Development of a real-time link-based predictive energy management strategy for extending FCEV lifespan using an experiment-driven degradation model

Fuel cell electric vehicles (FCEVs) face durability challenges primarily due to cell degradation influenced by power variations and operational ranges. This issue can be mitigated through an energy management strategy (EMS), with many durability-focused studies employing predictive EMS (P-EMS) for high performance. However, existing strategies often rely on highly uncertain future vehicle trajectories, such as velocity or power demand, leading to a shortened horizon length and significant loss of optimality. This study proposes a novel link-based, durability-focused P-EMS optimized on a per-link basis, achieving near-optimal performance. The key innovation lies in reformulating the problem from trajectory optimization to parameter optimization, expressed as a quadratic programming (QP) problem, which enables real-time implementation. The degradation model consists of dynamic and quasi-static operations, where the quasi-static model is developed based on experimental data. A multi-objective optimal control problem is then formulated, revealing a Pareto optimal relationship between degradation and system efficiency through a dynamic programming (DP) algorithm that ensures global optimality. Building on insights from DP results, the proposed approach analytically reformulates the problem, requiring easily predictable driving parameters such as travel time and energy demand that represent link conditions. The simulation results reveal that, when prioritizing cell degradation protection, the proposed method achieves high performance comparable to DP, with a minimal loss of optimality (1.5 % in fuel economy and 6.7 % in fuel cell degradation) while showing an impressive average computational time of merely 2.5 ms.