A processor implemented method to generate pricing for charging electric vehicles characterized in that : receiving by an electric vehicle, EV, charging aggregator having an Reinforcement Learning, RL, agent via one or more hardware processors , a user request comprising an EV charging demand request and a time of day to generate an EV charging price to maximize revenue of the EV charging aggregator; modelling using a state generator, via the one or more hardware processors , a state of the RL agent for processing the user request and assigning a reward to the RL agent for the performed action, wherein the state of the RL agent includes i. the actual demand pool size N t P 1 , ii. the service pool size N t S , iii. the total number of currently available EV chargers K t at current time step, and iv. the time of day t d of the user request so that time-of-day effects in user behavior are learnt over time, wherein the state of the RL agent is spatially tagged with the EV charging demand request and availability of EV supply equipment, EVSE, wherein the EV charging demand request is Global Positioning System, GPS, location stamped and the EV charger aggregator obtains current availability of participating EVSEs, wherein the RL agent learns to map action to observed state and maximizes the reward earned over training period, wherein the RL agent is trained and implemented using a Deep Q-network, DQN; and dynamically generating by the RL agent, via the one or more hardware processors , the EV charging price for the user request, without a priori knowledge, to maximize revenue of the EV charging aggregator by, computing i. an actual demand pool P 1 based on a next time step of actual demand pool size N t + 1 P 1 , ii. a service pool S based on a next time step of service pool size N t S , iii. a total number of currently available EV chargers K t , and iv. a time of day t d of the user request. receiving by an electric vehicle, EV, charging aggregator having an Reinforcement Learning, RL, agent via one or more hardware processors , a user request comprising an EV charging demand request and a time of day to generate an EV charging price to maximize revenue of the EV charging aggregator; modelling using a state generator, via the one or more hardware processors , a state of the RL agent for processing the user request and assigning a reward to the RL agent for the performed action, wherein the state of the RL agent includes i. the actual demand pool size N t P 1 , ii. the service pool size N t S , iii. the total number of currently available EV chargers K t at current time step, and iv. the time of day t d of the user request so that time-of-day effects in user behavior are learnt over time, wherein the state of the RL agent is spatially tagged with the EV charging demand request and availability of EV supply equipment, EVSE, wherein the EV charging demand request is Global Positioning System, GPS, location stamped and the EV charger aggregator obtains current availability of participating EVSEs, wherein the RL agent learns to map action to observed state and maximizes the reward earned over training period, wherein the RL agent is trained and implemented using a Deep Q-network, DQN; and dynamically generating by the RL agent, via the one or more hardware processors , the EV charging price for the user request, without a priori knowledge, to maximize revenue of the EV charging aggregator by, computing i. an actual demand pool P 1 based on a next time step of actual demand pool size N t + 1 P 1 , ii. a service pool S based on a next time step of service pool size N t S , iii. a total number of currently available EV chargers K t , and iv. a time of day t d of the user request. <EOS>