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

Prakhar Kapoor, Aaron W. Costall, Nikolaos Sakellaridis, Rogier Lammers, Antonio Buonpane, Stéphane Guilain

Research output: Contribution to journalArticlepeer-review

7 Citations (SciVal)


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.

Original languageEnglish
Article number100261
JournalEnergy Conversion and Management: X
Early online date28 Jun 2022
Publication statusPublished - 31 Aug 2022

Bibliographical note

Funding Information:
The authors would like to thank Mitsubishi Turbocharger & Engine Europe BV for sponsoring this research and provision of turbine geometric and performance data, and Renault Group for providing the engine and vehicle models, and test data.


  • Engine-turbocharger matching
  • Low carbon vehicles
  • Meanline model
  • Neural network
  • Turbine optimization
  • Worldwide harmonized Light vehicles Test Procedure

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology


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