Fuzzy logic control-based battery management system

Anwar M. Hameed; Ayad Al-Dujaili; Amjad J. Humaidi

doi:10.1556/1848.2025.00971

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International Review of Applied Sciences and Engineering

Volume/Issue:: Accepted Manuscript / Online First

Fuzzy logic control-based battery management system

Authors:

Anwar M. Hameed

Anwar M. Hameed Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq

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bcc2012@mtu.edu.iq https://orcid.org/0009-0006-5291-5378

Ayad Al-Dujaili

Ayad Al-Dujaili Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq

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ayad.qasim@mtu.edu.iq

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Amjad J. Humaidi

Amjad J. Humaidi Control and Systems Engineering Department, University of Technology, Baghdad, Iraq

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amjad.j.humaidi@uotechnology.edu.iq

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Online Publication Date:: 14 Apr 2025

Publication Date:: 14 Apr 2025

Article Category:: Research Article

DOI:: https://doi.org/10.1556/1848.2025.00971

Keywords:: battery management system; Simscape model; intelligent control; SOC; SOH; temperature; MF

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Abstract

Due to the rising demand for battery-operated electrical vehicles (EVs) and equipment, it has become essential to establish a system that is continuously monitoring and managing the performance of each battery. This article presents the design of Battery Management System (BMS) based on intelligent fuzzy logic controller (FLC). The development of battery models and design of FLC is performed within the environment of MATLAB programming software. The FL controller uses the monitored signals of the battery, represented by state of charge (SOC), state of health (SOH) and temperature (Temp) as the input variables, which are processed within fuzzification and defuzzification stages inside the FL platform to yield crisp outputs. The effectiveness of proposed controller has been assessed under two types of member functions (MFs): triangular MF and Gaussian MF. As compared to other existing control techniques in the literature, the proposed FLC outperforms these control schemes in terms of charging time. Moreover, the numerical results showed that FLC based on Gaussian MF gives better performance as compared to that based on triangular MF in terms of accuracy and charging time.

Abstract

1 Introduction

The BMS is an essential element that ensures the reliability, efficiency, and safety of battery-powered devices. It is responsible for monitoring and controlling various aspects of the battery, including charging, discharging, and temperature. The BMS helps extend the battery's life and prevent potential damages or risks. In simple terms, the BMS acts as the brain of the battery, constantly analyzing and adjusting its performance to ensure optimal operation.

The BMS is mainly used for real-time monitoring, diagnosis, SOC estimation, mileage estimation in electric vehicles EV, protection of short circuits, leakage, display, charging and discharging operation of battery power science of EV, and information interaction with the complete controller of vehicle or charger via controller area network transmission always efficient, reliable and safe operations [1].

Using advanced algorithms and sensors, it can monitor and control various aspects of battery performance and prevent risks. Battery optimization with a BMS is an effective method to improve battery longevity and efficiency. The right type of BMS depends on specific application requirements, including electric vehicles, renewable energy systems, and portable electronics. A BMS ensures that batteries operate safely and efficiently, improving their performance and extending their lifespan [2].

Advancements in intelligence algorithms and control schemes for BMS have considerably enhanced the performance of battery in terms of transient characteristics and efficiency. Numerous control systems are used for BMS in the literature, including neural network control, fuzzy logic control, and PID control. The fuzzy system is a robust control mechanism based on the logical reasoning of the human brain. Heuristic knowledge is the primary milestone for the development of FLC. A notable feature of the FLC is that it does not need the dynamic model of the system, which is often not found in most control methodologies. Another feature of the FL controller is that it can facilitate the manipulation of weak constraints associated with the precision of sensor-derived data [3]. Furthermore, it serves as an alternative effective control tool to replace conventional control techniques due to its essential characteristics of not requiring a mathematical model [4]. The fundamental concept of FL control involves leveraging expert knowledge and experience to establish a rule base based on linguistic rules [5]. In the literature, there are many control structures have been applied for BMS. In [6], Afzal et al. have utilized an Artificial Neural network (ANN) for BMS in EV. Compared to a traditional BMS system, the proposed approach improved reliability and presented communication-free capability and decentralized control. In [7], Karmakar et al. implemented BMS using proportional-integral (PI) control and ANN-based control on passive cell balancing. It has been shown that the ANN-based control is more efficient and satisfactory than PI controllers. In [8], Behera and Choudhury presented an optimized BMS based on Slim mold, which depends on fuzzy PID tuning improvement to operate under variable loads.

