Search arXiv⌕ Search

arXiv · 2610.06555

Adaptive Inductor and Frequency Management for Integrated Power Converters

Abstract

This work presents a digitally controlled adaptive power conversion framework that dynamically co-optimizes inductor configuration and switching frequency to achieve high efficiency across a wide range of load conditions. Conventional buck converters are typically optimized for a single operating point, leading to efficiency degradation under dynamic loads due to mismatches between inductance, switching frequency, and load current. To address this limitation, a reconfigurable inductor architecture is proposed. With this approach, the effective inductance and switching frequency are adjusted at runtime with on-chip digital controller. An end-to-end design methodology is developed, integrating physics-based analytical modeling, finite element method (FEM) simulations, and an optimization framework to determine preferred operating points across the load range. The resulting configurations are stored in a lookup table and implemented in real time through a low-overhead control scheme. The system explicitly accounts for both inductor and switching device losses, enabling co-optimization of conduction and switching losses within current ripple and continuous conduction mode (CCM) constraints. To ensure robust operation under fast load transients, a hysteresis-based control strategy is introduced to mitigate temporary CCM violations caused by sensing latency. An entry-margin condition is derived under which continuous conduction is guaranteed for a bounded load slew rate. Simulation results demonstrate a 4.5 percentage-point improvement in average efficiency and up to a 32% reduction in average power loss relative to fixed configurations, highlighting the effectiveness of adaptive inductance-frequency management in integrated power delivery systems with dynamic load profiles.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rami Rasheedi, Salma Abdelzaher, Inna Partin-Vaisband. 2026-10-05. Adaptive Inductor and Frequency Management for Integrated Power Converters. https://arxiv.org/abs/2610.06555

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Residual Bias Compensation Dual Extended Kalman Filter for Physics-Based SOC Estimation in Lithium Iron Phosphate Batteries

This paper addresses state of charge (SOC) estimation for lithium iron phosphate (LFP) batteries, where the relatively flat open-circuit voltage (OCV-SOC) characteristic reduces observability. A residual bias compensation dual extended Kalman filter (RBC-DEKF) is developed. Unlike conventional bias compensation methods that treat the bias as an augmented state within a single filter, the proposed dual-filter structure decouples residual bias estimation from electrochemical state estimation. One EKF estimates the system states of a control-oriented parameter-grouped single particle model with thermal effects, while the other EKF estimates a residual bias that continuously corrects the voltage observation equation, thereby refining the model-predicted voltage in real time. Unlike bias-augmented single-filter schemes that enlarge the covariance coupling, the decoupled bias estimator refines the voltage observation without perturbing electrochemical state dynamics. Validation is conducted on an LFP cell from a public dataset under three representative operating conditions: US06 at 0 degC, DST at 25 degC, and FUDS at 50 degC. Compared with a conventional EKF using the same model and identical state filter settings, the proposed method reduces the average SOC RMSE from 3.75% to 0.20% and the voltage RMSE between the filtered model voltage and the measured voltage from 32.8 mV to 0.8 mV. The improvement is most evident in the mid-SOC range where the OCV-SOC curve is flat, confirming that residual bias compensation significantly enhances accuracy for model-based SOC estimation of LFP batteries across a wide temperature range.

eess.SY↗

Distributed Coordination Algorithms with Efficient Communication for Open Multi-Agent Systems with Dynamic Communication Links and Processing Delays: Extended Version

In this paper we focus on the distributed quantized average consensus problem in open multi-agent systems consisting of dynamic directed communication links among active nodes. We propose three communication-efficient distributed algorithms designed for different scenarios. Our first algorithm solves the quantized averaging problem over the currently active node set under finite network openness (i.e., when the active set eventually stabilizes). Our second algorithm extends the aforementioned approach for the case where nodes suffer from arbitrary bounded processing delays. Our third algorithm operates over indefinitely open multi-agent networks with dynamic communication links (i.e., with continuous node arrivals and departures), computing the average that incorporates both active and historically active nodes. We analyze our algorithms' operation, establish their correctness, and present novel necessary and sufficient topological conditions ensuring their finite-time convergence. Numerical simulations on distributed sensor fusion for environmental monitoring demonstrate fast finite-time convergence and robustness across varying network sizes, departure/arrival rates, and processing delays. Finally, it is shown that our proposed algorithms compare favorably to algorithms in the existing literature.

eess.SY↗

Toward Single-Step MPPI via Differentiable Predictive Control

Model predictive path integral (MPPI) is a sampling-based method for solving complex model predictive control (MPC) problems, but its real-time implementation is challenged by computational and sample requirements that grow with the prediction horizon, as well as sensitivity to manually tuned sampling parameters. To address these issues, we propose Step-MPPI, a framework that learns a sampling distribution and MPPI parameters for efficient single-step lookahead MPPI. Specifically, a neural network parameterizes the MPPI sampling mean and covariance at each time step, while the single-step cost weights and temperature are jointly learned in a self-supervised manner over long horizons using the MPC cost, constraint penalties, and maximum-entropy regularization. By embedding long-horizon objectives into the learned cost and sampling policy, Step-MPPI achieves the foresight of multi-step optimization with the millisecond-level latency of single-step lookahead. We demonstrate its efficiency across challenging tasks involving high-dimensional systems and/or long control horizons.

eess.SY↗