Abstract
A data-driven PEMFC output voltage control method is proposed. Moreover, an Improved deep deterministic policy gradient algorithm is proposed for this method. The algorithm introduces three techniques: Clipped multiple Q-learning, policy delay update, and policy smoothing to improve the robustness of the control policy. In this algorithm, the hydrogen controller is treated as an agent, which is pre-trained to fully interact with the environment and obtain the optimal control policy. The effectiveness of the proposed algorithm is demonstrated experimentally.
Highlights
Fuel Cell is the fourth type of power generation technology after hydroelectric, thermal and nuclear power generation
In order to obtain accurate and fast response results, various advanced control strategies have been applied in the research of PEMFC output control strategies. (Zhang et al, 2019; Li and Yu, 2021b; Zhang et al, 2021)
Some higher order sliding mode control methods have been applied to PEMFC, (Ou et al, 2015) such as first order sliding mode control, higher order super twisted sliding mode control and (Chen et al, 2018) higher order sliding mode control with an observer
Summary
Fuel Cell is the fourth type of power generation technology after hydroelectric, thermal and nuclear power generation. It converts chemical energy stored in fuel and oxidizer directly into electricity through electrode reactions in an isothermal environment (Yang et al, 2021a; Yang et al, 2021b). As the PEMFC system is a complex system with multiple inputs and outputs, nonlinear, approximately east, with random disturbances, time-varying and high order (Yang et al, 2019b; Li and Yu, 2021a), it is difficult to achieve satisfactory control results with traditional PID control (Li et al, 2021). In order to obtain accurate and fast response results, various advanced control strategies have been applied in the research of PEMFC output control strategies. Scholars at home and abroad have done a lot of research on the control of PEMFC, which is mainly divided into the following categories: 1) Model-based control methods (Wang and Kim, 2014): including internal model control (IMC) (Danzer et al, 2008), model predictive control (MPC) (Kim, 2010), model-based adaptive control (Zhang et al, 2008), nonlinear model predictive control (NMPC) (Park and Gajic, 2014), model multivariable control (nonlinear multivariable control) (Talj et al, 2009), time delay control (Liu et al, 2016), generalized model control (Damour et al, 2014), etc
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