Next Article in Journal
Thermal Management System of the UNICARagil Vehicles—A Comprehensive Overview
Previous Article in Journal
Design Optimization of a New Hybrid Excitation Drive Motor for New Energy Vehicles
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Development of a Personnel Management and Position and Energy Tracking System for Electric Vehicles

Olugbenga Kayode Ogidan
Celestine Aghaukwu
Oluwagbotemi Oluwapelumi
Success Jeremiah
Edesemi Adokeme
1 and
Omowunmi Mary Longe
Department of Electrical and Electronics Engineering, Faculty of Engineering, Elizade University, Ilara-Mokin 340112, Ondo State, Nigeria
Department of Electrical and Electronic Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2023, 14(1), 5;
Submission received: 11 November 2022 / Revised: 17 December 2022 / Accepted: 20 December 2022 / Published: 25 December 2022


The challenges faced by managers of transportation systems in developing nations such as Nigeria are numerous. These include driver scheduling, which, in many cases, is still being done manually; diversion of vehicles to unauthorized routes by drivers for selfish reasons results in illegal use of fuel meant for official duties, causing the organization lose considerable revenue. In its drive to reduce its carbon footprint, Elizade University, Nigeria, is embarking on the development and use of electric vehicles (EV) as a means of transportation within the university campus. This research is geared towards supporting this initiative by developing an EV tracking system that combines tracking of EV drivers with vehicle position and battery power (energy) tracking in order to mitigate the challenges outlined above. Personnel tracking was achieved using an RFID-enabled staff identity card that authenticates authorized drivers before activating the vehicle ignition system, position tracking was achieved using a geographical positioning system (GPS), and current and voltage sensors were used for tracking of electric vehicle power. Tests revealed that the EV system administrator operated through a personal computer was able to track the EV driver, position and power through a web interface/Google Maps and e-mail in real time. Whereas previous studies either considered tracking of vehicle position or power without personnel, others tracked personnel with less emphasis on the vehicle position or energy. In this study, we combined different technologies such as RFID, GPS and power sensors to consider EV administration in a holistic manner, thereby providing intervention in an infrastructurally deficient setting.

