Real-Time Optimal Control of a Plug-in Hybrid Electric Vehicle Using Trip Information

The plug-in hybrid electric vehicle (PHEV) is a promising option for future sustainable transportation. It offers better fuel economy and lower emissions than conventional vehicles. This thesis has developed a novel energy-optimal powertrain controller for PHEVs. The controller will be broadly applicable to all PHEV models; however, it will be fine-tuned to the Toyota Prius Plug-in Hybrid for testing and validation. The controller will take advantage of advancements in vehicle intelligent and communications technologies, such as Global Positioning System (GPS), Intelligent Transportation System (ITS), Geographic Information System (GIS), radar, and other on-board sensors, to provide look-ahead trip data. These data are critical to increasing fuel economy as well as driving safety. This PhD research has developed three energy-optimal systems for PHEVs: Trip Planning module, Route-based Energy Management System (Route-based EMS), and Ecological Cruise (Eco-Cruise) Controller. The main objective of these energy-optimal systems is to minimize the total energy cost, including both electricity derived from the grid and fuel. The upper-level system is Trip Planning, using an algorithm designed to take advantage of previewed trip information to optimize State of Charge (SOC) profiles. The Route-based EMS optimally distributes propulsion power between the batteries and engine. Finally, the Eco-Cruise controller adjusts the speed considering upcoming trip data. Real-time implementation has remained a major challenge in the design of complex control systems. To address this hurdle, simple and efficient models and fast optimization algorithms are developed for each energy-optimal strategy. A Real-time Cluster-based Optimization is developed to solve the Trip Planning problem in real-time. The Route-based EMS is developed based on Equivalent Consumption Minimization Strategy (ECMS) to optimally distribute propulsion power between two energy sources. And, a Nonlinear Model Predictive Control (NMPC) is utilized to obtain optimum traction or regenerative torques in Eco-Cruise controller. Model-in-the-Loop (MIL) and Hardware-in-the-Loop (HIL) testing are critical steps in control validation and in ensuring real-time implementation capability. The MIL results show that the novel energy-optimal powertrain controller can improve the total energy cost by up to %20 compare to benchmark rule-based controller. The HIL test results demonstrate that the computational time for energy-optimal strategies are less than the target sampling-time, and they can be implemented in real-time

Traffic modeling, estimation and control for large-scale congested urban networks

Part I of the thesis investigates novel urban traffic state estimation methods utilizing probe vehicle data. Chapter 2 proposes a method to integrate the collective effect of dispersed probe data with traffic kinematic wave theory and data mining techniques to model the spatial and temporal dynamics of queue formation and dissipation in arterials. The queue estimation method captures interdependencies in queue evolutions of successive intersections, and moreover, the method is applicable in oversaturated conditions and includes a queue spillover statistical inference procedure. Chapter 3 develops a travel time reliability model to estimate arterial route travel times distribution (TTD) considering spatial and temporal correlations between traffic states in consecutive links. The model uses link-level travel time data and a heuristic grid clustering method to estimate the state structure and transition probabilities of a Markov chain. By applying the Markov chain procedure, the correlation between states of successive links is integrated and the route-level TTD is estimated. The methods in Part I are tested with various probe vehicle penetration rates on case studies with field measurements and simulated data. The methods are straightforward in implementation and have demonstrated promising performance and accuracy through numerous experiments. Part II studies network-level modeling and control of large-scale urban networks. Chapter 4 is the pioneer that introduces the urban perimeter control for two-region urban cities as an elegant control strategy to decrease delays in urban networks. Perimeter controllers operate on the border between the two regions, and manipulate the percentages of transfer flows between the two regions, such that the number of trips reaching their destinations is maximized. The optimal perimeter control problem is solved by the model predictive control (MPC) scheme, where the prediction model and the plant (reality) are formulated by macroscopic fundamental diagrams (MFD). Chapter 5 extends the perimeter control strategy and MFD modeling to mixed urban-freeway networks to provide a holistic approach for large-scale integrated corridor management (ICM). The network consists of two urban regions, each one with a well-defined MFD, and a freeway, modeled by the asymmetric cell transmission model, that is an alternative commuting route which has one on-ramp and one off-ramp within each urban region. The optimal traffic control problem is solved by the MPC approach to minimize total delay in the entire network considering several control policies with different levels of urban-freeway control coordination. Chapter 6 integrates traffic heterogeneity dynamics in large-scale urban modeling and control to develop a hierarchical control strategy for heterogeneously congested cities. Two aggregated models, region- and subregion-based MFDs, are introduced to study the effect of link density heterogeneity on the scatter and hysteresis of MFD. A hierarchical perimeter flow control problem is proposed to minimize the network delay and to homogenize the distribution of congestion. The first level of the hierarchical control problem is solved by the MPC approach, where the prediction model is the aggregated parsimonious region-based MFD and the plant is the subregion-based MFD, which is a more detailed model. At the lower level, a feedback controller tries to maximize the network outflow, by increasing regional homogeneity