Robust energy management system for electric vehicle

Ali Falih Challoob; Nur Azzammudin Bin Rahmat; Vigna K. A/L Ramachandaramurthy; Amjad J. Humaidi

doi:10.1556/1848.2024.00839

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International Review of Applied Sciences and Engineering

Volume/Issue:: Volume 16: Issue 1

Robust energy management system for electric vehicle

Authors:

Ali Falih Challoob

Ali Falih Challoob Middle Technical University, Iraq

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Bcc0025@mtu.edu.iq

Nur Azzammudin Bin Rahmat

Nur Azzammudin Bin Rahmat Universiti Tenaga Nasional, Malaysia

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azzammudin@uniten.edu.my

Vigna K. A/L Ramachandaramurthy

Vigna K. A/L Ramachandaramurthy Universiti Tenaga Nasional, Malaysia

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Vigna@uniten.edu.my

, and

Amjad J. Humaidi

Amjad J. Humaidi University of Technology, Iraq

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amjad.j.humaidi@uotechnology.edu.iq https://orcid.org/0000-0002-9071-1329

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Pages:: 98–117

Online Publication Date:: 09 Aug 2024

Publication Date:: 06 Mar 2025

Article Category:: Research Article

DOI:: https://doi.org/10.1556/1848.2024.00839

Keywords:: energy management system; battery management; robust control; artificial neural network; flatness approximation; PI controller

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Abstract

The Energy Management System (EMS) is critical for electric vehicle (EV) in order to optimize energy consumption, improve efficiency, and enhance vehicle performance. The EMS provides the optimization of energy distribution among various vehicle components, reduces energy losses and maximizes the vehicle's efficacy. The EMS reduces battery stress to prevent excessive charging and discharging cycles; thereby, decreases the necessity for premature battery replacement which, in turn, contributes to the battery's life time. The goal of this research is to develop robust control technique to maximize the use of energy storage systems, renewable energy sources and the bidirectional power flow associated with EVs. The proposed robust control approach is based on combination of flatness theory with artificial neural network. The controller is responsible for maintaining the voltage DC bus stabilized and enhancing the quality of the power fed to the EV side. The performance of controlled EMS is verified via computer simulation within MATLAB/SIMULINK environment. As compared to classical proportional-integral (PI) control, the computer results show the proposed controller (FEMS-ANN) gives higher power quality of EV, lower overshot level in the DC voltage, faster response to abnormal conditions, and less steady state error.

Abstract

1 Introduction

Electric vehicles (EVs) when combined with renewable energy sources offer numerous benefits to address environmental and sustainability challenges [1]. EVs maintain energy sustainability by minimizing the usage of fossil fuels and greenhouse gas emissions [2]. Also, they improve local air quality by eliminating the emissions of pollutants like NOx, leading to better health outcomes in urban areas. The EVs also reduce noise pollution, contributing to improved quality of life. Since electric vehicles are higher energy efficiency than conventional cars, electric vehicles can travel farther on the same quantity of energy [3, 4]. When combined with renewable energy like solar photovoltaic (PV) systems, the process of charging electric vehicles takes longer time in some cases, depending on the type of charger so the charging process of an EV is longer than the process of filling up a fuel tank in the traditional combustion engine of conventional vehicles, leading to potential long-term cost savings. The increased demand for EVs can incentivize the growth of renewable energy infrastructure, speeding the shift to an energy system with lower carbon emissions. Generating electricity from PV sources has a lower environmental impact compared to fossil fuels, protecting ecosystems and reducing resource extraction and combustion [5, 6]. Using solar PV energy to power EVs aligns with long-term sustainability goals, reducing the depletion of other resources like wind, turbine sources and other renewable sources. However, the environmental benefits of electric vehicles are maximized when all energy used is supplied from photovoltaic cells. An Energy Management System (EMS) is a crucial component in electric vehicles (EVs) that optimizes the distribution and utilization of electrical energy, enhancing efficiency, range, and performance [7–9]. It plays a vital role in preventing overcharging, over-discharging, and thermal issues, which can lead to reduced battery life. The EMS controls power transfer between the electric motor, battery, and any auxiliary systems assuring the conversion and distribution of energy efficiently [10]. It can also control regenerative braking, which captures and stores energy during braking, improving overall energy efficiency. In addition, the EMS also manages the thermal conditions of the battery and other critical components, preventing overheating and ensuring optimal performance. It considers factors like driving conditions, traffic patterns, route information, and driver preferences to optimize power and energy distribution [11]. Some EMS systems provide a user-friendly interface for drivers, allowing them to make informed decisions. The EMS may also manage the charging process, using predictive algorithms and real-time data to anticipate future driving conditions and adjust energy management accordingly. It can also detect and diagnose faults in the electrical system, ensuring safety and reliability [12, 13].

In the literature, several energy management system (EMS) techniques have been investigated to determine the optimal distribution of power between electric vehicle (EV) consumption and energy storage or renewable energy sources. However, homes with extra solar energy may sell it to charging stations, which in turn would sell it to drivers of electric cars. Since utilization of electric vehicles over conventional EVs is growing, also the requirement for charging stations for electric vehicles has increased dramatically in many global cities. Therefore, the authors proposed a scheme to generate and sell solar energy for homes in the surrounding area, in order to resolve the present issues of cheap pricing for electricity recycling and low photovoltaic power generation on residential roofs. This leads to enhancing the house owner's revenue by improving the harvesting of energy [14]. Moreover, energy sharing across regions is crucial for both residential PV power generation and electric automobiles. Incorporation of plug-in electric vehicle (PEV), solar panels, and heat pump into Home Energy Management System (HEMS) is presented in [15]. The study proposed using the HEMS approach with stochastic model predictive control (MPC) to decrease the power costs and save the cost of PEV battery. The MPC took into account the needs of residential loads such as EV battery charging and thermal comfort. In [16], a heuristic-based programmable energy management controller is demonstrated to decrease carbon emissions, optimize utility savings, and minimize the peak-to-average ratio (PAR) in residential structures. Different optimization algorithms are proposed to satisfy the objectives. The consumers in the proposed system generate their own electricity from solar panels connected to local microgrids (MGs). Moreover, the findings confirmed the effectiveness and efficiency of the suggested system based on optimization approaches. In an electric vehicle (EV), using more than one energy source often provides a safe ride without concerns about range. The energy management control algorithms called Artificial Neural Network (ANN) and Aquila Optimizer Algorithm (AOA) are proposed. As shown, large current battery discharge has been stopped. With the aid of the control methodology, switching between the UC charging and discharging states might also be realized seamlessly. While contrasted to the GA-PID and MHS algorithms that are already in use, the numerical simulations in MATLAB demonstrate that the suggested ANN–AOA approach is effective in anticipating the energy consumption for the next simultaneous interval and accomplishes the EV's faster speed of 91 km h⁻¹ [17].

