Economic model predictive control of Li‐ion battery cyclic aging via online rainflow‐analysis

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.