Efficient Fast Charging of Plug in Electric Vehicle Using Adaptive

Slide Mode Controller

Selvadurai C. K., Sankarganesh R., Sripathy D. and Gukankavin M. N.

Department of Electrical and Electronics Engineering, K.S.R College of Engineering, KSR Kalvi Nagar,

Tiruchengode - 637 215, Namakkal (Dt), Tamil Nadu, India

Keywords: Energy Storage System, SRA, GRA.

Abstract: With technological improvement, the interest in electric vehicles (EVs) has regained in the 21st century, as

well as the focus on renewable energy sources and a potential to reduce the negative impacts of transportation

on environmental issues like the climate change. The fast charging stations are gaining popularity with electric

cars as they can shorten the time, and help to reduce range anxiety. For effective alleviating grid stress and

reduction of carbon emissions, it is essential to integrate solar systems with conventional direct current fast

charging stations. However, challenges exist since GIC are underutilized and ESS are too expensive. To fully

leverage the advantages of electric vehicle (EV) installations and their supporting charging infrastructure,

these challenges must be effectively addressed. A proposed direct current (DC) fast-charging station

architecture, designed to operate solely with photovoltaic (PV) systems and without incorporating energy

storage systems (ESS), aims to minimize costs. In scenarios where an ESS is absent from the fast-charging

setup, the suggested Smart Charging Algorithm (SCA) ensures optimal alignment between power sources and

loads from both the grid and EVs. This optimization enhances power output from the PV system while

maximizing the efficiency of grid-integrated chargers (GICs). The GICs function based on a grid-regulated

algorithm (GRA), while EVs follow a self-regulated algorithm (SRA). As long as DC bus voltage fluctuations

remain within an acceptable range, the SRA dynamically adjusts the charging power for each EV according

to its state-of-charge (SOC) feedback, ensuring power balance within the fast-charging system (FCS). Both

simulation and experimental results validate the effectiveness of the proposed SCA. Additionally, the GRA

takes part in the regulation process when the DC bus voltage remains within the predefined excitation voltage

range, ultimately reducing the overall charging time for EV batteries.

1 INTRODUCTION

Consequently, electric vehicles (EVs) and hybrid

electric vehicles (HEVs) are widely advocated to

reduce the reliance on fossil fuels and lower carbon

emissions. Direct Current Fast Charging Systems

(DC FCS) can enhance the charging speed and extend

the driving range of EVs; however, they also increase

stress on the utility grid and introduce new

challenges. To mitigate grid stress and decrease

carbon emissions, DC FCS integrated with

photovoltaic systems is employed.

At the megawatt scale, photovoltaic technology

proves to be both practical and cost-effective.

Overall, hybrid power supply systems (HPSS) or DC

FCS that utilize renewable energy sources help to

eliminate dynamic power fluctuations.

Significant research results have been obtained

about the optimization of the ESS cost, but the

elimination of the ESS cost of FCS has not been

explored. Clearly, the removal of the ESS will save a

considerable amount of money for FCS. However, the

previous ESS based energy management strategies

for the HPSS are retained to highlight the important

role of ESS in power flow balancing. However,

without ESS power support, traditional energy

management strategies become ineffective for the

FCS. The DC FCS architecture based on ESS free is

introduced and studied to address the large cost of

ESS. At the same time, the smart charging algorithm

(SCA) is proposed for the effective coordination of

the grid and EVs' source or load properties to

minimize power fluctuations of FCS in the absence of

ESS.

C. K., S., R., S., D., S. and N., G. M.

Efﬁcient Fast Charging of Plug in Electric Vehicle Using Adaptive Slide Mode Controller.

DOI: 10.5220/0013893700004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 3, pages

171-178

ISBN: 978-989-758-777-1

171

2 PROBLEM DEFINITION

In several nations, the technology of electric vehicles

(EVs) has been advocated as the way to minimize

local air pollution and increase transportation energy

security. Electric vehicles are still very immature,

with their cost still very high, especially for pure

electric vehicles, and there is still not very suitable

infrastructure. This paper covers some of the

roadblocks and challenges that are likely to obstruct

progress in this challenging area. The current

problems can be divided into two groups Battery

performance and prices, as well as battery

manufacturing, including material supply concerns.

