1 Introduction

The concept of digital twin refers to the creation of a virtual replica of an object or system, which accurately mimics its behavior, characteristics, and performance [1,2,3]. Digital twins are becoming more common with the development of technologies such as artificial intelligence and the Internet of Things (IoT). Thanks to these technologies, more accurate and effective digital twins can be created using real-time data [4]. In the current technological landscape, digital twin (DT) technology plays a pivotal role within the domain of electric vehicles (EVs). By relying on a reduced number of physical components and harnessing the capabilities of the Internet of Things (IoT), underpinned by a dependable and uninterrupted internet connection, the digital twin representation of an electric vehicle functions as a virtual counterpart that meticulously emulates the intricate characteristics of an actual EV. This virtual replica adeptly replicates performance metrics and energy consumption patterns unique to real-world electric vehicles, while also encompassing operational dynamics encompassing both mechanical and electrical facets [5,6,7]. The concept of digital twins (DTs) holds significant importance for both scholarly investigation and practical implementation, as it encompasses the entire product lifecycle. This lifecycle spans various phases, comprising design, prototyping, manufacturing, service, and disposal [8, 9]. Consequently, the digital twin has emerged as a pivotal element in the amalgamation of technology and data within value chains, streamlining the proficient conception of industrial production lines while guaranteeing peak performance. This development has engendered substantial interest among corporations, research institutions, and scholarly circles [10,11,12].

IoT technology enables the continuous monitoring of the performance of vehicles in the field, with the data being mirrored into their digital twins; these vehicles have the opportunity for preemptive maintenance, allowing repairs to be conducted before faults escalate [13]. An illustrative example of digital twin utilization can be demonstrated through its application to optimize vehicle energy consumption and enhance driving range. This digital replica, utilizing real-time driving data from the actual vehicle, can generate driving models tailored to the prevailing driving conditions to achieve the most efficient energy consumption [14,15,16,17].

The concept of the digital twin was originally introduced by NASA [18]. In their research, Biesinger and Weyrich explored various aspects of digital twins in the automotive industry and highlighted the advantages they offer. Several studies have been conducted to make digital twin technology practical [19]. Another investigation examined the significance of the digital twin concept in the automotive sector, revealing its value addition to the industry. This study involved a field survey with an automotive manufacturer, uncovering the benefits of digital twins [20]. Piromalis et al. emphasized that digital twin technology plays a pivotal role throughout the entire lifecycle of a vehicle, from initial production to end-of-life. Given the limited utilization of assistive technologies such as artificial intelligence and the Internet of Things in the industry, it is projected that digital twin technology is in its early stages of adoption and will continue to evolve [21].

To assess the effectiveness of the digital twin, Bartolucci et al. conducted an investigation involving the digital twin of a fuel cell hybrid electric vehicle. They analyzed the mechanical and thermal systems of the vehicle using Simulink/Simscape software. Experiments were also conducted to validate the accuracy of the digital twin, specifically examining the impact of temperature variations and how they affect energy consumption [15]. In a separate study, Venkatesan et al. developed a digital twin model to assess the parameters necessary for the healthy service life of an electric vehicle equipped with a permanent magnet synchronous motor (PMSM). Their model, created in the MATLAB/Simulink environment, identified the required parameters and enabled remote monitoring of motor performance through IoT technology [22]. Similarly, Zhang et al. employed the digital twin model to investigate energy consumption with temperature parameter tracking. Accumulating tracked data improved the model's ability to make more precise energy consumption predictions over time [5].

In another study, Fiori et al. utilized vehicle speed and road slope as input parameters to design a model capable of calculating the instantaneous energy consumption of an electric vehicle based on this data [23]. Adegbohun et al. developed a methodology for designing and enhancing electric vehicle powertrains, employing modeling and simulation studies within the MATLAB/Simulink environment, using a real electric vehicle as a reference [24]. Kaloko et al., also utilizing MATLAB/Simulink software, presented the modeling required to determine the range of a 900-kg electric vehicle with a 5.5-kW motor based on battery capacity and optimal performance at a constant speed. This modeling involved defining the vehicle's free body equations and applying the vehicle's parameters for simulation, ultimately revealing the distance the vehicle could travel with the specified battery capacity [25].

