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Life Cycle Energy Optimisation: A multidisciplinary engineering design optimisation framework for sustainable vehicle development

Time: Fri 2023-02-24 10.00

Location: F3, Lindstedtsvägen 26 & 28, Stockholm

Video link:

Language: English

Subject area: Vehicle and Maritime Engineering

Doctoral student: Hamza Bouchouireb , Teknisk mekanik

Opponent: Assoc.Prof. Dipl.-Ing. Dr.techn. Mario Hirz, Graz University of Technology, Institute of Automotive Engineering

Supervisor: Associate Professor Ciarán J. O'Reilly, VinnExcellence Center for ECO2 Vehicle design, Linné Flow Center, FLOW; Professor Peter Göransson, VinnExcellence Center for ECO2 Vehicle design, Strömningsmekanik och Teknisk Akustik; Professor Rupert J. Baumgartner, University of Graz, Institute of Systems Sciences Innovation & Sustainability Research, Austria.; Professor José Potting, Hållbar utveckling, miljövetenskap och teknik

QC 230127


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.