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.

This thesis explores how the systemic-level environmental footprint of light-duty vehicles could be reduced through integrative design using the Life Cycle Energy Optimisation (LCEO) methodology. This methodology aims at finding a design solution that uses a minimum amount of cumulative energy demand over the different phases of the vehicle's life cycle; while complying with a set of functional constraints, thereby avoiding any sub-optimal energy demand shifts between the vehicle's different life cycle phases. This thesis further develops the LCEO methodology and expands its scope through four main methodological contributions. This work also contributes in establishing the methodology as a standalone design approach and provides guidelines for its most effective use.

Initially, an End-of-Life (EOL) model, based on the substitution with a correction factor method, is included to estimate the energy credits and burdens that originate from EOL vehicle processing. Multiple recycling scenarios with varying levels of induced recyclate material property degradation were built, and their associated resulting optimal vehicle subsystem designs were compared to those associated with landfilling and incineration with energy recovery scenarios. The results show how the structural material use patterns, as well as the very mechanisms enabling the embodiment of the Life Cycle Energy (LCE) optimal designs, are impacted by taking into consideration the effect of a vehicle's EOL phase. In particular, the material intensity-space allocation trade-off was identified as a key factor in the realisation of the LCE optimal designs.

This coupling existing between optimal use of material and space allocation was further explored by functionally expanding the LCEO methodology's scope to handle aerodynamic functional requirements. This involved the definition of a novel allocation strategy for the energy necessary to overcome aerodynamic drag, as well as the development of a parametrised vehicle body model that ensures that the LCE knock-on effects of aerodynamically motivated design decisions are fully accounted for at the targeted subsystem level.

The expanded methodology was subsequently applied to perform the aero-structural life cycle-driven design optimisation of a vehicle subsystem, with the impact of the constitutive material's circularity potential being included through the previously developed EOL model and scenarios. The results demonstrate the significant extent of the coupling existing between a vehicle's fundamental aerodynamic shape, and a vehicle's structural material composition, including its EOL characteristics, within the LCEO context.

Beyond the vehicle level implications, the LCEO methodology's position within the broader vehicle-design methodology context was further characterised by comparing its outcomes to those of the purely lightweight and purely aerodynamic approaches. It was found that the LCE optimal designs were distinctly clustered from their mono-disciplinary counterparts. They offered up to 20% energy savings over the lightweight alternatives by being, on average, larger, heavier and more aerodynamics designs; while also being shorter and lighter than the optimal aerodynamic configurations.

Subsequently, a mixed integer nonlinear programming formulation of this expanded LCEO methodology was developed to include the effects of battery energy storage systems on the LCE optimal vehicle designs. In particular, the vehicle's battery size and number of such batteries needed over its life cycle were introduced as variables subject to a range and a cycle life constraint. The former is derived from the battery-capacity-to-structural-mass ratio of recent production vehicles, while the second ensures that the batteries' cycle lives are sufficient for the entirety of the vehicle's use phase. Additionally, three battery chemistries with varying characteristics were included: lithium nickel manganese cobalt oxide (NMC), lithium iron phosphate (LFP) and lithium cobalt oxide (LCO); along with an EOL recycling scenario. The results of the coupled aero-structural-battery energy storage LCE-driven design optimisations demonstrate that battery chemistry and recycling potential have a significant impact on the system's design in terms of overall LCE footprint, battery size and number, as well as aerodynamic shape. More specifically, a change in battery composition was found to lead to up to 12.5% variation in drag coefficient, while battery recycling can on average reduce a vehicle's associated LCE by 32%.

Finally, elements of robust design and uncertainty quantification were included into the LCEO methodology, in order to evaluate the impact of uncertainty on the resulting LCE optimal designs. Specifically, uncertainty was introduced through the assumption that the material properties of a subset of the optimisation's candidate materials are described by statistical distributions, as opposed to a priori fixed values, thereby changing the nature of the optimisation problem from deterministic to stochastic. This change is handled through a multilevel representation hierarchy for the targeted subsystem's model, and using the Multilevel Monte Carlo (MLMC) approach in the optimisation process to evaluate the expected compliance of a given design with the transport-related functional requirements. the results demonstrate how the robust design configurations both constitute a significant departure from their deterministic counterparts and depend on the EOL scenario considered, while only incurring a marginal LCE premium. Moreover, this work also further illustrated the performance increase associated with the use of the MLMC estimator in lieu of the classical Monte Carlo one within an optimisation under uncertainty framework.

