Health-conscious predictive energy management strategy for fuel cell electric trucks

This paper proposes a novel health-conscious energy management strategy for fuel cell electric trucks to address the durability and efficiency challenges of heavy-duty long-haul transportation. The proposed strategy has a dual-stage predictive control structure that enables the implementation of dynamic programming for optimal and predictive energy management. The multi-objective optimization of fuel consumption, fuel cell degradation, and battery degradation represents the main novelty of this work, making the strategy highly flexible in finding suitable trade-offs between the three optimization targets depending on the vehicle operating conditions. For example, assuming that the fuel cell and battery systems cannot be individually replaced, the proposed strategy can ensure balanced degradation, considerably extending the vehicle mileage while keeping the fuel consumption low. Another novelty of this paper is the study of the progressive degradation from “begin-of-life” to “end-of-life” conditions of the powertrain components. This analysis can have significant implications for the decision-making and design of fuel cell electric trucks because it demonstrates that fuel consumption and degradation rates considerably increase as the components age. • Novel dynamic programming formulation for health-conscious energy management strategies. • Multi-objective optimization of fuel consumption, fuel cell degradation, and battery degradation. • Study of performance deterioration due to progressive fuel cell and battery degradation. • Pareto fronts between the energy management targets in long-haul fuel cell electric trucks.