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.

Road traffic noise exposure assessment typically relies on aggregated traffic flow data, which prevents the estimation of high-temporal-resolution noise indicators increasingly recognized as important for health impact studies. To bridge this gap, this research proposes two stochastic disaggregation methods that reconstruct refined vehicle kinematics from aggregated traffic flows, enabling 1-s resolution noise estimation comparable to computationally intensive microscopic traffic modelling chains. Using SUMO microscopic simulation as reference, the disaggregation methods are evaluated in a dense urban area, in Stockholm’s Södermalm Island. The resulting acoustic indicators calculated, including LAeq,1h, LA10,1h, LA1,1h, and LAeq,1s, show estimates comparable to those obtained from the microscopic traffic noise modelling chain. Robustness and sensitivity analyses show that the proposed methods maintain stable performance even with reduced input data granularity. The proposed methods offer a practical intermediate solution between static annual noise maps and detailed microscopic simulations, enabling cost-effective dynamic noise exposure assessment at an urban scale.

Parametric model order reduction by matrix interpolation allows for efficient prediction of the behavior of dynamic systems without requiring knowledge about the underlying parametric dependency. Within this approach, reduced models are first sampled and then made consistent with each other by transforming the underlying reduced bases. Finally, the transformed reduced operators can be interpolated to predict reduced models for queried parameter points. However, the accuracy of the predicted reduced model strongly depends on the similarity of the sampled reduced bases. If the local reduced bases change significantly over the parameter space, inconsistencies are introduced in the training data for the matrix interpolation. These strong changes in the reduced bases can occur due to the model order reduction method used, a change of the system's dynamics with a change of the parameters, and mode switching and truncation. In this paper, individual approaches for removing these inconsistencies are extended and combined into one general framework to simultaneously treat multiple sources of inconsistency. For that, modal truncation is used for the reduction, an adaptive sampling of the parameter space is performed, and eventually, the parameter space is partitioned into regions in which all local reduced bases are consistent with those of their neighboring samples within the same region. The proposed framework is applied to a cantilever Timoshenko beam and the Kelvin cell for one- to three-dimensional parameter spaces. Compared to the original version of parametric model order reduction by matrix interpolation and an existing method for inconsistency removal, the proposed framework leads to parametric reduced models with significantly smaller errors.

The transition from light internal combustion engine (ICE) vehicles, such as cars and vans, to light electric vehicles (EVs) presents an opportunity to reduce road traffic noise exposure in urban environments, which still needs to be quantified. Although the noise emissions of light ICE vehicles are generally well understood, including during acceleration and deceleration, the noise emitted by light EVs has so far not been studied as thoroughly, in particular during acceleration. This study thus proposes a correction model for the noise emissions of light EVs during acceleration, based on pass-by measurements under reference conditions. Data were collected for 6 vehicle models at both steady speed and full acceleration. The difference in noise levels between these two conditions was analysed to develop the correction model. This correction model accounts for both speed and acceleration at an octave-band level. The resulting model shows that acceleration has no impact on the noise emissions of light EVs in the 63 and 125 Hz octave bands, and that acceleration may increase the overall A-weighted emissions of a light EV by up to 5 dBA, at 20 km/h. Furthermore, the analysis suggests that deceleration does not increase noise emissions for light EVs. This contribution paves the way for the integration of EV-specific noise emissions into noise exposure assessment frameworks, enabling a more comprehensive understanding of the potential benefits associated with the transition towards EVs.

Wave-based structural identification for real honeycomb sandwich structures has become an important research focus. However, most existing wave-based identification methods suffers from experimental uncertainties and a limited frequency range of applicability. To this end, we present a new wave-based structural identification framework, which includes two promising material identification methods – linear and nonlinear – suitable for honeycomb sandwich structures. The advantages of the identification process are reflected on two aspects: Firstly, the Algebraic Wavenumber Identification (AWI) technique reliably extracts complex wavenumbers over a wide frequency range under stochastic conditions, serving as input for the identification process. Secondly, a novel frequency-dependent, stepwise estimation strategy is proposed for honeycomb sandwich structures, greatly enhancing the precision of material parameter determination. Noteworthy, the proposed structural identifications enable the recovery of both equivalent dynamic and static mechanical properties. The experimental applications on a real beam, plate, and shell are presented. Key results show that (1) The proposed stepwise strategy reduces the relative error of wavenumbers of the tested beam to below 3.5%, improving parameter accuracy and ensuring estimation success; (2) For the tested plate, the estimated Young's modulus of skins, shear modulus of the core, and dynamic Hooke's matrix demonstrate satisfied precision; (3) It is the first to extract mechanical parameters of real curved structures using wave-based propagation parameters.

Electric vehicles (EVs) are expected to reduce urban noise pollution due to their quieter operation at low speeds. However, research has shown that EVs produce significant additional noise during acceleration, even at low speeds, primarily due to tyre–road interaction under high torque loads. This study investigates the impact of acceleration noise on two key exposure indicators—the equivalent continuous sound level (LAeq) and the number of noise events—using microscopic traffic simulations coupled with noise emission and propagation models. Acceleration noise was modelled using correction terms for both internal combustion engine (ICE) vehicles and EVs, based on recent literature. Simulations were performed on a real-world microscopic traffic model of Tartu (Estonia), and the fraction of EVs in the fleet was varied. Results show that neglecting acceleration noise can lead to underestimating LAeq by up to 4.3 dBA and missing close to half of noise events, particularly in electric fleets. The influence of acceleration noise is more pronounced at louder locations and for high-exceedance noise events. These findings highlight the need to integrate acceleration noise into noise exposure assessments, especially as EV penetration increases in urban traffic.