As mentioned, digital twins of electric vehicles can have various application areas, including monitoring vehicle performance, planning maintenance, determining achievable range, and optimizing energy consumption. Further research is needed in this area to explore different application domains and elucidate the benefits, advantages, and efficiency of digital twins.

The motivation behind this study is to enhance the energy efficiency of the powertrain systems used in electric vehicles to address one of the most critical issues, the range problem. To achieve this goal, an examination of electromechanical energy conversion by motors, based on motor types, is intended. Various types of electric motors were modeled using MATLAB/Simulink, and their energy consumption values were analyzed. In this study, the mathematical model of a L6e-class light electric vehicle of the CERYAN brand was transferred into a digital twin model in a computer environment. Through Model-Based Design, the aim was to align the simulation results of motor types commonly used in electric vehicles with real-world data. The compatibility of the computer-generated models with different vehicle models was also tested.

Upon reviewing existing literature, it was observed that applied studies are relatively fewer compared to exploratory investigations. In light of this observation, the primary contributions of this study can be summarized as follows:

  • In contrast to prior studies, this research encompasses the development of an actual digital twin model for a light electric vehicle. The study goes beyond theoretical concepts, applying the digital twin model to practical experiments and research. Emphasis has been placed on translating theoretical knowledge into real-world applications, ultimately implementing theoretical insights into a commercial model of a light electric vehicle using the digital twin method.

  • Other key contributions of this study revolve around evaluating the energy efficiency and fuel consumption of different motor types commonly used in electric vehicles, such as BLDC (brushless DC), DC (direct current), PMSM (permanent magnet synchronous motor), and AC induction motors. The research aims to determine the most efficient motor option for urban transportation in a light electric vehicle, comparing  these motor types. The study provides valuable insights by conducting comprehensive analyses of energy efficiency and fuel consumption, facilitating the selection of the most suitable motor type for urban transportation in light electric vehicle applications.

  • In practice, information from four distinct motors was processed to obtain data related to the range, climbing gradient, maximum speed, and kWh/100 km efficiency of the motors. The energy consumption of a CERYAN brand L6e-class electric vehicle was examined concerning slope values of 2%, 4%, 6%, 8%, 10%, and 12%. Based on this data, the depletion time of the vehicle's battery was determined. Furthermore, torque requirements for initial acceleration and climbing were established for various slopes, as mentioned. The study also investigated the ability of different motor types to meet the torque requirements for initial acceleration and climbing. Consequently, this analysis facilitated the identification of the most efficient and high-performance motor based on the specified slope, speed, and electric vehicle range values. This, in turn, contributes to determining the suitable motor type for light electric vehicles.

2 Material and Methods

Digital twin technology entails the creation of a virtual replica of tangible entities, enabling continuous digital monitoring and simulation of their performance across the design, production, and utilization phases. It finds applicability across diverse sectors encompassing industry, energy, healthcare, construction, and transportation [26].

Utilizing digital twins for industrial production lines permits the simulation of multiple scenarios mirroring real-world operations. This capability aids in minimizing production defects and enhancing productivity through proactive issue resolution and workflow optimization [27].

Moreover, digital twin technology extends its utility to large-scale endeavors, including urban planning, the healthcare industry [28], battery systems [29], and transportation management. For instance, a city's digital twin can facilitate analyzing and enhancing aspects such as traffic flow, water and electricity distribution, air quality, and related factors [30].

Table 1 provides examples of the domains wherein digital twin technology is applied and the associated benefits within these domains.

Table 1 Examples of digital twin technology
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Digital twin technology is applied across various facets within the manufacturing and automotive industries, including design and engineering, production and maintenance [10, 11], as well as efficiency improvements [25, 26].

Furthermore, digital twin technology is employed within the automotive manufacturing sector. Each vehicle in the production line is represented by a digital twin, with the digital twin closely monitoring every step of the production process. This approach ensures the timely detection and prevention of production errors, facilitates an increase in productivity, and ultimately leads to reductions in production costs [43, 44].

Table 2 provides a comparative analysis of the characteristics of selected studies involving digital twin technology within the automotive industry.

Table 2 Digital technology in automotive industry
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The instances presented in the table above illustrate a selection of studies within the automotive industry that leverage digital twin technology. Various automobile manufacturers employ digital twin technology in distinct manners, employing diverse methodologies. Nevertheless, their collective objectives encompass enhancing efficiency in production processes, cost reduction, and elevating product quality.

