The freight bogie type Y-25 is the most used running gear in European freight wagons since its standardisation in 1967. Nonetheless, apart from several studies mostly focused on the primary suspension to improve the running characteristics, its design has barely changed over time. Up to date, there is rather little information and analyses related to the secondary suspension, limited to some standardised dimensional characteristics of its components, known as side bearers. In order to enhance the knowledge of these elements and their influence on the wagon operation, an analysis of the effect of the side bearers preload on different parameters related to the dynamic response of the wagon, such as the axle lateral force, wheel load transfer or bogie yaw velocity, is presented in this article for a Y-25 bogie. To this aim, a multibody model of a freight wagon with two Y-25 bogies is developed using Adams® and verified against the current normative for European freight wagons. The verified model is later subjected to varying suspension preloads, and the dynamic-related parameters for different wagon carrying loads, speed and track layouts are analysed. The results obtained provide relevant information that contributes to increase the understanding of the secondary suspension elements in Y-25 bogies.

A model of the running resistance of a locomotive powered by liquefied natural gas is proposed. The model uses operating data and does not require specific instrumentation. The input data consists of a succession of instantaneous speed and electrical power measurements of a diesel-electric locomotive. The slope at each point along the route is unknown and the speed is measured with a digital sensor that quantifies the signal, so acceleration estimates are also unreliable. From these data, a weakly supervised learning problem is defined that makes use of a fuzzy rule-based system to indirectly predict the effective slope, and is able to estimate the power demand of the locomotive with a margin of error close to 5%.

A new methodology for constructing stability maps (phase-plane analysis) is presented and validated for application to complex multibody vehicle models implemented in Multibody Dynamics simulation software (Adams®). Traditional methodologies are developed to be applied to explicit mathematical models. Given the complexity of some special multibody systems, particularly in vehicle dynamics, simplifications are needed to apply this stability analysis technique. The main limitation when using simplified models is the need to neglect components which could have a significant influence on the dynamic behavior of the system and therefore on its stability. In the proposed methodology it is not necessary to have access to explicit mathematical models of multibody systems. Thus, the stability map of a vehicle model can be constructed by considering highly nonlinear dynamic elements, such as tires and silent-blocks components, modeled using the nonlinear finite element technique.

In order to reduce the environmental impact of fossil fuel consumption, the rail transport sector is analysing various alternatives for non-electrified lines. As an alternative to diesel, this study analyses the use of Liquefied Natural Gas (LNG) in rail traction. A predictive model is developed that estimates emissions on different routes. The model is fitted with real data obtained in pilot tests. In these tests, a train with two engines, one diesel and one LNG, was used. The methodology is applied to assess the impact on consumption and emissions of the two fuels on a narrow-gauge commuter line. A significant improvement is observed in some indicators, while in others the differences are minor. The conclusions that can be drawn are that operational CO2 (greenhouse gas) emissions are lower in the LNG engine than in the diesel engine; CO emissions are lower in the diesel engine and emissions of other pollutants (nitrogen oxide and particulate matter) are higher in the diesel engine by several orders of magnitude. Keywords: Alternative fuel; Liquefied Natural Gas; Energy consumption; Emissions; Railways

The global challenge of reducing pollutant and greenhouse gas emissions has forced the development of alternatives to traditional internal combustion engine vehicles, such as electric or hybrid vehicles. Electric engines are the most efficient for delivery trucks or city buses. Their acceleration and deceleration patterns make them inefficient for the use of internal combustion engines. However, their range and purchase cost are the main factors limiting their use in these applications. The range and acquisition cost of an electric vehicle are mainly related to the energy storage system. Therefore, the optimal size of the battery pack should be considered as a design objective when its application is known. This paper presents a methodology to optimize the battery pack of an electric vehicle based on a given travel distance in a target time. Therefore, it would be applicable to delivery vehicles, buses and any vehicle whose route and travel time are known in advance. The proposed methodology allows minimizing energy consumption by determining the optimal gear ratio for a given route, setting the travel time as a target. A complete vehicle model and a multi-objective genetic algorithm are used for this purpose.