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An energy-efficient train trajectory describing the motion of a single train can be used as an input to a driver guidance system or to an automatic train control system. The solution for the best trajectory is subject to certain operational, geographic, and physical constraints. There are two types of strategies commonly applied to obtain the energy-efficient trajectory. One is to allow the train to coast, thus using its available time margin to save energy. The other one is to control the speed dynamically while maintaining the required journey time. This paper proposes a distance-based train trajectory searching model, upon which three optimization algorithms are applied to search for the optimum train speed trajectory. Instead of searching for a detailed complicated control input for the train traction system, this model tries to obtain the speed level at each preset position along the journey. Three commonly adopted algorithms are extensively studied in a comparative style. It is found that the ant colony optimization (ACO) algorithm obtains better balance between stability and the quality of the results, in comparison with the genetic algorithm (GA). For offline applications, the additional computational effort required by dynamic programming (DP) is outweighed by the quality of the solution. It is recommended that multiple algorithms should be used to identify the optimum single-train trajectory and to improve the robustness of searched results.

Concerns over future energy security, energy costs, and competitiveness with other modes have prompted the railway industry to search for cost-effective energy efficient traction solutions which will ensure continuing business feasibility. For non-electrified routes, where the business case for electrification is unfavourable, traction is usually provided by diesel fuel combustion. Hybridization offers the potential to achieve a step change in energy efficiency. This article presents an analysis of the potential benefits of hybridization for rail vehicles. The performance requirements of the energy storage device in a hybrid rail vehicle which is storage device dominant are derived. A rail vehicle simulator has been developed in order to compute the drive train duty cycle in typical high-speed and commuter passenger services. The outputs from the simulator have been inputted into a series hybrid model, which has been optimized to preserve the state of charge of the energy storage device over a single typical rail journey. The analysis suggests the energy savings of up to 28 per cent for high-speed intercity vehicles and 35 per cent for commuter vehicles are achievable with practical system components. A sensitivity analysis exploring the effect of the inherent efficiency of the regenerative braking capability and the energy storage device revealed that primary energy savings are only realized with in/out storage efficiencies of greater than ∼40 per cent.

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This study presents the recent application of energy storage devices in electrified railways, especially batteries, flywheels, electric double layer capacitors and hybrid energy storage devices. The storage and reuse of regenerative braking energy is managed by energy storage devices depending on the purpose of each system. The advantages resulting from the use of energy storage devices are presented by observing the results of both verification tests and practical applications in passenger services. Several real installations of energy storage for railways are shown and compared by using the Ragone plot. The effect of the use of energy storage devices on electrified railways of the future is discussed. Finally, a discussion on the recent applications and developments of energy storage devices is presented in this study. The effective use of energy storage devices is characterised on the basis of the specific applications and current trends of the research undertaken by public bodies and manufacturers.