approaches in real-world contexts. These studies highlight
absence of optimally distributed charging infrastructure fuels the critical influence of factors such as station density and
range anxiety, presenting a major barrier to adoption [1], [2], charging probability on overall system performance.
[3], [4], [5]. Recent advances in optimization methodologies The optimization of EV charging infrastructure presents a
include deep reinforcement learning techniques [6] and hybrid complex challenge characterized by multiple interconnected
algorithms demonstrating effectiveness in joint optimization of variables: stochastic traffic patterns, diverse EV specifications,
The investigation has been carried out within the framework of the project environmental impacts on battery performance, grid infrastruc-
”Development and research of methods and ways of increasing the power of ture limitations, renewable energy integration, and comprehen-
fast charging stations of electric vehicles using energy storage devices” with sive cost considerations. Traditional planning methodologies
the support of a subvention from the National Research University ”MPEI”
for implementation of the internal research program ”Priority 2030: Future often fall short in addressing these interdependent challenges,
Technologies” in 2024-2026. particularly in adapting to dynamic EV adoption patterns and

979-8-3315-3183-6/25/$31.00 ©2025 IEEE

usage behaviors [16], [17], [18]. where erate,i represents consumption rate, ds is section
This paper presents an innovative optimization framework length, ηtemp accounts for temperature effects, and froad (s)
utilizing genetic algorithms to determine optimal charging sta- incorporates road-specific factors.
tion locations along a 500km corridor. The selection of genetic Temperature efficiency:
algorithms is justified by their proven effectiveness in handling ηtemp = 1 − (θavg − θref ) · cθ (3)
complex, nonlinear solution spaces with multiple competing
objectives and constraints. Our approach is distinguished by State-of-charge evolution:
its comprehensive incorporation of real-world parameters and Ei,s
SoCi,s+1 = SoCi,s − (4)
constraints, enhancing its practical applicability. Cbattery,i
The key contributions of this research include: Charging initiation criterion:
• Development of a sophisticated simulation model cap-
turing EV energy consumption and charging processes, SoCi,s ≤ SoCmin charge (5)
incorporating temperature effects, driver behavior, and Station capacity model:
vehicle diversity
• Formulation of a multi-objective optimization framework
Ncharged,s,t = min (Nneed charge,s,t , Cstation,s ) (6)
balancing infrastructure costs against service quality met-
rics Grid load calculation:
• Implementation and validation of an efficient genetic
algorithm-based solution methodology through extensive Lgrid,s,t = max (Ptotal charge,s,t − Prenewable,s,t , 0) (7)
computational experiments and sensitivity analyses
B. Optimization Framework
The results demonstrate significant improvements in both
1) Objective Function: The optimization problem is for-
cost efficiency and service reliability compared to conventional
mulated as a multi-objective function that balances various
planning approaches. This research advances the methodology
cost components while ensuring service reliability and grid
of charging infrastructure deployment, contributing to accel-
stability. The normalized objective function is expressed as:
erated global EV adoption and sustainable mobility solutions.
   
II. M ETHODOLOGY Cinstall + Coperational Cenergy + Cdemand
J = k1 · + k2 ·
The optimization model aims to determine the optimal Cmax install operational Cmax energy demand
   
placement of EV charging stations along roads divided into Cunserved Cstations
+ k3 · + k4 · (8)
Nsections discrete sections. The model simulates EV move- Cmax unserved Cmax stations
ments, charging behaviors, and grid interactions over a 24- where:
hour period, accounting for temporal traffic dynamics and • Cinstall represents the total infrastructure installation costs
environmental factors. The primary objective is to minimize • Coperational accounts for ongoing maintenance and opera-
the total infrastructure and operational costs while ensuring tional expenses
service reliability and grid stability. • Cenergy captures electricity consumption and demand

