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Optimization of Fuel Economy Using Optimal Controls on Regulatory and Real-World Driving Cycles
ISSN: 2641-9637, e-ISSN: 2641-9645
Published April 14, 2020 by SAE International in United States
Citation: Lodaya, D., Zeman, J., Okarmus, M., Mohon, S. et al., "Optimization of Fuel Economy Using Optimal Controls on Regulatory and Real-World Driving Cycles," SAE Int. J. Adv. & Curr. Prac. in Mobility 2(3):1705-1716, 2020, https://doi.org/10.4271/2020-01-1007.
In recent years, electrification of vehicle powertrains has become more mainstream to meet regulatory fuel economy and emissions requirements. Amongst the many challenges involved with powertrain electrification, developing supervisory controls and energy management of hybrid electric vehicle powertrains involves significant challenges due to multiple power sources involved. Optimizing energy management for a hybrid electric vehicle largely involves two sets of tasks: component level or low-level control task and supervisory level or high-level control task. In addition to complexity within powertrain controls, advanced driver assistance systems and the associated chassis controls are also continuing to become more complex. However, opportunities exist to optimize energy management when a cohesive interaction between chassis and powertrain controls can be realized. To optimize energy management along a given route, certain information such as the projected vehicle route, driver behavior, and battery charge level should be considered. In this paper, simulation models of a parallel P0/P4 hybrid electric vehicle are presented, which optimize powertrain controls using the Dynamic Programming approach. This virtual vehicle model is exercised through the HWFET and FTP-75 regulatory driving cycles to establish a performance baseline in a controlled driving environment. For comparison to off-cycle driving, the virtual vehicle is then also exercised through a real-world driving scenario over real-world roads, with similar trip characteristics to the regulatory tests, but with real traffic conditions during the day. This comparison provides insights into how optimized real-world fuel economy results can differ compared to the controlled testing environment, and how predictive powertrain controls can offer “in-situ” optimization of energy management.