Alam, Md. Golam RabiulEvan, Md. SaharanEfad, Akil RahmanShukti, Nusrat Jahan2025-01-162025-01-16©20242024-05ID 20201020ID 20201041ID 21101003http://hdl.handle.net/10361/25185Cataloged from PDF version of thesis.Includes bibliographical references (pages 38-39).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.This paper is about creating a system that helps to manage the charging of electric vehicles that are connected and can drive autonomously taking into consideration the safe reinforcement learning outcomes in this process. The system is regarded as an intelligent decision support system (IDSS). In this system, a holding corporation that works the whole charging infrastructure, installs charging equipment for both regular electric vehicles driven by humans and autonomous vehicles. The problem arises when human-driven vehicles ask for more charging time and energy than they really need and to success charging request competition, which can particularly lead to cause issues. To address this problem, a proposed solution aims to make sure the charging equipment is used efficiently minimizing the risk of not having enough power available as well as considering all the safety of the charging equipment. Here a system will be introduced where it encourages human-driven vehicles to make rational charging requests based on data and noting down the parameters which are the number of DSOs (Distribution System Operator), the nearest finding of EVSE(Electrical Vehicle Supply Equipment), the association among the EVSEs, the starting and ending time of the plugin, energy absorption, time duration, request for charging for the CAV, CV and AVs. Furthermore, the introduction of a learning system where the charging equipment learns how to schedule charging sessions based on the procession from the main operator or the distribution system operator. The conducted experiments will show that this system improves the charging rate, active charging time, and energy usage compared to existing systems ensuring all the protection of the electrical and connected autonomous vehicles. Therefore, the study will contribute to making transportation systems smarter and addressing the challenges and safeties of connected and autonomous vehicles.45 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Safe reinforcement learningCharging systemDistribution system operatorIntelligent decision support systemElectric vehiclesAutonomous vehiclesDecision support systems.Artificial intelligence.Electric vehicles--Technological innovations.Electric vehicles--Power supply.Battery charging stations (Electric vehicles).Thesis