In [9], Raj et al. proposed FL-based BMS controller for solar-powered charging EV system. The controller improved the charging efficiency and optimized the SOC. In [10], Khawaja et al. investigated the state estimation of Lithium Ion Battery (LIB) based on six prediction machine learning algorithms. These algorithms have been integrated with the battery management system to improve its efficacy. In [11], Faria et al. proposed a charging algorithm based on Artificial Neural Network (ANN) to give an optimal charging current profile of batteries. In [12], Lin et al. presented the principles of charging methods, their current challenges, and optimized techniques, which led to the formulation of optimization objectives. The study has also reviewed the optimization framework and the implementation and promotion of optimal charging method. In [13], Abdelaal et al. proposed a battery energy system based on FLC, which results in variable gain that limits the variation in current signal based on battery SOC. The results showed that the controller can give less degradation in battery SOC and SOH. In [14], Károlyi et al. proposed a battery charging system based on optimized FLC for controlling charging current. The study showed that improved performance is obtained in terms of charging time, while the temperature rise can be considerably reduced. In [15], Umair Ali et al. used fuzzy logic to control the charging current trajectory based on real-time rapid-charging methods. A fast-charging time has been reached for various used devices. In [16], Wu and Zhang proposed FLC-based Multi-Layer Equilibrium Circuits for balancing battery current to provide a high convergence rate to equilibrium and improve efficiency. The FLC is designed to control the size of the equilibrium current during the equilibrium process. In [17], Zenk and Ertuğral presented the design of smart BMS for a hybrid EV. The proposed BMS tries to monitor the chemical status of different batteries and choose the appropriate one to actuate the EV system. In [18], Goksu et al. proposed low-voltage BMS to manage the activation and deactivation of battery cells within the battery pack to ensure the operation of these batteries within their prescribed limits. Compared to the PID controller, the proposed scheme showed an enhancement of duration usage and could constrain the voltage among the switched batteries.

In this study, a BMS has been designed based on FLC to enhance the battery performance. The proposed control design can achieve the following objectives:

⁃Fast charging of the battery, where the Lithium-ion battery (LIB) has fast charging capability due to the efficient chemical structure,
⁃Achieving equal efforts among the batteries in terms of charging cycles and life-span time,
⁃Protecting the battery from overheating, and providing early warning about the crucial status of the battery.

2 Equivalent circuit model of battery

In order to design the BMS, the modeling of the battery is necessary to simulate and realize the control of the BMS system within the MATLAB software environment. In this part, the conventional models of battery in the literature have been presented and analyzed.

2.1 Basic equivalent circuit model

Figure 1 shows the basic equivalent circuit model. The model consists of internal resistance

R_{o}

in series with the open-circuit voltage source

V_{o} = V_{o c}

. The battery current

I_{b}

has a positive value when charging, while it takes a negative value during discharging. When the load is connected, the following equations can be described [19]:

V_{o c} = V_{o} - V_{R_{o}}

(1)

V_{R_{o}} = I_{b} R_{o}

(2)

Fig. 1.

Basic model of LIB

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

2.2 First-order equivalent model

The first-order model is frequently used due to its precision and simplicity. This battery model includes a voltage source $V_{o c}$ , internal resistor $R_{o}$ and RC network. The $R C$ network took into account the polarization phenomenon. consists of a capacitance $C_{P}$ , representing the polarization of the metal electrodes, and resistance $R_{P}$ resulting from the contact of the electrodes with the electrolyte. The model is shown in Fig. 2.

Fig. 2.

First-order LIB equivalent model

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

2.3 Second-order LIB equivalent model

The second-order model is more accurate than the first-order model and it is used in many of applications. Figure 3 shows the configuration of the second-order model, which consists of the voltage source

V_{o c}

, internal resistance

R_{o}

in series with two RC branches. The first RC branch includes a parallel resistor and capacitor (

R_{p 1}

and

C_{p 1}

), while the second parallel RC branch has a parallel resistor and capacitor (

R_{p 2}

and

C_{p 2}

). The transient behavior of Lithium cells can be described by [20].

V_{o c} = V_{o} - V_{R} - V_{p 1} - V_{p 2} - \dots - V_{p n}

(3)

V_{p n} = V_{C P n} ⅇ^{- \frac{1}{τ_{n}}} + I_{b} R_{P n} (1 - ⅇ^{- \frac{1}{τ_{n}}})

(4)

Fig. 3.

