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<p>Optimizing nonlinear time-dependent control in complex energy systems such as direct methanol fuel cells (DMFCs) is a crucial engineering challenge. The long-term power delivery of DMFCs deteriorates as the electrocatalytic surfaces become fouled. Dynamic voltage adjustment can clean the surface and recover the activity of catalysts; however, manually identifying optimal control strategies considering multiple mechanisms is challenging. Here we demonstrated a nonlinear policy model (Alpha-Fuel-Cell) inspired by actor–critic reinforcement learning, which learns directly from real-world current–time trajectories to infer the state of catalysts during operation and generates a suitable action for the next timestep automatically. Moreover, the model can provide protocols to achieve the required power while significantly slowing the degradation of catalysts. Benefiting from this model, the time-averaged power delivered is 153% compared to constant potential operation for DMFCs over 12 hours. Our framework may be generalized to other energy device applications requiring long-time-horizon decision-making in the real world.</p>

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An actor–critic algorithm to maximize the power delivered from direct methanol fuel cells

  • 期刊论文

  • 发表日期：

    2025年7月14日

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  • Hongbin Xu,
  • Yang Jeong Park,
  • Zhichu Ren,
  • Daniel J. Zheng,
  • Davide Menga,
  • Haojun Jia,
  • Chenru Duan,
  • Guanzhou Zhu,
  • Yuriy Román-Leshkov,
  • Yang Shao-Horn,
  • Ju Li

摘要

Optimizing nonlinear time-dependent control in complex energy systems such as direct methanol fuel cells (DMFCs) is a crucial engineering challenge. The long-term power delivery of DMFCs deteriorates as the electrocatalytic surfaces become fouled. Dynamic voltage adjustment can clean the surface and recover the activity of catalysts; however, manually identifying optimal control strategies considering multiple mechanisms is challenging. Here we demonstrated a nonlinear policy model (Alpha-Fuel-Cell) inspired by actor–critic reinforcement learning, which learns directly from real-world current–time trajectories to infer the state of catalysts during operation and generates a suitable action for the next timestep automatically. Moreover, the model can provide protocols to achieve the required power while significantly slowing the degradation of catalysts. Benefiting from this model, the time-averaged power delivered is 153% compared to constant potential operation for DMFCs over 12 hours. Our framework may be generalized to other energy device applications requiring long-time-horizon decision-making in the real world.