In [18] a machine learning-based approach to energy management (EM) in renewable (MGs) is presented, with a structure that can be remotely switched by cutting and splitting circuits. The suggested approach takes into account the state-of-the-art support vector machine to model and estimate EV charging demand. Two scenarios, coordinated and intelligent charging, are implemented to reduce effect of charging EV on the system. A novel modified optimization approach using dragonflies is proposed as a means of tackling the problem's complicated structure. A new EMS was proposed by the authors in [19], which uses the Harris Hawks Optimization to offer an external energy maximization method with the goal of decreasing hydrogen consumption while increasing system efficiency. The effectiveness of the suggested EMS was measured against that of preexisting algorithms applying the Federal Test Procedure (FTP-75)-based comparative simulation for the city driving cycle. The simulation findings show that the suggested EMS has the potential to reduce fuel usage by 19.81%, making it superior to other current methods.

The optimal approach to energy management for EV has been proposed in [20]. The idea is to supply the EV with stable, superior power. The hybrid power system (HPS) of lithium-ion battery and super-capacitor (SC) bank was used. The EMS makes an effort to keep the voltage bus stable while meeting high-quality power of the load requirements under a variety of conditions. The parameters used to generate trajectories by the controller based on flatness theory are improved by the controller for management, which is based on a metaheuristic optimization approach. The authors in [21] proposed a new flatness based FEMS using flatness control method for a SC/Li-ion battery HPS. To improve efficiency and maintain a steady DC bus voltage, the proposed FEMS aims to share a common power reference between SC's DC/DC converters and the battery. The model order is first reduced as a flat system by theoretical analysis using the differential flatness technique. Second, MATLAB/Simulink is used to verify the suggested FEMS in a variety of loading scenarios. In this paper, a robust EMS is proposed based on flatness approximation and artificial neural network (ANN). The goal of this method is to stabilize DC voltage at the side of EV subjected to abnormal speed conditions. The use of Flatness based artificial neural network (Flatness-ANN) is to avoid the peak overshoot at starting, which occurs in the conventional flatness-based or PI controllers. Accordingly, the proposed controller will satisfy two objectives simultaneously; mitigation of overshoot and stabilization of DC voltage. However, the study suggests smooth EV voltage and high quality of power. The following points highlight the contribution this study addresses:

Hybrid control algorithm development based on Flatness Energy Management System and ANN to improve the performance of EV system.
Conducting a comparison study in performance with conventional PI controller in terms of transient characteristics
Verifying the proposed controller performance for EV energy management system with three scenarios of tests.

2 Proposed system description

2.1 System configuration

Figure 1 shows the recommended EMS for the PV, SC, and battery integrated with the EV model. The boost DC/DC converter is utilized with the PV system. The DC/DC bidirectional converters are used in both SC and battery to adjust voltage of DC bus on the EV load side. The modified P&O MPPT controller is used to maximize the PV power and track the MPP under different irradiance and temperature. Moreover, the PV system's output voltage is increased via the boost converter so that it is equivalent to 500V on the side of DC bus, and to monitor the MPP of the PV system. The motor current of the EV is sensed based on the required speed of the EV. The varying in the load or in EV is measured and compared with the generation power ( $P_{P V}$ ) or the energy storage system (ESS) power of the battery and SC. The goal of utilizing hybrid ESS in this work is to supply high power density by SC to compensate the load (EV) demand in case of PV cannot provide more energy, while the main battery provides the average power, hence extending the lifespan of the batteries.

Fig. 1.

Proposed system configuration

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

2.2 Modelling of the PV system

2.2.1 Solar cell circuit

Modeling a solar PV module using a single-diode model is a common approach to describe the electrical solar cell behavior or module [22]. This model simplifies the complex conduct of a solar cell by using a single diode to represent the various loss mechanisms and characteristics of the cell/module [23]. The single-diode model consists of several parameters that are used to characterize the PV module's performance under different operating conditions as seen in Fig. 2. The key components of the single-diode solar PV module model are:

Photocurrent ( $I_{P h}$ ): This shows that the solar cell is producing due to the incident sunlight. It is directly related to the incident light's intensity level of and the cell's efficiency.
Diode Ideality Factor (α): This factor represents the ideality of the diode. A value of around 1 is typical for silicon solar cells.
Reverse Saturation Current ( $I_{o}$ ): This parameter characterizes the reverse current through the diode. It is dependent upon the solar cell's quality semiconductor material and increases with temperature.
Series Resistance ( $R_{s}$ ): This resistance accounts for the internal resistance within the solar cell/module, including the resistance of the conducting materials and connections.
Shunt Resistance ( $R_{s h}$ ): This represents the shunt resistance, which occurs due to defects or imperfections in the solar cell/module. Higher values of $R_{s h}$ indicate better performance.

Fig. 2.

Solar array model

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

The equation that explains the current-voltage (I–V) properties of a solar cell/module using the single-diode model is as follows [24, 25]:

I_{p v} = {N_{P} I}_{p h} - N_{P} I_{o} [\exp (\frac{V_{p v} + R_{s} (\frac{N_{S}}{N_{P}}) . I_{p v}}{α V_{T h} N_{S}}) - 1] - \frac{V_{p v} + R_{s} (\frac{N_{S}}{N_{S}}) . I_{p v}}{R_{s h} (\frac{N_{S}}{N_{P}})}

(1)

where,

I_{P h}

is the current output (photocurrent),

V_{p v}

is the voltage output,

I_{p h}

is the photocurrent generated by the cell,

I_{o}

is the diode's reverse saturation current, α is the ideality factor of diode,

R_{s}

is the series resistance,

R_{s h}

is the shunt resistance,

N_{S}

is the series modules number,

N_{P}

is the number of parallel strings of array, and

V_{T h}

represents the voltage of thermal, which is approximately 25.85 mV at room temperature (T = 25°C) [26].

2.2.2 PV array characteristics

The characteristics of I–V arrays and P–V at Standard Test Conditions (STC) are crucial for understanding the performance of PV systems. P–V array characteristics provide a comprehensive overview of power output at various voltage levels under standardized conditions, while I–V array characteristics delineate the voltage and produced current relationship for a photovoltaic array. Analyzing these characteristics helps determine key parameters like short-circuit current, fill factor, open-circuit voltage and efficiency, which are vital for designing and optimizing solar energy systems. To model the behavior of a complete solar PV module, by using the I–V characteristics of individual series cells and parallel connections, taking into account factors like temperature and irradiance changes. Accurate modeling and simulation of PV modules are essential for predicting their performance in various environmental conditions, optimizing system design, and evaluating energy generation. Simulation software of MATLAB/Simulink was used for detailed modeling and analysis. In this work, a solar PV array of 7 × 3 modules are used to provide required power of EV load which is 8000 W and each PV panel has 405 W. The curves of this array under STC (1,000 W/ $m^{2}$ , and temperature 25 °C) are presented in Fig. 3. This array consists of 21 PV panels type of the technical parameters of this module can be shown in Table 1.

