# Online Model Adaption for Energy Management in Fuel Cell Electric Vehicles (FCEVs)
## 4 Conclusions  
This paper addressed a problem wherein an EMS of an FCEV employs a 1D look-up table to model a fuel cell and compute the power-split using the widespread ECMS. The goal was to adapt the calibration of this model using fuel cell measurements by applying a strategy based on an extended Kalman filter approach. A high-fidelity vehicle plant was modelled to represent the vehicle, and experimental data were employed to build the FC model. Two studies were carried out. The first evaluated EMS performance using three different FC maps. The first map was obtained from a set of steady-state tests on the actual FC. Second, a biased map reproduced the negative impact of modelling errors in model-based control approaches such as the ECMS. Last, we tested the map obtained using the proposed method. The second aspect of the study discussed the learning rate: a parameter that regulates the gain of the adapter algorithm to make higher or lower adaptations to the fuel cell measurements. From the results, we can state that:  
* Although a dynamic system such as a fuel cell has been simplified to a 1D look-up table, the proposed method seems to be an option to adapt maps in a model-control-based approach. The proposed method allows us to adjust the look-up table parameters for correcting drift and for slowly varying them when using the vehicle in different driving conditions.
* The primary contribution of this work lies in the adaptive method capability to gradually adjust the map to compensate for any drift resulting from prolonged use. Furthermore, in the case that the EMS utilizes a biased map, significant improvements are shown in short periods of operation: approximately 1 h across various operating conditions.
* The learning algorithm was shown to be advantageous since it improved the estimation of the fuel cell power after only one hour of operation. In addition, it reduced the estimated fuel cell power error by close to 85% compared to the results employing the biased map, providing fuel savings of up to 1.2%.
* The learning rate analysis showed that by using lower learning rates, the algorithm gradually adjusted the map, placing greater trust in the previously implemented map in the EMS. Conversely, when using higher learning rates, the adapter algorithm relied more on the fuel cell power measurement, enabling faster map adaptation. Regarding future work, the authors may consider the following possible lines:
* Enhance the vehicle model to consider other relevant parameters in the EMS, such as fuel cell air and temperature management as well as battery temperature;
* Increase the map dimensions to incorporate this new parameter in the calibration process, improving the control of the fuel cell system and subsequently improving fuel consumption.  
Conceptualization, B.P. and P.B.; methodology, B.P. and P.B.; software, D.P.; formal analysis, R.N.; data curation, D.P.; writing--original draft preparation, D.P.; writing--review and editing, all; funding acquisition, R.N. and B.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by IAI 2019 Proyectos de investigacion, desarrollo e innovacion competitivos, project number TED2021-130801B-I00.  
Not applicable.  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.