1. Introduction

Administration of drivers within an organization, as well as management of resources such as power and security, is a major challenge in many organizations, especially in Africa which, could negatively impact the security of lives and properties. Therefore, with this work, we seek to address these challenges by developing a personnel administration, tracking and power management system. In other words, an organization such as a university community should be able to track the condition of their electric vehicles, such as the amount of power contained in the car battery, the vehicle’s position and the identity of the personnel (driver) driving in real time. This will guarantee security of lives, as well as effective management of human and material resources. In many organizations in Nigeria, including university campuses, the administration of drivers has posed a number of challenges. In many of these cases, the allocation of drivers to vehicles is recorded on paper. Some of drivers divert vehicles allocated to them for official use to other routes for personal gain, and it is difficult for the organization to detect these unwholesome attitudes. The fuel in such cars is also used for unofficial purposes. With the emerging adoption of electric vehicles [1], in this work, we consider the use of EVs within a university campus and the measures that could be put in place to track the personnel driving an EV at a particular time, the amount of energy in the EV and the position in real time. The proposed system can further strengthen security in the sense that only authorized (pre-registered) personnel would be granted access to activate the vehicle’s ignition, and the vehicle could be detected remotely in case the driver strays from the approved route. Previous works have provided some solutions to each of these challenges but in isolation. However, there are many benefits when parameters such as position, personnel and energy are combined and tracked for successful management of EVs, which is what this paper has achieved. We consider some previous related works regarding our subject matter.
In recent years, the prevalence of radio frequency identification (RFID)-based applications [2] has increased because of their use for automatic identification and tracking. These applications include vehicle tracking [3], home automation and security systems [4], health applications [5], personnel attendance management systems [6], transportation [7,8] and commerce [9], to mention a few. Some RFID systems are combined with fingerprint technology to make them more secure [9], whereas some are linked with a database and web interface for online monitoring [7].
Vehicle tracking systems are being used by urban public transit authorities increasingly frequently, especially in major cities. Tracking the position of a moving vehicle, such as a car, truck or other vehicles, is achieved in many cases using a geographical positioning system (GPS) [10,11,12,13,14,15,16]. Such a device can be widely deployed to keep track of truck fleets to ensure that vehicles are properly used, and if stolen, they can be easily recovered. The use of geographical positioning systems (GPS) has become very popular in tracking the position of different items. These include vehicles [10,11,12,13,15,16,17,18] and human beings [14]. Some of GPS devices work with microcontrollers [10,12,14,17], whereas some are also accompanied by GPRS/GSM technologies as a networked means of accessing a remote web server [11]. Different means have been used over the years as means of retrieving or visualizing the information acquired by GPS, including on-site visualization such as a liquid crystal display (LDC), as the in [15], and in some cases, information is transmitted over a network and viewed in a Visual Basic-based platform, as in [10]. Web interfaces and Google Maps are employed in some cases [10,13,18]. The advantage of web/Google Maps visualization over other setups is that users are able to see the exact position of the object being racked in real time.
In an electric vehicle, power is crucial because in many cases, it determines the efficiency of the EV. As a result, several studies have been conducted involving the measurement or monitoring of EV power to ensure improved quality of service. The authors of [16] developed a data acquisition system for electric vehicles using a battery management system (BMS) bus. This BMS was able to operate as a controller area network (CAN) bus using an onboard diagnostic (OBD) system data standard. Measured parameters include position, temperature, voltage and current of the battery pack, speed, motor temperature, control temperature, state of charge, fault codes, etc. [16]. The acquired parameters were sent through a remote database to a web application and smart phones. EV data collected for approximately 5 months were used to evaluate EV performance and driving behavior. Other studies on EV power tracking include [17,18] whereas in [16], the authors explored how to track the battery and solar photovoltaic power of a solar-powered smart irrigation system. In many such cases, voltage sensors have been used [18,19]. In the case of Ref. [19], a current sensor was used, and voltage sensors for the battery and solar PV were developed using resistors, the values of which were calculated and arranged following the voltage divider rule. Over the years, the modes of presentation of the voltage, current, power and other measured parameters have included graphical presentation using MS-Excel [19] and web applications/Google Maps [12,17,18]. In [18], a Google map was provided, on which the coordinates (longitude and latitude) of an EV, as well as the voltage of the battery, were displayed in real time.
At times, different technologies have been combined with some benefits for EVs. In an attempt to avoid errors in vehicle recognition at toll gates, the authors of [20] combined three methods of recognition, namely RFID, optical character recognition (OCR) and Bluetooth, as redundant tools to eliminate mistakes and achieve better results. The device used a state machine for comparison of the different methods starting with RFID and Bluetooth approaches and later OCR before a final decision was arrived at. The authors of [21] combined RFID and Bluetooth technology to develop a smart ticketing application that aided in payment for shuttle busses on campus. This was in a bid to make payments easy and avoid littering the environment with paper. The system allowed users to pay for transport services using an RFID card, but it did not track position of the car or energy (fuel) level.
In Ref. [22], the authors developed an in-car module that communicated through a 5G network to cloud infrastructures with the aim of assisting drivers to locate the fastest route to their destinations, thereby reducing fuel consumption. It used RFID-enabled parking space monitoring, as well as cloud services, to provide supervisory control for cars, and device took into account the energy of the EV. In Ref. [23], the authors combined RFID and GPS technologies to form an RFID-GPS localization device to be installed as part of autonomous vehicles for their navigation. Experiments revealed the feasibility of this approach and its promising accuracy relative to GPS combined with sonar or light detectors and ranging. In this case, RFID, like GPS, was used for location detection, and nothing was mentioned about personnel or energy tracking. In Ref. [24], the authors proposed RFID-based wireless charging for EVs and an optimized path-finding algorithm for charging.
Previous works have considered tracking the position of EVs with less emphasis on personnel [10,11,12,13,14,15,16,17,18]. Some have focused on personnel [8] with less emphasis on the position or energy of the vehicle, whereas others concentrated on tracking the energy of EVs and other parameters such as speed and temperature while neglecting the personnel driving the EV [16,17]. In this research, a tracking system is proposed for an EV that combines personnel tracking and management with location and energy (power) tracking for efficient management of EVs. The proposed information retrieval system includes on-the-field feedback using a liquid crystal display, as well as remote real-time tracking of personnel, energy and vehicle position through a secured web interface by the administrator of the EV. As shown in Table 1, the novelty of this research is that it considers the African context with infrastructural deficit and addresses the three-dimensional challenges of ineffective driver scheduling, fuel (energy) mismanagement/theft and diversion of vehicles for personal gain, which are often encountered by transport providers, by combining different technologies such as RFID, GPS and power sensors to provide a low-cost solution. Previous studies did not combine these challenges to address them within the African context. The rest of this paper is arranged as follows. Section 2 discusses the materials and methods, Section 3 includes results and discussion, Section 4 concludes the paper.