Fig. 3.

(a) I–V curve (b) P–V curve

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

Table 1.

Setting of PV's parameters (LG Electronics Inc.LG405N2W-V5)

Parameter description	Values
Maximum Power (w)	405.49
Cells per module ( $N_{c e l l}$ )	72
Open Circuit voltage $V_{o c}$ (V)	49.4
Short circuit current $I_{s c}$ (A)	10.51
Voltage at maximum power point $V_{m p}$ (V)	41
Current at maximum power point $I_{m p}$ (A)	9.89
Temperature coefficient of $V_{o c}$ (%deg.C)	−0.26
Temperature coefficient of $I_{o c}$ (%deg.C)	0.022997
Light-generated current IL (A)	10.5231
Diode saturation current I0 (A)	5.7904 $\times 10^{- 12}$
Diode ideality factor	0.94671
Shunt resistance $R_{s h}$ (ohms)	236.2548
Series resistance $R_{s}$ (ohms)	0.29365

2.3 Modelling of the battery

Modeling a battery in a MG is crucial for understanding its behavior, optimizing usage, and ensuring system stability and reliability. Key steps include selecting the battery chemistry, developing a voltage model, tracking the state of charge (SOC), monitoring the state of health (SOH), developing current and power models, considering implementing control and management strategies. These models are essential for effective MG design, control, and optimization, contributing to improved EMS and grid reliability [27, 28]. The battery model should be integrated into MG simulation or EMS software to assess its impact on MG stability, energy flow, and economics. However, the electrical circuit of a battery is crucial for its operation, regulating, protecting, and managing the flow of electrical energy. It plays a vital role in powering various devices, including electronic gadgets, electric vehicles, and power grids. The circuit regulates voltage, ensuring consistent output for electronic devices. The circuit manages charging and discharging processes, preventing overcharging and ensuring proper charging and discharging. The circuit also enables remote monitoring and control in large-scale applications. Figure 4 shows the electrical circuit of the battery. The output voltage of the battery is written as:

V_{B} = V_{O C} - K \frac{Q}{Q - i_{t}} (\int i d t) A . e^{- B \int i d t}

(2)

where, the battery nominal voltage (in volts) is denoted by

V_{B}

, the battery's

V_{O C}

is the voltage in an open circuit, the number Q represents the amp-hour capacity of the battery in (Ah), the amplitude (A) of the exponential region and the polarization constant is denoted by K (V/Ah). Moreover, the characteristics of the battery used in this work are shown in Fig. 5 [29].

Fig. 4.

Model circuit of the battery

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

Fig. 5.

Battery module curves

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

2.4 Modelling of the super-capacitor

Super-capacitors (SCs) are also referred to as electrochemical capacitors or ultra-capacitors; they play a crucial role in MG systems. They are designed to enhance grid reliability, improve energy efficiency, and incorporate renewable energy sources. SCs contribute significantly to the functionality and efficiency of MGs in several ways [30]:

Energy Storage: SCs are capable of storing and rapidly delivering large amounts of electrical energy.
Power Quality: MGs need to maintain a stable power supply to ensure the quality of electricity.
Extended Battery Life: When used in conjunction with batteries, SCs can extend the lifespan of batteries by handling short-duration high-power demands. Batteries are better suited for longer-term energy storage, while SCs excel in providing bursts of power.
Grid Resilience: In areas prone to power disruptions, such as regions with frequent blackouts or unstable grids, MG with SCs can provide reliable backup power and contribute to the resilience of critical infrastructure.

In summary, SCs are essential components in MG systems, enhancing energy storage, power quality, grid stability, and overall system efficiency. Their ability to provide rapid energy bursts and complement other energy storage technologies makes them invaluable in ensuring reliable and resilient MG operations, particularly in light of the growing need for decentralized energy solutions and the incorporation of renewable energy.

Equation (3) outlines the basic idea of SCs, which is derived from electrostatic capacitor. Figure 6 shows the variables used in this equation, including the air permittivity

(ε_{o})

, the dielectric material's relative permittivity (

ε_{r})

, area of surface (A), and distance (d) between two electrodes [31]. Varying the surface area and thickness of the dielectric material affects the capacitance through the connection given by formula (1).

C = \frac{ε_{o} {\times ε}_{r} \times A}{d}

(3)

Fig. 6.

(a) Electrostatic capacitor structure (b) SC structure (c) circuit model of SC

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

Modeling is required for SC systems to monitor system dimensions. There is also a basic electrical model of SCs provided to help characterize their behavior. Module behavior was simulated using a two-fork SC circuit model. The SC module uses a reduced version of this circuit, which is shown in equation (4).

V_{S C} = N_{S, S C} \times v_{S C} = N_{S, S C} \times (v_{1} + R_{1} i_{S C}) = N_{S, S C} \times (v_{1} + R_{1} \frac{I_{S C}}{N_{P, S C}})

(4)

where,

v_{S C}

and

i_{S C}

N_{S, S C}

N_{P, S C}

are primary voltage and current of SC, series elements, and parallel branches respectively whereas the SC module voltage and currents are represented by V_SC and I_SC [32, 33].

R_{1}

is the equivalent resistance of RC circuit for the SC.

The voltage value of the RC circuit

v_{1}

can be determined by the following equation:

v_{1} = \frac{- C_{0} + \sqrt{{C_{0}}^{2} + 2 C_{v} Q_{1}}}{C_{v}}

(5)

where,

C_{0}

is total capacitance at no load,

C_{v}

is the total capacitance at full load and

Q_{1}

is the quick charge. Figure 7 shows the characteristics of the used SCs.

Fig. 7.

Characteristics of the supper capacitor

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

2.5 Modelling of electric vehicle (EV)

In the fact, the electric vehicle that is discussed in this study has been designed and simulated and taken its data from real resources. In EV applications, several kinds of batteries able to serve as power sources. Batteries are frequently divided into two categories according to how simple it is to recharge them: batteries, both main and secondary. Using the secondary battery in EV and HEV applications requires it to have a low energy loss and high power density. The most common types of EV batteries are NiMH, Ni–Cd, Lead acid, and lithium-ion (Li-ion). The cell voltages of LIBs are higher than those of nickel and zinc batteries. On the other hand, NiMH and lead-acid batteries, offer the poorest life cycle performance while lithium batteries have a maximum cycle life of 3,000 so Li-ion batteries power modern EVs because they offer the best performance in almost every category [34].

