In early-stage vehicle design, there is a significant lack of knowledge about the impact of design requirements on the design of subsystems, theresulting knock-on effects between subsystems and the vehicle’s overall performance. This leads to a sub-optimal vehicle design with increaseddesign iterations. To mitigate this lack of knowledge, a cross-scalar design tool consisting of an induction motor model is presented in this paper.The tool calculates the motor’s attributes, namely its volume, mass, and the performance it can deliver to satisfy a given drive cycle’s requirements.This is achieved by breaking down the drive cycle requirements into motor parameters from which the various power losses are derived. Thesekey losses are then utilised to develop the torque/speed curve. Furthermore, it is proposed that the motor’s attributes can be used to design othersubsystems and consequently analyse their interaction effects. For example, the motor’s attributes can be used to design regenerative brakes andconsequently analyse their influence on brake wear, lifetime, and energy savings. Thus, the design tool enables the design of efficient vehicles withminimised design iterations by analysing the influence of design requirements on the subsystem’s design and the consequent interaction effectsamong the subsystems and on the vehicle’s overall performance.

Rail vehicle models have become increasingly complex, posing challenges in extracting insights using traditional model representations as they require numerous iterations to achieve a satisfactory solution. This complexity leads to high computational and time costs and possibly resulting in inefficient vehicle design. To alleviate these limitations, network models are proposed as an alternative representation in this paper. These models enable the analysis of structure, behaviour, and patterns of interactions, facilitating an understanding of knock-on effects across disciplines and subsystems. The terminology, benefits, and capabilities of network theory in early-stage vehicle design are presented in this paper, along with the aspects to consider and methods for developing network models. The applicability of network theory metrics and algorithms is demonstrated using a railway traction system example. Results indicate that the proposed representations can capture complex system knock-on effects across disciplines and subsystems.

In conventional vehicle design approaches, there is typically little understanding of the consequences of early stage design choices. This may be attributed to the conventional approach’s limitation in capturing complex interactions, further leading to increased design iterations. To overcome this, holistic multidisciplinary models were developed. However, they introduce the burden of complexity and costs because of their intricate nature. Furthermore, it is challenging to gain meaningful insights without a deeper understanding of the model’s nature and structure. Therefore, in this article, an alternative form of model representation was proposed to address these shortcomings. This was achieved by integrating two concepts: network theory and sensitivity analysis. A detailed and robust framework that represents complex multidisciplinary models as network models, reduce their complexity, and navigate insights from them, was provided. This is further demonstrated by a case study of a rail vehicle traction system including a traction motor and an inverter coupled with operational drive cycle. Among the identified 246 factors in the traction system network model, the three most influential inputs were identified for the chosen output factor of interest. Subsequently, the knock-on effects of these inputs were determined. The results indicate a significant reduction in the network graph size compared with the complete network graph of the traction system model. This indicated a significant reduction in the number of factors to consider in the analysis. This demonstrates the capability of the proposed framework to reduce the complexity of the analysis while retaining the ability to analyze intricate interaction effects.

 Vehicles are complex systems composed of interdependent subsystems, where a change in one component can propagate through others, causing knock-on effects and impacting overall performance. This interconnected nature makes early-stage design challenging due to limited knowledge of the consequences of design choices. Existing tools address different aspects of this gap in isolation and typically do not quantify the cascading changes i.e., knock-on effects or the total impact of an input on the output, highlighting the need for an integrated approach. This paper addresses these limitations by proposing a framework to quantify the magnitude and direction of the knock-on effects and consequently calculate the total impact by integrating curve fitting, sensitivity analysis, and structural equation modeling. This framework derives representative functions, quantifies the influence between every interacting factor pair, and calculates the total impact. To demonstrate the framework, a rail traction system is used as case study. Results indicate that while rated power had comparatively less impact on the output than frequency, rated power had 6 times as many paths and 1.86 times as many vertices as frequency, indicating higher knock-on effects. To validate the structural model, for each input and output, the total impact value was compared with the direct sensitivity coefficient, yielding a maximum error of 3%. Additionally, comparison with results of Global Sensitivity Analysis confirmed consistent input rankings and influence directions. This demonstrates the framework’s capability to quantify knock-on effects and total impact while preserving true input-output relationships.

The advent of silicon carbide (SiC) semiconductors in electric traction enables several benefits, including the shift to passive cooling. However, it requires a conjugate heat transfer analysis to understand the temperature distribution and variation. While steady-state solutions exist, transient conditions in rail vehicles remain challenging. This paper develops two analytical models to predict temperature distribution and variation, validated against numerical simulations. An electric motor model estimates power losses in the converter, defining heat dissipation. The complete model is tested under realistic drive cycles, linking operational conditions to power losses and free flow speed. The results show the model effectively captures temperature variations, with higher losses during acceleration and larger temperature surges of around 70 K at lower speeds. Furthermore, the temperature at the junction was observed to be 20 K higher than at the base position and to exceed 420 K at a more downstream location. Thus, the proposed method captures the temperature variations considering different physical effects with reasonable accuracy and significantly faster computation times than transient numerical simulations.

Vehicles are complex systems composed of multiple interdependent subsystems. A design change in one subsystem can lead to either beneficial or detrimental effects on others, making it essential to understand how design choices propagate through the system. This paper focuses on the traction chain in rail vehicles, specifically the interaction between two critical subsystems: the traction motor and the inverter. Since the inverter supplies the current and voltage required by the motor, changes in motor design can affect the inverter’s thermal behaviour. We analyse this interaction to understand how motor design influences the temperature evolution of inverter power electronic components. To achieve this, we apply a framework that integrates network theory, structural equation modelling (SEM), and sensitivity analysis. First, analytical models of a three-phase induction motor and an inverter thermal model are developed. A reverse breadth-first search is used to identify all input parameters that influence the inverter temperature. Global sensitivity analysis (GSA) isolates the most influential inputs, enabling the construction of a reduced network graph. Then, SEM and local sensitivity analysis (LSA) are applied to quantify the relationship between motor design parameters and inverter temperature, yielding a coefficient that captures the strength of the dependency. This approach provides an alternative representation of the multidisciplinary interactions between subsystems. It also helps identify key change propagation paths, making it easier for designers to anticipate and manage the consequences of design changes. By reducing analytical complexity and clarifying subsystem interdependencies, the framework supports more efficient early-stage design, potentially reducing the number of iterations needed to reach a satisfactory solution.