Multi-objective optimization of turbocharger turbines for low carbon vehicles using meanline and neural network models

Due to slow turnover of the global vehicle parc internal combustion engines will remain a primary means of motive power for decades, so the automotive industry must continue to improve engine thermal efficiency to reduce CO₂ emissions, since savings will be compounded over the long lifetime of millions of vehicles. Turbochargers are a proven efficiency technology (most new vehicles are turbocharged) but are not optimally designed for real-world driving. The aim of this study was to develop a framework to optimize turbocharger turbine design for competing customer objectives: minimizing fuel consumption (and thus CO₂ emissions) over a representative drive cycle, while minimizing transient response time. This is achieved by coupling engine cycle, turbine meanline, and neural network inertia models within a genetic algorithm-based optimizer, allowing aerodynamic and inertia changes to be accurately reflected in drive cycle fuel consumption and transient performance. Exercising the framework for the average new passenger car across a drive cycle representing the Worldwide harmonized Light vehicles Test Procedure reveals the trade-off between competing objectives and a turbine design that maintains transient response while minimizing fuel consumption due to a 3 percentage-point improvement in turbine peak efficiency, validated by experiment. This optimization framework is fast to execute, requiring only eight turbine geometric parameters, making it a commercially viable procedure that can refine existing or optimize tailor-made turbines for any turbocharged application (whether gasoline, diesel, or alternatively fuelled), but if applied to turbocharged gasoline cars in the EU would lead to lifetime CO₂ savings of > 290,000 tonnes per production year, and millions of tonnes if deployed worldwide.