Most electric vehicles work with lithium ion batteries due to their specification related to long lifespan, higher capacity for storage of more energy and operation within the range of temperature (for charging

0 \sim 45 ℃

, for discharging

- 20 \sim 60 ℃

) and this range of temperatures can work safely with high efficiency. It is well known that all battery types are affected by their operating temperature so variations in a battery's behavior affect its autonomy and, thus, the battery-powered electric vehicle's operating range [35]. Therefore, we can represent the electric vehicle as mathematical mechanical representation forces, so a function of the external forces acting on the EV, the total traction forces (FT) may be determined [36–39]. It may be made available as,

F_{T} = F_{r} + F_{a d} + F_{g} + F_{a c}

(6)

where,

F_{r}

is the resistance force of rolling,

F_{a d}

represents the force of aerodynamic,

F_{g}

is the force of gravity and

F_{a c}

is acceleration vehicle force. These forces are shown in Fig. 8.

Fig. 8.

Total traction forces on the vehicle

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

These forces can be expressed as follows:

F_{r} = μ_{r r} M_{v} g

(7)

F_{a d} = 0.5 ρ v^{2} A_{f} C_{d}

(8)

F_{g} = M_{v} g \sin (\emptyset)

(9)

F_{a c} = M_{v} a = M_{v} \frac{d v}{d t}

(10)

where,

μ_{r r}

is the rolling friction coefficient (0.05 and 0.005 for radial and EV tires),

M_{v}

is the vehicle mass,

g

represents the acceleration of gravity, ρ is the density of air,

\emptyset

is the road slop, v is the speed of vehicle,

A_{f}

is the frontal area,

C_{d}

indicates coefficient drag of aerodynamic in the range (0.19–0.3),

\emptyset

is the road slope and

a

is the acceleration of EV. Furthermore, the traction engine's generated load power on the DC link (

P_{L} (t)

) is written as follows:

P_{L} (t) = F_{T} (t) \times v (t) \times η_{t}

(11)

where,

η_{t}

is the mechanical transmission efficiency.

3 Proposed energy management system

3.1 Flatness control method

The nonlinearity of the system makes linear control techniques more challenging, so a reduced-order model was developed using differential flatness theory, such that the dynamics of the trajectories could be defined explicitly. The following equations can be utilized to describe flat model [40–42]:

y = ϕ (x, u, \dot{u}, \dots . . u^{(α)})

(12)

x = φ (y, \dot{y}, \dots . . y^{(β)})

(13)

u = ψ (y, \dot{y}, \dots . . y^{(β + 1)})

(14)

where, y is the output flat of model, x is the variable state, and u is the variable of control. Also, ϕ, φ, and ψ are the functions of the smooth mapping, and

y^{(β + 1)}

denotes the derivative of the output

(β + 1)

^th. The letter α represents the order of derivative, where rank

(ϕ) = m, rank (φ) = n, and rank (ψ) = m

3.2 Artificial neural network (ANN) control method

An ANN is a kind of data processing system that is inspired from biological neurons. An exceptional skill of ANN is to extract meaning from complicated data. Additionally, it is capable of identifying patterns and trends in data that is too complicated for computers or humans to classify. An artificial neural network (ANN) that has been trained may evaluate the given data and offer predictions and solutions for any follow-up issues. Furthermore, ANNs are regarded as practical computational models that can be applied to resolve a variety of difficult problems. Artificial Neural Networks (ANNs) are essential for solving complex system prediction and control (CPM), regression, and classification problems [43].

An ANN is a collection of three layers. For the basic models, there are three layers that can be identified: the layer of input, the hidden layer or layers, and the layer of output. Data are received at the input layer, which may include sensors that detect environmental signals. Regarding a robotic system, the output layer can function as an effector and responds to all synaptic activities inside the network. The tasks (calculations, adjustments) necessary to represent the environment to be represented are completed by the hidden layer. The training dataset is utilized to construct the neural network, and the validation dataset is used to assess its performance. The validation dataset parameters of input are imported, the network is trained, and the error is within allowable bounds. At that point, the output parameter's associated values are predicted. This procedure contributes to the accuracy and dependability of the ANN. The expected output parameter values from the validation dataset are compared with the corresponding actual values to assess the performance of the trained ANN. If the difference between the actual and predicted values is smaller than the permitted maximum, the best prediction model might be the trained ANN. The ANN can foresee the matched input values and output parameters for the selected training strategy and number of training iterations [44, 45].

3.3 Classical PI control method

As previously said, the PI controllers are commonly used since they are simple to change and tweak. Due to its straightforward design and capacity in order to ensure outstanding performance, the PI control strategy is frequently utilized with DC-DC converters. PI controller-based regulation techniques are frequently used to control bus voltage. The PI controller then matches the integral and proportional processes to produce the signal output as shown in Fig. 9. Because of its simplicity, a similar trial-and-error approach has been used to adjust the gain of PI. Since the PI controller's methodology is already widely known, only the completed form is shown. The transfer function of the PI controller is denoted by

G (s)

which is given by [46–48]:

G (s) = K_{P} + \frac{K_{I}}{s}

(15)

Fig. 9.

Schematic diagram of classical PI control

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

The control action due to PI controller can be expressed as

u_{C} (t) = (K_{P} + \frac{K_{I}}{s}) e

(16)

where,

K_{P}

and

K_{I}

are proportional and integral gains of PI control respectively which equal in this study to (

K_{P} = 20

K_{I} = 100)

. Also, to evaluate the variation between the measured DC bus voltage and the reference DC bus voltage depends on the following equation:

e (t) = V_{b u s, r e f} - V_{b u s, m e a s u r e d}

(17)

Large setpoint changes may cause the controller to become saturated, which will lead to an overshoot and a protracted stabilization period for the system. To solve this issue and provide a more responsive and stable system, a Flatness-ANN controller is utilized to adjust DC bus voltage and reducing overshoot.

3.4 Proposed system of analysis based flatness control

To implement the MPPT algorithm, the PV system is interfaced with a circuit for a boost converter. The bidirectional converter is used to regulate both SC and the Li-ion battery. The reason for using a boost DC/DC converter is to stabilize the voltage output of the PV system at the desired value of 500V on the side of DC bus. The EMS works to monitor the DC/DC boost converter's output according to the PV system's maximum power point's efficiency and the controller manage to compensate the dropped voltage under abnormal conditions. In other words, the aim of the flatness control based EMS is to supply the optimal power sharing from each source or energy storage system. The power reference for each unit can be expressed as follows:

P_{P V_r e f} = P_{P V} = v_{p v} i_{p v}

(18)

P_{B_r e f} = P_{B} = v_{B} i_{B}

(19)

P_{S C_r e f} = P_{S C} = v_{S C} i_{S C}

(20)

where, values of the instantaneous voltage and current for each unit are:

v_{P V}, i_{P V}

are parameters of the PV system,

v_{B}, i_{B}

are parameters of the battery,

v_{S C}

, i_{S C}

are parameters of the SC.