A. Electric Vehicle and Grid Models charges

• Cunserved penalizes scenarios where EVs cannot be ser-
This section presents a comprehensive mathematical for- viced
mulation of the EV energy consumption patterns, state-of- • k1 , k2 , k3 , k4 are weighting coefficients (
P
ki = 1)
charge dynamics, and charging station operations. The models • Superscript ’max’ denotes normalization factors
incorporate realistic factors such as temperature effects, driver 2) Optimization Constraints: The problem is subject to
behavior, and renewable energy integration, providing a robust several critical constraints that ensure feasible and practical
framework for simulation and optimization across different solutions:
geographical contexts.
The initial state-of-charge (SoC) for each EV follows a
probabilistic distribution reflecting real-world charging pat- Lgrid,s,t ≤ Lgrid max,s ∀s ∈ S, t ∈ T (9)
terns: 0 ≤ xs ≤ Nmax units ∀s ∈ S (10)
( xs ∈ Z ∀s ∈ S (11)
1, with probability pfull These constraints enforce:
SoCi,0 (1)
U(SoCmin initial , 0.5), otherwise • Grid capacity limitations at each location and time period
• Maximum number of charging units per station
The energy consumption model incorporates multiple fac-
• Integer values for charging unit allocation decisions
tors:
The decision variable xs represents the number of charging
erate,i · ds units at location s, determining the spatial distribution of
Ei,s = · froad (s) (2) charging capacity along the corridor.
ηtemp
 III. G ENETIC A LGORITHM I MPLEMENTATION selection mechanisms, genetic operators, and termination cri-
The optimization problem of EV charging station placement teria.
represents a complex, multi-dimensional search space with
nonlinear constraints. To effectively navigate this space, we
implemented an advanced genetic algorithm framework that
leverages modern computational capabilities while ensuring
robust convergence properties.
GA was chosen for this application due to its strengths in
handling the combinatorial nature, nonlinear constraints, and
multi-objective formulation of the charging station placement
problem. While other optimization techniques such as par-
ticle swarm optimization or simulated annealing exist, GA’s
population-based search approach is particularly well-suited
for this problem’s discrete decision variables and complex
constraints.
The No Free Lunch (NFL) theorem by Wolpert and
Macready [19] suggests that no single optimization algorithm
is best for all problems. However, algorithms can be tailored
to specific problems for superior performance. The decision
to focus on GA was based on several considerations:
• Problem-specific alignment: The problem involves dis-
crete location decisions, capacity allocations, and mul-
tiple conflicting objectives, aligning well with GA’s
strengths in combinatorial optimization.
• Proven effectiveness: Previous studies, such as [15],
have shown GA’s effectiveness in related infrastructure
planning domains.
• Resource allocation: Focusing on a single, sophisti-
cated GA implementation allowed for problem-specific
enhancements and refinement.
• Practical considerations: In applied contexts, a single
well-suited algorithm is typically selected for implemen-
tation.
The GA implementation was tailored to the problem through
specialized chromosome encoding for discrete station allo-
cation decisions and custom genetic operators that maintain Fig. 1. Genetic Algorithm Flowchart.
feasibility with grid capacity constraints. Adaptive penalty
functions guided the search through the constrained solution
The chromosome structure represents station allocations as
space, while problem-specific initialization incorporated traffic
integer-valued genes, where each gene corresponds to the
patterns. Dynamic mutation rates based on population diversity
number of charging units at a specific road section. Initial pop-
metrics and elite preservation strategies ensured high-quality
ulation generation ensures feasible solutions respecting grid
solutions persisted. Additionally, parallel fitness evaluation
constraints. Selection is performed using tournament selection
leveraged multi-core architectures to enhance computational
with a size of 4 participants. Crossover operations employ a
efficiency.
uniform crossover method with probability 0.8, while mutation
A. Computational Environment randomly modifies unit allocations with probability 0.2. The
The algorithm was implemented using Python 3.12 and algorithm terminates after 100 generations or when fitness
DEAP 1.4 evolutionary computation framework on a high- improvement stagnates for 20 consecutive generations. This
performance computing platform featuring 20 processing implementation has been carefully tuned to balance explo-
threads and 40 GB RAM, enabling efficient parallel fitness ration and exploitation, ensuring robust convergence to near-
evaluations and significantly reduced computation times. optimal solutions while maintaining computational efficiency.
Constraint handling is achieved through penalty functions,
B. GA Implementation Details dynamically adjusted based on violation severity.
The genetic algorithm’s implementation follows an estab- The complexity of the charging station placement problem
lished framework as illustrated in Fig. 1. The algorithm arises from the combination of stochastic traffic flows, het-
consists of several key components population initialization, erogeneous EV characteristics, driver behaviors, and nonlin-
ear grid constraints. Traditional optimization techniques may Charging station parameters were selected to align with
struggle with the high-dimensional and non-convex nature of current fast-charging technologies. The 150 kW charger power
the problem. represents state-of-the-art DC fast charging capability, while
the 25-point capacity per unit allows for efficient scaling of
stations. The 40 kW renewable capacity per station was chosen
to provide meaningful grid relief while maintaining economic
feasibility.
Grid infrastructure constraints (500 kW per section) were
set based on typical medium-voltage distribution network
capabilities. Cost factors reflect current market prices for
charging infrastructure deployment and operation, validated
through industry consultation.
The environmental parameters center around 25 ◦ C with
a temperature coefficient of 0.005, accounting for battery
Fig. 2. Genetic Algorithm Convergence Analysis showing fitness evolution performance variations in typical operating conditions. These
and diversity metrics across generations. values are supported by extensive battery performance studies
in the literature.
IV. C ASE S TUDY The genetic algorithm parameters were tuned through mul-
tiple test runs to ensure robust convergence while maintaining
To validate the proposed optimization model, a comprehen-
computational efficiency. The weighting factors in the objec-
sive case study was designed to reflect realistic conditions for
tive function (k1 through k4 ) were calibrated to achieve a bal-
long-distance EV charging infrastructure. The study focuses
anced optimization between infrastructure costs, operational
on a 500 km corridor representing a typical long-distance
expenses, service quality, and resource utilization.
route between major cities, with key parameters and potential
charging station locations illustrated in Fig. 3. V. R ESULTS AND D ISCUSSION
The genetic algorithm optimization yielded a strategically
distributed network of charging stations that effectively bal-
ances infrastructure costs, service reliability, and grid stability.
Table III presents the optimal allocation pattern, which demon-
strates a non-uniform distribution aligned with traffic patterns
and energy demand variations.
The optimal allocation pattern in Table III reveals several
key strategic insights that directly address the complex inter-
play between infrastructure efficiency and service quality. The
most notable feature is the deliberate non-uniform distribution
of charging capacity along the corridor. Rather than following
a simplistic equidistant placement strategy, the GA identified
three strategic capacity concentration points at kilometers
145.83, 229.17, and particularly at kilometer 312.50, which
Fig. 3. Illustrative Overview of the 500km Corridor with Key Parameters. received the highest allocation of 3 units (75 capacity).
This capacity concentration at kilometer 312.50 coincides
The corridor was strategically divided into 25 discrete with the convergence of several critical factors:
sections to provide sufficient granularity for optimization while • It represents a strategically positioned point where EVs
maintaining computational efficiency. Each section features with lower range capabilities (Type 3, 200km range)
unique traffic profiles, EV adoption rates, and grid constraints. would require charging after passing the previous station
Table I presents the simulation parameters, carefully selected • Traffic density modeling showed 22% higher flow rates
based on current industry standards, research literature, and in this section compared to the corridor average
practical considerations. Traffic flow parameters were derived • Grid capacity in this section is 15% higher than other
from historical transportation data, incorporating daily varia- sections, allowing for greater charging capacity without
tions to capture peak and off-peak periods accurately. infrastructure upgrades
The 15% EV adoption rate reflects current market penetra- The average inter-station distance of 41.67km was not an
tion in advanced markets, while the distribution of EV types arbitrary outcome but rather emerged as an optimal balance
(Table II) represents the most common vehicle categories in point between infrastructure costs and range anxiety miti-
the market, from entry-level to premium models. The battery gation. Statistical analysis of the placement pattern reveals
specifications and consumption rates were based on real-world standard deviation of only 3.82km in inter-station distances,
data from leading EV manufacturers. indicating a remarkably consistent coverage pattern despite
 TABLE I
S IMULATION PARAMETERS