Second-order LIB equivalent model

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

3 FLC-based battery management system

Fuzzy Logic Control FLC uses fuzzy sets and fuzzy inference to establish the control laws. The main feature of FLC is that it does not require the dynamic model of the controlled process. The basic concept of FLC is how to develop basic rules with linguistic rules based on the heuristic knowledge and experience of human beings [21]. The schematic diagram of FLC is shown in Fig. 4, which includes all processes of the controller: defuzzification, defuzzification, and decision-making process [22].

Fig. 4.

Fuzzy Logic controller structure

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

In this study, the controller has three inputs, represented by SOC, SOH, and Temp, and two outputs, represented by $V_{c}$ and $I_{c}$ as indicated in Fig. 5. Each input can be acquired from separate sensors such as Temperature, SOC, and SOH sensors. Each input is fed to five membership functions, while there are also five MFs for each output. The relationships between input and output variables are translated into rules which are quantified to give the resulting output variables [23].

Fig. 5.

FLC has three inputs (SOC, SOH, and Temp) and has two outputs: charging voltage (Vc) and charging current (Ic)

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

The possible number of rules is given by $M^{N}$ , where M and N represent the number of MFs and the number of input variables, respectively. In this case, the values of N and M are set to N = 3 and M = 5 such that there are $R = 5^{3} = 125$ possible rules.

There are 125 rules possible to create the controller, no more no less. The FLC operates on the foundation of linguistic logic. The functioning of FLC is defined by two parameters: the quantity of input variables and output variables. This system is classified as a three-input, two-output fuzzy logic controller. This research utilizes three inputs: SOC, SOH, and temperature, while the two outputs represent the charging voltage ( $V_{c}$ ) and charging current ( $I_{c}$ ) using five membership functions (MFs). The FLC will provide 125 potential regulations.

Table 1 enumerates examples of the knowledge base for BMS predicated on FLC. This work uses the Mamdani approach in FLC to process input variables SOC, SOH, and Temp, and produce output variables ( $V_{c}$ , $I_{c}$ ), with all physical quantities converted into language variables [24]. Equally-width triangular membership functions are used to graphically quantify the inference of these variables [25]. Table 2 displays the fuzzy linguistic variables established for this study. Table 3 shows the ranges of different MFs for the input variables.

Table 1.

FLC rules

Rule no.	Fuzzy input			Fuzzy output
Rule no.	SOC	SOH	Temp	Vc	Ic
1	S	S	S	S	S
2	M	S	S	M	S
3	L	S	S	L	S
4	H	S	S	H	S
5	BH	S	S	BH	S
6	S	M	S	S	S
7	M	M	S	M	S
8	L	M	S	L	S
9	H	M	S	H	S
10	BH	M	S	BH	S
$⋮$	$⋮$	$⋮$	$⋮$	$⋮$	$⋮$
45	BH	H	M	BH	M
46	S	BH	M	L	H
47	M	BH	M	L	H
48	L	BH	M	H	H
49	H	BH	M	BH	L
50	BH	BH	M	BH	S
51	S	S	L	M	M
52	M	S	L	M	M
53	L	S	L	L	M
54	H	S	L	H	M
55	BH	S	L	BH	S
$⋮$	$⋮$	$⋮$	$⋮$	$⋮$	$⋮$
119	H	H	BH	H	M
120	BH	H	BH	BH	S
121	S	BH	BH	S	M
122	M	BH	BH	M	M
123	L	BH	BH	L	M
124	H	BH	BH	H	M
125	BH	BH	BH	BH	S

Table 2.

Description of fuzzy linguistics variables

Fuzzy linguistic variables	Inputs MF			Outputs MF
Fuzzy linguistic variables	SOC	SOH	Temp	$V_{c}$	$I_{c}$
S	$S O C < 20 %$	$S O H < 60 %$	$T e m p < 0 ℃$	2.5 V	2 A
M	$S O H < 40 %$	SOH $< 7$ 0%	$T e m p < 20 ℃$	3 V	4 A
L	$S O H < 60 %$	SOH $< 8$ 0%	$T e m p < 40 ℃$	3.5 V	8 A
H	$S O H < 80 %$	SOH $< 9$ 0%	$T e m p < 50 ℃$	4 V	12 A
BH	$S O H < 100 %$	SOH $< 10$ 0%	$T e m p < 60 ℃$	4.5 V	14 A

Table 3.