The energy of the DC bus (

E_{b u s}

) and the SC (

E_{S C}

) can be expressed as follows:

E_{b u s} = \frac{1}{2} C_{b u s} v_{b u s}^{2}

(21)

P_{B_r e f} = P_{B} = v_{B} i_{B}

(22)

where,

C_{b u s}

is the capacitance of dc bus, and

C_{S C}

is the capacitance of SC.

To guarantee the EMS for the planned system, nevertheless, a differential equation between the actual output power from the PV, battery, SC, and DC load is presented. The derivative of the capacitive energy of DC bus

E_{b u s}

is formulated as follows [49]:

\dot{E_{b u s}} = P_{P V o} {+ P}_{S C o} + P_{B o} - P_{l o a d}

(23)

Where the actual load of the DC bus can be written as:

P_{l o a d} = i_{l o a d} . v_{b u s} = \sqrt{\frac{2 E_{b u s}}{C_{b u s}}} . i_{l o a d}

(24)

In order to decrease the model order and exhibit the recommended FEMS, flatness control to the suggested power system is applied. The proposed model will be clarified as follows, based on the information presented previously:

y = [\begin{array}{c} y_{1} \\ y_{2} \end{array}], u = [\begin{array}{c} u_{1} \\ u_{2} \end{array}], x = [\begin{array}{c} x_{1} \\ x_{2} \end{array}]

(25)

The subsequent equation is derived through the application of this idea to the given system:

y = [\begin{array}{c} E_{b u s} \\ E_{S C} \end{array}], u = [\begin{array}{c} P_{S C_r e f} \\ P_{B_r e f} \end{array}], x = [\begin{array}{c} v_{b u s} \\ v_{S C} \end{array}]

(26)

3.5 Stabilize DC bus voltage

The terms used in this work are the flat output

y_{1}

, DC bus state variable

x_{1}

, and input control unit

u_{1}

are used to regulate the voltage of DC bus as in equation (23)

y_{1} = E_{b u s}, x_{1} = v_{b u s}, u_{1} = P_{S C_r e f}

(27)

This control law can be written as:

u_{1} = P_{S C, r e f} = 2 P_{S C, \max} [1 - \sqrt{1 - \frac{{\dot{y}}_{1} + \sqrt{\frac{2 y_{1}}{C_{b u s}}} ∙ i_{l o a d} - (P_{B o} + P_{P V o})}{P_{S C, \max}}}] = ψ_{1} ({\dot{y}}_{1}, y_{1})

(28)

where,

P_{S C, \max} = \frac{v_{S C}^{2}}{4 r_{S C}}

The purpose of the controller is to keep the DC bus voltage constant or bus capacitive energy, denoted by the equation $E_{b u s}$ = $y_{1}$ .

In conventional EMS based flatness control, the voltage DC bus is stabilized and adjusted with the help of a PI controller. In this work, the ANN controller with flatness was used to stabilize the voltage DC bus according to the weather or load conditions. Therefore, the general strength of the suggested model may be expressed as ${\dot{y}}_{b u s} = P_{S C o}$ . Moreover, this is the trajectory planning of the SC which is used to adjust the DC bus by controlling the PWM of the bidirectional converter through the current controller, as shown in Fig. 10.

Fig. 10.

Proposed EMS for SC unit

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

3.6 Battery management and control

The trajectory planning is constructed using the SC's demand power depend on the following equation:

P_{S C, d e m a n d} = K_{P P} ({V_{s c} - V}_{s c, r e f})

(29)

As a result, tracking of the SOC or charging of the SC is done based on the battery. In order for the sources of PV to enable Li-ion battery charging, equation (26) could be written as follows:

P_{l o a d} {+ P}_{B o} = P_{P V o} + P_{S C o}

(30)

Figure 11 shows the battery EMS and its control that was used to monitor the SC's voltage and track the voltage reference of the SC, which is 48 V. This figure shows how the power demand of the SC was used to estimate the needed power of battery. We had to do this to get the electricity or power we needed for EV. To further manage the charge and discharge values of the battery, PI current control was also included. However, in certain situations, the PI controller may experience issues known as integrator windup. Integrator windup occurs when the integral term of the controller accumulates an excessively large error signal, leading to saturation or other undesirable effects, especially in the systems with actuator constraints. To mitigate integrator windup, an anti-windup, a correction term is often added to the PI controller. The proposed PI controller with anti-windup correction is shown in Fig. 12. As indicated in the figure, the parameters of PI controller are assigned as $K_{i 3}$ and $K_{i 2}$ are 1.54 and 605, respectively.

Fig. 11.

Battery management control unit

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

Fig. 12.

Current controller of the battery

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

4 Results and discussion

In this study, a MATLAB Simulink tool that was utilized to model and simulate the performance of the suggested DC microgrids. The proposed EMS's efficacy for EV load is tested under different scenarios to achieve an optimal response and stabilize the reference value for the DC bus voltage of 500V.

4.1 First test: performance of system under step change in speed of EV

Testing the performance of a PV/battery/SC system in an EV under varying speed, acceleration, and driving patterns is crucial for various reasons. These include dynamic behavior assessment, energy management, range estimation, charging and discharging performance, regenerative braking evaluation. Testing under different speeds helps evaluate the battery's response to power demand changes, energy management, and the effectiveness of regenerative braking systems. Firstly, the weather condition of the solar system is kept under STC conditions, as shown in Fig. 13. The temperature is 25 °C and solar irradiance is 1,000 W/ $m^{2}$ . The step changes in speed of the EV is used to show the performance of the used EMS. The speed profile as well as the motor current curves are mentioned in Fig. 14.

Fig. 13.

Curves of the irradiance and temperature at STC

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

Fig. 14.

Obtained results at fixed weather conditions (a) speed of EV (b) motor's current (c) the power curves of load, SC, battery and PV system

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

Figure 14 demonstrates obtained results curves of the speed of EV, motor's current, and the power curves of load, SC, battery and PV system. When the speed is high – 140 km h⁻¹, the load power is high at 9500 W as well as the PV is fixed power generation of maximum power of 8 KW. At this moment, the load is increased more than the generation power, the SC responds quickly to avoid the overshoot in the DC bus voltage and then minimizes it to the reference value of 500 V. Moreover, the battery slowly provides power to the EV in order to charge the SC and tracks the voltage of the SC based on its power demand. The response of each unit of the storage is displayed based on their SOC as indicated in Fig. 15. The bus DC voltage is stabilized under these conditions, as presented in Fig. 16. As presented in this figure, the proposed EMS provide high response, less overshoot and stabling bus voltage.

Fig. 15.

SOC of the battery and SC at case (1)

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

Fig. 16.