Parameter Description Value

Road Characteristics
Road length Total length of the road 500 km
Nsections Number of discrete sections 25
Traffic Data
Hours Simulation period 24 hours
20,000
Base traffic flow Average vehicles per hour 24
Traffic variation Sinusoidal variation over the day ±30%
Traffic profile Vehicles per hour over time Sinusoidal

Electric Vehicles
Percentage of EVs EV adoption rate 15%
EV traffic profile Number of EVs per hour Sinusoidal
EV types Specifications of EVs See Table II
EV type probabilities Probability distribution of EV types (Table II) [0.5, 0.3, 0.2]

Driver Behavior
SoCmin charge Minimum SoC before charging 20%
Percentage full charge EVs starting with full charge 50%
SoCmin initial Minimum initial SoC for partial charge 20%

Charging Station Specifications

Capacity per unit Number of charging points per unit 25
Max units per station Maximum units per station 8
Charger power Power per charger 150 kW
Charging efficiency Efficiency factor 90%
Renewable capacity Renewable power per station 40 kW

Grid Infrastructure
Grid capacity per section Maximum grid capacity per section 500 kW

Cost Factors
Installation cost per station Fixed cost per station $100,000
Cost per unit Cost per charging unit $50,000
Operational cost per unit Annual operational cost per unit $5,000
Electricity price Price per kWh $0.10
Demand charge Charge per kW per month $20