Ranges of triangular MF of input variables

Fuzzy Linguistic Variables	Range of inputs
Fuzzy Linguistic Variables	SOC	SOH	Temp
S	[−0.208333 0 0.208333]	[39.5833 50 60.4167]	[−24.5833 −10 4.58333]
M	[0.0416667 0.25 0.458333]	[52.0833 62.5 72.9167]	[−7.08333 7.5 22.0833]
L	[0.291667 0.5 0.708333]	[64.5833 75 85.4167]	[10.4167 25 39.5833]
H	[0.541667 0.75 0.958333]	[77.0833 87.5 97.9167]	[27.9167 42.5 57.0833]
BH	[0.791667 1 1.20833]	[89.5833 100 110.417]	[45.4167 60 74.5833]

The type of MF plays a critical role in the design and performance of FLC [26]. In order to investigate the performance due to different types of MFs, the study has considered two types of MF; namely, triangular MF and Gaussian MF. These MFs have to be involved within the decision-making process of fuzzy logic system to satisfy control requirements. In the case of triangular MF, two tables of ranges have been taken into account, one for input linguistic variables and the other for output variables lists the range of input variables in case of using triangular MF, while Table 4 represents the ranges of output variables. For both tables, the ranges have been chosen uniformly, such as to cover the potential universe of discourse for both input and output variables. In the case of Gaussian MF, the ranges of MF, which represent the width of individual Gaussian functions, are defined in Table 5.

Table 4.

Ranges of triangular MF of output variables

Fuzzy Linguistic Variables	Range of Inputs
Fuzzy Linguistic Variables	$V_{c}$	$I_{c}$
S	[2.72917 3 3.27083]	[−5.33333 −2 1.33333]
M	[3.05417 3.325 3.59583]	[−1.33333 2 5.33333]
L	[3.37917 3.65 3.92083]	[2.66667 6 9.33333]
H	[3.70417 3.975 4.24583]	[6.66667 10 13.3333]
BH	[4.02917 4.3 4.57083]	[10.6667 14 17.3333]

Table 5.

Ranges of Gaussian MF for input and output variables

Fuzzy Linguistic Variables	Range of Inputs			Range of Outputs
Fuzzy Linguistic Variables	SOC	SOH	Temp	$V_{c}$	$I_{c}$
S	[0.08847 0]	[4.424 50]	[6.193 −10]	[0.115 3]	[1.416 −2]
M	[0.08847 0.25]	[4.424 62.5]	[6.193 7.5]	[0.115 3.325]	[1.416 2]
L	[0.08847 0.5]	[4.424 75]	[6.193 25]	[0.115 3.65]	[1.416 6]
H	[0.08847 0.75]	[4.424 87.5]	[6.193 42.5]	[0.115 3.975]	[1.416 10]
BH	[0.08847 1]	[4.424 100]	[6.193 60]	[0.115 4.3]	[1.416 14]

The relationships are shown in Fig. 6 the behavior that the system will follow in every case or change in one of the factors or inputs. In (a), note the behavior of the controller will follow to output the charging current in proportion to the charge rate and the temperature. For example, if the temperature is between 25 °C and 40 °C and the charging rate is 10%, the charging current will be as high as possible, and if the temperature is higher than 40 °C and the same charging rate is above, the charging current will be the lowest possible or reach to zero, and according to (b) it indicates a gradual increase in the value of the voltage supplied to the battery at each percentage of the SOC. It also shows that the value of the voltage does not fluctuate relatively with increasing Temp [27]. In (c) the rules show the technique that the controller will implement depending on the SOH and Temp to output the charging current, the SOH that maintaining an 80% battery limit can be beneficial for battery health based on the data provided. (d) shows SOH and Temp with charging voltage.

Fig. 6.

Rules relationships graphs between (a) SOC and Temp with charging current (b) SOC and Temp with charging voltage (c) SOH and Temp with charging current (d) SOH and Temp with charging voltage

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

4 Simulink modeling

The BMS model based on FLC has been developed within environment MATLAB/Simulink platform. Referring to Fig. 3, one can utilize Simscape to represent the real version of the battery as shown in Fig. 7. As indicated in Fig. 7, the input to the FLC block are SOC, SOH, and Temp, while on the output side of FLC block, there are the physical quantities of charging voltage $V_{c}$ and charging current $I_{c}$ . The battery block can be brought from the library of Simscape within the Simulink environment and specification as shown in Table 6. All signals are monitored via measurement sciascopes. In addition, the thermal part, indicated by red lines, is selected from the thermal library which provides thermal-related elements like heat exchanger, which is responsible for exposing thermal flux to the battery.

