Health- and Thermal-Aware Multiobjective Energy Management for Fuel Cell Postal Delivery Vehicle Using Velocity Preview Information

Fuel cell hybrid vehicles (FCHVs) have drawn tremendous attention due to the advantages of zero emissions. Existing energy management strategies (EMSs) typically fail to adequately address the coupled relationship between power allocation and thermal dynamics in the powertrain system, which is a gap that affects the economic and durability performance of FCHVs. To address this, this research proposes a hierarchical EMS that innovatively integrates: 1) a fuzzy-encode Markov chain (FMC) speed predictor for speed forecasting at the upper level; and 2) a multiobjective model predictive control (MPC) that optimizes system operating cost, durability, and thermal safety of battery and fuel cell systems at the lower level. In the validation phase, the impacts of different membership functions and state numbers on speed prediction accuracy are explored firstly. Then, the effects of weighting factors in multiobjective function are studied. Furthermore, an effectiveness evaluation method is set up to score each strategy with dynamic programming (DP) as the upper benchmark. The comparison with the other three benchmark strategies proves that the suggested strategy is more cost-effective, thermally safe, and life-extended, bringing an overall performance improvement of at least 20.61% and a score improvement of at least 14.02 points in the [0,100] range.