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

Abstract

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 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 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 savings of 290,000 tonnes per production year, and millions of tonnes if deployed worldwide.