Overall, the work presented in this doctoral thesis has contributed to the development of the state-of-the-art of the LCEO methodology to enable the early-stage conceptual design of more sustainable vehicle configurations, and demonstrated how the methodology is at its most effective when leveraging its cross-scalar and cross-disciplinary nature to enable integrative functional vehicle design.

Denna avhandling behandlar frågeställningen hur lätta fordons miljöpåverkan, utvärderad på en hög system-nivå, kan minskas genom livscykelbaserad energioptimering och integrerad designtänkande. I centrum för arbetet står metoden för Life Cycle Energy Optimisation (LCEO) som söker minsta mängden kumulativ energi, sett över ett fordons livscykel, under beaktande av uppsatta funktionella bivillkor. Optimeringsproblemet balanserar energibehovet i olika faser och undviker i och med detta sub-optimala lösningar sett mellan olika delar av livscykeln. LCEO-metoden utvecklas och utvidgas i detta arbete som utgör ett väsentligt steg mot en komplett designprincip och riktlinjer för hur denna kan tillämpas på ett effektivt sätt.

Ett av avhandlingens bidrag till LCEO-metodens utveckling berör frågeställningen kring hur omhändertagandet av ett uttjänt fordon och dess komponenter, här sammanfattat i det engelska begreppet End-of-Life (EOL), kan integreras i energibalansen. En EOL-modell, baserad på återanvändning med en korrektionsfaktor, används för att uppskatta återvunnen och förbrukad energi i EOL-processen. Ett flertal EOL-scenarier, med varierande grad av försämrade egenskaper i återvunna material, har definierats och jämförts med de två ytterligheterna deponi och förbränning med energiåtervinning. Resulterande optimala designlösningar visar som förväntat hur de olika EOL-scenarierna påverkar vilka material som används, men att det även uppstår avvägningar mellan materialens funktion och en rumslig påverkan i form av den volym som de upptar.

Denna koppling, mellan å ena sidan en optimal användning av material (inklusive återvunna med förändrade egenskaper) och å andra sidan allokering av volym, har studerats genom att utvidga LCEO-metoden med funktionella krav relaterade till aerodynamiska egenskaper. För att möjliggöra detta föreslår avhandlingen en ny strategi för allokering av den energi som krävs för att övervinna ett fordons luftmotstånd. Denna strategi ger tillsammans med en parametriserad fordonsmodell möjligheter att inkludera så kallade dominoeffekter i LCE-balansen som härrör från aerodynamiskt motiverade designlösningar. 

Denna utvidgning möjliggjorde en unik studie av en komplett aero-struktur-livscykelbaserad optimering av ett subsystem hos ett fordon, där dessutom hänsyn tas till de ingående materialens cirkularitetspotential. Studien visar på en stark koppling i LCEO-mening, mellan ett fordons yttre form, och därmed dess aerodynamiska egenskaper, och de ingående materialen, inklusive deras EOL prestanda.

LCEO lösningar jämfördes under arbetets gång med motsvarande konfigurationer från dels rena lättvikts- och dels rena aerodynamikoptimerade analyser. De LCE-optimala lösningarna skiljer sig markant från respektive mono-disciplinära designer. De har upp till 20% lägre energi, genom att vara större, tyngre och mer aerodynamiska än motsvarande lättviktsdesign, och genom att vara kortare och lättare än motsvarande aerodynamikbaserad design.

Elektrifieringen av transportsystemet har introducerat nya funktionella krav på ett fordon i form av räckvidd och återvinning av batterier. För att möjliggöra studier av dessa, implementerades en lösning för problem med heltal och olinjär programmering i LCEO. I denna inkluderades variabler beskrivande batteristorlek och antal batterier som behövs för en hel livscykel, där batteristorleken modellerades på senare generationer av elfordon i drift. Till detta introducerades tre olika typer batterier, med tillhörande EOL-scenarier, nämligen NMC, LFP och LCO. 

Resultaten från genomförda kopplade aero-struktur-batteri LCE-optimeringar visar att valet av teknologi för batterierna och deras respektive återvinningspotential, har en stark påverkan på såväl den totala miljöpåverkan i LCEO-mening, batteristorleken och antalet batterier som krävs över en livscykel som den resulterande optimala aerodynamiska formen. Som exempel, ändringar i batterikompositionen resulterade i upptill 12.5% variation i luftmotstånd, medan olika former av batteriåtervinning kan ge i medeltal 32%-iga minskningar av LCE.

