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Electric vehicle (EV) adoption is expanding, posing new issues for grid operators, fleet operators, charging station operators, and EV owners. The challenge is to devise an efficient and cost-effective strategy for managing EV charging that takes into account the demands and objectives of all parties. This study offers a context-aware EV smart charging system based on deep reinforcement learning (DRL) that takes into account all participants&#x2019; requirements and objectives. The DRL-based system adjusts to changing contexts such as time of day, location, and weather to optimize charging decisions within an instantaneous fashion by balancing the trade-offs among charging cost, grid strain reduction, fleet operator preferences, and energy efficiency of charging station maintainer while providing EV owners with a convenient and cost-effective charging experience for its ability to handle sequential decision-making, capture complex patterns in data, and adapt to changing contexts. The proposed system&#x2019;s performance has been evaluated using simulations and compared with existing solutions. The results demonstrate that the proposed system is capable of balancing the trade-offs between different objectives and providing an energy-efficient solution which is approximately 15% better than traditional approach, and about 10% more cost-effective charging experience for EV owners while reducing grid strain by 20% and CO2 emissions by 10% as a result of using a natural energy source. The proposed system has then resulted in achieving the needs for efficient and optimised resource scheduling of fleet operators and charging station maintainers.

Context Aware - Resource Optimality in Electric Vehicle Smart2Charge Application. A Deep Reinforcement Learning Base Approach