Non-energy-consumption Equalization Method for Series Super Capacitor Banks Based on Reinforcement Learning
As a representative of emerging energy storage components,super capacitor are increasingly used in the context of the implementation of the innovation-driven development strategy.For solving such problems as large voltage deviation and low utilization efficiency of each single unit in the use of series-con-nected super capacitor banks and fully using artificial intelligent technology,a kind of reinforcement learn-ing-based non-energy equalization method for series super capacitor banks is proposed,and the proximal policy optimization operator(PPO)is applied to the voltage equalization of series super capacitor banks.The super capacitor bank and reinforcement learning environment are set up in Matlab/Simulink simulation platform and the effectiveness of the algorithm is verified.The super capacitor,compared to the traditional energy consumption voltage equalization method,has good equalization efficiency and small loss.The experimental results show that the PPO algorithm can equalize the voltage of series super capacitor bank.
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