This dissertation focuses on reducing vehicle energy consumption using optimal control and real-time optimization based on vehicle connectivity. The proposed methods include optimal vehicle transient motion control and eco (economic) route planning, where vehicle route and speed are optimized based on a proposed data-driven Grey-Box model considering vehicle speed, and its driving environment such as temperature, road grade, gust wind, etc. The two methods, optimal transient control and eco route planning form the whole route and speed optimization system. Vehicle transient motion control plays an important role in reducing energy consumption for hybrid and electric vehicles, as well as vehicles powered by internal combustion engines since vehicle acceleration and deceleration can be optimized based on the driving environment. In this thesis, nonlinear quadratic tracking (NQT) control is used for optimal acceleration and minimal principle for deceleration to optimize energy recovery, where the acceleration control generates the optimal propulsion torque based on the current powertrain states and the error between vehicle speed and given reference provided by the connected system based on the surrounding traffic; and the deceleration (braking) control optimizes regenerative brake to maximize the recovered energy while obeying speed and braking distance constraints. Both control strategies are designed for real-time applications and can be updated online to respond to the rapid change in traffic environment using analytic solutions of optimal control. Computer-in-the-loop (CIL) and Hardware-in-the-loop (HIL) simulations validate their adaptability to reduce energy consumption and update to a changing traffic environment in real-time. Considering the various system disturbances (e.g., road grade, gust wind, etc.) occurring during drive and model uncertainties due to model simplification and parameter errors ignored by the optimal controls above. In this thesis, a linear quadratic integral tracking (LQIT) control is utilized to generate regulation laws for both acceleration and deceleration operations to reduce tracking error. The LQIT acceleration control tracks the reference speed trajectory generated by the optimal acceleration strategy with minimal tracking error; and the LQIT deceleration control tracks the brake distance reference from the optimal braking control, achieves the target speed, and keeps brake distance below its reference for safety concerns. A unified Kalman filter is used to estimate system state based on noisy measurement. Simulation studies validate the proposed LQIT controls and indicate that the static tracking errors for both speed and distance are reduced with confirmation of being able to handle changing traffic environments. To perform the vehicle eco route and speed optimization, a vehicle energy consumption model is necessary to estimate the energy usage. In this thesis, a Grey-Box vehicle energy consumption model is developed based on vehicle dynamics with environmental influence based on the Kriging model. This model retains its high fidelity by utilizing basic vehicle dynamics in the model structure including rolling resistance, aerodynamics, gravity and energy consumption of air conditioning (AC) and heater as functions of environmental conditions such as temperature, wind speed, etc. The proposed data-driven model is trained based on Gaussian process assumption with a modeling error below 2.5\%. After the real-time model is trained, Recursive Least-Squares (RLS) algorithm is used to update the model based on new driving data to reflect the current vehicle status such as aging. The accuracy of the proposed Gray-Box Kriging model is verified in CIL simulation and a case study on vehicle route shows the capability of reducing energy consumption by using the Grey-Box model with changing environments. Based on the developed Grey-Box energy model, a novel vehicle eco motion planning (VEMP) method is proposed to optimize the vehicle route and speed simultaneously for minimizing its energy usage with a given origin-destination pair and a travel time limit. The proposed VEMP method is based on the modified Dijkstra algorithm and gradient descent speed optimization to find and update the optimal route and corresponding speed profile in real-time based on the changing traffic and driving environment information. Co-simulation studies are conducted for the developed VEMP method in MATLAB with the SUMO traffic model using a real-world map. The simulation results show that for studied driving environments, the VEMP speed optimization is able to reduce energy usage, and results of five scenarios indicate that the VEMP can reduce total energy consumption. A sudden traffic jam study demonstrates the ability of real-time updating for the proposed VEMP method to handle sudden traffic changes such as vehicle cut-in.
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