Development of improved reinforcement learning smart charging strategy for electric vehicle fleet

Due to its environmental and energy sustainability, electric vehicles (EV) have emerged as the preferred option in the current transportation system. Uncontrolled EV charging, however, can raise consumers; charging costs and overwhelm the grid. Smart charging coordination systems are required to prevent the grid overload caused by charging too many electric vehicles at once. In light of the baseload that is present in the power grid, this research suggests an improved reinforcement learning charging management system. An optimization method, however, requires some knowledge in advance, such as the time the vehicle departs and how much energy it will need when it arrives at the charging station. Therefore, under realistic operating conditions, our improved Reinforcement Learning method with Double Deep Q-learning approach provides an adjustable, scalable, and flexible strategy for an electric car fleet. Our proposed approach provides fair value which solves the over-estimation action value problem in deep Q-learning. Then, a number of different charging strategies are compared to the Reinforcement Learning algorithm. The proposed Reinforcement Learning technique minimizes the variance of the overall load by 68 % when compared to an uncontrolled charging strategy.

Automated summary

Electric vehicles (EV) have become the preferred option in transportation due to their environmental and energy sustainability. However, uncontrolled EV charging can increase consumer costs and overload the grid. This research proposes an improved reinforcement learning charging management system to prevent grid overload. Under realistic operating conditions, the proposed approach provides an adjustable, scalable, and flexible strategy for an electric car fleet. Compared to an uncontrolled charging strategy, the proposed reinforcement learning technique reduces the variance of the overall load by 68%.