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[EN] Fuel cell (FC) technology has been identified as a technically attractive solution to decarbonize the transportation sector, especially for heavy-duty vehicles. In this context, the industry and the scientific community are in need of advanced fuel cell systems (FCS) models that are able to replicate real-world operating conditions. Due to the scarcity of said models in the open literature, this study aimed to develop a comprehensive methodology to calibrate and validate multi-physics dynamic FCS models. Therefore, the key contribution of this paper is the detailed description of the calibration process for each component and the calibration order. The specific focus here was to accurately describe the behavior of the FC stack as well as the cathode, anode, and cooling circuits of the balance of plant. The model was calibrated with the aid of experimental data from a Toyota Mirai FC electric vehicle, which was predominantly retrieved from the vehicle¿s Controller Area Network (CAN) bus system thereby negating the need for major intrusion into the powertrain system. The validation process was deemed successful with the model being able to truthfully replicate the characteristics of the FC vehicle operated on the World-wide harmonized Light duty Test Cycle (WLTC) 3b and US06 driving cycle. The time-resolved physical parameters such as the cathode pressure, mass flow, or the FC stack temperature were captured with high fidelity, while the overall performance parameters such as the H2 consumption in the stack and the system, and the compressor energy consumption were predicted accurately with a deviation lower than 0.47%, 1.75% and 1.89% with respect to the experimental data, respectively. This research is part of the project TED2021-131463B-I00 (DI-VERGENT) funded by MCIN/AEI/10.13039/501100011033 and the European Union "NextGenerationEU"/PRTR. It has also been partially funded by the Spanish Ministry of Science, Innovation, and University through the University Faculty Training (FPU) program (FPU19/00550) . Toby Rockstroh and Ram Vijayagopal acknowledge support through the US DOE Vehicle Technologies Program. Argonne National Laboratory is operated by UChicago Argonne, LLC under Contract no. DE-AC02-06CH11357. The US Government retains for itself, and others acting on its behalf, a paid-up non-exclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly, by or on behalf of the Government. The authors would like to express their gratitude to Kevin Stutenberg from Argonne National Laboratory for the informative discussions surrounding the experimental test campaign.

[EN] The battery electric vehicle is the leading technology for reducing greenhouse gas emissions using clean and renewable energy. However, concerns due to battery thermal runaway are becoming more severe as the battery energy density increases. Fast-calculation models capable of predicting the heat released during the thermal runaway phenomenon can help to develop safety mechanisms according to the battery chemistry. The current study assesses the battery thermal runaway variability for two different battery chemistries, nickel cobalt aluminium oxides and nickel manganese cobalt oxides, for 3 different states of charge (100%, 80% and 50%), two different battery sizes (18,650 and 21,700), and two different battery health (pristine and aged). The tests are performed in the accelerating rate calorimeter using the heat-wait-seek protocol and repeated 5 times (each battery condition) for statistical analysis of the main thermal runaway parameters. A model using the Arrhenius equation was developed, calibrated, and validated. The model was developed considering 5 steps during temperature evolution to the reliable prediction of thermal runaway characteristics, considering inputs as states of charge, capacity fade (solid electrolyte interface growth), energy density, battery end mass and initial voltage. The experimental tests show that temperature rise rate, when the exothermic is detected, and battery end mass play an important role in the self-heating duration and maximum temperature, respectively, which are key parameters to understanding scattering behaviour. Considering these effects during modelling, the model can forecast the primary features of a thermal runaway, including maximum temperature, onset temperature, and duration of the whole battery thermal runaway process, all within the average difference of no more than 3%. For this reason, the model proposed seems to be a suitable tool for battery safety mechanism design as it considers the state of charge, energy density and ageing effects. The authors acknowledge the Vicerrectorado de investigacion de la Universitat Politecnica de Valencia for supporting this research through Programa de Ayudas de Investigacion y desarrollo (PAID-01-22). This research is part of the projects TED2021-132220B-C21 and TED2021-130488 A-I00, funded by the MCIN/AEI/10.13039/501100011033 and the European Union "NextGenerationEU"/PRTR. This research is part of the project PID2021-124696OB-C21, funded by Ministerio de Ciencia e Innovacion, Agencia Estatal de Investigacion and FEDER (MCIN/AEI/1 0.13039/501100011033/FEDER, UE).

The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid hydrogen–electricity Storage System (HSS) in a residential microgrid (RMG). The HEMS dispatches an electrolyzer-fuel cell-based hydrogen energy storage (ES) unit for seasonal energy shifting and an on-site battery stack for daily energy allocation against the uncertainty from the renewable energy source (RES) and demand side. The online decision-making of the proposed HEMS is realized through two parallel fuzzy logic (FL)-based controllers which are decoupled by different operating frequencies. An original local energy estimation model (LEEM) is specifically designed for the decision process of FL controllers to comprehensively evaluate the system status and quantify the electricity price expectation for the HEMS. The proposed HEMS is independent of RES prediction or load forecasting, and gives the optimal operation for HSS in separated resolutions: the hydrogen ES unit is dispatched hourly and the battery is operated every minute. The performance of the proposed method is verified by numerical experiments fed by real-world datasets. The superiority of the HEMS in expense-saving manner is validated through comparison with PSO-based day-ahead optimization methods, fuzzy logic EMS, and rule-based online EMS.