2. Materials and Method

The developed system comprises three aspects that are integrated together, i.e., are personnel tracking and management, position tracking and energy tracking and management, as shown in Figure 1.

2.1. RFID-based Personnel Tracking and Management

The main hardware components used for the system implementation are RFID sourced from Kuongshun Electronic, Che Gong Miao in China [25] RC522 reader module, RFID tags/cards, a NodeMCU (ESP8266, Shanghai, China) microcontroller, an I2C liquid crystal display (LCD), LED, 2-channel relay, lithium-ion batteries (3.7 V DC), an electrical switch and a buck converter. A block diagram of the system is shown in Figure 2. To ensure the user is in close proximity to the reader and to eliminate multipath effects, 125 kHz low-frequency RFID tags/cards were used. The tags were modified to suit the needs of the task using codes written on the Arduino IDE (integrated development environment) version 1.8. 19 [26]. The NodeMCU (ESP8266) microcontroller functioned as the system’s brain, LEDs as indicators and the LCD as the user interface. The internal power source comprised two rechargeable 3.7 V lithium-ion batteries with a capacity of 2 Ah connected in series, producing 7.4 V, with an LM2596 buck converter stepping to step down the 7.4 V to the 5 V required by the ESP8266 microcontroller. A 12 V, 4.5 Ah rechargeable external power supply was connected to power the relay, which controlled the load (vehicle ignition system). The load in this work was a 12 V DC bulb, representing an automobile ignition switch. Figure 2 represents the block diagram of the entire system, whereas the circuit diagram is shown in Figure 3. As shown in Figure 3 and Table 2, NodeMCU digital output pins D4, D5, D7, D6 and D3 and ground (GND) were connected to the serial data (SDA), serial clock (SCK), master input/slave output (MISO), master output/slave input (MOSI), slave select (SS) and ground (GND) of the RFID, respectively, as shown in Table 2, whereas the interrupt request pin (IRQ) was left vacant. The digital output pins are D0 to D8 while the transmit and receive output pins are Tx and Rx respectively. Different line colors are showing the flow of signals from RFID reader to NodeMCU and then from Node MCU to the LCD.
Node MCU was connected to the liquid crystal display serial data (SDA) and serial clock (SCK) through its digital output pins, D1 and D2, respectively. NodeMCU digital pin D0 served as was the signal input to the relay.

2.2. Position Tracking using GPS

The development of a real-time tracking system for an electric vehicle comprises both hardware and software parts. The hardware components include a NodeMCU controller board (ESP8266), a geographical positioning system module, a GSM module and an LCD display. The software components include an Arduino IDE [26], If This Then That (IFTTT), XAMPP and Google Maps. Figure 4 shows a block diagram of a real-time tracking system using NodeMCU.
The NodeMCU microcontroller and GSM module were powered by +5 volt DC power. NodeMCU, which is the brain of the system, coordinated the activities of the GSM module and GPS receiver. The model of GPS receiver used for this work was a ublox Neo 6M GPS module [27], which was used to receive satellite data from space and in this research. It has a default baud rate of 9600 but operates at a range of 4800 to 230,400 with 2.5 m horizontal position accuracy [28]. By sending the data received to NodeMCU, coordinates (latitude and longitude of EV) were extracted from the GPS receiver and passed to the global system of mobile communication (GSM). Through the GSM module, the coordinates were sent to the user/owner of the EV by SMS so that the vehicle could be tracked around the globe, and the real-time position could be viewed on Google Maps. Furthermore, the coordinates were constantly sent as e-mail directly to the user’s preregistered e-mail address. A flow chart illustrating this process is shown in Figure 5. Once the system was switched on, GPS obtained the location data from the satellite and sent it to NodeMCU, which is the brain of the system. NodeMCU was able to make the information available to users in a number of ways. It displayed the longitude and latitude on an LCD for the driver of the EV. Moreover, NodeMCU sent the location information to Google Maps via a web server/web page. The administrator was also able to receive e-mail notifications from NodeMCU via the Internet/server.

