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Global automotive fuel economy and emissions pressures mean that 48 V hybridisation will become a significant presence in the passenger car market. The complexity of powertrain solutions is increasing in order to further improve fuel economy for hybrid vehicles and maintain robust emissions performance. However, this results in complex interactions between technologies which are difficult to identify through traditional development approaches, resulting in sub-optimal solutions for either vehicle attributes or cost. The results presented in this paper are from a simulation programme focussed on the optimisation of various advanced powertrain technologies on 48 V hybrid vehicle platforms. The technologies assessed include an electrically heated catalyst, an insulated turbocharger, an electric water pump and a thermal management module.

Tightening emission regulations and accelerating production cycles force engine developers to shift their attention towards virtual engineering tools. When simulating in-cylinder processes in commercial LDD DI engine development, the trade-off between run time and accuracy is typically tipped towards the former. High-fidelity simulation approaches which require little tuning would be desirable but require excessive computing resources. For this reason, industry still favors low-fidelity simulation approaches and bridges remaining uncertainties with prototyping and testing. The problem with low-fidelity simulations is that simplifications in the form of sub models introduce multi variable tuning parameter dependencies which, if not understood, impair the predictive nature of CFD simulations. In previous work, the authors have successfully developed a boundary condition dependent input parameter table.

The Direct Numerical Simulation (DNS) approach to solving the fundamental transport equations down to the smallest scales of motion is favorable should the requirement be a truly predictive solution of fluid dynamic problems, but the simulation run times are unacceptable for most practical industrial applications. Despite the steadily increasing computational capabilities, Reynolds Averaged Navier-Stokes (RANS) based frameworks remain the most commercially viable option for high volume sectors, like automotive. The sub models within RANS simplify the description of key physical phenomena and include several numerical constants. These so-called “tuning constants” introduce multivariable dependencies that are almost impossible to untangle with local sensitivity studies.

Since previous publications, Ricardo have continued to investigate the development of advanced after-treatment technologies through model based system simulation using an integrated model based development (IMBD) approach. This paper presents the results of the evaluation of after-treatment systems and management strategies for a range of diesel passenger cars. The targets of this study are applicable to Real Driving Emissions (RDE) legislation, but now targeting emissions levels beyond Euro 6d. The work was carried out as part of the EC Horizon 2020 co-funded REWARD (Real World Advanced technologies foR Diesel engines) project. Owing to the wide variation in feed-gas properties expected over an RDE cycle, the results seen for current production system architectures such as Lean NOX traps (LNT) or actively dosed Selective Catalytic Reduction (aSCR) systems highlight the challenge to adhere to emissions limitations for RDE legislation whilst fulfilling stringent CO2 targets.

Fuel consumption, and the physical behaviours behind it, have never been of greater interest to the automotive engineering community. The enormous design, development and infrastructure investment involved with a new engine family which will be in production for many years demands significant review of the base engine fundamental architecture. Future CO2 challenges are pushing car manufacturers to consider alternative engine configurations. As a result, a wide range of diesel engine architectures are available in production, particularly in the 1.4 to 1.6 L passenger car market, including variations in cylinder size, number of valves per cylinder, and bore:stroke (B/S) ratio. In addition, the 3 cylinder engine has entered the market in growing numbers, despite its historic NVH concerns. Ricardo has performed a generic architecture study for a midsize displacement engine in order to assess the pros and cons of each engine configuration.

Diesel vehicle buyers in Europe are now able to select from a wide range of desirable products with excellent performance and levels of refinement competitive with gasoline engine products. However, the trend towards higher ratings and increased low speed torque appears to have resulted in increased levels of turbocharger generated flow or ‘whoosh’ noise. This paper discusses the phenomenon of turbocharger whoosh and its impact on powertrain [1] and vehicle level NVH [2] focusing on: Possible mechanisms for whoosh noise occurrence The effect of turbocharger compressor selection on whoosh noise Mechanisms for the radiation of whoosh noise Potential countermeasures for reducing turbocharger whoosh Case studies are drawn from a range of relevant powertrain NVH projects to illustrate the turbocharger whoosh noise development process. The paper includes an insight to the future development of predictive tools for the whoosh noise phenomenon.

Diesel vehicle sales in the European passenger car market are rapidly approaching 50% market share. This rise in penetration has been achieved because buyers are attracted by the fuel consumption benefits of diesel powertrains and are now able to select from a wide range of desirable products with excellent performance and levels of refinement competitive with gasoline powered products. In a highly competitive marketplace there is increasing emphasis on the development of appealing diesel products, with the dominant factor being powertrain NVH performance. This paper compliments an earlier paper [1] on the vehicle NVH process required to achieve high ‘diesel appeal’ by providing a more detailed powertrain NVH perspective.