DC bus curve at (a) proposed EMS (b) classical PI method

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

To emphasise the novelty of this work, the obtained results of the bus voltage are compared with the classical PI-based EMS. As evident, the suggested EMS is better than the PI method in terms of overshoot at the start of operation and at the disturbance due to the varying in the speed. The time response at beginning is very short in the proposed method – 0.05 s, but PI method requires more time – about 0.62 s to reach the desired voltage of the bus. Also, the overshoot in the bus voltage is 1 V at the suggested method while this overshoot is the worst in the case of the classical method, at about 11.5 V. We make a comparison between them as mentioned in the Table 2.

Table 2.

Comparison of proposed FEMS-ANN with traditional PI control law

EMS control strategy	PI control law	Proposed FEMS-ANN
Maximum over shoot	11.5 V	1 V
Steady state error	0.62 s	0.05 s
Response of Stabilize voltage	Low response	Wey high response
Varying of load due to change of speed EV	Maximum over shoot	Minimum over shoot
Stability of DC bus voltage and steady state error	Steady state error is very high	Low steady state error

4.2 Second test: performance of system under random speed of EV

In this section, evaluating the system's responsiveness to dynamic changes in driving circumstances entails testing the performance of a PV battery and super-capacitor system under randomly variable EV speeds. The profile of the EV speed is shown in Fig. 17. The obtained curves of the load, SC, battery and PV are shown in Fig. 18. As seen, the response of the SC is similar to the load curve because SC has been used to maintain the DC bus load stability then keep the DC voltage, with its value of 500V. The battery in this case is in charge mode due to the produced power form the PV system; it is enough for the required load demand and therefore, the excess energy is used in the storage of the battery.

Fig. 17.

EV speed profile randomly

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

Fig. 18.

Power curves of the system at case (2)

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

The SOC of the SC and battery are shown in Fig. 19. In this case, the curve of the voltage bus can be seen in Fig. 20. However, when compared the, proposed control technique (Flatness-ANN EMS) with the classical PI method shows that the proposed control strategy is better than PI and this method can remove high overshoot that appears in the traditional method with very fast response to the variation in the load side. This makes energy management control very robust and the loss in the power is very low.

Fig. 19.

SOC of the battery and SC at case (2)

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

Fig. 20.

DC bus curve under case 2 (a) proposed EMS (b) classical PI method

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

4.3 Third test: performance of system under varying solar irradiance

In this section, Simulation MATLAB was employed to evaluate a PV system's performance/battery/super-capacitor under different conditions, such as different levels of irradiance (solar intensity). The goal of this test is to prove the novelty of the EMS compared with PI method. The varying in the solar irradiance will decrease the PV production and this will affect many factors related to battery and SC charging and discharging, life span cycles and stability of the supplied energy. Figure 21 shows the temperature and the irradiance of solar system used in this section. The speed of the EV is kept constant with rated 100 km h⁻¹.

Fig. 21.

Weather conditions of the solar PV system (a) solar irradiance and (b) Temperature

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

The irradiance varies from 900 W/ $m^{2}$ to 700 W/ $m^{2}$ from 0 s to 0.4 s of the simulation. As clear, the varying in the solar irradiation decreased the PV system power output from 7200 to 5600W and the PV power varies from 5600 to 6300W when the irradiance varied from 700 to 800W/ $m^{2}$ . In this duration, the load demand value is higher than PV power generation, so the SC begins operation and charges from battery to supply the energy to the DC bus, based on the value of the solar irradiance as seen in Fig. 22. Based on the approach proposed in this research, it is to make the super-capacitor preserve bus voltage stability and also ensure continuous supply of energy. As a result, the using of robust EMS can fix these issues and enhance the quality of the power from the source to the EV side. Figure 23 shows the stabilization of DC bus voltage, based on the suggested method of EMS control. As shown in this curve, the proposed EMS give better quality of the power with a very slight change in DC bus voltage, even if there is a change in solar radiation. This makes energy management of the system very robust and the overshoot in the EV voltage is very low. The SOC of the energy storage systems at this case are shown in Fig. 24

Fig. 22.

Achieved results at third case study

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

Fig. 23.

DC bus voltage at case (3)

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

Fig. 24.

SOC of the Super-capacitor and battery at case (3)

Citation: International Review of Applied Sciences and Engineering 16, 1; 10.1556/1848.2024.00839

Other modern control techniques can be suggested to extend this study for future work [50–59]. To further show the effectiveness of the proposed controller, it can be compared to the performance of the suggested controller. Moreover, the proposed controller can be validated in real energy management systems by conducting real-time implementation of controller on different embedded systems like FPGA, Raspberry Pi, Arduino Platform or LabVIEW environment [60–63]. Modern optimization methods can be suggested to improve the proposed controller by optimal tuning of its design parameters [64–69].

5 Conclusion

This study proposed an EMS for DC microgrid application, which comprise photovoltaic (PV) systems, batteries, and super-capacitors (SC), and supplied EV. The purpose is to design a sophisticated control and EMS approach that maximizes the energy and achieves a stable DC bus voltage. Therefore, to maintain the stability of DC bus voltage and improve the quality of the power that is provided to the electric vehicle side, an effective energy management system (EMS) that is depend on the control of flatness theory and incorporates an artificial neural network (ANN) is used. On the basis of MATLAB Simulink, the performance of the EMS that was used was shown. A comparison is made between the previously established proportional-integral (PI) control approach and the results that were produced. The time response of the proposed method is 0.05 s and overshoot in the voltage is 1V. Against, the PI based EMS has low efficiency with large time response of 0.62 s and worst overshoot in the voltage of 12 V. Finally, the findings that were obtained demonstrate that the suggested EMS is capable of providing a high power quality to the EV while minimizing the amount of overshoot in the DC voltage and exhibiting a rapid reaction to abnormal situations.