2.3. Energy Tracking and Management

The purpose of the energy tracking and management in this work is to measure the power available in the battery for use of the EV and display the energy level to users or driver of the EV and send same information to the EV administrator for management purposes. Figure 6 shows a block diagram, Figure 7 is the circuit diagram and Figure 8 is a flow chart of an energy tracker for electric vehicles using an Arduino Nano microcontroller. In Figure 7, pin numbers 1 to 4 are the transmit (Tx), receive (Rx), reset and ground pins. Pins 5 to 16 are the digital input / output pins D0 to D13. Pins 19 to 26 are analog input pins while pin17 is 3 v power input, pin 18 is reference pin, pin 27 is 5 v power input pins, pin 28 is reset, pin 29 is ground while pin 30 is another 5 V output voltage pin denoted with an arrow. It should be noted that the 5 V output power pin from the microcontroller connects with arrow the 5 V arrow from the LCD to provide power to it. As soon as the system was switched on, it used its current and voltage sensors to acquire the instantaneous value of the voltage and current of the EV, which it used to calculate the power according to Equations (1) and (2). The result was displayed on the LCD display and the web interface. The system then checked whether the voltage was up to 10 V; if less than this value, the battery was ready to be charged; if greater than 10 V, the systems kept working.
A 5 volt battery was used to power the Arduino Nano, whereas other components in the system such as the voltage sensor, current sensor, ESP01 Module and LCD received their power from the Arduino Nano. The EV power source represented by a 12 V battery from which current and voltage were to be measured for power computation was connected across the current and voltage sensors. When the developed device was switched on, the Arduino Nano sent a command to the voltage and current sensors to measure the parameters (current and voltage) and compute the battery power. The measured and computed parameters were received by the microcontroller (Arduino Nano) and sent to the LCD for display. Then, these parameters were also sent through the ESP 01 module to the cloud server and made accessible to users on a web interface through a PC or mobile phone. The current sensor used in this work was an ACS712 current sensor module. The voltage sensor employed was based on the principle of the resistive voltage divider rule. A circuit diagram illustrating the connection of these sensors to the Arduino Nano microcontroller is shown in Figure 7, where R1 and R2 are 30 kilohm and 7.5 kilohm, respectively. Once current and voltage values were obtained, Equations (1) and (2) were used for the computation of the battery power where V i n is the battery input voltage; V o u t is battery output voltage; P is battery power; I is battery current; R 1 is voltage divider resistor 1 and R 2 is voltage divider resistor 2.
V o u t = V i n ( R 2 R 1 + R 2 )
P = I * V o u t
In terms of software requirements, the Arduino independent development environment (IDE) was used to develop the programs to be uploaded to the microcontrollers. For the remote logging of tracked parameters, MySQL was used for the database, whereas the web interface was developed with a combination of PhP, JavaScript and HTML. Figure 9 is a prototype of the developed tracking device after packaging, whereas Figure 10 and Figure 11 are the database and web interface representations, respectively, of the three tracking systems. It should be noted that the developed device is intended to be interfaced with the ignition system of an EV.
The components used for the device were chosen based on the following considerations. RFID technology comprising an RC522 reader module and RFID tags/cards was chosen for personnel identification instead of a fingerprint sensor because in the considered use scenario, a fingerprint would aid in a faster transmission of diseases, such as COVID-19 as a result of multiple individuals having direct contact with the reader, whereas with an RFID card /tag, each user would not necessarily have to touch the RFID reader; drivers would only bring their personalized RFID card/tag within close proximity of the RFID reader for authentication to take place. The other reason is that the card could also serve as a staff identity card for the driver, thereby serving dual purposes. A NodeMCU microcontroller was chosen rather than an Atmega 328 on an Arduino Uno board or an Arduino Mega because of its Wi-Fi capability, which makes it suitable for online data transfer to a web server. NodeMCU has more RAM space—typically 128 kB of RAM and 4 MB of ROM (flash). This means that it has more capacity to store code. In other words, its high processing power with built-in Wi-Fi/Bluetooth and deep sleep operating features make it ideal for IoT projects. The model of GPS receiver used for this study was a ublox Neo 6M GPS module with a default baud rate of 9600 but operating at a range of 4800 to 230,400 and 2.5 m horizontal position accuracy, which made it suitable for state-of-the-art communication at a baud rate of 115,200 bps. The current sensor used in this study was an ACS712 current sensor. This was chosen because of its low cost and effectiveness in measuring current, as well as the ease of interfacing it with the microcontroller. To measure voltage, resistors were connected based on the principle of the resistive voltage divider rule instead of a ZMPT101B voltage sensor module in order save some cost because the resistors were readily available, and it would be easy to replace in case of future maintenance.