Arbetet med LCEO-metoden kompletterades slutligen med en studie av robust design och kvantifiering av osäkerhet, för att utvärdera känsligheten hos den optimala lösningen i sig. För detta syfte ändrades optimeringsproblemet från deterministiskt till stokastiskt, och materialegenskaperna antogs vara representerade av statiska fördelningar. Lösningen baserades på en så kallad Multilevel Monte Carlo (MLMC) metod. De robusta designkonfigurationerna är signifikant annorlunda jämfört med de deterministiska och påverkade av gjorda EOL-scenario antaganden. Som en del av detta arbete utvärderades även prestanda hos MLMC metoden i sig och jämfördes med en Monte Carlo-baserad lösning. MLMC befanns vara överlägsen för den typ av osäkerhetsanalys som studerats här.

Given the increasing importance of sustainability in product design, tools for designing products with low environmental impact are important for tackling problems in the future. One important measure of environmental impact is life cycle energy (LCE), which uses the cumulative amount of energy a product consumes over its’ lifetime as a proxy for environmental impact. In this work, the core topology and face sheet thickness of a sandwich beam are optimized for different material compositions with the goal to minimize the life cycle energy of the beam. A constraint on the mean compliance of the beam is used as a proxy for functional requirements. The problem is solved using a mixed-integer programming extension of the established Topology Optimization of Binary Structures (TOBS) method. Numerical examples indicate that the method is able to find feasible minimum LCE solutions with varying topologies and face sheet thicknesses.

State-of-the-art urban road traffic noise propagation simulation methods such as the CNOSSOS-EU framework rely on ray tracing to estimate noise levels at specific locations on façades, so-called receiver points; this method is relatively computationally expensive and its cost increases with the number of receiver points, which limit the spatial resolution of such simulations in the context of real-time or near-real-time urban noise simulation applications. This contribution aims to investigate the applicability of multiple data-driven methods to the surrogate modelling of traffic noise propagation for fast façade noise calculation as an alternative to these traditional, ray-tracing-based methods. The proposed approach uses compressed sensing to select a small subset of optimal receiver points from which the dataset of the entire façade may be reconstructed, associated with either a kriging model or neural networks, used to predict noise levels for these sensors. The prediction performance of each of these steps is evaluated on an academic test case, with two levels of complexity based on the dimensionality of the problem.

The Life Cycle Energy Optimisation (LCEO) methodology aims at finding a design solution that uses a minimum amount of cumulative energy demand over the different phases of the vehicle's life cycle, while complying with a set of functional constraints. This effectively balances trade-offs, and therewith avoids sub-optimal shifting between the energy demand for the cradle- to-production of materials, operation of the vehicle, and end-of-life phases. The present work describes the inclusion of robust design aspects and uncertainty quantification into the LCEO framework. In particular, uncertainty is introduced through the assumption that the material and energy properties of a subset of the optimisation’s candidate materials are described by statistical distributions as opposed to a priori fixed values. Subsequently, the nature of the LCEO-associated optimisation problem is changed from deterministic to stochastic. This change is handled by defining a multilevel representation hierarchy, and using the Multilevel Monte Carlo (MLMC) approach in the optimisation process to evaluate the expected compliance of a given design with the transport-related functional requirements. The extended framework is applied to the robust design optimisation of a subsystem of a vehicle model which is both mechanically and geometrically constrained. The ability of the LCEO methodology to include robust design aspects early during the vehicle design process, while simultaneously handling functional conflicts, to result in a robust life cycle energy optimal design is demonstrated. Furthermore, the performance increase obtained by the use of the MLMC approach instead of the classical Monte Carlo approach within an optimisation under uncertainty framework is illustrated.

In this work, an End-Of-Life (EOL) model is included in the Life Cycle Energy Optimisation (LCEO) methodology to account for the energy burdens and credits stemming from a vehicle's EOL processing phase and balance them against the vehicle's functional requirements and production and use phase energies. The substitution with a correction factor allocation method is used to model the contribution of recycling to the EOL phase's energy. The methodology is illustrated through the optimisation of the design of a simplified vehicle sub-system. For the latter, multiple recycling scenarios with varying levels of assumed recycling induced material property degradation were built, and their impact on the vehicle sub-system's optimal solutions was compared to that of scenarios based on landfilling and incineration with energy recovery. The results show that the vehicle sub-system's optimal designs are significantly dependent on the EOL scenario considered. In particular, the optimal designs associated with the recycling scenarios are on average substantially heavier, and less life cycle energy demanding, than their landfilling or incineration with energy recovery-related counterparts; thus, demonstrating how the inclusion of EOL modelling in the LCEO methodology can significantly alter material use patterns, thereby effecting the very mechanisms enabling the embodiment of the resulting life cycle energy optimal designs.

