In view of the complex working conditions of the hybrid power system of rubber-tyred gantry (Hy-RTG) crane and the limitations of traditional parameter matching methods, this study proposes a working condition-based parameter matching and optimization method for the hybrid power system of port gantry cranes to address the parameter matching and efficiency optimization issues. Firstly, by constructing the house of quantity model and comprehensively applying the analytic hierarchy process, entropy method and grey relational method, the weights of technical characteristic indicators oriented towards the operational demands of the port are quantitatively determined, and the core indicators such as the dynamic performance, energy consumption are clarified. Then, a joint simulation model of the Hy-RTG crane was established based on Matlab-Simulink and Cruise software. 40 sets of orthogonal experiments were designed to analyze the influence laws of core parameters such as engine power, drive motor power, power battery capacity and supercapacitor of the range-extending system on the system performance. The significant coupling effects among various technical parameters were discussed. Then, the particle swarm optimization algorithm is adopted to conduct offline optimization for the core parameters with higher sensitivity. The approximately optimal parameter combination scheme was further determined from the perspective of multiple objectives. Finally, the simulation experiment verification results show that the optimized Hy-RTG crane has improved dynamic performance, reduced procurement and operation costs, improved economic performance, increased continuous operation time, and improved comprehensive performance indicators. The research results provide theoretical and practical guidance for the optimal design of Hy-RTG crane and contribute to the green and efficient development of port lifting equipment.

To address energy management challenges for intelligent connected automated range-extended electric vehicles under vehicle-road cooperative environments, a hierarchical energy management strategy (EMS) based on speed prediction is proposed from the perspective of multi-objective optimization (MOO), with comprehensive system performance being significantly enhanced. Focusing on connected car-following scenarios, acceleration sequence prediction is performed based on Kalman filtering and preceding vehicle acceleration. A dual-layer optimization strategy is subsequently developed: in the upper layer, optimal speed curves are planned based on road network topology and preceding vehicle trajectories, while in the lower layer, coordinated multi-power source allocation is achieved through EMS_MPC-P, a Bayesian-optimized model predictive EMS based on Pontryagin’ s minimum principle (PMP). A MOO model is ultimately formulated to enhance comprehensive system performance. Simulation and bench test results demonstrate that with SoC₀ = 0.4, 7.69% and 5.13% improvement in fuel economy is achieved by EMS_MPC-P compared to the charge depleting-charge sustaining (CD-CS) method and the charge depleting-blend (CD-Blend) method. Travel time reductions of 62.2% and 58.7% are observed versus CD-CS and CD-Blend. Battery lifespan degradation is mitigated by 16.18% and 5.89% relative to CD-CS and CD-Blend, demonstrating the method’s marked advantages in improving traffic efficiency, safety, battery life maintenance, and fuel economy. This study not only establishes a technical paradigm with theoretical depth and engineering applicability for EMS, but also quantitatively reveals intrinsic mechanisms underlying long-term prediction accuracy enhancement through data analysis, providing critical guidance for future vehicle–road–cloud collaborative system development.