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OTA (over the air) updates help automotive manufacturers to reduce vehicle warranty and recall costs. Vehicle recall is expensive, and many automotive manufacturers have implemented OTA updates. Updating parameters for connected vehicles can be challenging when dealing with thousands of vehicles across different regions. For example, how does the manufacturer prioritise which vehicles need updating? Environmental and geographical factors affect degradation rates and vehicles in hotter regions or congested cities may degrade faster. For EVs, updating the BMS (battery management system) parameters requires careful analysis prior to the update being deployed, to maximise impact and reduce the likelihood of adverse behaviour being introduced. The analysis overhead increases with the number of vehicles. This is because it requires simulation and optimisation of the fleet BMS calibration in a digital twin environment.

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 increasing need for cleaner and more efficient combustion systems has promoted a paradigm shift in the automotive industry. Virtual hardware and engine calibration screening at the early development stage, has become the most effective way to reduce the time necessary to bring new products to market. Virtual engine development processes need to provide realistic engine combustion rate responses for the entire engine map and for different engine calibrations. Quasi Dimensional (Q-D) combustion models have increasingly been used to predict engine performance at multiple operating conditions. The physics-based Q-D turbulence models necessary to correctly model the engine combustion rate within the Q-D combustion model framework are a computationally efficient means of capturing the effect of port and combustion chamber geometry on performance.

The automotive market’s need for ever cleaner and more efficient powertrains, delivered to market in the shortest possible time, has prompted a revolution in digital engineering. Virtual hardware screening and engine calibration, before hardware is available is a highly time and cost-effective way of reducing development and validation testing and shortening the time to bring product to market. Model-based development workflows, to be predictive, need to offer realistic combustion rate responses to different engine characteristics such as port and fuel injector geometry. The current approach relies on a combination of empirical, phenomenological and experienced derived tools with poor accuracy outside the range of experimental data used to validate the tool chain, therefore making the exploration of unconventional solutions challenging.

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.