The environmental benefits of electric vehicles are

dependent on the energy sources used to generate

electricity and their carbon intensity. The length of

time it takes to charge an automobile is determined by

the battery capacity and other factors.

3 LITERATURE SURVEY

This study (Akhtar Hussain et al., 2020) presents an

optimized approach for determining the appropriate

size of a battery energy storage system (BESS) in a

fast-electric vehicle (EV) charging station that

experiences power outages. The research focuses on

minimizing energy storage system costs, enhancing

EV resilience, and reducing peak power demand to

establish the most effective BESS configuration.

Additionally, it emphasizes the robustness of EV

operations during power interruptions. In the initial

phase, the stochastic demand of the fast-charging

station (FCS) and the resilience of EV loads are

analyzed using probability distribution models. To

ensure EVs remain operational despite power losses,

the energy levels in the storage system are maintained

at a stable level. Based on this, the annualized cost

rate of the BESS is determined, considering yearly

interest rates and component lifetimes. The optimal

BESS size is then derived by factoring in the

annualized cost, penalties for peak-hour power

purchases, and penalties for resilience violations.

Furthermore, simulations and sensitivity analyses are

conducted to assess the impact of various parameters,

such as the number of EVs at the charging station,

converter ratings, and uncertain factors like market

price fluctuations, EV arrival times, and residual

energy levels. Simulation results indicate that

increasing costs during peak intervals effectively

reduces overall FCS expenditures while managing

peak capacity efficiently.

Primary and Secondary Control in Dc Microgrids

a Review offered a Direct and indirect control in DC

microgrids are discussedThe concept of microgrids is

well-known in the field of electrical engineering due

to the rapid advancement of power electronics

technology. DC microgrids (MGs) are becoming

increasingly common due to the advantages of DC

power distribution networks, such as reduced losses

and simplicity in connecting with energy storage

resources. A DC microgrid with multiple sources is

gaining importance as a research issue with the

increasing acceptance of distributed generation. The

challenge with a multi-source DC microgrid is to

supply voltages that effectively facilitate power

sharing. Given the significance of the control method

in ensuring the power system reliability of the

microgrid, an extensive analysis of current condition

control techniques in DC microgrids is necessary to

ensure their efficacy. This work covers both the direct

and indirect control techniques used in hierarchical

control.

Integrated Pv Charging of Ev Fleet Based on

Energy Prices, V2g, An Offer of Reserves developed

an integrated reserve offer, V2G, and energy price PV

charging solution for EVs. There are several

advantages to using office building photovoltaic (PV)

panels to charge electric vehicles (EVs) while at

work. This includes using PV energy that is generated

locally, using it to charge electric vehicles, and

establishing energy exchanges with the grid through

the use of dynamic grid pricing.This study presents an

original mixed-integer linear programming (MILP)

formulation designed to tackle distribution network

constraints, aiming at effective electricity

management and grid overload prevention. Utilizing

a receding-horizon methodology, the MILP model

governs the charging of electric vehicle fleets through

photovoltaic sources.

4 DESCRIPTION

In light of the rapidly increasing global demand for

energy and the need to locate a substation of fossil

fuel resources before their eventual long-term

depletion, this recommended solution reduces the

time required for charging an electric automobile as

well as the energy storage system.

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5 BLOCK DIAGRAMS

Solar panels capture sunlight, and a direct current (dc-

dc) converter is employed to transfer this energy to

the battery. The electric vehicle is charged using a

dc/dc converter within a charging station.

Additionally, the power grid serves as an alternative

power source. Refer to Figure 1 for the block diagram

of the suggested system.

