Engine and Aftertreatment Co-Optimization of Connected HEVs via Multi-Range Vehicle Speed Planning and Prediction 2020-01-0590
Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs (including those of the engine and aftertreatment systems) has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the short-range prediction based on vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications-based information, is difficult and error-prone.
The multiple timescales inherent to power and thermal systems call for a variable timescale optimization framework with access to short- and long-term vehicle speed preview. To this end, a model predictive controller (MPC) with a multi-range speed preview for integrated power and thermal management (iPTM) of connected hybrid electric vehicles (HEVs) is presented in this paper. The MPC is formulated to manage the power-split between the engine and the battery while enforcing the power and thermal (engine coolant and catalytic converter temperatures) constraints. The MPC exploits prediction and optimization over a shorter receding horizon and longer shrinking horizon. The vehicle speed is predicted (or planned in case of eco-driving) based on V2I communications over the shorter receding horizon. Over the longer shrinking horizon, the vehicle speed estimation is based on the data collected from the connected vehicles traveling on the same route as the ego-vehicle. Simulation results of applying the MPC over real-world urban driving cycles in Ann Arbor, MI are presented to demonstrate the effectiveness and fuel-saving potentials of the proposed iPTM strategy under the uncertainty associated with long-term predictions of the CV’s speed.
Citation: Hu, Q., Amini, M., Feng, Y., Yang, Z. et al., "Engine and Aftertreatment Co-Optimization of Connected HEVs via Multi-Range Vehicle Speed Planning and Prediction," SAE Technical Paper 2020-01-0590, 2020, https://doi.org/10.4271/2020-01-0590. Download Citation
Qiuhao Hu, Mohammad R. Amini, Yiheng Feng, Zhen Yang, Hao Wang, Ilya Kolmanovsky, Jing Sun, Ashley Wiese, Zeng Qiu, Julia Buckland
University of Michigan, Ford Motor Company