A Real-Coded Dynamic Genetic Algorithm for Optimizing Driver´s Model in Emission Test Cycle

This paper proposes a real-coded Genetic Algorithm for finding optimal parameters´ values of a driver´s model. Driver´s models are one of the important requirements in de-signing controllers for vehicle electrification technology. Latest driver´s model that includes driver´s response delay, driver´s response time and the level of driver´s skills has been proposed previously. This model contains several critical parameters that need to be optimally determined in accordance with the vehicle´s properties as well as the test cycle data. The objective of this research is to search for the near-optimal values of driver´s model parameters that minimize the error between the target speed and the model´s speed, as well as maximize the smoothness of the driving operation. The algorithm is evaluated using two driving cycle data, Japan´s 10-15 mode cycle and Europe´s NEDC. The solutions are compared with other existing heuristic techniques. Experimental results demonstrate a good performance and the consistency of the proposed algorithm.