Predictive control of hybrid vehicle powertrain for intelligent energy management

This thesis studies the minimization of the fuel consumption for a Hybrid Electric Vehicle (HEV) using Model Predictive Control (MPC). The presented MPC – based controller calculates an optimal sequence of control inputs to a hybrid vehicle using the measured plant outputs, the current dynamic states, a system model, system constraints, and an optimization cost function. The MPC controller is developed using Matlab MPC control toolbox. To evaluate the performance of the presented controller, a power-split hybrid vehicle, 2004 Toyota Prius, is selected. The vehicle uses a planetary gear set to combine three power components, an engine, a motor, and a generator, and transfer energy from these components to the vehicle wheels. The planetary gear model is developed based on the Willis’s formula. The dynamic models of the engine, the motor, and the generator, are derived based on their dynamics at the planetary gear. The MPC controller for HEV energy management is validated in the MATLAB/Simulink environment. Both the step response performance (a 0 – 60 mph step input) and the driving cycle tracking performance are evaluated. Two standard driving cycles, Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Driving Schedule (HWFET), are used in the evaluation tests. For the UDDS and HWFET driving cycles, the simulation results, the fuel consumption and the battery state of charge, using the MPC controller are compared with the simulation results using the original vehicle model in Autonomie. The MPC approach shows the feasibility to improve vehicle performance and minimize fuel consumption.

INFLUENCE OF 24-HOUR SLEEP DEPRIVATION ON CARDIOVASCULAR AND NEURAL CONTROL AT REST AND DURING ACUTE STRESS IN HUMANS

Recent epidemiological studies report a consistent association between short sleep and incidence of hypertension, as well as short sleep and cardiovascular disease-related mortality. While the association between short sleep and hypertension appears to be stronger in women than men, the mechanisms underlying the relations between sleep deprivation, stress, risks of cardiovascular diseases, and sex remain unclear. We conducted two studies to investigate the underlying neural mechanisms of these relations. In study 1, we examined sympathetic neural and blood pressure responses to experimentally-induced sleep deprivation in men and women. We further investigated the influence of sleep deprivation on cardiovascular reactivity to acute stress. In study 2, we examined the neural and cardiovascular function throughout the ovarian cycle in sleep deprived women. Twenty-eight young healthy subjects (14men and 14 women) were tested twice in study 1, once after normal sleep (NS) and once after 24-h total sleep deprivation (TSD). We measured the blood pressure, heart rate (HR), muscle sympathetic nerve activity (MSNA) and forearm blood flow (FBF) during 10min baseline, 5min of mental stress (MS) and 2 min cold pressor test (CPT). We demonstrated that TSD increased resting arterial blood pressure to a similar extent in both men and women, but MSNA decreased only in men following TSD. This MSNA response was associated with altered baroreflex function in women and divergent testosterone responses to TSD between men and women. Regarding TSD and cardiovascular reactivity, TSD elicited augmented HR reactivity and delayed recovery during both MS and CPT in men and women, and responses between sexes were not statistically different. Fourteen young healthy women participated in study 2. Subjects were tested twice, once during their early follicular (EF) phase after TSD, once during their mid-luteal (ML) phase after TSD. Blood pressure, HR, MSNA, and FBF were recorded during 10min baseline, 5 min MS, and 2 min CPT. We observed an augmented resting supine blood pressure during EF compared to ML in sleep deprived women. In contrast, resting MSNA, as well as cardiovascular responses to stressors, were similar between EF and ML after TSD. In conclusion, we observed sex differences in MSNA responses to TSD that demonstrate reductions of MSNA in men, but not women. TSD elicited augmented HR reactivity and delayed HR recovery to acute stressors similarly in men and women. We also reported an augmented supine blood pressure during EF compared to ML in sleep deprived women. These novel findings provide new and valuable mechanistic insight regarding the complex and poorly understood relations among sleep deprivation, sex, stress, and risk of cardiovascular disease.

MILITARY VEHICLE OPTIMIZATION AND CONTROL

It is remarkable that there are no deployed military hybrid vehicles since battlefield fuel is approximately 100 times the cost of civilian fuel. In the commercial marketplace, where fuel prices are much lower, electric hybrid vehicles have become increasingly common due to their increased fuel efficiency and the associated operating cost benefit. An absence of military hybrid vehicles is not due to a lack of investment in research and development, but rather because applying hybrid vehicle architectures to a military application has unique challenges. These challenges include inconsistent duty cycles for propulsion requirements and the absence of methods to look at vehicle energy in a holistic sense. This dissertation provides a remedy to these challenges by presenting a method to quantify the benefits of a military hybrid vehicle by regarding that vehicle as a microgrid. This innovative concept allowed for the creation of an expandable multiple input numerical optimization method that was implemented for both real-time control and system design optimization. An example of each of these implementations was presented. Optimization in the loop using this new method was compared to a traditional closed loop control system and proved to be more fuel efficient. System design optimization using this method successfully illustrated battery size optimization by iterating through various electric duty cycles. By utilizing this new multiple input numerical optimization method, a holistic view of duty cycle synthesis, vehicle energy use, and vehicle design optimization can be achieved.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.