Fuel cell electric vehicles are usually hybrid vehicles requiring an energy management strategy (EMS) to determine the power split between the fuel cell system and a battery. The performance of an EMS can be improved by taking into account forecasts of the vehicle velocity. Simple estimates derived from static route information, e.g., speed limits, can already provide a significant performance increase because of being available before departure and for the entire driving mission. However, such long-term predictions can deviate considerably from the actual velocity because of dynamic influences, such as traffic, roadworks, or weather. Here, short-term predictions from vehicular communication systems provide more accurate real-time information and allow the EMS to react better to the actual driving conditions. This article proposes a predictive EMS that efficiently combines the information of long-term and short-term forecasts. Before departure, a dynamic programming algorithm optimizes the energy management based on a-priori available route data yielding a distance-based map describing the optimal cost-to-go. While driving, a model predictive controller (MPC) optimizes the energy management online considering the information of the short-term prediction and including the optimal cost-to-go as terminal cost. A computationally efficient linear MPC implementation is proposed, and the significant performance benefit over an MPC that tracks an optimized battery state-of-charge reference is demonstrated in a numerical study.

Fuel cells (FCs) are promising eco-friendly power sources. Nevertheless, there are challenges to overcome if they are to be widely deployed in areas such as degradation avoidance and control, where the knowledge of the unavailable concentrations is crucial. In this respect, observers can provide unavailable quantities based on an estimation algorithm and available measurements. This paper presents an FC concentration observer design workflow, covering the model-based design of experiments (DOE), their execution, systematic nonlinear identification, and measurement-based validation. The model-based DOE and the validation with a mass spectrometer, including dynamic operation, are unique for PEMFC observers. The workflow is demonstrated with a constrained extended Kalman filter observer on a $\mathrm {30 \text {k} \text {W} }$ polymer electrolyte membrane FC (PEMFC) test stand. A control-oriented model serves as the workflow basis, and the DOE is based on optimizing the parameter sensitivity. The test stand delivers the measurements, the parametrization comprises a sensitivity analysis, and the experimentally validated observer yields outstanding concentration estimation performance.

This paper proposes a new efficient two-step method for parametrizing control-oriented zero-dimensional physical polymer electrolyte membrane fuel cell (PEMFC) models with measured stack data. Parametrizations of these models are computationally intensive due to the numerous unknown parameters and the typically nonlinear, stiff model properties. This work reduces an existing model to decrease its stiffness for accelerated numerical simulations. Subdividing the parametrization into two consecutive subproblems (thermodynamic and electrochemical ones) reduces the solution space significantly. A parameter sensitivity analysis further reduces each sub-solution space by excluding non-significant parameters. The method results in an efficient parametrization process. The two-step approach minimizes each sub-solution space’s dimension by two-thirds, respectively three-fourths, compared to the global one. An achieved $R^2$ value between simulation and measurement of 91% on average provides the required accuracy for control-oriented models.

Polymer electrolyte membrane fuel cells (PEMFCs) are prone to membrane dehydration and liquid water flooding, negatively impacting their performance and lifetime. Therefore, PEMFCs require appropriate water management, which makes accurate water modeling indispensable. Unfortunately, available control-oriented models only replicate individual water-related aspects or use oversimplistic approximations. This paper resolves this challenge by proposing, for the first time, a control-oriented PEMFC stack model focusing on physically motivated water modeling, which covers phase change, liquid water removal, membrane water uptake, and water flooding effects on the electrochemical reaction. Parametrizing the resulting model with measurement data yielded the fitted model. The parameterized model delivers valuable insight into the water mechanisms, which were thoroughly analyzed. In summary, the proposed model enables the derivation of advanced control strategies for efficient water management and mitigation of the degradation phenomena of PEMFCs. Additionally, the model provides the required accuracy for control applications while maintaining the necessary computational efficiency.

Power hardware-in-the-loop (PHIL) testing has become indispensable for the rapid, modular, and cost-saving development of automotive components. This paper focuses on PHIL tests composed of entire powertrains that exchange speed and torque signals with vehicle simulations. Previous studies pointed out the importance of promptly following the references from the virtual simulation environment to replicate realistic driving conditions and introduced control strategies to cope with the challenges associated with this setup. However, a comprehensive comparison of the different control strategies has not yet been carried out. To fill this gap, the concepts are first investigated in-depth in simulations and are then rigorously validated on a state-of-the-art powertrain test bed under highly dynamic driving scenarios, including full-braking. Furthermore, an improvement of existing shaft torque control approaches, which are mainly based on feedforward control, is proposed to better compete with the other methods. The proposed extension shows higher resilience to low accuracy of torque actuators, while the other concepts exhibit greater robustness against time delays. The results from the direct comparisons are summarized and allow the appropriate selection of control strategies for specific use cases.