Modeling and identifying EV drivers' behavior is an essential prerequisite for charging stations (CSs) management, which improves their efficiency and service quality. In this thesis, we introduce a non-homogeneous behavioral Markov model (BMM) for describing a typical EV driver's charging behavior, where the model parameters depend on the behavioral characteristic of the driver, the vehicle specifications, and the EV battery level of charge. Validating the model based on real data shows its ability to describe the drivers' charging pattern well. The sensitivity analysis of the model indicates that the EV drivers' charging behavior affects the statistical characteristics of the CSs' behavioral-related parameters. This model is then applied to simulate the congestion status in public CSs and predict their future capacity to guarantee an appropriate service quality level. The results show that studying and controlling the EV drivers' behavior leads to a significant saving in CS capacity and results in consumer satisfaction, thus affecting the station owners' profitability. Applying BMM to design a system to identify the EV drivers' charging behavior needs an accurate and feasible parameter estimator. In this regard, we propose an RL-based algorithm to estimate the behavioral parameters. The evaluation results demonstrate the convergence of the proposed algorithms and validate the estimated behavioral parameters. Keywords: Electric Vehicle (EV), Characteristic Modeling, Electric Vehicle Charging, Congestion Control, Reinforcement Learning, Parameter Estimation.