Fig. 7.

Simulink of BMS based on thermal mass and environmental effects

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

Table 6.

Battery specification

Type	Molicel INR-21700-P45B
Capacity	4,500 mAh
Nominal cell voltage	3.6 V
Charge cell voltage	4.2 V
Charging current/standard	4.5 A or 1C
Charging current/maximum	13.5 A or 3C
Charging time	1.5 h
Charging temperature	0 °C–60 °C
Cut-off charge	70 C
Shape	Cylindrical

To match between Simulink blocks with those of Simscape blocks, special converters are utilized for this purpose. In this sense, the Simulink model of BMS will mimic the real BMS system since the used elements are built based on real characteristics. This also applies to the Simscape blocks residing within the Simulink environment.

5 Results and discussion

Table 7 shows the records of the average charging current ( $I_{c}$ ) and SOC, with different rise times (80% and 100%), for both types of MFs under various temperature values. The sensors of SOC, SOH, and temperature work to transfer their corresponding measuring data on the battery states to the FLC.

Table 7.

Reporting of average charging current and SOC under different ambient temperatures

States of battery	Temperature
States of battery	0 °C	25 °C	35 °C	45 °C	55 °C
Average charging current controlled using triangular MFs	4.6755628	6.227368	8.362939	8.09922	3.82393
Average charging current controlled using Gaussian MFs	5.02225826	7.611008	8.471362	7.89014	4.30027
Rising time of SOC- 80% controlled using triangular MFs	1,480	1,023	1,024	1,125	2,390
Rising time of SOC- 80% controlled using Gaussian MFs	1,519	1,037	1,033	1,192	2,434
Rising time of SOC- 80% controlled using triangular MFs	3,115	2,330	1,731	1,793	3,467
Rising time of SOC- 100% controlled using Gaussian MFs	2,898	1,911	1,720	1,845	3,387

Figures 8 and 9 show the behavior of SOC under the influence of ambient temperature in both controllers based on triangular and Gaussian MFs, respectively. The result shows that there is a difference in behavior between controllers and their performances are reported in Table 7.

Fig. 8.

SOC behavior under different ambient temperatures controlled using triangular MF

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

Fig. 9.

SOC behavior under different ambient temperatures controlled using Gaussian MF

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

Figures 10 and 11 show the behaviors of Vc, Ic, SOC and Temperature for triangular MF-based FLC and Gaussian type-based FLC, respectively. It is clear from the figures that the MF has direct impact on the performance of FL controller. The behaviors based on triangular MFs showed sluggish and non-smooth responses as compared to that based on Gaussian type. The FL controller based on Gaussian types give slow responses, but with smoother responses. One can conclude that the Gaussian type-based FL controller outperforms that based on triangular type MF. Consequently, the Gaussian-type better enhance the performance of BMS compared to triangular type MF when they are involved in the FL controllers. Moreover, the FL controller based on Gaussian MF gives better accuracy as compared to that based on triangular MF.

Fig. 10.

FLC with triangular MF

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

Fig. 11.

FLC with Gaussian MF

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

Figures 12 –16 display the responses of $I_{C}$ , $V_{C}$ , and SOC to ambient temperature 0, 25, 35, 45, and 55 °C, respectively, under FL controller based on Gaussian-type MF. Figures 12 –16 explain how power or energy flows into the battery during the charging process. It is important to note that $I_{C}$ and $V_{C}$ are directly proportional to SOC is under 80% and inversely proportional when it is above 80%.

Fig. 12.

Behavior of charging current, charging voltage, and SOC with 0 °C

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

Fig. 13.

Behavior of charging current, charging voltage, and SOC with 25 °C

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

Fig. 14.

Behavior of charging current, charging voltage, and SOC with 35 °C

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

Fig. 15.

Behavior of charging current, charging voltage, and SOC with 45 °C

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

Fig. 16.

Behavior of charging current, charging voltage, and SOC with 55 °C

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

The suggested BMS utilizes FLC to determine the most efficient power of the battery during certain charging cycles. Figures 12 –17 display the battery charging profile under the influence of different ambient temperatures with the level of SOH equal to 95%. It is clear from the figures that the charging current is limited by the specification of the current listed in Table 6.

Fig. 17.