Fast-charging stations (FCS) commonly utilize

energy storage systems (ESS) to mitigate power

fluctuations caused by the unpredictable and frequent

charging demands of electric vehicles (EVs), as well

as the variability of photovoltaic (PV) power

generation. The primary goal of the scheduling

coordination approach (SCA), which integrates the

scheduling regulation approach (SRA) and global

regulation approach (GRA), is to optimize the use of

ESS within the FCS through coordinated

management of EV charging and grid-interface

converters (GICs). The power fluctuation range of the

FCS is effectively represented by variations in DC

bus voltage. Due to the frequent fluctuations in

power, GICs may experience lower utilization rates,

given that EVs exhibit distinct temporal and dynamic

load characteristics.

Since EV charging durations typically fall within

the hourly range, this study employs the SRA method

to regulate EV charging power, thereby mitigating

minor power fluctuations in the FCS. By

implementing a modified droop charging strategy

with state-of-charge (SOC) feedback, SRA enables

proportional and dynamic power adjustments across

EVs. However, while SRA effectively reduces

fluctuations, it may either slow down EV charging or

negatively impact battery lifespan—both of which are

undesirable for users. To address this limitation, GRA

is introduced to ensure that EVs receive adequate

charging power for handling larger power

fluctuations while simultaneously improving the

utilization efficiency of GICs.

5.1 Solar Panel

The primary HPSS component in FCS is the PV

system. A photovoltaic system with many parallel

array sets is connected to the DC bus. In the planned

FCS, PV systems will be given preference while

charging EVs, which will ease the load on the electric

grid, particularly during peak hours.

Figure 1: Proposed Block Diagram.

5.2 Converter

A converter is an electrical circuit that takes a direct

current (DC) input and delivers a DC output at a

different voltage level. This is typically achieved

through high-frequency switching, along with the use

of inductive and capacitive filtering components.

Depending on its design, a converter can serve

multiple functions while altering the input voltage. In

the proposed system, a DC-DC converter is utilized,

which integrates both boost and buck conversion

capabilities to regulate the voltage as needed.

5.2.1 Boost Converter

This proposed system employs a dc-dc converter that

combines the operations of a buck and a boost

converter. Figure 2 depicts the boost converter used

in the PV system to achieve maximum power point

tracking (MPPT).

Figure 2: Boost Converter.

Efﬁcient Fast Charging of Plug in Electric Vehicle Using Adaptive Slide Mode Controller

173

Each boost converter is assessed based on its

capacity to operate efficiently, as well as its size and

implementation cost. Traditional boost converters

and interleaved boost converters are extensively used

topologies in solar systems, although they have the

disadvantage of varying efficiency levels depending

on the weather.

5.2.2 Buck Converter

The charging station has the buck converter shown in

Figure 3. The DC/DC stage is the second stage of

power conversion in an EV charging station. It does

this by changing the incoming DC link voltage to a

lower DC voltage, which charges the battery of an

electric car. The buck converter is a typical DC-DC

converter that turns a high voltage to a low voltage.

Figure 3: Buck Converter.

5.2.3 Interlinking Converter

By granting control over the exchanged active and

reactive power, interlinking converters enable the

direct management of power flow between grids. A

key component of the stability of the entire hybrid

microgrid is the interlinking power converter, which

connects the AC and DC sub grids in Figure 4.

Figure 4: Interlinking Converter.

In grid-connected mode, the utility grid could

assure power balance, and the converter could ensure

DC bus voltage stability. In both micro grids, an

interlinking converter is employed for power balance,

transferring power from one micro grid to the other if

one is overwhelmed.

5.3 Battery

In applications involving electric vehicles (EVs), lead-

acid batteries make up 25–50% of the vehicle mass

overall. At 30–50 Wh/kg, their specific energy is less

than that of petroleum fuels, which is the standard for

all batteries. Figure 5 shows an example of a lead-acid

battery. Even the most sophisticated batteries, when

used in cars with a conventional range, typically lead

to larger masses; however, this difference is lessened

because an EV's drivetrain is lighter.

Figure 5: Lead Acid Battery.

Batteries release hydrogen, oxygen, and sulfur

during charging and usage. These gases are naturally

occurring and, when properly vented, are typically

safe. Among the various types of batteries, lead-acid

batteries are the most affordable. In situations where

vehicle speed is not a concern, this type of battery is

commonly used in professional settings.