3. Results and Discussion

Figure 10 and Figure 11 show results of tracking in a MySQL database and the dashboard of EV tracking on the web interface. Figure 11, shows that from a remote location, the administrator of the EV can see the information about the driver of the EV, including name date and time of starting the EV. Other information from the web interface includes the position of the EV on Google Maps and the energy tracking information. All this information was also replicated on different records of the MySQL database, as shown in Figure 10.
Personnel tracking and administration were achieved by scanning of the RFID-enabled identity card issued to drivers within the organization to be used on the developed tracking device. Each driver was required to register on a web portal, and their names were linked to a unique code associated with their RFID card. Each driver’s RFID tag/card was brought close to the RFID reader of the developed tracking device for authentication. If the driver was using an authentic card, the device displayed “access granted”, as shown in Figure 12, and a green light was displayed, indicating that the car ignition system had been activated. An unauthorized user was denied access to activate the ignition. Concurrently, the NodeMCU microcontroller logged the identity of the driver, as well as the date and time of ignition activation in the database, which was made accessible to relevant stakeholders in real time. This record can assist in administration of the drivers within the organization in a secured manner by providing the identity of the driver, as well as the date and time of car use.
As soon as the driver began to drive, the geographical position of the car was being tracked by the developed device, displayed on the LCD display and sent to the web portal, which was linked to a Google map for online and real-time display of the EV position. The Google map could be accessed by clicking on “map” on the dashboard of the web interface, as shown in Figure 11. The developed device was tested within the campus of Elizade University, Nigeria. Figure 13 showed part of the reading obtained on the Google map, and Figure 14a shows the longitude and latitude of the vehicle displayed on the LCD of the developed device for the driver’s use. Figure 14b shows a longitude of 5.108779 and a latitude of 7.365971 sent to the user through an preregistered e-mail address when operated at a 9600 bps baud rate. Using u-centre software, the baud rate was changed to 115,200 bps; the longitude was 5°6′21.96″ E, and the latitude was 7°21′57.1968″ N. Observation revealed that the EV positions did not change as frequently when the baud rate was increased to 115,200 bps. This could be due to the capacity of some of the hardware connected to the GPS. GPS signals are normally affected by an ionospheric effect, usually resulting in error due to signal delay. This ionospheric effect is proportional to the total electric content of the signal and inversely proportional to the carrier frequency [29]. In this work, this value could not be estimated and compensated for due to the capacity of other hardware connected to the GPS receiver. As a result, the obtained longitude and latitude used in this work might have some delay due to atmospheric dispersion. A space-based augmentation system (SBAS) was used to provide correction information to improve GNSS positioning accuracy for satellite orbit, clock and ionospheric delay information, as presented in [30]. For power management, two 12 V, 3 amp batteries were used as sources of power, and a 7 V, 3 amp bulb was connected to the device as a form of load. A voltage of 6.39 V, 0.001 A was displayed on the web interface, as shown in Figure 14, which would also be displayed on the LCD of the developed tracking device for the driver’s use. The results obtained in this work show that at any instant, the identity of an authorized driver, as well as the time and date of activating vehicle ignition, his position on the Google map and the level of power (fuel) in the electric vehicle could be tracked by both the driver on the device LCD and the administrator through a remote web interface/ Google map in real time and by e-mail.
Figure 15 shows the energy tracking section as viewed on the web page. It basically captures the three components of the energy data (current, voltage and power rating) at every point in time. This plays crucial role in energy efficiency management during the use of the EV.

4. Conclusions

In this work, a portable tracking device was developed that can be interfaced with the ignition and battery of an electric vehicle (EV). Tests revealed that the device can track personnel, as well as the position of the EV and energy of an EV battery, making the acquired information available to relevant stakeholders in real time on a web interface/Google map or by e-mail. This feedback information includes the identity of personnel (driver), the date and time a driver activated the ignition of an EV and the battery level and the position of the EV at any instant. These pieces of information are vital for the management of personnel (drivers), providing an oversight function for the EV and supporting the security of the lives and property of staff and students on the Elizade University campus. The proposed system could also help to detect unauthorized diversion of vehicles and provide accountability for energy, thereby avoiding revenue loss caused by the operators of the EV and the University management. Future work should be conducted to compensate for GPS signal error due ionosphere and energy optimization to implement the developed device on a prototype of an electric vehicle.