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A. J. Amjad and T. M. Kadhim, “Recognition of Arabic characters using spiking neural networks,” in International Conference on Current Trends in Computer, Electrical, Electronics and Communication, CTCEEC 2017, 2018, pp. 7–11, 8455116. https://doi.org/10.1109/CTCEEC.2017.8455116.
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A. J. Humaidi, S. K. Kadhim, and A. S. Gataa, “Development of a novel optimal backstepping control algorithm of magnetic impeller-bearing system for artificial heart ventricle pump,” Cybernet. Syst., vol. 51, no. 4, pp. 521–541, 2020. https://doi.org/10.1080/01969722.2020.1758467.
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[46]↑
R. A. Kadhim, M. Q. Kadhim, H. Al-Khazraji, and A. J. Humaidi, “Bee algorithm based control design for two-links robot arm systems,” IIUM Eng. J., vol. 25, no. 2, pp. 367–380, 2024. https://doi.org/10.31436/iiumej.v25i2.3188.
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[47]
A. Al-Jodah, S. J. Abbas, A. F. Hasan, A. S. M. Al-Obaidi, A. A. Al-Qassar, and R. F. Hassan, “PSO-based optimized neural network PID control approach for a four wheeled omnidirectional mobile robot,” Int. Rev. Appl. Sci. Eng., vol. 14, no. 1, pp. 58–67, 2023. https://doi.org/10.1556/1848.2022.00420.
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[48]
A. J. Humaidi and A. I. Abdulkareem, “Design of augmented nonlinear PD controller of Delta/Par4-like robot,” J. Control Sci. Eng., 2019, Art no. 7689673. https://doi.org/10.1155/2019/7689673.
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P. Thounthong, S. Sikkabut, P. Mungporn, L. Piegari, B. Nahid-Mobarakeh, S. Pierfederici, and B. Davat, “DC bus stabilization of li-ion battery based energy storage for a hydrogen/solar power plant for autonomous network applications,” IEEE Trans. Indus. Appl., vol. 51, no. 4, pp. 2717–2725, 2015. https://doi.org/10.1109/IAS.2014.6978368.
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A. J. Jaleel and H. A. Hussein, “Adaptive control of parallel manipulator in Cartesian space,” in 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). Coimbatore, India, 2019, pp. 1–8.
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[51]
A. J. Humaidi, E. N. Talaat, M. R. Hameed, and A. H. Hameed, “Design of adaptive observer-based backstepping control of cart-pole pendulum system,” in Proceeding of IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT 2019). IEEE, 2019, pp. 1–5. https://doi.org/10.1109/ICECCT.2019.8869179.
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[52]
T. Ghanim, A. R. Ajel, and A. J. Humaidi, “Optimal fuzzy logic control for temperature control based on social spider optimization,” IOP Conf. Ser. Mater. Sci. Eng., vol. 745, no. 1, 2020, Art no. 012099. https://doi.org/10.1088/1757-899X/745/1/012099.
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[53]
S. M. Mahdi, N. Q. Yousif, A. A. Oglah, M. E. Sadiq, A. J. Humaidi, and A. T. Azar, “Adaptive synergetic motion control for wearable knee-assistive system: a rehabilitation of disabled patients,” Actuators, vol. 11, no. 7, p. 176, 2022. https://doi.org/10.3390/act11070176.
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[54]
AQ Al-Dujaili, AJ Humaidi, ZT Allawi, and ME Sadiq, “Earthquake hazard mitigation for uncertain building systems based on adaptive synergetic control,” Appl. Syst. Innovation, vol. 6, no. 2, p. 34, 2023. https://doi.org/10.3390/asi6020034.
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[55]
A. F. Mutlak and A. J. Humaidi, “A comparative study of synergetic and sliding mode controllers for pendulum systems,” J. Européen des Systèmes Automatisés, vol. 56, no. 5, pp. 871–877, 2023. https://doi.org/10.18280/jesa.560518.
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[56]
A. F. Mutlak and A. J. Humaidi, “Adaptive synergetic control for electronic throttle valve system,” Int. Rev. Appl. Sci. Eng. (published online ahead of print 2023) https://doi.org/10.1556/1848.2023.00706.
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[57]
M. Y. Hassan, A. J. Humaidi, and M. K. Hamza, “On the design of backstepping controller for Acrobot system based on adaptive observer,” Int. Rev. Electr. Eng., vol. 15, no. 4, pp. 328–335, 2020. https://doi.org/10.15866/iree.v15i4.17827.
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[58]
A. J. Amjad, M. R. Hameed, and A. H. Hameed, “Design of block-backstepping controller to ball and arc system based on zero dynamic theory,” J. Eng. Sci. Technol., vol. 13, no. 7, pp. 2084–2105, 2018.
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[59]
A. J. Humaidi and M. Hameed, “Development of a new adaptive backstepping control design for a non-strict and under-actuated system based on a PSO tuner,” Information, vol. 10, no. 2, pp. 1–17, 2019, 38.
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[60]↑
A. J. Hameed, S. K. Kadhim, and A. S. Gataa, “Optimal adaptive magnetic suspension control of rotary impeller for artificial heart pump,” Cybernetics Syst., vol. 53, no. 1, pp. 141–167, 2022. https://doi.org/10.1080/01969722.2021.2008686.
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[61]
M. L. Muhammed, A. J. Humaidi, and E. H. Flaieh, “Towards comparison and real time implementation of path planning methods for 2R planar manipulator with obstacles avoidance,” Math. Model. Eng. Probl., vol. 9, no. 2, pp. 379–389, 2022. https://doi.org/10.18280/mmep.090211.
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[62]
M. Laith Muhammed, A. Jaleel Humaidi, and E. Hassan Flaieh, “A comparison study and real-time implementation of path planning of two arm planar manipulator based on graph search algorithms in obstacle environment,” ICIC Express Lett., vol. 17, no. 1, pp. 61–72, 2023. https://doi.org/10.24507/icicel.17.01.61.
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[63]
M. L. Muhammed, E. H. Flaieh, and A. J. Humaidi, “Embedded system design of path planning for planar manipulator based on Chaos A* algorithm with known-obstacle environment,” J. Eng. Sci. Technol., vol. 17, no. 6, pp. 4047–4064, 2022.
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[64]↑
A. J. Humaidi and A. H. Hameed, “Robustness enhancement of MRAC using modification techniques for speed control of three phase induction motor,” J. Electr. Syst., vol. 13, no. 4, pp. 723–741, 2017.
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[65]
Q. Y. Noor, A. F. Hasan, A. H. Shallal, A. J. Humaidi, and T. L. Rasheed, “Performance improvement of nonlinear differentiator based on optimization algorithms,” J. Eng. Sci. Technol., vol. 18, no. 3, pp. 1696–1712, 2023.
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[66]
A. F. Hasan, N. Al-Shamaa, S. S. Husain, A. J. Humaidi, and A. Al-dujaili, “Spotted hyena optimizer enhances the performance of fractional-order PD controller for tri-copter drone,” Int. Rev. Appl. Sci. Eng., vol. 15, no. 1, pp. 82–94, 2024. https://doi.org/10.1556/1848.2023.00659.
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[67]
A. J. Humaidi, S. Hasan, and A. A. Al-Jodah, “Design of second order sliding mode for glucose regulation systems with disturbance,” Int. J. Eng. Technol. (UAE), vol. 7, no. 2, pp. 243–247, 2018.
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[68]
H. Al-Khazraji, W. Guo, and A. J. Humaidi, “Improved cuckoo search optimization for production inventory control systems,” Serb. J. Electr. Eng., vol. 21, no. 2, pp. 187–200, 2024. https://doi.org/10.2298/SJEE2402187A.
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[69]
A. E. Korial, I. I. Gorial, and A. J. Humaidi, “An improved ensemble-based cardiovascular disease detection system with chi-square feature selection,” Computers, vol. 13, no. 6, supp. 126, 2024. https://doi.org/10.3390/computers13060126.
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International Review of Applied Sciences and Engineering

