The standard fatigue estimation procedure is implemented in Model Predictive Control via externalization of the Rainflow algorithm from the optimization problem. Additionally, stress history is considered in a consistent manner by employing a so-called stress residue. The formulation is implemented in the state-of-the-art MPC framework acados and tested in closed-loop with the 5MW onshore turbine in OpenFAST. Simulation results indicate that the new formulation outperforms conventional PID- and MPC-controllers over the entire wind regime, and that the consideration of stress history is highly beneficial.

Cycle identification via the rainflow-algorithm is implemented online in a model predictive controller (MPC) for Li-ion batteries. This is achieved by externalization of the cycle identification from the optimization problem. The limitation for cyclic aging estimation due to short prediction horizons is overcome by updating and utilizing a State of Charge memory. Furthermore, a comprehensive plant model for Li-ion batteries is presented with novel submodels for calendric and cyclic aging. The novel MPC is implemented in the ACADO Toolkit and tested with the aforementioned plant model. Simulation results indicate that-even without tuning-the novel MPC clearly outperforms a rule-based controller and an extensively tuned MPC from literature.

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