Operation and energy evaluation of diesel and hybrid trains with smart switching controls

Hybrid railway vehicle trajectory optimisation using a non‐convex function and evolutionary hybrid forecast algorithm

“…Prior research on railway vehicle optimisation [37,38,66] has indicated that there is often a significant trade-off between energy consumption and journey time during the optimisation process. Nevertheless, the author's proposed hybrid optimisation algorithm for hybrid railway vehicles effectively minimises the utilisation of power sources without compromising journey time constraints.…”

“…This study focuses on determining an optimal hybrid train trajectory for a mid‐range light hybrid rail vehicle on typical British cross‐country and intercity railway routes. The hybrid train simulator [66] is used for benchmark simulation, and the proposed algorithm can simultaneously optimise multiple hybrid train trajectories.…”

“…In this research, the case study is conducted by employing a comprehensive simulation of benchmark trajectories for the hybrid railway vehicle, utilising the hybrid train simulator developed by the author in a previous study [66]. The simulation results derived from these benchmark trajectories are subsequently fed into the novel hybrid optimisation algorithm, specifically designed to optimise the single train trajectory of the hybrid railway vehicle.…”

Prediction of the optimal hybrid train trajectory by using artificial neural network models

“…Although still in its early stages, the hybrid railway system has shown promising potential as a technology that saves energy, can interact with renewable energy sources, and generates zero carbon emissions at the point of use [4]. As fuel cost is a critical aspect to be considered in energy management, much of the research has focused on improving the fuel efficiency of railway vehicles [5].…”

“…The use of data classification technology in the railway industry is widespread, covering various areas such as maintenance, operation, and safety [27]. The author also uses large datasets from previous research [5] and is aimed to develop a new and improved custom algorithm for hybrid railway vehicles, as the conventional models may not be suitable for processing the large data sets from these vehicles. The author chose to utilise ANNs for this purpose due to their efficiency in handling large data sets, ability to self-learn, adaptivity, non-linearity, and advanced input/output mapping [28].…”