One of the main challenges for the energy management of heavy-duty fuel cell/battery hybrid vehicles is to control the battery charge within safe operating levels without hindering fuel efficiency. This article proposes a novel method that uses basic route information from navigation systems to implement an optimal and predictive energy management scheme to improve battery charge control, fuel economy, and fuel cell system lifetime. Indeed, it is possible to obtain a rough estimate of the electric load profile using elevation and speed forecasts and optimize the power split before starting the trip. The result of this offline optimization is saved as a location-based battery state of charge (SoC) reference for the online power-split strategy. The novelty of this work is related to the formulation of a quadratic program to generate the predictive SoC reference by optimizing the hydrogen economy while respecting the battery charge constraints. Afterward, a simple but effective online energy management strategy is used to track the predictive references during the simulations. The use of real-world driving cycles is essential to demonstrate the benefits of the proposed method. In particular, it is critical to consider the road elevation because it highly influences the power demand of heavy-duty vehicles. The simulation results prove that using the predictive SoC reference significantly improves the hydrogen economy and battery charge control compared to a non-predictive energy management scheme. In addition, the high-power operation time of the fuel cell system is reduced, resulting in slower system degradation compared to the non-predictive strategy. The effectiveness and robustness of the proposed method are tested against 1750 hours of real-world driving scenarios, considering uncertain speed and vehicle mass information for the reference generation. Statistical and global analyses of the results prove that the implementation of the predictive SoC reference generation method is highly beneficial for the performance of heavy-duty fuel cell vehicles.

The use of ultracapacitors in fuel cell electric vehicles holds the potential to promote their advancement by significantly extending the fuel cell lifetime. However, the design of suitable control strategies is critical to take full advantage of the additional degree of freedom and limit the principal causes of fuel cell degradation, such as transient operation and start-up/shut-down cycles. This paper studies the energy management of fuel cell-ultracapacitor-battery hybrid vehicles, investigating different criteria for fuel cell operation to find the best trade-off between hydrogen consumption and the number of start-up/shut-down cycles. In particular, the investigated control criteria are fixed-setpoint and charge-balancing operation. A third strategy is considered as a reference for potential performance improvements of predictive energy management. The study reveals that, in fuel cell-ultracapacitor-battery vehicles, the only significant source of fuel cell degradation are start-up/shut-down cycles. Here, harmful transients are limited because ultracapacitors enable stationary fuel cell operation without compromising battery life. Moreover, the investigation shows that the ultracapacitor and battery systems can sustain short driving cycles alone, avoiding fuel cell starts to preserve its life. The simulation results indicate that the fixed-setpoint strategy yields a better trade-off between hydrogen consumption and start-up/shut-down cycles than the charge-balancing one. Additionally, the study demonstrates that a predictive energy management strategy can significantly reduce degradation without hindering system efficiency.

The design of a combined observer for battery state of charge (SoC) and state of health (SoH) using data-driven models is addressed. Knowledge of both SoC and SoH is an important topic in battery management system design for (hybrid) electrical vehicles. The proposed cascaded observer is designed for estimating SoC and SoH during normal vehicle operation based on measured signals which are directly available in the car. For an SoC estimation, that is accurate in the entire battery lifespan, the underlying battery model has to be adapted based on the estimated SoH. For that purpose, ageing data analysis using data-driven models and the associated model adaptation is discussed. A cascaded observer structure for combined estimation of SoC and SoH is presented and the performance of the proposed concepts is demonstrated by means of battery ageing data covering the entire lifetime of a lithium-ion cell.

Pressure and mass flow control in fuel cell air supply systems highly affect the dynamic performance, reliability, and efficiency of proton exchange membrane fuel cell vehicles (FCV). However, the coupling effect between pressure and mass flow makes their control difficult and can seriously compromise the performance of proton exchange membrane fuel cells. In this paper, the optimization diagonal matrix decoupling (DMD) is proposed to avoid the occurrence of detrimental operating conditions and improve performance. This study includes data-driven modeling of the air supply system with transfer functions and the analysis of the coupling mechanisms between pressure and mass flow. The simulation results show that the proposed strategy has good disturbance rejection and low coupling between flow and pressure. Compared with conventional DMD, the standard deviation of the relative control error of flow and pressure can be reduced by 9.7%, and 14.4% in the proposed strategy. The new contribution of this paper is to reveal the coupling mechanism, which can be used to guide the design of decoupling control strategies designed for air supply systems of fuel cell engines in FCV.

Fuel cell systems are regarded as a promising candidate in replacing the internal combustion engine as a renewable and emission free alternative in automotive applications. However, the operation of a fuel cell stack fulfilling transient power-demands poses significant challenges. Efficiency is to be maximized while adhering to critical constraints, avoiding adverse operational conditions (fuel starvation, membrane flooding or drying, etc.) and mitigating degradation as to increase the life-time of the stack. Owing to this complexity, advanced model-based diagnostic and control methods are increasingly investigated. In this work, a real time stack model is presented and its experimental parameterization is discussed. Furthermore, the stack model is integrated in a system simulation, where the compressor dynamics, the feedback controls for the hydrogen injection and back-pressure valve actuation, and the purging strategy are considered. The resulting system simulation, driven by the set-point values of the operating strategy is evaluated and validated on experimental data obtained from a fuel cell vehicle during on-road operation. It will be shown how the internal states of the fuel cell simulation evolve during the transient operation of the fuel cell vehicle. The measurement data, for which this analysis is conducted, stem from a fuel cell research and demonstrator vehicle, developed by a consortium of several academic and industrial partners under the lead of AVL List GmbH.