6 SMART CHARGING

ALGORITHM

Power fluctuations caused by the intermittent nature

of the PV system and the irregular and frequent access

of EVs are the most important reasons that the ESS

needs to be used in order to eliminate this. In this

work, the ASMC aims to cooperatively allocate the

power distribution of EVs and GICs in order to

replace the ESS in FCS. The SCA flow chart is as

shown in Figure 6. The FCS power fluctuation range

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can be well indicated by the DC bus voltage. EVs

have different temporal and dynamic load

characteristics, therefore frequent power fluctuations

will lead to a poorer usage of GICs.

Figure 6: Flow Chart of ASMC.

Because EV charging time is measured in hours, this

article employs the Self-Regulated Algorithm (SRA)

to adjust EV power input in order to smooth out minor

power variations in FCS. With SOC feedback, the

ASMC for EVs can accomplish proportionate

dynamic power control among EVs via modified

droop charging.

1. Since FCS has large power fluctuations,

GICs should be involved in the regulation of the

charging power of EVs.

2. To maintain the stability of the High-

Performance Storage System (HPSS), it is essential to

adjust the droop coefficient based on the operational

status of the Grid Interface Converter (GIC) and

fluctuations in DC bus voltage. When the GIC is not

operating at full capacity, it retains its initial droop

coefficient, and the DC bus voltage remains

proportional to the output power. However, once the

GIC reaches its maximum power output and an

additional GIC is activated, the droop coefficient

adjusts in response to changes in DC bus voltage. By

implementing a dynamic adaptive management

approach, the output voltage of each GIC can be

effectively synchronized with the DC bus voltage,

ensuring optimal system performance.

6.1 AC Grid

Grids are typically consistently synchronous,

indicating that all distribution areas operate on

synchronized three-phase alternating current (AC)

frequencies, allowing voltage fluctuations to happen

nearly simultaneously. In FCS, the AC module is

movable and is made up of many parallel GICs. The

GICs will turn on when EVs are unable to manage

power fluctuations to the point where the DC bus

voltage rises over the predetermined threshold range.

To guarantee that EVs are charging at the proper

power, GICs are able to endure significant power

fluctuations. To increase the converter's usage, use

numerous GICs.

6.2 Charging Point

Every CP is connected to the common DC bus, which

is managed and maintained by the FCS, and each CP

has a buck converter installed independently. When

an EV battery pack is being charged, the CP keeps

track of its voltage, current, and state of charge (SOC)

and relays this data to the control center. The

enhanced CC&CV charging mode provided by the

CP guarantees the efficiency of EV charging.

7 SIMULATION DIAGRAMS

By working together to coordinate the power

distribution of EVs and GICs, the ASMC hopes to

accomplish ESS replacement within FCS. In Figure 6,

the ASMC flow chart is shown. The power fluctuation

range of FCS is effectively represented by the DC bus

voltage. Due to frequent power fluctuations, the

unique temporal and dynamic load characteristics of

EVs might lead to a decline in the usage of GICs.

Figure 7 depicts the suggested system's simulation

diagram.

The Smart Regulation Algorithm (SRA) for electric

vehicles (EVs) relies on feedback mechanisms to

ensure balanced and dynamic power distribution.

However, continuously using Adaptive Sliding Mode

Control (ASMC) to suppress power fluctuations can

lead to undesirable effects—either slowing down

charging due to reduced power levels or causing

battery degradation from excessive charging power.

To address these challenges, an optimized control

approach is required.

To manage significant power variations, ASMC is

employed to regulate charging power levels and

improve the efficiency of grid-interface converters

(GICs). During this process, there is a continuous

exchange of power between the fast-charging station

(FCS) and the utility grid. ASMC is activated only

when voltage fluctuations caused by power variations

remain within a defined range.This study presents an

enhanced droop control strategy that integrates state-

of-charge (SOC) data to achieve power balance in an

Efﬁcient Fast Charging of Plug in Electric Vehicle Using Adaptive Slide Mode Controller

175

FCS handling multiple EVs. Additionally, it provides

a comprehensive analysis of droop behavior in both

constant current (CC) and constant voltage (CV)

charging phases.