3 Experimental Setup

This section is structured into three distinct segments. The initial segment entails the formulation of a comprehensive digital twin model for the system under consideration. Subsequently, the second segment involves the practical application of the aforementioned generalized model to an electric vehicle, specifically the BMW i3, in order to assess and validate its accuracy. The concluding section is dedicated to the development of a digital twin model for the validated CERYAN L6e class vehicle, encompassing diverse engine configurations as depicted in Fig. 1.

Fig. 1
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Digital twin technology

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3.1 Digital Twin Modeling

The digital twin vehicle model encompasses components such as the battery, motor, motor driver, gear system, controller, vehicle body, brake system, and drive cycle subsystems. The objective of this digital twin model is to analyze and select the most efficient and high-performance motor by processing data from different motor types within the simulation environment. Key considerations when evaluating these motors include determining the vehicle's range, its capability to ascend various inclines, and its maximum attainable speed. These outputs play a pivotal role in assessing the performance and efficiency of the motors within the digital twin simulation (Fig. 2).

Fig. 2
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Flow diagram of model

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The simulation system comprises multiple blocks, each serving distinct roles within the digital twin model. The "Drive Cycle" block applies the desired driving cycle to the vehicle model during simulation. In this block, the WMTC (Worldwide Motorcycle Test Cycle) driving cycle is input into the MATLAB block, constrained to a maximum speed of 45 km/h. The WMTC is a standardized driving cycle utilized for assessing emissions and fuel efficiency in motorcycles. Introduced in 2016, it replaced the previous New European Driving Cycle (NEDC) and is now adopted globally as the standard for evaluating motorcycle emissions and fuel consumption. The entire WMTC cycle spans 1200 s, equivalent to 20 min. Throughout this test, the motorcycle is equipped with instrumentation to measure emissions and fuel consumption, and the resulting data are employed to assess compliance with emission regulations and fuel efficiency standards [52].

The “Controller” block encompasses both speed control and brake control systems that can operate simultaneously. This control system assesses the vehicle's instantaneous speed and compares it with the reference speed to adjust the vehicle's performance accordingly. It employs a PID controller where only PI values are utilized. Control over the accelerator and brake pedals is incorporated here. The accelerator pedal control provides direct PI output to the brake block, while brake pedal control is transmitted to the brake block as brake pressure (in pascals) after converting the value to an absolute value if it remains at a negative level in the PI graph.

Motor label values and drive voltage values are located within the “Motor and Driver” block. This block also manages the current limitation process. Comprising a 48 V motor and motor driver, the “Motor and Driver” block can be customized based on the specific motor and drive in use. It collaborates with the “Battery” block to compute the energy consumption throughout the simulated drive cycle. To address the variance between motor rotor speed and the desired wheel speed, the “Gear System” block defines the necessary gear ratio for electric vehicles, allowing adjustments based on motor characteristics. The transmission rate used to follow the definition of the motor rotor is also determined within this block.

The “Vehicle Body” block receives input regarding the physical characteristics of the vehicle, while it also defines road conditions, including positive and negative slopes. This block encompasses parameters such as vehicle weight, wheel diameter, wind resistance, and front surface area. Additionally, the “Brake System” block outlines the physical attributes of the brake system, including brake center pressure. This block is where the mechanical brake attached to the vehicle is situated.

3.2 Model Validation

To validate the electric vehicle model, the researchers employed the BMW i3 vehicle as a reference point and endeavored to closely align the model's energy consumption with the factory-provided data. The model was crafted by integrating specific parameters, including power, weight, transmission ratios, and battery characteristics as delineated in the article. Upon scrutinizing the achieved outcomes, it became evident that they exhibited a high degree of congruence within acceptable margins of error. This validation process substantiates the model's accuracy in estimating energy consumption and reinforces its dependability for subsequent analyses and simulations [53] (Table 3).

Table 3 BMW vehicle specifications
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The table below compares the range and average consumption values obtained in the reference model and the Simscape model.

In order to obtain estimated range values, the simulation data obtained from the BMW i3 model and the driving cycle used during the validation process were evaluated using calculation blocks. It was observed that the outputs closely matched the factory data. The model calculated the distance traveled during the NEDC Cycle in kilometers, the energy consumption in kWh/100 km, and the estimated range in kilometers.

