Predictive Control Framework for Thermal Management of Automotive Fuel Cell Systems at High Ambient Temperatures

Environmental conditions have a significant effect on the performance of fuel cell systems. This paper studies the vehicle hydrogen consumption, the thermal management system, and the thermal loads of an automotive fuel cell system. A predictive control framework for thermal management is investigated to minimize the overall hydrogen consumption. Initially, a numerical modeling approach for the automotive fuel cell system is presented from electrochemical and thermal perspectives. Then, the problem formulation related to the thermal management strategy is presented and solved with an optimization method based on dynamic programming (DP). The implemented DP exploits the a priori knowledge of the driving mission to appropriately control the fuel cell system gross power and the operation of the radiator fan, the coolant pump, and the compressor. Optimization constraints involve maintaining the fuel cell stack temperature below the operational limit and avoiding the thermal system from being activated when the vehicle is at rest. The fuel cell system is tested while the vehicle performs different numbers of repetitions of the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) at high ambient temperature. Using the proposed predictive control framework for thermal management, results demonstrate that an average 62.5% to 63.0% efficiency can be attained by the fuel cell stack in extreme ambient conditions both in short distance and long distance driving missions.