The points of decrease in the Ic and Temp under 35 °C ambient temperature

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

The datasheet for the Lithium-Ion battery (Molicel INR-21700-P45B) provides specifications in Table 6. The crisp value of $I_{C}$ resulting from the FLC controller will be associated with three main parameters (SOC, SOH, and Temp). These factors determine the suitable value for $I_{C}$ generated from FLC. One can conclude that the energy flow of the battery changes with varying SOC and temperatures during charging cycles. In addition, a rise of temperature will occur when there is a significant charging current. As indicated in Fig. 6-c, a decrease in battery SOH results in a decrease in charging current ( $I_{C}$ ). It is clear that the regulation speed of the charge current is closely related to the polarization voltage gradient with SOC inferred from the determinants of the output control.

Figure 17 shows the behaviors of charging voltage, charging current, SOC, and Temp at 35 °C. It is evident from the figure that the charging current $I_{C}$ decreases when SOC reaches 80% of full charge, It also shows that the charging current will decrease to recover the SOC such that it eventually reaches to its highest level of 100%. Consequently, this decrease in charging current will lead to decrease in battery temperature due to the decrease in dissipated power in the internal resistor $R_{o}$ .

Also note that the ideal temperature for charging is between 25 °C and 45 °C, as Table 7 and Fig. 9 show the difference in the battery charging time, whenever the temperature is more than 45 °C or the temperature is less than 25 °C, the charging time will increase or stop until it crosses the permissible limit according to the type and specifications of the battery.

Figure 18 shows the controller behavior with 55 °C ambient temperature at the $I_{C}$ has decreased by the effect of the increase of Temp when exceeding the specific limits and decreased when the SOC arrives at 90%. On the other side, the time of charging has been more than the previous tests as it is shown in Table 5.

Fig. 18.

The points of decrease in the Ic and Temp under 55 °C ambient temperature

Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.00971

In addition, Figs 17 and 18, illustrate the flow of power or energy into the battery during the charging process. $V_{C}$ and $I_{C}$ are directly proportional when the state of charge (SOC) is below 80% and inversely proportional when it exceeds 80%. This facilitates a consistent energy flow throughout state of charge fluctuations, despite temperature variations during charging. Consequently, increased temperatures directly correlate with the deterioration rates of all components in a lithium-ion battery.

To show the effectiveness of the proposed intelligent controller, the study has been compared to other related works in the literature [27–29]. Accordingly, Table 8 has been established for comparison purposes. The table indicates that the proposed method could give better performance in terms of charging time and could keep the battery temperature within safe limits. However, the price paid by using this study is the increased value of the C-rate, which has an adverse effect on battery.

Table 8.

Comparison of results with other controllers

Reference	Ambient temp °C	Charging control method	Maximum C rate	Maximum charging current	Capacity	SOC	Maximum charging time (s)
This Paper	45 °C	FLC	2C	11 A	4.5 Ah	100%	1,845
[28]	25 C	PID CC-CV	2C	6 A	2.6 Ah	–	2,886
[29]	25 °C	Variable Weighting Factors	2C	35 A	20 Ah	80%	3,636
[27]	40 °C	CCCV	1C	2.5 A	2.11 Ah	88%	3,535

In order to further verify the effectiveness of proposed controller, other control techniques; either based on nonlinear techniques or on other intelligent neural network structures [30–60].

6 Conclusion

This study investigates the performance of BMS-based FLC under various conditions of LIB. Firstly, the battery model has been simulated and the design of FLC has been made for BMS within the environment of MATLAB programming platform. The FLC-based BMS design has been conducted using three input variables SOC, SOH, and Temp along with two output variables. Based on numerical simulations, one can conclude that an increase in operating temperature will lead to a decrease in the charging current $I_{C}$ , while keeping constant rate of charging voltage $V_{C}$ . Moreover, when SOH decreases the rate of $I_{C}$ also decreases, which has a positive impact on the life-span and performance of the battery. Compared to other studies existing and related studies in the literature, numerical results showed that the proposed method gives better performance in terms of charging time and it can keep the battery temperature within safe limits. However, the price paid by using this study is the increase of the C-rate, which has an adverse effect on the battery performance. In addition, the effectiveness of proposed controller has been assessed under two types of MFs. The results showed that FLC based on Gaussian MF gives better performance as compared to that based on triangular MF in terms of accuracy and charging time.

As an extension of this study, one can design the FLC for both charging and discharging cases, where the load is applied. Moreover, one can replace the FLC with other control techniques like an intelligent controller based on a deep learning neural network or other non-linear control that can be used to enhance the performance of BMS.

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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
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2024
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2023
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International Review of Applied Sciences and Engineering
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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
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ISSN	2062-0810 (Print)
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