Upon comparing the reference study with the validation study, it was found that the margin of error, as indicated in Table 4, was below 2%. This level of error is considered acceptable [54]. Consequently, the system demonstrated accurate operation and reliability without any noticeable issues.

Table 4 Comparison of reference article and validation model
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3.3 Digital Twin of Motor Types for Electric Vehicle (CERYAN)

The process of digital twin modeling and validation was executed successfully, employing the BMW i3 model as the benchmark. In real-world applications, the primary objective is to ascertain the engine configuration that yields the maximum range and climbing capacity. This is achieved by inputting the requisite parameters for four distinct engine variants. To curtail research expenses, the digital twin was employed in advance of practical implementation to identify the most efficient and high-performing engine among the four alternatives. Figure 3 illustrates the MATLAB/Simulink interface utilized for the modeling of the CERYAN electric vehicle belonging to the L6e class.

Fig. 3
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Block diagram of MATLAB/Simulink model

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Table 5 presents the input and output variables for the digital twin model of the CERYAN electric vehicle. As per the contents of this table, parameters such as wheel dimensions, vehicle mass, and center of gravity height are determined in accordance with the specific attributes of the CERYAN vehicle. The current limiter is configured based on the driver's current-carrying capacity. Frontal surface area is calculated using data derived from simulations conducted in Catia. The gear ratio is computed in relation to engine RPM (Revolutions Per Minute) relative to the desired wheel RPM. Other input variables are held constant throughout. In the resultant digital twin model, all output variables outlined in Table 5 can be obtained. Of particular significance among these output variables are instantaneous torque values, brake pressure, kWh/100 km, and the electric vehicle's operational range.

Table 5 Input and output of CERYAN electric vehicle model
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The maximum speed is capped at 45 km/h by applying the electric vehicle's range parameter as an output in the context of the WMTC (Worldwide Motorcycle Test Cycle) drive cycle, which is utilized for L6e and L7e vehicle categories. During the range test to determine the distance electric motors can cover, motor-specific information is entered. However, unlike other tests such as battery consumption and battery discharge time, the slope information is not employed (Table 6).

Table 6 Specification of CERYAN electric vehicle
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The motor tests for the CERYAN brand L6e vehicle primarily utilized the digital twin model. Within the capabilities of the digital twin developed in this phase, input parameters such as motor rated speed, maximum speed, motor power, and gear ratio can be configured. Additionally, several common parameters were applied consistently across all tests. These include the total weight of the vehicle (600 kg in total with a driver and a passenger) and battery specifications, including Ah (Ampere-hour) and Voltage values (48 V–66.5 Ah). With the incorporation of these parameters into the model, it becomes possible to assess the vehicle's average range, energy consumption (expressed in kWh/100 km), and maximum attainable speeds under varying slope conditions. During the motor tests, mechanical transmission conversion rates were manually calculated based on motor data.

The fundamental input parameters consist of the motor's operating range, gear ratio, and motor rated power. The gearmotor ratio is selected to maintain a wheel RPM value of 500 RPM. The gear ratio serves to lower RPM while augmenting wheel torque. The specific ratio values, as outlined in Table 7, are determined accordingly.

Table 7 Motor test parameters
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Throughout this phase, four distinct motors underwent testing for the CERYAN L6e vehicle. These motors are commonly employed in practical applications and readily available in the market [55,56,57].

For L6-e B class vehicles, the continuous rated power or net power must not exceed 6000 W (watts) [58]. The motors chosen for this study were specifically selected from available market products with a rated power of 6 kW or lower.

Slope information, categorized according to road classes as stipulated by the Highway Design manual for Turkey, is presented in Table 8 [59]. In light of this table and considering that the CERYAN vehicle, belonging to the L6e class, will be utilized on straight and gently undulating terrains, the maximum allowable climbing slope is set at 12%.

Table 8 Maximum longitudinal slope
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4 Results and Discussion

According to the four different motor data entered, range of EV, kWh/100 km, slope, and speed values were obtained as output for each motor. The range of EV value is calculated using the motor data without entering the slope information on the straight road by taking the speed of the vehicle as constant as 45 km/h. The range of EV value promised by the manufacturer in advertising activities for the CERYAN brand vehicle is 60 km. The range of EV value is obtained separately by entering the motor value for each Case. For this reason, in the tests carried out, it is desired that the motors provide a range of 60 km and more with the battery pack determined by the vehicle. In addition, the maximum speed values that the vehicle can climb at a 12% slope were considered together with the motors used in the tests. Also, battery energy consumption values and discharge time were obtained according to 2%, 4%, 6%, 8%, 10%, 12% slope values for each motor. According to the obtained graphs, the results are given in Table 9.

