Driving Style Adaptive Electrified Powertrain Control

The performance of a powertrain is dependent on how its controller parameters are tuned for the given duty cycle. In real world, the duty cycle is hard to predict, however, if the historical performance is evaluated and used to fine tune the controller parameters, the performance of the powertrain can be improved. This thesis presents a retrospective information-based powertrain performance improvement. The performance is evaluated using the fuel economy and charge sustaining operation of an electrified powertrain. A Retrospective Cost Adaptive Controller (RCAC) has been designed for parallel HEV, which improves it performance based on the former performance. The simulation results demonstrate that the controller parameters are re-calibrated to show improved performance for standard test driving cycles as well as cycles with different driving styles. A dynamic programming (DP) solution is also given as a benchmark to evaluate RCAC results.