## 4 Conclusions and Outlook on Future Work  
In this paper, a novel Kalman filter-based online identification and maximum power point tracking algorithm is proposed. It was validated experimentally by using measured data from a real-life SOFC test rig. Due to the small computational burden of this algorithm, it is readily applicable to industrial applications and typically outperforms offline identified multivariate look-up table solutions. The advantage of the proposed procedure can not only be seen by its reduced memory demand but also due to the inherent ability to track variations of the power characteristic resulting from instationary thermal and fluidic operating conditions of the SOFC.  
Future work will deal with interfacing this estimation and optimization procedure with model-based power control strategies [17]. Moreover, the potential performance increase and resulting robustness of the augmented system representation in Equation (26) will be investigated. The aim of this investigation will be specifically the reduction of the transient first phase of the proposed algorithm with the goal to determine the optimal operating point as quickly as possible.  
As mentioned in the Introduction, maximization of the electric power by means of current adaptations, for the case of predefined, or slowly varying hydrogen mass flows is not the only possible optimization task that can be solved by the proposed technique. Future work can also deal with defining an extended cost function, in which the percentage of non-utilized fuel--easily computable by means of [PERSON]'s law, as shown in [9]--is added in terms of a weighted square norm onto the power to be maximized. Then, using Equation (26) as the augmented system model, it becomes possible to perform a two-parameter optimization task, which determines not only the optimal current for a predefined hydrogen mass flow but allows for optimizing this second input as well. There, it should be assumed that a subsidiary control loop for controlling the hydrogen mass flow exists, either in the case of a direct gas supply (as considered in this paper) or in terms of an external gas reformation process.  
Due to its generality, the proposed procedure is not only applicable to the maximization of the electric fuel cell power, but it is also straightforward to transfer this algorithm to any other real-time optimization task on the basis of noisy data, as long as the cost functions under investigation can be identified in terms of expressions that depend linearly on slowly varying estimated parameters as well as have a unique, regular extremum in the interior of the admissible operating domain.  
**Author Contributions:** Conceptualization, A.R.; Methodology, A.R.; Software, A.R.; Validation, A.R., W.F. and J.K.; Investigation, A.R., W.F. and J.K.; Writing--original draft preparation, A.R., W.F. and J.K.; Writing--review and editing, A.R., W.F. and J.K.; Visualization, A.R., W.F. and J.K.; Project administration, A.R.; Funding acquisition, A.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is partially funded by the European Union within the European Fund for Regional Development.  
**Conflicts of Interest:** The authors declare no conflict of interest.