Crayfish-Optimized Adaptive Equivalent Consumption Minimization Strategy for Medium-Duty Commercial Vehicles

Under China’s dual-carbon policy, medium-duty commercial vehicles (MDCVs)—widely used in urban distribution with high load fluctuation and long operating hours—are key to transportation energy conservation and emission reduction. Optimizing powertrain parameters and energy management is essential for fuel-cell MDCVs. However, traditional powertrain parameter selection relies on fixed thresholds and lacks optimization, while the equivalent consumption minimization strategy (ECMS) suffers from poor driving cycle adaptability despite addressing hydrogen consumption and online application challenges. To overcome these issues, this study proposes an innovative approach for fuel cell-powered MDCVs: a driving cycle model was constructed based on hydrogen consumption and fuel cell degradation rates. Subsequently, the powertrain system parameters were optimized, culminating in the development of an adaptive ECMS (A-ECMS). Specifically, the method includes: (1) a driving cycle construction approach analyzing driving cycle clustering’s impact on adaptive control parameters; (2) a powertrain parameter optimization method considering vehicle performance under synthetic driving cycles; and (3) an A-ECMS enhanced by a crayfish optimization algorithm (COA) to improve driving cycle adaptability. Simulations show that A-ECMS achieves hydrogen consumption close to the dynamic programming algorithm (DP) optimum, reducing consumption by 2.12% and 1.45% compared to traditional ECMS under synthetic and World Transient Vehicle Cycle (WTVC) cycles, significantly improving MDCV economy.