Search Results (3)

Railway catenary maintenance vehicles are essential for ensuring the safety and efficiency of electrified railway systems. The implementation of pre-optimized speed profiles significantly reduces the energy consumption while improving key operational performance metrics, such as ride comfort, punctuality, and safety. This study introduces a novel multi-objective optimization method that optimizes the speed profile in scenarios in which railway catenary maintenance vehicles are performing operations on line sections. Initially, a multi-objective optimization model is developed based on a four-stage operational strategy. Subsequently, the enhanced selection strategy of the Non-Dominated Sorting Genetic Algorithm III (ESS-NSGA-III) algorithm is proposed to refine the mating and environmental selection processes. Finally, the effectiveness of the proposed method is validated using the Huoqiu-Caomiao section of the Fuyang-Lu’an Railway in China. A comparative analysis demonstrates that the ESS-NSGA-III algorithm outperforms NSGA-III and NSGA-II in terms of the diversity and convergence of the solution set. Specifically, the Hypervolume (HV) index improves by 0.77% and 4.12% compared to NSGA-III and NSGA-II, respectively. Moreover, the results highlight the advantages of the proposed method based on a comparison of three alternative operational strategies. Compared to the minimum running time strategy, the punctual and delayed strategies achieve energy consumption reductions of 29.51% and 52.86%, respectively. These results validate the algorithm’s capability to provide valuable insights for practical applications. Full article

A full electrification of many local railway lines is often not feasible or sustainable in terms of construction and maintenance costs or alternatively for the presence of additional constraints and limitations deriving from environmental or infrastructural limitations. Battery Operated or other kind of hybrid solutions powertrains are currently proposed as sustainable alternatives to Internal combustion engines for the propulsion of rolling stock on not electrified lines. In this work, authors propose the adoption of a partial electrification of lines to assure higher performances and reliability of battery-operated rolling stock designed to be recharged and feed using standard technologies such as pantographs gathering power from suspended catenaries. This innovative solution is designed and sized for a vehicle inspired from an existing one and simulated for two different existing lines, also proposing an optimal distribution of electrified sections dedicated to train recharge. This Case Study is simulated considering some possible applications to some existing railway lines in Italy. Full article

Overhead catenary system (OCS) automatic detection is of important significance for the safe operation and maintenance of electrified railways. The vehicle-borne mobile mapping system (VMMS) may significantly improve the data acquisition. This paper proposes a VMMS-based framework to realize the automatic detection and modelling of OCS. The proposed framework performed semantic segmentation, model reconstruction and geometric parameters detection based on LiDAR point cloud using VMMS. Firstly, an enhanced VMMS is designed for accurate data generation. Secondly, an automatic searching method based on a two-level stereo frame is designed to filter the irrelevant non-OCS point cloud. Then, a deep learning network based on multi-scale feature fusion and an attention mechanism (MFF_A) is trained for semantic segmentation on a catenary facility. Finally, the 3D modelling is performed based on the OCS segmentation result, and geometric parameters are then extracted. The experimental case study was conducted on a 100 km high-speed railway in Guangxi, China. The experimental results show that the proposed framework has a better accuracy of 96.37%, outperforming other state-of-art methods for segmentation. Compared with traditional manual laser measurement, the proposed framework can achieve a trustable accuracy within 10 mm for OCS geometric parameter detection. Full article