Investigation of the real-time feasibility of NMPC for air-path control in automotive fuel cell systems

Abstract:
This paper presents the development and evaluation of a nonlinear model predictive control (NMPC) algorithm in the context of air-path control for automotive fuel cell hybrid systems, with a focus on its real-time performance. The study is motivated by the need to establish real-time capability as a key criterion for the practical deployment of such advanced control systems. Our approach involves the design of an optimal control problem, followed by its efficient conversion into a nonlinear program, to which the sequential quadratic programming method is applied. The resulting quadratic programs are solved by the open-source numerical solver HPIPM. A notable outcome of this study is the controller’s mean turnaround time of 10.3 ms in numerical simulations on embedded hardware, utilizing a Gauss-Newton Hessian approximation. While this marginally exceeds the desired sampling time of 10 ms, our results demonstrate the potential of NMPC as a viable solution for managing the complexities inherent in automotive fuel cell hybrid systems, potentially contributing to enhance their operational reliability, efficiency and durability.