The present work describes a widening of the scope of the Life Cycle Energy Optimisation (LCEO) methodology with the addition of shape-related design variables. They describe the curvature of a vehicle which impacts its aerodynamic drag and therewith its operational energy demand. Aerodynamic drag is taken into account through the estimation of the drag coefficient of the vehicle body shape using computational fluid dynamics simulations. Subsequently, the aforementioned coefficient is used to calculate the operational energy demand associated with the vehicle. The methodology is applied to the design of the roof of a simplified 2D vehicle model which is both mechanically and geometrically constrained. The roof is modelled as a sandwich structure with its design variables consisting of the material compositions of the different layers, their thicknesses as well as the shape variables. The efficacy of the LCEO methodology is displayed through its ability to deal with the arising functional conflicts while simultaneously leveraging the design benefits of the underlying functional alignments. On average, the optimisation process resulted in 2.5 times lighter and 4.5 times less life cycle energy-intensive free shape designs. This redesign process has also underlined the necessity of defining an allocation strategy for the energy necessary to overcome drag within the context of vehicle sub-system redesign.

The Life Cycle Energy Optimisation (LCEO) methodology aims at finding a design solution that uses a minimum amount of cumulative energy demand over the different phases of the vehicle's life cycle, while complying with a set of functional constraints. This effectively balances trade-offs, and therewith avoids sub-optimal shifting between the energy demand for the cradle-to-production of materials, operation of the vehicle, and end-of-life phases. The present work describes the extension of the LCEO methodology to perform holistic product system optimisation. The constrained design of an automotive component and the design of a subset of the processes which are applied to it during its life cycle are simultaneously optimised to achieve a minimal product system life cycle energy. A subset of the processes of the end-of-life phase of a vehicle’s roof are modelled through a continuous formulation. The roof is modelled as a sandwich structure with its design variables being the material compositions and the thicknesses of the different layers. The results show the applicability of the LCEO methodology to product system design and the use of penalisation to ensure solution feasibility.

The aerodynamic sound sources on a realistic landing gear are investigated using numerical phased array methods, based on array data extracted from compressible Detached-Eddy Simulations of the flow. Assuming monopole or monopole in a moving medium propagation, the sound sources are identified in the source region through various beamforming approaches: dual linear programming (dual-LP) deconvolution, orthogonal beamforming and CLEAN-SC. The predicted source locations are in good agreement with previous experimental results performed on the same nose landing gear configuration by industrial and academic partners within the ALLEGRA project. Additionally, the modeled sources are used to generate far-field spectra which are subsequently compared to the ones obtained with the Ffowcs Williams-Hawkings acoustic analogy. The results of the dual-LP approach show a good match between the far-field spectra up to a certain frequency threshold cor- responding to the quality of the mesh used. The results demonstrate the potential of numerical phased array methods as a legitimate modeling tool for aeroacoustic simulations in general and as a tool to gain insight into the noise generation mechanisms of landing gear components in particular. 

The Life Cycle Energy Optimisation (LCEO) methodology is used to explore the coupling existing between an electric vehicle's aerodynamic shape, structural material composition and design, and the properties of its onboard battery's chemistry in order to enable the development of more sustainable vehicle configurations. To this end, a mixed integer nonlinear programming formulation of the LCEO methodology was developed to include the effects of battery energy storage systems on the Life Cycle Energy (LCE) optimal vehicle designs. In particular, the vehicle's battery size and number of such batteries needed over its life cycle were introduced as variables subject to a range and a battery cycle life constraint. The former is derived from the battery-capacity-to-structural-mass ratio of recent production vehicles, while the latter ensures that the batteries' cycle lives are sufficient for the entirety of the vehicle's use phase. Additionally, three lithium-based battery chemistries with varying properties were included: lithium nickel manganese cobalt oxide (NMC), lithium iron phosphate (LFP) and lithium cobalt oxide (LCO); along with a closed-loop end-of-life recycling scenario for the battery materials. The results of the coupled aero-structural-battery energy storage LCE-driven design optimisations demonstrate that battery chemistry and recycling potential have a significant impact on the system's design in terms of overall LCE footprint, battery size and number, as well as aerodynamic shape. More specifically, a change in battery composition was found to lead to up to 12.5% variation in drag coefficient, while battery recycling can on average reduce a vehicle's associated LCE by 32%. Furthermore, battery material recycling was found to decrease the role played by the specific energy and cycle lives of the batteries, and increase that played by their embodied energy. Consequently, the LFP battery chemistry was found to be the best performer from an LCE perspective in the presence of battery material recycling; while the NMC chemistry was found to perform marginally better in the absence of the latter.