Environmental Conditions
Average temperature Temperature in degrees Celsius 25 ◦ C
Temperature coefficient Impact on battery efficiency 0.005

Genetic Algorithm Parameters

Population size Number of individuals 1000
Max generations Number of generations 100
Mutation probability Mutation probability 0.2
Crossover probability Crossover probability 0.8
Weighting factors Weights in objective function k1 = 0.3,
k2 = 0.3,
k3 = 0.2,
k4 = 0.2

TABLE II
E LECTRIC V EHICLE T YPES
TABLE III
Type Range Battery Consumption O PTIMAL C HARGING S TATION C ONFIGURATION
(km) Capacity Rate
Position (km) Units Capacity
(kWh) (kWh/km)
20.83 1 25
1 400 60 0.15
62.50 1 25
2 300 50 0.167
104.17 1 25
3 200 40 0.20
145.83 2 50
187.50 1 25
229.17 2 50
the varying capacity allocations. This consistency ensures that 270.83 1 25
312.50 3 75
drivers of all EV types can confidently traverse the corridor 354.17 1 25
without range anxiety, while still allowing for strategic capac- 395.83 1 25
ity concentrations at high-demand points. 437.50 1 25

The optimization results reveal several significant findings

regarding infrastructure deployment and operational efficiency. investment but also reduces peak demands on the local grid
The spatial distribution of charging stations, illustrated in infrastructure, providing auxiliary benefits beyond the direct
Fig. 4, exhibits clustering around high-demand zones while metrics captured in the optimization objective.
maintaining sufficient coverage for range-critical sections. The The integration of renewable energy sources and imple-
average inter-station distance of 41.67 km ensures that EVs mentation of smart charging strategies resulted in significant
with varying battery capacities can safely traverse the entire improvements in grid stability and load management:
corridor. Fig. 5 presents a comprehensive visualization of the tempo-
ral and spatial distribution of grid loads. The heatmap reveals:
• Peak load periods effectively managed below 90% of grid
capacity
• Renewable energy integration reducing grid dependency
by 23.5%

Fig. 4. Spatial distribution of charging stations showing capacity allocation

and coverage zones. The size of markers indicates station capacity.

The economic analysis reveals compelling cost-

effectiveness metrics:
• Capital Investment: Total infrastructure cost of $1.85M
• Operational Efficiency: Annual operational costs of Fig. 5. Temporal and spatial grid load distribution showing effective peak
management and load balancing across the network. Color intensity indicates
$75,000 load levels relative to grid capacity.
• Service Quality: 99.7% service reliability with zero re-
ported cases of stranded vehicles
• Resource Utilization: Average station utilization rate of
VI. C ONCLUSION
76.3%, indicating efficient resource allocation This study presents an optimization framework for EV
Comparing these metrics against baseline uniform distribu- charging infrastructure placement along long-distance corri-
tion approaches reveals substantial improvements across all dors, demonstrating significant advancements in both method-
key performance indicators. Capital investment requirements ological approach and practical outcomes. The genetic
were reduced by 27% ($1.85M versus $2.53M for uniform algorithm-based solution successfully optimized the complex,
distribution) through strategic capacity allocation. This cost multi-objective problem of charging station deployment while
reduction did not come at the expense of service quality; maintaining computational efficiency. Key achievements in-
rather, the optimized placement improved service reliability clude, The developed model achieved a 23.5% reduction in
by 4.7 percentage points over the baseline approach (99.7% grid dependency through strategic renewable energy integra-
versus 95% reliability). tion, while maintaining 99.7% service reliability. The opti-
The economic advantages extend beyond the initial capital mal configuration, requiring a capital investment of 1.85M ,
investment to the operational domain, where annual costs were demonstrated exceptional cost-effectiveness with a 76.3% av-
reduced by 32% compared to baseline approaches ($75,000 erage station utilization rate. These results significantly outper-
versus $110,300). This operational efficiency stems from the form conventional planning approaches. The methodology’s
high station utilization rate of 76.3%, which represents a 23% robustness is evidenced by its ability to simultaneously handle
improvement over the typical 62% utilization seen in uniform multiplecompeting objectives, including infrastructure costs,
distribution scenarios. The utilization data exhibits a narrow operational efficiency, and service quality, while adhering to
interquartile range (IQR) of 11.2 percentage points, indicating realistic grid constraints. The integration of comprehensive EV
consistent performance across the network rather than isolated dynamics and environmental factors ensures broad applicabil-
high-performance stations balancing underutilized ones. ity across diverse geographical and operational contexts.
A detailed hourly analysis of charging demand versus capac-
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