Engine Calibration Using Global Optimization Methods with Customization 2020-01-0270
The automotive industry is subject to stringent regulations in emissions and growing customer demands for better fuel consumption and vehicle performance. Engine calibration, a process that optimizes engine performance by tuning engine controls (actuators), becomes challenging nowadays due to significant increase of complexity of modern engines. The traditional sweep-based engine calibration method is no longer sustainable. To tackle the challenge, this work considers two powerful global optimization methods: genetic algorithm (GA) and Bayesian optimization. In real engine testing platform, only the limited number of function evaluations (less than 400) is available. We customized GA to cope with limited resource. Another challenge of engine calibration is that, in real engine testing platform, some solutions cannot even run completely due to the engine hardware limitations. These solutions, called non-operational solutions, are part of infeasible solutions and do not have any information about either objectives or constraints. A constraint repair algorithm is applied to handle non-operational solutions. The experimental study on high-fidelity engine models demonstrated that both GA and Bayesian optimization effectively find solutions very close to global optimum, and Bayesian optimization is more stable and has better worst-case performance.
Ling Zhu, Yan Wang, Anuj Pal, Guoming Zhu
Ford Motor Company, Michigan State University