Author Contributions

Conceptualization, O.K.O. and C.A.; methodology, O.O. and C.A.; formal analysis, S.J., E.A. and O.O.; investigation, S.J., E.A. and O.O.; writing—supervision, O.O. and C.A.; writing—review and editing, O.O. and O.M.L. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Data Availability Statement

Not applicable.


The authors acknowledge the support of O.A. Adeleke and Ganiyu Azeez of the Faculty of Engineering, Elizade University, Nigeria, for technical assistance with GPS tracking.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Longe, O.M. An Expository Comparison of electric vehicles and internal combustion engine vehicles in Africa - motivations, challenges and adoption strategies. In Proceedings of the 2022 IEEE PES/IAS PowerAfrica, Kigali, Rwanda, 22–26 August 2022; pp. 1–5. [Google Scholar]
  2. American Barcode and RFID. What is RFID and how does RFID work? Available online: (accessed on 19 September 2022).
  3. Ismail, N.; Wahid, S.; Ahmad, N. Arduino Based RFID Vehicle Tracking for Home Security. J. Phys. Conf. Ser. 2020, 1529, 022060. [Google Scholar] [CrossRef]
  4. Dusit, T.M.; Norjali, R. Garage Automation Door by Using Radio Frequency Identification (RFID). Prog. Eng. Appl. Technol. 2021, 2, 437–442. [Google Scholar]
  5. Álvarez López, Y.; Franssen, J.; Álvarez Narciandi, G.; Pagnozzi, J.; González-Pinto Arrillaga, I.; Las-Heras Andrés, F. RFID Technology for Management and Tracking: E-Health Applications. Sensors 2018, 18, 2663. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Maramis, G.; Rompas, P. Radio Frequency Identification (RFID) Based Employee Attendance Management System. IOP Conf. Ser. Mater. Sci. Eng. 2018, 306, 012045. [Google Scholar] [CrossRef] [Green Version]
  7. Dhanasekar, N.; Valavan, C.; Soundarya, S. IoT based Intelligent Bus Monitoring System. Int. J. Eng. Res. Technol. (IJERT) 2019, 7, 1–5. [Google Scholar]
  8. Sawant, N.; Sutar, S.; Ghumare, G. Fingerprint Based Car Ignition System Using Arduino and RFID. Int. J. Adv. Sci. Res. Eng. Trends 2021, 6, 203–205. [Google Scholar]
  9. Hussien, N.; Alsaidi, S.; Ajlan, I.; Mohamed Firdhous, M.; Alrikabi, H. Smart Shopping System with RFID Technology Based on Internet of Things. Int. J. Interact. Mob. Technol. (IJIM) 2020, 14, 17. [Google Scholar] [CrossRef]
  10. Ahmed, A.A.; Ahmed, A.M.; Mohammed, A.H.; Akram, M.A. Design and implementation of vehicle tracking and theft control system. In Proceedings of the International Conference on Computing, Control, Networking, Electronics and Embedded System Engineering, Khartoum, Sudan, 7–9 September 2015. [Google Scholar]
  11. Jisha, R.C.; Mathews, P.M.; Kini, S.P.; Kumar, V.; Harisankar, U.V.; Shilpa, M. An Android Application for School Bus Tracking and Student Monitoring System. In Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research, Madurai, India, 13–15 December 2018. [Google Scholar]
  12. Karthikeyan, R. GPS Based Automotive Monitoring and Control system using Smart phone. Int. J. Mech. Eng. 2021, 6, 454–458. [Google Scholar]
  13. Kamble, S.J.; Kounte, M.R. Machine learning approach on traffic congestion monitoring system in internet of vehicles. Procedia Comput. Sci. 2020, 171, 2235–2241. [Google Scholar] [CrossRef]
  14. Khan, M.E.U.H.; Anjum, N.; Arida, F.; Khan, M.M. Hajji Tracker: Development of Web-Based GPS Tracking System for Pilgrims. In Proceedings of the 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 27–30 October 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 0896–0901. [Google Scholar]
  15. Rahman, M.M.; Mou, J.R.; Tara, K.; Sarkar, M.I. Real time Google map and Arduino based vehicle tracking system. In Proceedings of the 2016 2nd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), Rajshahi, Bangladesh, 8–10 December 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 1–4. [Google Scholar]
