Optimal combustion engine control using Model Predictive Control

Background of thesis project

The combustion engine is a complex multivariable system which conventionally has been controlled with separate feed-forward and feedback. However, as it is challenging to incorporate the multivariable impact in by conventional approach and there is no supporting methodology for constraint handling, a more coordinated control method is desired. Model Predictive Control (MPC) is an attractive method that can control multivariable systems and take constraints into account. It also is a flexible platform where optimization or constraints easily can be added or subtracted. To comply with the more stringent emissions regulation and improved fuel consumption on a combustion engine, the air charge and start of injection must be accurately controlled to ensure that the engine's outflow meets the aftertreatment system's needs. ​

This work aims at calibrating an engine model and thereafter designing and implementing a nonlinear MPC to control the combustion engine to meet the aftertreatment, fuel consumption, hardware and driverability needs.​

Suitable background
  • Strong skills in modeling, analysis and simulation of dynamical systems​
  • Strong skills in control systems and MPC​
  • Proficiency in MATLAB/Simulink​
  • Suitable for two students

  • Basic acquaintance with combustion engine and its objectives​
  • Detailed acquaintance with engine model​
  • Calibrate engine model using test cell data​
  • Developing MPC formulation for engine control​
  • Implementation and evaluation of the resulting MPC controller ​
  • in simulation environment (MATLAB/Simulink)​
  • Potential bonus: run the nonlinear MPC in engine in test cell
Thesis Level: Master

Language: English

Starting date: 2024-01-08

Number of students: 2


Johan Dahl, Expert system engineer L1, +46 73 9027756

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