基于学习增强型模型预测控制的混合储能系统实时能量管理策略及其在波浪能转换器中的应用

Integrating hybrid energy storage systems (HESSs) into wave energy converters (WECs) can mitigate power fluctuations of WECs across multiple timescales, provided that an effective energy management strategy (EMS) is implemented. Model predictive control (MPC) is the mainstream EMS for HESSs, as it typically yields a control solution close to the global optimum while satisfying constraints. However, MPC faces a high computational burden when the optimal control problem is nonlinear. More importantly, considering multiple competing objectives, tuning weighting factors (WFs) in the MPC cost function is also challenging. To tackle these challenges, this article proposes a learning-augmented MPC strategy to optimize energy management for the HESS in WECs. The strategy first utilizes a fuzzy logic-based asymmetric action trimming technique to reduce MPC computational time. Further, a warm-start Q-learning (QL) framework with high learning efficiency is applied to obtain the WF online tuning method. To bridge the simulation-to-reality gap in the QL framework, the article designs a neural network-based current predictor, aiming to sense the nonlinear power loss during power conversion. Finally, simulations and experiments demonstrate the superior performance of the proposed strategy in reducing energy loss, battery degradation, and MPC computational burden.