Estimation of model parameters is a critical topic in battery modeling research, as the accuracy of parameters determines the efficacy of the widely used model-based battery engineering. The measurement-based approaches aim at measuring the battery physical parameters using advanced experiment and instrumentation techniques, but the associated complexity, time, and cost make it undesirable in many cases. Therefore, the data-based identification approach, which only uses easily available input and output measurement data, has been widely adopted due to its convenience and noninvasiveness. As the quality of data has significant impact on the estimation accuracy, data optimization, or optimal experiment design, is often utilized to improve and guarantee the accuracy of estimation. The common practice of data optimization aims at designing input excitation by maximizing a certain conventional criterion, e.g. Fisher information which measures the information content of the data and relates to the variance of the estimation error. However, such approach suffers from fundamental limitations, including inability to explicitly address estimation bias and system uncertainties in measurement, model, and parameter, which severely restrict the applicability and effectiveness of the method in practice.

To overcome the existing limitations, new criteria and a novel framework are proposed in this research for estimation error quantification and data optimization. A generic formula is first derived for quantifying the estimation error subject to sensor, model, and parameter uncertainties for the commonly used least-squares algorithm. Based on the formula, data structures, represented in terms of parameter sensitivity, which could minimize the estimation errors caused by each type of uncertainty are identified. These data structures are then employed as new criteria to supplement the Fisher information and formulate a novel data optimization framework. In order to facilitate the solution of the formulated data optimization problem, this research also explores new methods for efficient computation of parameter sensitivity, which is a key for representing the data structures and enabling data optimization. Efforts have been made to derive the analytic expressions of the sensitivity of battery electrochemical parameters by leveraging reasonable assumptions and model reformulation and simplification techniques. The proposed methodology is applied to estimating the electrochemical parameters of a single particle lithium-ion battery model in simulation and experiments, showing excellent estimation and voltage prediction accuracy compared with the traditional approach.

Electric multirotor aerial vehicles are an emerging technology with extensive potential applications across a wide range of fields, but flight time and range limitations currently impose significant constraints on the use of such vehicles. Improving the vehicle energy performance is therefore a critical research topic, and one promising strategy is to optimize operational energy efficiency through model-based motion planning and control. While there has been extensive research on the topic and important progress has been made, existing works generally oversimplify or disregard key vehicle subsystem behaviors, and therefore fail to capture the complete energy dynamics and exploit the full energy saving potential. To address this gap in the state of the art, a complete system-level vehicle model is developed and applied to planning and control, aiming at achieving significant energy performance improvements in this dissertation.The model captures all relevant subsystem dynamics related to the vehicle energy performance, including propeller aerodynamics, motor assembly electro-mechanical dynamics, battery electrical dynamics, and airframe rigid-body dynamics. Through experimental validation, the model demonstrates a high degree of fidelity over a wide range of operating conditions. The model is then used to demonstrate the importance and necessity of incorporating individual dynamics into model-based planning and control, highlighting the impact of battery dynamics on the propulsion limits, the influence of propeller (inflow) aerodynamics on the energy performance, and the breakdown of vehicle energy efficiency to each subsystem dynamics. An energy-optimal trajectory generation and feedback control framework is then developed based on this model, and is shown to reduce energy usage significantly relative to a baseline controller in both simulations and experimental validation over a range of waypoint-to-waypoint flight operations. Polynomial approximations of the optimized trajectories are then developed to enable rapid and computationally efficient trajectory generation. Relative to the true energy-optimal trajectories, these approximations significantly reduce computational complexity with only a slight increase in energy consumption. Finally, the framework is extended to mission planning, in which the minimum-energy order for traversing a series of waypoints in 3D space is identified. Of particular interest is to compare with the minimum-distance order, which is often assumed to be energy optimal according to conventional wisdom and frequently adopted in practice. Over a large number of missions with randomized waypoint locations, it is found that the minimum energy order differs from the minimum-distance order in a majority of the cases, and the difference in energy consumption between the two orders can be substantial among missions of varying ranges and number of waypoints.