Table 9 Battery discharge time and velocity for PMSM motor
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4.1 Case 1 (PMSM Motor)

The range of EV of the first motor, the parameters of which are entered, is 68.4 km, the kWh/100 km value is 4.66. When these values are examined, it is seen that the range value is over 60 km, which is the value promised by the manufacturer. In the graphs given in Figs. 4 and 5, the discharge time and the speed values of the vehicle are given according to the battery capacity based on 6 different inclination values for the first motor. The reason why the slope values end at 3.2 kWh is that the battery capacity of the CERYAN model vehicle is 3.2 kWh. Based on the graph, it is observed that the amount of energy consumption increases as the amount of slope increases and the battery exhaustion time is faster. PMSM motor was the motor with the highest battery discharge time at all grade values compared to other motors.

Fig. 4
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PMSM motor battery discharge--slope curve

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Fig. 5
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PMSM motor velocity--slope curve

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4.2 Case 2 (BLDC Motor)

The range of EV value of the second motor, the parameters of which are entered, is 52.95 km, kWh/100 km value is 6.03. When these values are examined, it is seen that the range of EV value is below 60 km and the lowest range value among the motors. The reason why the slope value is not kept at 11% here is that when the second motor is used in the vehicle, it is observed that the vehicle cannot climb the 12% slope and moves backwards. This is illustrated in Fig. 6. In the graphs given in Figs. 7 and 8, the discharge time and the speed of the vehicle are given according to the battery capacity based on six different inclination values for the second motor (Table 10).

Fig. 6
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BLDC motor velocity--slope curve at %12 slope

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Fig. 7
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BLDC motor battery discharge-slope curve

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Fig. 8
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BLDC motor velocity--slope curve

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Table 10 Battery discharge time and velocity for BLDC motor
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4.3 Case 3 (PMSM Brushless 6 kW)

The range of EV value of the third motor, the parameters of which are entered, is 63.75 km; kWh/100 km is 5. In the graphs given in Figs. 9 and 10, the discharge time and the speed values of the vehicle are given according to the battery capacity based on six different inclination values for the third motor (Table 11).

Fig. 9
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PMSM brushless (6 kW) battery discharge--slope curve

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Fig. 10
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PMSM brushless (6 kW) velocity--slope curve

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Table 11 Battery discharge time and velocity for PMSM brushless (6 kW)
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4.4 Case 4 (PMSM Brushless 5 kW)

In the fourth motor, the parameters of which are entered, the range of EV value is 70.2 km, kWh/100 km value 4.42. In the graphs given in Figs. 11 and 12, the discharge time and the speed values of the vehicle are given according to the battery capacity based on six different inclination values for the third motor (Table 12).

Fig. 11
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PMSM brushless (5 kW) battery discharge--slope curve

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Fig. 12
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PMSM brushless (5 kW) velocity--slope curve

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Table 12 Battery discharge time and velocity for PMSM brushless (5 kW)
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When compared to the BLDC motor, it was observed that PMSM Motor of Case 1 could travel 29.25% more. This is one of the factors for choosing the most efficient motor in Table 8, where slope information is given according to road classes published by the Highway The motor performance analysis yielded the following results:

For the motor with an average slope of 6% in accordance with the Design Book for Turkey’s urban roads, PMSM motor exhibited a longer battery discharge time compared to BLDC motor, PMSM brushless (6 kW), and PMSM brushless (5 kW). Specifically, PMSM motor had 10.3%, 3.1%, and 1.9% longer battery discharge times, respectively. In terms of speed, this motor outperformed the motors in BLDC motor and PMSM brushless (6 kW) by 17.8% and 3.3%, respectively, while being 6.4% slower than the motor in PMSM brushless (5 kW). Consequently, it was established that PMSM motor met the performance and efficiency criteria for use in the vehicle (Table 13).

