Amal H. Alharbi ¹ Doaa Khafaga ¹ El-Sayed M. El-kenawy ^2* Marwa Eid ³ Abdelhameed Ibrahim ⁴ Laith Abualigah ⁵ Nima Khodadadi ⁶ Abdelaziz Abdelhamid ^7*

• ¹ Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia, Riyadh, Saudi Arabia
• ² Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt
• ³ Delta University for Science and Technology, Al Mansurah, Egypt
• ⁴ School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain, Bahrain, Bahrain
• ⁵ Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Jordan, Amman, Jordan
• ⁶ Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA, Miami, United States
• ⁷ Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Riyadh, Saudi Arabia

Introduction: As the number of individuals who drive electric vehicles increases, it is becoming increasingly important to ensure that charging infrastructure is both dependable and conveniently accessible. Methodology: In this paper, a recommendation system is proposed with the purpose of assisting users of electric vehicles in locating charging stations that are closer to them, improving the charging experience, and lowering range anxiety. The proposed method is based on restricted Boltzmann machine learning to collect and evaluate real-time data on a variety of aspects, 1 Amal H. Alharbi et al.including the availability of charging stations and historical patterns of consumption. To optimize the parameters of the restricted Boltzmann machine, a new optimization algorithm is proposed and referred to as parallel greylag goose (PGGO) algorithm. The recommendation algorithm takes into consideration a variety of user preferences. These preferences include charging speed, cost, network compatibility, amenities, and proximity to the user's present location. By addressing these preferences, the proposed approach reduces the amount of irritation experienced by users, improves charging performance, and increases customer satisfaction. Results: The findings demonstrate that the method is effective in recommending charging stations that are close to drivers of electric vehicles. On the other hand, the Wilcoxon rank-sum and Analysis of Variance tests are utilized in this work to investigate the statistical significance of the proposed parallel greylag goose optimization method and restricted Boltzmann machine model. The proposed methodology could achieve a recommendation accuracy of 99% when tested on the adopted dataset. Conclusion: Based on the achieved results, the proposed method is effective in recommending systems for the best charging stations for electric vehicles.