To maintain optimal charging power, GICs play a

vital role in stabilizing power fluctuations within the

FCS. Deploying multiple GICs significantly enhances

system efficiency compared to using a single unit. To

ensure the stability of the hybrid power supply system

(HPSS), the droop coefficient must be dynamically

adjusted based on the DC bus voltage and the

operational state of the GICs. When a GIC is not fully

utilized, the DC bus voltage remains proportional to

output power, preserving the initial droop coefficient.

However, when a GIC reaches its capacity limit, the

next GIC is activated, which may lead to variations in

the droop coefficient relative to the DC bus voltage.

By implementing an adaptive droop coefficient

management strategy, each GIC’s output voltage

aligns with the DC bus voltage, ensuring optimal

system performance.

Figure 7: Simulation Diagram of Proposed System.

8 RESULTS AND SIMULATION

The simulation output and analysis of solar panel,

converter, battery, inverter, grid and charging time

with ASMC were shown in following figures. The

output of solar panel after using dc-dc boost converter

is shown in Figure 8.

The power taken from the grid to charge the

battery of electric vehicle by using smart charging

algorithm is shown in the Figure 9 The power is

drawn from the grid only when there is high power

fluctuation is observed.

Figure 8: Solar Voltage.

Figure 9: Grid Voltage.

The gate pulse shown in Figure 10 is given to the

switch by using the smart charging algorithm. This

gate pulse is used to trigger the gate of the switch to

fasten the process of open and close of the switch.

This process can speed the flow of charge through the

circuit. So, that charging speed can be increased.

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Figure 10: Gate Pulse.

The battery gets charged by using the source such

as solar power and grid. The charging source of

battery is decided by using the controller such as

smart charging controller. The current, voltage and

state of charge of a battery is shown in the Figure 11

Figure 11: SOC of Battery.

Figure 12: Battery Current.

The aim is to reduce the charging time of electric

vehicle. This is obtained by using the newly proposed

algorithm called smart charging algorithm. This

algorithm is basically a switching technique which

controls the switching operation of the system. The

comparison of charging time of an electric vehicle

with ASMC and without ASMC is shown in the

figure 13.

Figure 12 shows the Battery Current.

Figure 13: Time Comparison of Charging EV.

9 CONCLUSIONS

In a DC fast-charging station (FCS) that employs a

hybrid power supply system (HPSS), it is crucial to

lower the costs associated with energy storage

systems (ESS) while enhancing the performance of

grid-integrated chargers (GICs). The proposed DC

FCS incorporates an Adaptive Sliding Mode Control

(ASMC) strategy that functions independently of the

ESS. Simulation outcomes demonstrate the

algorithm's effectiveness in sustaining power

stability. By removing the reliance on ESS, the

ASMC optimizes power delivery from both GICs and

electric vehicles (EVs) within the FCS. This approach

mitigates power fluctuations resulting from the erratic

arrival of EVs and the variable output of photovoltaic

(PV) systems, thereby ensuring a stable power

supply. The ASMC employs a dynamic droop control

mechanism that modifies EV charging power based

on state-of-charge (SOC) feedback, allowing it to

effectively respond to the changing load

characteristics of EVs and enhance power support

within the FCS. Utilizing an adaptive droop control

strategy, the ASMC improves the efficiency of

multiple GICs in comparison to depending on a single

unit. A significant benefit of this method is its

capacity to decrease the number of battery charging

cycles in EVs, which in turn promotes battery

longevity and enhances overall system efficiency.

10 FUTURE SCOPE

In further research, the vehicle-to-grid mode (V2G)

will be taken into consideration to enhance power

support to the grid. The load (motor) will be charged

using a battery that provides feedback to the ASM for

the protection of battery life. This approach is

Efﬁcient Fast Charging of Plug in Electric Vehicle Using Adaptive Slide Mode Controller

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suggested to keep the electrical grid's voltage stable.

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