Table 13 Results of motor tests
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The BLDC motor in Case 2 demonstrated a consistently lower battery discharge time across all slope conditions compared to other motors. Specifically, at a 6% grade, it was 17.8%, 14%, and 25.3% slower than PMSM motor, PMSM brushless (6 kW), and PMSM brushless (5 kW), respectively. These findings indicate that BLDC motor is not suitable for urban use due to its inadequate performance and efficiency.

The PMSM brushless (6 kW) motor in Case 3 exhibited a reduced travel range compared to BLDC motor (20.3% less) and PMSM motor (7.35% less), as well as PMSM brushless (5 kW) (10.2% less). At a 6% slope, its battery discharge time was 3.16% higher than BLDC motor but 6.9% and 1.2% lower than PMSM motor and PMSM brushless (5 kW), respectively. In terms of speed, PMSM brushless (6 kW) was 14% faster than BLDC motor but 3.2% and 9.9% slower than PMSM motor and PMSM brushless (5 kW), respectively. Consequently, PMSM brushless (6 kW) did not meet the performance and efficiency requirements for use in the vehicle compared to PMSM motor and PMSM brushless (5 kW).

In the case of the motor in PMSM brushless (5 kW), it exhibited a travel range that exceeded the other motors by 3.1%, 32.6%, and 10.2% compared to PMSM motor, BLDC motor, and PMSM brushless (6 kW), respectively. Among the first three motor tests, PMSM brushless (5 kW) demonstrated the highest EV range. This was a significant factor in selecting PMSM brushless (5 kW) as the most suitable motor. At a 6% slope, its battery discharge time was 8.2% higher than BLDC motor, 1.2% higher than PMSM brushless (6 kW), and 1.9% lower than PMSM Motor. In terms of speed, it outperformed the first three motors by 6.4%, 25.37%, and 9.9%, respectively. These results indicated that PMSM brushless (5 kW) met the performance and speed criteria more effectively than the other three motors and was thus deemed suitable for use in the vehicle.

As a result of these tests, when four different types of motors were examined, the parameters of range of EV, velocity according to different slopes, battery energy consumption and discharge time were obtained in the simulation environment. According to the data obtained, the motor of BLDC motor was the most inefficient motor compared to other motor in all three tests. The BLDC motor was found to be unsuitable for use for this vehicle as it met the requirements less than the first and fourth motors. As a result of the tests carried out for the motors of PMSM motor and PMSM brushless (5 kW), close values were obtained. In terms of battery energy consumption and discharge time, the motor of PMSM motor is more prominent with a low value of 1.9%, while the motor of PMSM brushless (5 kW) is more prominent than the first three motors in the range of EV and speed values. Therefore, it can be said that the most effective motor is the motor of PMSM brushless (5 kW). Since the motor of PMSM brushless (5 kW) for this vehicle met the requirements, it was decided that this motor was the most suitable.

5 Conclusions

The digital twin concept entails a virtual representation of real-world processes, productivity, consumption, and attrition associated with a specific process or product, with its central objective being the enhancement of real-world products. Consequently, it facilitates the utilization of actual data in pre-production operations, thereby mitigating time and cost inefficiencies by scrutinizing collected results and optimizing designs for optimal outcomes. These advantages stem from a comprehensive examination of the acquired findings, thus affording the potential for pre-production processing grounded in real-world data.

This research is centered on the comparative assessment of the performance of L6e-class vehicles equipped with varying motors, including PMSM, PMSM brushless (5 kW), PMSM brushless (6 kW), and BLDC motors. In the case of the CERYAN brand L6e-class electric vehicle, designed as an urban transportation solution, the road slope characteristics that the vehicle will encounter are determined by the Turkish Highway Manufacturing Regulations. The simulation study's test conditions conform to the standards outlined in the World Motorcycle Test Cycle (WMTC) to represent real-world scenarios accurately. The sizing of the battery pack is determined based on typical urban transportation distances. A physical device model is created using MATLAB/Simulink software, a well-established and versatile modeling tool.

The results of the study reveal that the PMSM brushless motor (5 kW) exhibits a 25% longer range compared to the BLDC motor, a 26% improved energy efficiency compared to the PMSM brushless motor (6 kW), and a 30% superior climbing performance compared to the PMSM motor.

For future work, as the digital twin model matures and garners broader market acceptance, it will be incorporated as an additional parameter influencing motor selection, accounting for geographical region characteristics. Research efforts will be directed toward dynamic adjustments based on real-time geographic information.