Print ISSN:: 2062-0810

Online ISSN:: 2063-4269

Editorial Board

Mohammad Nazir AHMAD, Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia

Murat BAKIROV, Center for Materials and Lifetime Management Ltd., Moscow, Russia

Nicolae BALC, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Umberto BERARDI, Toronto Metropolitan University, Toronto, Canada

Ildikó BODNÁR, University of Debrecen, Debrecen, Hungary

Sándor BODZÁS, University of Debrecen, Debrecen, Hungary

Fatih Mehmet BOTSALI, Selçuk University, Konya, Turkey

Samuel BRUNNER, Empa Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland

István BUDAI, University of Debrecen, Debrecen, Hungary

Constantin BUNGAU, University of Oradea, Oradea, Romania

Shanshan CAI, Huazhong University of Science and Technology, Wuhan, China

Michele De CARLI, University of Padua, Padua, Italy

Robert CERNY, Czech Technical University in Prague, Prague, Czech Republic

Erdem CUCE, Recep Tayyip Erdogan University, Rize, Turkey

György CSOMÓS, University of Debrecen, Debrecen, Hungary

Tamás CSOKNYAI, Budapest University of Technology and Economics, Budapest, Hungary

Anna FORMICA, IASI National Research Council, Rome, Italy

Alexandru GACSADI, University of Oradea, Oradea, Romania

Eugen Ioan GERGELY, University of Oradea, Oradea, Romania

Janez GRUM, University of Ljubljana, Ljubljana, Slovenia

Géza HUSI, University of Debrecen, Debrecen, Hungary

Ghaleb A. HUSSEINI, American University of Sharjah, Sharjah, United Arab Emirates

Nikolay IVANOV, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

Antal JÁRAI, Eötvös Loránd University, Budapest, Hungary

Gudni JÓHANNESSON, The National Energy Authority of Iceland, Reykjavik, Iceland

László KAJTÁR, Budapest University of Technology and Economics, Budapest, Hungary

Ferenc KALMÁR, University of Debrecen, Debrecen, Hungary

Tünde KALMÁR, University of Debrecen, Debrecen, Hungary

Milos KALOUSEK, Brno University of Technology, Brno, Czech Republik

Jan KOCI, Czech Technical University in Prague, Prague, Czech Republic

Vaclav KOCI, Czech Technical University in Prague, Prague, Czech Republic

Imre KOCSIS, University of Debrecen, Debrecen, Hungary

Imre KOVÁCS, University of Debrecen, Debrecen, Hungary

Angela Daniela LA ROSA, Norwegian University of Science and Technology, Trondheim, Norway

Éva LOVRA, Univeqrsity of Debrecen, Debrecen, Hungary

Elena LUCCHI, Eurac Research, Institute for Renewable Energy, Bolzano, Italy

Tamás MANKOVITS, University of Debrecen, Debrecen, Hungary

Igor MEDVED, Slovak Technical University in Bratislava, Bratislava, Slovakia

Ligia MOGA, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Marco MOLINARI, Royal Institute of Technology, Stockholm, Sweden

Henrieta MORAVCIKOVA, Slovak Academy of Sciences, Bratislava, Slovakia

Phalguni MUKHOPHADYAYA, University of Victoria, Victoria, Canada

Balázs NAGY, Budapest University of Technology and Economics, Budapest, Hungary

Husam S. NAJM, Rutgers University, New Brunswick, USA

Jozsef NYERS, Subotica Tech College of Applied Sciences, Subotica, Serbia

Bjarne W. OLESEN, Technical University of Denmark, Lyngby, Denmark

Stefan ONIGA, North University of Baia Mare, Baia Mare, Romania

Joaquim Norberto PIRES, Universidade de Coimbra, Coimbra, Portugal

László POKORÁDI, Óbuda University, Budapest, Hungary

Roman RABENSEIFER, Slovak University of Technology in Bratislava, Bratislava, Slovak Republik

Mohammad H. A. SALAH, Hashemite University, Zarqua, Jordan

Dietrich SCHMIDT, Fraunhofer Institute for Wind Energy and Energy System Technology IWES, Kassel, Germany

Lorand SZABÓ, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Csaba SZÁSZ, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Ioan SZÁVA, Transylvania University of Brasov, Brasov, Romania

Péter SZEMES, University of Debrecen, Debrecen, Hungary

Edit SZŰCS, University of Debrecen, Debrecen, Hungary

Radu TARCA, University of Oradea, Oradea, Romania

Zsolt TIBA, University of Debrecen, Debrecen, Hungary

László TÓTH, University of Debrecen, Debrecen, Hungary

László TÖRÖK, University of Debrecen, Debrecen, Hungary

Anton TRNIK, Constantine the Philosopher University in Nitra, Nitra, Slovakia

Ibrahim UZMAY, Erciyes University, Kayseri, Turkey

Andrea VALLATI, Sapienza University, Rome, Italy

Tibor VESSELÉNYI, University of Oradea, Oradea, Romania

Nalinaksh S. VYAS, Indian Institute of Technology, Kanpur, India

Deborah WHITE, The University of Adelaide, Adelaide, Australia

International Review of Applied Sciences and Engineering
Address of the institute: Faculty of Engineering, University of Debrecen
H-4028 Debrecen, Ótemető u. 2-4. Hungary
Email: irase@eng.unideb.hu

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2024
Scopus
CiteScore
CiteScore rank
SNIP
Scimago
SJR index	0.261
SJR Q rank	Q2

2023
Scimago
Scimago H-index	11
Scimago Journal Rank	0.249
Scimago Quartile Score	Architecture (Q2) Engineering (miscellaneous) (Q3) Environmental Engineering (Q3) Information Systems (Q4) Management Science and Operations Research (Q4) Materials Science (miscellaneous) (Q3)
Scopus
Scopus Cite Score	2.3
Scopus CIte Score Rank	Architecture (Q1) General Engineering (Q2) Materials Science (miscellaneous) (Q3) Environmental Engineering (Q3) Management Science and Operations Research (Q3) Information Systems (Q3)
Scopus SNIP	0.751

International Review of Applied Sciences and Engineering
Publication Model	Gold Open Access Online only
Submission Fee	none
Article Processing Charge	1100 EUR/article
Regional discounts on country of the funding agency	World Bank Lower-middle-income economies: 50% World Bank Low-income economies: 100%
Further Discounts	Limited number of full waivers available. Editorial Board / Advisory Board members: 50% Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information	Gold Open Access

International Review of Applied Sciences and Engineering
Language	English
Size	A4
Year of Foundation	2010
Volumes per Year	1
Issues per Year	3
Founder	Debreceni Egyetem
Founder's Address	H-4032 Debrecen, Hungary Egyetem tér 1
Publisher	Akadémiai Kiadó
Publisher's Address	H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible Publisher	Chief Executive Officer, Akadémiai Kiadó
ISSN	2062-0810 (Print)
ISSN	2063-4269 (Online)