  16. Wu, X.; Freese, D.; Cabrera, A.; Kitch, W.A. Electric vehicles’ energy consumption measurement and estimation. Transp. Res. Part D Transp. Environ. 2015, 34, 52–67. [Google Scholar] [CrossRef]
  17. Perişoară, L.A.; Stamati, E.M.; Chiţu, L.R.; Săcăleanu, D.L. Pilot Platform for Remote Monitoring of an Electric Vehicle. In Proceedings of the 2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging (SIITME), Iasi, Romania, 25–28 October 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 394–397. [Google Scholar]
  18. Abd Wahab, M.H.; Anuar, N.I.M.; Ambar, R.; Baharum, A.; Shanta, S.; Sulaiman, M.S.; Hanafi, H.F. IoT-based battery monitoring system for electric vehicle. Int. J. Eng. Technol. (IJET) 2018, 7, 505–510. [Google Scholar]
  19. Ogidan, O.K.; Amusan, A.A.; Temikotan, K.O.; Nkanga, I.E. Optimal solar power for control of smart irrigation system. IOP Conf. Ser. Mater. Sci. Eng. 2021, 1088, 012070. [Google Scholar] [CrossRef]
  20. Wiseman, Y. Vehicle Identification by OCR, RFID and Bluetooth for Toll Roads. Int. J. Control. Autom. 2018, 11, 67–76. [Google Scholar] [CrossRef]
  21. Abayomi-Alli, O.; Odusami, M.; Chima, R.; Misra, S.; Ahuja, R.; Damasevicius, R.; Maskeliunas, R. Smart ticketing for academic campus shuttle transportation system based on RFID. In Advances in Data Sciences, Security and Applications; Springer: Singapore, 2020; pp. 237–252. [Google Scholar]
  22. Pawłowicz, B.; Salach, M.; Trybus, B. Smart city traffic monitoring system based on 5G cellular network, RFID and machine learning. In Proceedings of the KKIO Software Engineering Conference, Pultusk, Poland, 27–28 September 2018; Springer: Cham, Switzerland, 2018; pp. 151–165. [Google Scholar]
  23. Khosyi’in, M.; Prasetyowati, S.A.D.; Nawawi, Z.; Suprapto, B.Y. Review and design of GPS-RFID localization for autonomous vehicle navigation. In Proceedings of the 2019 2nd International Conference on Electronics and Electrical Engineering Technology, Penang, Malaysia, 25–27 September 2019; pp. 42–46. [Google Scholar]
  24. Arora, S.; Goel, S.; Chhikara, P.; Singh, H.; Kumar, N.; Rana, P.S. An efficient scheme for wireless charging of electric vehicles using RFID with an optimal path planning. In Proceedings of the 2019 IEEE Globecom Workshops (GC Wkshps), Waikoloa, HI, USA, 9–13 December 2019; pp. 1–6. [Google Scholar]
  25. Kuongshun 2022. MFRC-522 RC522 RFID. Available online: (accessed on 8 December 2022).
  26. Arduino. Arduino IDE 1.8. 19. 2022. Available online: (accessed on 8 December 2022).
  27. U-blox. U-Centre. 2022. Available online: (accessed on 8 December 2022).
  28. LastminuteEngineers. Available online: (accessed on 11 December 2022).
  29. El-Naggar, A.M. Enhancing the accuracy of GPS point positioning by converting the single frequency data to dual frequency data. Alex. Eng. J. 2011, 50, 237–243. [Google Scholar] [CrossRef] [Green Version]
  30. Kim, M.; Kim, J. SBAS-Aided GPS Positioning with an Extended Ionosphere Map at the Boundaries of WAAS Service Area. Remote Sens. 2021, 13, 151. [Google Scholar] [CrossRef]
Figure 1. Architecture of Electric (E) - mobility tracking system for an EV.
Figure 1. Architecture of Electric (E) - mobility tracking system for an EV.
Wevj 14 00005 g001
Figure 2. Block diagram of RFID-based personnel tracking.
Figure 2. Block diagram of RFID-based personnel tracking.
Wevj 14 00005 g002
Figure 3. RFID-based personnel administration circuit diagram.
Figure 3. RFID-based personnel administration circuit diagram.
Wevj 14 00005 g003
Figure 4. Block diagram of the GPS tracking system.
Figure 4. Block diagram of the GPS tracking system.
Wevj 14 00005 g004
Figure 5. Flow chart illustrating GPS tracking operation of the EV.
Figure 5. Flow chart illustrating GPS tracking operation of the EV.
Wevj 14 00005 g005
