Model Predictive Climate Control for Connected and Automated Vehicles
Wang, Hao
2019
Abstract
Emerging connected and automated vehicle (CAV) technologies are improving vehicle safety and energy efficiency to the next level and creating unprecedented opportunities and challenges for the control and optimization of the vehicle systems. While previous studies have been focusing on improving the fuel efficiency via powertrain optimizations, vehicle thermal management and its interaction with powertrain control in hot and cold weather conditions have not been fully explored. For light-duty vehicles, the power used by the climate control system usually represents the most significant thermal load. It has been shown that the thermal load imposed by the climate control system may lead to dramatic vehicle range reduction, especially for the vehicles with electrified powertrains. Besides its noticeable impact on vehicle range reduction, the performance of the climate control system also has a direct influence on occupant comfort and customer satisfaction. Aiming at reducing the energy consumption and improving the occupant thermal comfort (OTC) level for the automotive climate control system, this dissertation takes air conditioning (A/C) system as an example and is dedicated to developing practical A/C management strategies for electrified vehicles. In particular, the proposed strategies leverage the predictive information enabled by the CAV technologies such as the traffic and weather predictions. There are three novel MPC-based A/C management strategies developed in this dissertation, the hierarchical optimization, the precision cooling strategy (PCS), and the combined energy and comfort optimization (CECO). They can be differentiated by their OTC assumptions, robustness considerations, and implementation complexities on the testing vehicle. In the hierarchical optimization, a two-layer hierarchical MPC (H-MPC) scheme is exploited for potential integration between the A/C and the powertrain systems of an HEV. This hierarchical structure handles the timescale difference between power and thermal systems as well as the uncertainties associated with long prediction horizon. Comprehensive simulation results over different driving cycles have demonstrated the energy saving potentials of efficient A/C energy management, which is attributes to leveraging the vehicle speed sensitivity of the A/C system efficiency. In terms of the comfort metric, the average cabin air temperature is applied. In contrast to this hierarchical optimization, PCS and CECO utilize the simpler single-layer MPC structure assuming accurate predictive information. They are focusing on formulating more practical OTC metrics and the implementation on the testing vehicle. Specifically, the PCS renders the simplest control-oriented model structure and its energy benefits are validated based on an industrial-level A/C system model. The proposed PCS exploits a more practical comfort metric, DACP, which directly motivates the design of an off-line eco-cooling strategy, which coordinates the A/C operation with respect to the vehicle speed. Vehicle-level energy saving is confirmed according to repeatable vehicle experiments. Finally, the CECO strategy considers a comprehensive OTC model, PMV, and combines the energy and comfort optimizations simultaneously. Further energy saving and OTC improvement can be achieved by explicitly leveraging both traffic and weather predictive information.Subjects
model predictive climate control connected and automated vehicle energy and comfort optimization vehicle thermal management
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