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.

Well-designed energy management strategies are essential for the good operation of Hybrid Electric Vehicles (HEVs) in terms of fuel economy and pollutant emissions reduction, regardless of the specific powertrain architecture. The goal of this paper is to propose two innovative supervisory control strategies for HEVs derived from different optimization algorithms and to assess HEVs’ fuel consumption reduction (compared to conventional vehicles). These approaches are derived from the literature and modified by the authors to present novel algorithms for the optimization problem. One is based on Dynamic Programming (DP), here referred to as the Forward Approach to Dynamic Programming (FADP) and introduces a different implementation of the DP to achieve computational and accuracy benefits. The other is based on the Equivalent Consumption Minimization Strategy (ECMS) approach, and it adapts to the latest driving conditions using information gathered in a finite-length backward-looking horizon. These techniques are used to achieve the optimal power share between the thermal engine and the battery of a parallel HEV. Their performances are compared and analysed in terms of achieved fuel economy and computational time with respect to conventional DP and Pontryagin’s Minimum Principle (PMP) approaches.