Figure 6. Block diagram of the EV energy tracking system.
Figure 6. Block diagram of the EV energy tracking system.
Wevj 14 00005 g006
Figure 7. Circuit diagram of the EV energy tracking system.
Figure 7. Circuit diagram of the EV energy tracking system.
Wevj 14 00005 g007
Figure 8. Flow chart of the EV energy tracking operation.
Figure 8. Flow chart of the EV energy tracking operation.
Wevj 14 00005 g008
Figure 9. System front view.
Figure 9. System front view.
Wevj 14 00005 g009
Figure 10. Personnel, position and energy tracking values in the database.
Figure 10. Personnel, position and energy tracking values in the database.
Wevj 14 00005 g010
Figure 11. Dashboard of personnel, position and energy tracking on the web interface.
Figure 11. Dashboard of personnel, position and energy tracking on the web interface.
Wevj 14 00005 g011
Figure 12. RFID card logs used for personnel management.
Figure 12. RFID card logs used for personnel management.
Wevj 14 00005 g012
Figure 13. Vehicle position showing on Google Maps.
Figure 13. Vehicle position showing on Google Maps.
Wevj 14 00005 g013
Figure 14. (a) GPS data on the LCD display; (b) GPS data sent to user by e-mail.
Figure 14. (a) GPS data on the LCD display; (b) GPS data sent to user by e-mail.
Wevj 14 00005 g014
Figure 15. Energy tracking section on the web page.
Figure 15. Energy tracking section on the web page.
Wevj 14 00005 g015
Table 1. Analysis of reviewed papers.
Table 1. Analysis of reviewed papers.
PaperTracking DeviceParameters TrackedObject TrackedRetrieval MethodLimitations
[10]GPS and radio frequency VehicleVisual Basic GUI and Google MapsOnly vehicle position was tracked, using radio frequency to transmit.
[11]GPS and GPRS/GSM VehicleAndroid AppOnly vehicle position was tracked.
[12]GPS, GPRS VehicleWeb and smart phoneOnly vehicle position was tracked.
Machine Learning (ML)
VehicledatabaseOnly vehicle position was tracked.
[14]GPS HumanGoogle MapsOnly human position was tracked.
[15]GPS vehicleLCD, mobile phone and Google MapsOnly vehicle position was tracked.
[16]Battery management system and bus VehicleDatabase and web smart phonesEnergy was tracked.
[17]GPS, GPRS, speed, current, voltage, battery temperature and CAN temperature VehicleMySQL database, web application and SMS on smart phonesVehicle position, speed, energy, battery temperature, current and voltage were tracked.
[18]Voltage sensor, SIM 800 GSM shield and GPS VehicleGoogle Maps and
web interface
Only vehicle position was tracked.
[19]Voltage sensor and current sensor Irrigation solar battery and PVLCDBattery voltage and current (energy) of an irrigation system were tracked.
Developed deviceRFID, GPS,
voltage sensor and current sensor
Vehicle/ human/ energyLCD, web portal, Google Maps and SMSCombined tracking of EV position and personnel using RFID and energy using appropriate sensors.
Table 2. MFRC522 RFID reader to NodeMCU ESP8266 pin description.
Table 2. MFRC522 RFID reader to NodeMCU ESP8266 pin description.
NodeMCU PinMFRC522 Pin
3 V3.3 V
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ogidan, O.K.; Aghaukwu, C.; Oluwapelumi, O.; Jeremiah, S.; Adokeme, E.; Longe, O.M. Development of a Personnel Management and Position and Energy Tracking System for Electric Vehicles. World Electr. Veh. J. 2023, 14, 5.

AMA Style

Ogidan OK, Aghaukwu C, Oluwapelumi O, Jeremiah S, Adokeme E, Longe OM. Development of a Personnel Management and Position and Energy Tracking System for Electric Vehicles. World Electric Vehicle Journal. 2023; 14(1):5.

Chicago/Turabian Style

Ogidan, Olugbenga Kayode, Celestine Aghaukwu, Oluwagbotemi Oluwapelumi, Success Jeremiah, Edesemi Adokeme, and Omowunmi Mary Longe. 2023. "Development of a Personnel Management and Position and Energy Tracking System for Electric Vehicles" World Electric Vehicle Journal 14, no. 1: 5.

Article Metrics

Back to TopTop