Accurate real material modeling is essential for structural dynamic analysis and design. Reliable structural parameters estimation, involving geometric and material parameters, is a key prerequisite, yet many existing methods primarily address homogenized material properties, which is inadequate for multilayer composites with complex geometrical core. To this end, this paper introduces a robust wave-based approach to structural parameter identification of individual layers, using only full-field displacement data. Specifically, the Algebraic K-Space Identification 2D technique (AKSI 2D) initially extracts wavenumber space (k-space) from measured structural responses, while surrogate optimization subsequently aligns this experimental k-space with the Wave Finite Element Method (WFEM)-derived numerical k-space to estimate structural parameters. The superiority of the proposed identification method stems from: (1) the ability of the AKSI 2D to automatically and accurately identify wavenumbers in any wave propagation direction from displacement fields on 2D grids, even in noisy environments, eliminating the need for complex filtering and specific point layouts; (2) the capacity of the WFEM in modeling wave propagation within multilayer structures with complex geometries, using unit cell-based operations within finite element software; and (3) the efficiency of the surrogate optimization in solving high-dimensional problems by finding the global minimum with high computational efficiency. To validate the accuracy of the proposed method, the structural parameters of each layer in two numerical cases, a four-layer laminated carbon fiber panel and a kelvin cell-based sandwich composite panel, are estimated. The inverted structural parameters show good agreement with the reference values, with an averaged relative error of less than 3.5%, even when a high level of white noise is added to the simulated displacement field. In addition, the structural parameters of a real parallelogram core sandwich panel is updated experimentally. These studies confirm that the proposed approach aligns with the intuitive decision-making of structural engineers for material characterization and modeling, offering adaptability for diverse structural design tasks.

This paper proposes an inverse method to characterize wave and energy propagation in curved structures, addressing the challenges of accurately obtaining dispersion curves, wavenumber space, and damping loss factors caused by their complex dynamics. The proposed method, Generalized Algebraic K-Space Identification (GAKSI) technique, is developed within the algebraic identification framework, enables the extraction of complex wavenumbers of multidimensional signals from full-field measured maps for the first time. By introducing iterated integrals and multivariate Laplace transform, the method can effectively filter signal noise, enhancing the accuracy of extracted wave propagation parameters. In this paper, the proposed method is applied to isotropic open shells with different geometric parameters and a real honeycomb cylindrical shell. Extracted results are compared with those from the reference methods. An in-depth analysis compares the characterization of shells and plates under varying signal noise levels. The findings demonstrate that the proposed method achieves high precision even under noisy conditions: the relative error for the extracted wavenumber converges to around 2.5% when the signal-to-noise ratio (SNR) exceeds 5, while the relative error for the extracted damping loss factor converges to approximately 5.5% when the SNR exceeds 10. Furthermore, the observations reveal that curvature-induced bending-membrane coupling enhances the damping properties, with this effect becoming more pronounced as the wave propagation direction transitions from the axial to the circumferential direction. These findings validate the capability of proposed method to characterize dispersion and damping properties in curved structures, offering promising potential for further applications in structural analysis, such as structural optimization and design.

Reducing children's exposure to traffic-related noise and air pollution in urban areas is a critical challenge. Therefore, evaluating traffic management strategies in a computational environment offers a practical tool for planners, policymakers, and researchers. However, a key research gap remains: most studies evaluate traffic strategies on air pollution, noise, or traffic separately. Few quantify their combined impacts in a single framework. To address this, we propose a multi-criteria evaluation approach and apply it by comparing four scenarios against a baseline. Through a comprehensive panel of statistical, spatial, and temporal analyses of traffic conditions, air pollutant concentrations, and noise levels, we find that: (1) restricting vehicle access during student arrival times significantly reduces exposure to both noise and air pollution; and (2) speed limit reductions have only limited effects on noise and may, under certain conditions, increase air pollution levels.

The full model parameters estimation of heterogeneous multilayer composites (HMC), involving geometric parameters and static-dynamic viscoelastic properties, has attracted considerable attention for both damage diagnosis and the design of new materials. However, this remains a challenge in current research due to the complexity involved in identifying special layers. To this end, we developed a robust wave-based method to estimate the structural parameters of each layer in HMCs using full-field displacement data. The method follows a two-stage inversion process. In Stage I, it estimates geometric and elastic parameters, and in Stage II, it determines damping properties. These parameters can be static, dynamic, linear, nonlinear, or mixed. The objective is to optimize the identification process by combining the multi-scale wave and energy propagation modeling and characterization numerical methodology that automatically incorporates the limited knowledge on both the used predicted Finite Element model (whatever its complexity) and experimental data (inevitably noisy). The Condensed Wave Finite Element Method with Contour Integral solver (CWFEM-CI) is proposed to model wave and energy propagation in mesoscopic predicted models by solving a nonlinear eigenvalue problem. It enables complex wavenumber extraction in arbitrary directions while reducing computational cost through model order reduction approach, Component Mode Synthesis (CMS). At the macroscopic scale, Algebraic K-Space Identification 2D (AKSI 2D) is applied to retrieve complex wavenumbers from real materials, serving as reference data for inverse optimization. By embedding iterated integrals into the mathematical foundation of the method, signal noise is effectively suppressed, thereby ensuring accurate material identification. Finally, the identification problem is formulated and solved iteratively using the surrogate optimizer, which minimizes the difference between predicted and experimental wave propagation parameters. The accuracy and effectiveness of the proposed method are validated through numerical experiments on linear elastic, nonlinear viscoelastic, and heterogeneous multilayer models, using both synthetic and real full-field data.