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Model Predictive Control for Robust Power and Thermal Management of Connected and Electrified Vehicles.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Model Predictive Control for Robust Power and Thermal Management of Connected and Electrified Vehicles./
作者:	Hu, Qiuhao.
面頁冊數:	1 online resource (150 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
標題:	Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30547444click for full text (PQDT)
ISBN:	9798379564216

Model Predictive Control for Robust Power and Thermal Management of Connected and Electrified Vehicles.
Hu, Qiuhao.

Model Predictive Control for Robust Power and Thermal Management of Connected and Electrified Vehicles. - 1 online resource (150 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--University of Michigan, 2023.

Includes bibliographical references

Integrated power and thermal management (iPTM) of electrified vehicles, such as hybrid electric vehicles (HEVs) and electric vehicles (EVs), can significantly improve their energy efficiency. In addition, with the emergence of connected vehicles (CVs), new opportunities open up for vehicle enhanced situational awareness, including the growing availability of preview/forecast of future vehicle speed and load. Model predictive control (MPC) is appealing for iPTM because of its ability to handle state and input constraints, approximate optimal control, and incorporate preview information. In the application of MPC to iPTM, however, different timescales of power and thermal subsystems present special challenges. Specifically, with the conventional MPC, fast sampling required by the fast dynamics and the long prediction horizon dictated by the slow dynamics lead to a large computational effort, which is difficult to accommodate in computationally constrained automotive processors. To this end, this dissertation develops novel multi-horizon MPC-based iPTM approaches for connected and electrified vehicles. The proposed multi-horizon model-predictive control (MH-MPC)leverages multi-fidelity models and preview information over a short receding and a long shrinking horizon to balance a trade-off between performance and computational efficiency. Compared to the conventional MPC-based approaches with a short prediction horizon and terminal cost, the MH-MPC improves fuel/energy efficiency to a level comparable to Dynamic Programming (DP) while still being computationally affordable. A statistical sensitivity analysis over real-world city driving cycles is conducted to demonstrate the robustness of MH-MPC to moderate levels of uncertainty in the long-term preview. To complement the proposed MH-MPC approach. A data-driven multi-range vehicle speed prediction strategy is developed for arterial corridors with signalized intersections, providing vehicle speed prediction for short, medium, and long ranges. The short-range prediction is informed through V2V/I communications. The medium-range prediction is realized using a Neural Network (NN), while the long-range speed profile is predicted based on a Bayesian Network (BN). The predictions are incorporated into MH-MPC for iPTM of connected vehicles, and energy efficiency improvement is observed. Moreover, an integrated spatio-temporal framework is proposed in this dissertation for multi-range traction power and speed prediction for CVs. The proposed framework leverages the historical and real-time data to predict traction loads. The spatio-temporal framework is combined with MPC-based iPTM to investigate the impact of uncertainties for a commercial electric vehicle. To improve the robustness of the algorithm in the presence of uncertainties, a location-dependent constraint strategy is proposed and integrated into the MPC-based thermal management strategy. The simulation results show a reduction of energy consumption for thermal management without degrading the capacity of enforcing the thermal constraints. Finally, the dissertation explores the synergy between battery thermal management and battery charging in an EV. An MPC-based approach is applied to minimize the energy used for battery thermal management and optimize fast charging time. An adaptive strategy is developed to adjust the weight of the two competing objectives in the MPC cost function to manage the trade-off between energy consumption and charging time. The sensitivity of the proposed MPC-based battery thermal management (BTM) strategy to uncertainties in the fast charging station availability is also investigated. For a commercial EV model, the simulation results show a decrease in the charging time achieved by optimally performing BTM at the cost of negligibly higher battery thermal management energy usage.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379564216Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Optimal controlIndex Terms--Genre/Form:

542853
Electronic books.

Model Predictive Control for Robust Power and Thermal Management of Connected and Electrified Vehicles.
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