A Unified Framework for Robust Energy-Efficient Power Transfer Networks

Thesis Abstract

The rapid evolution of power transfer networks toward higher efficiency and reliability faces persistent challenges from non-ideal channel conditions, component nonlinearity, and varying load demands, which collectively undermine robustness and energy efficiency in practical deployments. This study develops a unified framework for robust energy-efficient power transfer networks (UEEP-TN) that integrates coordinated control strategies, adaptive impedance matching, and resilience-aware optimization to sustain performance under uncertainty. The aim is to establish a theory-driven framework that quantifies the trade-offs between energy efficiency, robustness, and quality-of-service in wireless and wired power transfer topologies, including resonant inductive coupling and capacitive links, under realistic perturbations. Specific objectives are (i) to formulate a comprehensive mathematical model that captures coupled electromechanical dynamics, nonlinear parasitics, and stochastic load fluctuations; (ii) to design a robust control architecture based on H-infinity and recessed-optimized model predictive control (MPC) that guarantees stability and performance margins; (iii) to develop an adaptive impedance matching and power routing scheme utilizing convex optimization and Lyapunov-based stability proofs; (iv) to evaluate energy efficiency and robustness across diverse networks through a multi-scenario simulation framework; and (v) to validate the framework experimentally on a laboratory testbed representing both wireless resonant and wired power transfer segments. The methodology adopts a mixed-methods research design combining theoretical development, numerical simulation, and empirical validation. The theoretical component builds a state-space representation of the network, incorporating uncertain parameters modeled as bounded disturbances and stochastic processes. The population comprises representative network configurations drawn from published topologies, with a sample of 12 distinct scenarios (6 wireless, 6 wired) used for simulation experiments. Data collection instruments include high-fidelity circuit simulators (PSpice, MATLAB/Simulink) augmented by a hardware testbed with three coupled resonant coils and a low-voltage/wideband power interface. Instrument validity is ensured via cross-validation against analytical benchmarks and published results. The primary data analysis employs robust control synthesis techniques, convex optimization, and Lyapunov stability analysis to derive performance guarantees, complemented by Monte Carlo simulations (10,000 iterations) to quantify robustness margins. The empirical evaluation uses regression analysis to correlate energy efficiency gains with perturbation levels, ANOVA to assess differences across network configurations, and nonparametric tests where assumptions are violated. The study anticipates that the integrated UEeP-TN framework will yield a stable operating region under input disturbances up to 20% variation, with energy efficiency improvements of 12–25% relative to baseline designs and robustness metrics exceeding predefined criteria (robust stability margins > 0.2 and disturbance rejection > 15 dB). Key findings are expected to reveal that joint optimization of impedance matching and control policy, guided by a Lyapunov-based proof of stability, enables significant energy savings without compromising reliability, especially in heterogeneous networks where wireless and wired segments interact. The theoretical contribution includes a formalized unified framework that combines H-infinity control with model predictive control under a common objective of energy-optimal robustness, along with a generalized impedance-matching algorithm that adapts to parameter drift. Practically, the framework informs the design of next-generation power transfer systems suited for electric vehicle charging, industrial automation, and smart-grid islands, where both wireless and wired links coexist. The study's contribution to knowledge lies in (i) delivering an integrated model that reconciles energy efficiency with robustness across multiple transfer modalities, (ii) providing a provably stable control scheme with explicit performance guarantees under uncertainty, and (iii) offering a scalable testing protocol and experimental validation methodology for complex power transfer networks. The main conclusion is that a unified, robustness-aware optimization framework can achieve meaningful energy efficiency gains while maintaining stringent reliability requirements in heterogeneous power transfer networks. Recommendations include extending the framework to include machine-learning-based disturbance prediction for proactive control, implementing real-time adaptive scheduling in larger networks, and exploring standardized benchmarking procedures to facilitate cross-system comparisons.