New methodologies for assessing the behaviour of vehicles under real driving emissions testing regimes

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Personal transportation is a major source of local pollution and it is vital that this is reduced. Over time, legislation aimed at imposing limits on vehicle emissions in laboratory experiments has failed to address the problem of emissions from vehicle when operating on the road. This has led to the recent introduction of Real Driving Emissions that include tests conducted on public roads using mobile emissions measurement devices. Firstly, this legislation simultaneously broadens the range of operating conditions over which the propulsion system needs to be compliant (altitude, temperature, fuel qualities...). Secondly, the legislation removes the detailed stipulation of the testing conditions, meaning the on-road tests will be stochastic and not repeatable. Both these aspects create significant disruption to the development and sign-off processes currently in place in the automotive industry and in particular mean that there remains significant uncertainty as to whether vehicles will fully comply with the new legislation and therefore deliver on the required levels of emissions. This project aims to create a new process making use of virtual methods to increase the robustness of vehicle development and sign-off with regards to Real Driving Emissions. The virtual approach will rely heavily on modelling and mass simulation in order to ensure the broad operating ranges are covered. The performance of such a virtual approach will be underpinned by the accuracy of the mathematical models used within the process. Vehicle emissions formation is a complex process involving both combustion but also after treatment, meaning that current state of the art relies heavily on empirical approaches. It is expected that empirical approaches will also be needed here firstly to characterise the emissions models and subsequently to validate the findings form the virtual approach. The research challenges to be addressed by this PhD are therefore:1. Expressing in a mathematical and structured way the broadness and seemingly randomness of on-road driving and using this to create synthetic driving cycles that cover the compete operating range covered by Real Driving Emissions legislation. This will involve data collection and literature review of real driving situations, statistical analysis of this data and the creation of new algorithms to generate synthetic cycles using techniques such as Markov chains. This will also need to be sanitised through an engineering analysis to ensure the cycles generated purely from mathematical are physically coherent. The output from this research will be a better understanding of what real driving is and a quantification and classification of different driving conditions.2. The characterisation of vehicle emissions models that present an appropriate balance of experimental effort and predictive accuracy for undertaking the simulations of emissions over the synthetic cycles. This will require investigation into different modelling approaches (physical, semi physical and empirical) to determine the most appropriate mathematical format for the models. This will be complemented by experimental work to characterise and validate the models.3. The experimental work in this PhD itself is not trivial, requiring the measurement of numerous vehicle characteristic in difficult conditions for measurement (emissions, road topology, vehicle states...). Therefore, a significant part of this work relates to the capture of this data and the analysis of its robustness. This will involve a physical analysis of the measurement principles and the proposal of new methods for capturing or processing raw data into the required quantities.Outcomes will be the generation of new methodologies to improve the robustness and speed for developing and certifying new vehicles. Will ultimately lead to less expensive, cleaner vehicles being brought to market sooner which will bring associated benefits in local air quality as these veh