year Proposed approaches Short descriptions Merits Demerits 2018 Wan et al. [32 ] A real-time EV charging scheduling mechanism using deep RL Realistic variability of the EV traveling patterns and wholesale prices are considered Scalability issues along with no data accessibility for user 2019 Wang et al. [22 ] Maximizing the objective of a charging scheduling and real-time pricing problem using an RL algorithm hyperopia state–action–reward–state–action (SARSA) 138.5 higher charging-station profit compared to benchmark algorithms Need to be included data security 2019 Dang et al. [18 ] Q-learning algorithm-based dynamic charging scheduling scheme Multidimensional Q-learning is applied for optimal dispatch Compute-intensive in case the size of Q-table is huge 2020 Mhaisen et al. [33 ] To control the charging and discharging operation of EVs using Q-learning Model-free and off policy mechanism is employed State elements shift is not considered 2020 Mohanty et al. [7 ] ML-based support vector machine (SVM) algorithm is applied for scheduling of EVs Various parameters such as energy consumption, trip time, and charging state for every interval are considered Advanced ML algorithms like deep neural network (DNN), etc. need to explored depending upon the length of the data 2020 Qiu et al. [12 ] A deep RL method for pricing EVs using discrete charging levels This method achieves 31% higher profit compared to Q-learning-based approach Realistic variability of the EV traveling patterns and wholesale prices are not considered 2021 Lin et al. [34 ] Deep RL for EVs routing problem along with time windows Solve EVs routing problem instances of large sizes Scalability and data security issues 2021 Ding et al. [35 ] Secure policy for energy trading in smart microgrids for peer-to-peer trading Optimal pricing method is used on real dataset Scalability and data storage cost issue on blockchain 2022 The proposed SV2G-ET scheme Secure V2G energy trading using deep RL and EBT Handling data storage cost issue on blockchain, increasing EV’s owner profit, improving scalability, saving EVs charging cost —