The Centre for ECO2 Vehicle Design (ECO2) is a Swedish multilateral competence centre based at KTH Royal Institute of Technology with a focus on the development of resource effcient vehicle for sustainable transport. This report covers the first 18 months of ECO2’s new agreement period (Jul 2024 – Dec 2029), marking a period of organizational renewal, research excellence, and strategic positioning for future growth. During this period, ECO2 successfully completed three PhD defences, produced 33 peer-reviewed journal publications, 50 conference papers, and strengthened its research portfolio with 12 projects multiple project applications. The new five-year centre agreement was finalized, securing partner and KTH financing co-financing and establishing enhanced governance structures. ther highlights include the holding of the second Resource Efficient Vehicles conference in Graz with 10 ECO2 contributions, establishing a collaboration agreement with Fraunhofer IAO, and expanding industrial partnerships through new doctoral projects. ECO2’s research continues to address critical attributes of vehicles as part the transport system – effciency, accessibility and resilience, which contribute sustainable transport and other major societal challenges.

Decarbonization of road transportation in Europe is slow, while interest in emerging technologies and operating strategies with environmental and operational benefits is increasing.

To assess their impacts, vehicle routing models support strategic fleet assessment, yet many rely on simplified energy models and neglect key vehicle- and system-level factors, limiting evaluation of technologies and operating strategies.

We develop a two-stage optimization framework that determines fleet size, mix, and routes for simultaneous pickup and delivery, estimating realistic energy use on road networks. The energy model is evaluated against electric-truck data, and the framework quantifies trade-offs among driverless multipurpose, human-driven combustion, and battery-electric fleets in combined heavy-goods freight-and-waste city logistics.

Although the energy model overestimates absolute energy use, it reproduces the main subroute ranking and captures fleet- and network-specific effects. The framework supports energy-informed strategic, tactical, and operational decisions; key limitations are scalability and that results reflect automation rather than full multipurpose capability.

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.

The production of a heavy-duty road vehicle is a major contributor to the overall financial cost and environmental impact of the vehicle. In recent times, closing the material loops using circular practices has emerged as a promising approach to reduce the environmental impact of the vehicle production. The most common circular practice is material recycling, but this is a low-value process, as any embodied value in the product apart from the material refinement is lost and, even then, the recycling process might be cumbersome if the product is composed of multiple materials that cannot be easily separated. An upcoming and promising approach to circularity is remanufacturing where a product is disassembled and the constituent components are cleaned and inspected.  The components which pass the inspection are then reused in new products. This preserves the value in the reused products and not only reduces the consumption of raw materials, but also manufacturing effort. In order for the remanufacturing process to be worthwhile, some conditions need to be satisfied. Firstly, the product needs to be valuable enough for the remanufacturing effort to make economic sense. Secondly, the state of a product that is returned from a customer must be able to be ascertained with a certain degree of confidence in a quick manner. One way to evaluate the state of a returned product is by using the historical usage data of the vehicle it comes from. The goal of the work presented here is to study the feasibility of remanufacturing for different heavy-duty road vehicle components based on the economical benefits as well as the availability of data for the component in question, as well as identifying the relevant data for evaluating the remanufacturability of the component in question.

Driverless multipurpose vehicles (DMVs) are an emerging vehicle concept for urban heavy-duty transport. However, little is known about their effect on urban road transport systems. Thus, the aim of this study is to analyse the total fleet energy consumption of DMVs for specific transport operations in urban logistics compared to heavy- duty battery and combustion vehicles. A novel electric vehicle routing problem was used to simulate in total 96 case-studies of operations with varying network and vehicle fleet properties. We found that the combustion vehicle fleets consumed significantly more energy for the same operation compared to the electric vehicle fleets. Although the DMV fleet and battery electric vehicle fleet showcased similar energy consumption for most case-studies, there were several operations where the DMV fleet consumed less energy and required a smaller fleet size. This study highlights the potential benefits of DMV fleets in urban logistics operations in terms of reducing total fleet energy consumption and fleet size.

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 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.

This study introduces an agent-specific assessment method of traffic noise exposure in agent mobility simulations. The assessment is achieved through a combination of an energy-based noise exposure impact assessment using noise exposure cost, and the state-of-the-art traffic noise prediction tool NoiseModelling coupled with the activity-based agent mobility simulation software MATSim. The agent-specific noise exposure cost is a measure to evaluate how the noise emissions from the transport of agents relate to the noise-related impact on other agents performing stationary activities. By introducing an agent-specific level, each agent’s individual responsibility for the noise exposure may be estimated. The potential of the agent-specific noise exposure cost concept, combined with the MATSim-NoiseModelling framework, is illustrated through a case study, applying activity-based agent mobility simulations across Nantes, France. The results of the case study highlight, among other considerations, the insights that an agent-specific, activity-based noise exposure cost approach provides by visualizing the noise exposure ”footprint” resulting from an agent’s transportation activities.

Despite the increasing availability of tools and methods for sustainable and circular product design (DfS), their uptake in practice is slow. This is also true in the automotive industry, where DfS is an important measure for addressing the industry's negative environmental and social impacts. To facilitate DfS implementation, this paper uses an analytic hierarchy process (AHP) and offers, for the first time, a classification and prioritisation of the barriers that need to be overcome when implementing DfS into vehicle development processes. Based on a systematic literature review and on an expert workshop, the top 15 DfS barrier factors were derived and divided equally into five groups, following a multi-level structure. These factors and groups formed the input for a survey-based analytic hierarchy process with 38 European industry experts. The results show that strategic issues are the most important barriers, followed by the group of operational, personal, external, and tool-related barriers. Among the 15 barrier factors identified, the top five were (1) an unclear link to profitability, (2) lack of top management support, (3) difficulties in handling trade-offs, (4) high operational costs, and (5) a lack of integration of DfS into corporate strategy. The results indicate that while external constraints already exert pressure on automotive companies, they still face particular challenges when attempting to integrate sustainability into corporate strategies and in transferring such strategies to DfS activities at the operational level. The study results may be used to inform managerial policy and further research.