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Cooperative merging strategies enabled by vehicle-to-vehicle (V2V) communication have shown promise in addressing congestion, fuel inefficiency, and collision risks. However, their performance can be severely degraded by time-varying and uncertain communication delays—an issue often overlooked in existing research, which primarily focuses on merging sequence determination and trajectory planning. Furthermore, practical considerations such as heterogeneous vehicle dynamics, varying road conditions, and real-time implementation complexities are frequently neglected. This paper presents a model-free, online planning framework for cooperative on-ramp merging of connected and automated vehicles (CAVs), explicitly accounting for time-varying V2V communication delays. Without relying on detailed vehicle dynamics, the proposed method introduces a data-driven delay compensation scheme. A cosimulation platform integrating high-fidelity vehicle dynamics, traffic simulation (SUMO), and V2V communication within MATLAB/Simulink is developed to evaluate the proposed method. Simulation results demonstrate that unaddressed V2V communication delays significantly impair merging performance. In contrast, the proposed framework enhances intervehicle distance tracking and maintains low fuel consumption, under communication delay across different communication frequencies. Its lightweight design also facilitates real-time implementation, making it well-suited for deployment in practical CAV systems.
Geotechnical site characterization using in-situ tests, such as cone penetration tests (CPTs), is essential for foundation design but is often limited by sparse spatial coverage, hindering accurate soil variability assessment. This study benchmarks six prediction techniques, Bayesian compressive sampling with Markov chain Monte Carlo (BCS_MCMC), Bayesian neural network (BNN), genetic algorithm (GA), gene expression programming (GEP), empirical Bayesian kriging (EBK), and inverse distance weighting (IDW), to predict corrected cone tip resistance (q(t)) at untested locations across ten Louisiana sites. The performance of these techniques is evaluated using the root mean square error (RMSE), mean absolute percentage error (MAPE), mean bias factor (lambda), coefficient of efficiency (COE), coefficient of variation (COV), and a unified Performance Index (PI) analysis. Results show that BNN, EBK, and IDW consistently achieve higher accuracy, stability, and minimal bias; whereas GA, GEP, and BCS_MCMC exhibited larger errors than BNN/EBK/IDW when validated against measured q(t) profiles. Prediction quality depends strongly on CPT layout, with favorable accuracy at minimum spacing near similar to 100 ft and distribution indices between similar to 0.05-0.10. The proposed BNN architecture is implemented in the CPT Site Variability Suite (CSVS), a MATLAB tool developed by the authors that automates data processing, interpolation, visualization, and downstream analyses (e.g., variogram derivation and LRFD workflows), all within a single platform. This integrated pipeline enhances reproducibility and supports data-driven foundation design in geotechnical site investigations. Findings pertain to the Louisiana dataset examined and provide a transferable workflow that should be validated for other geologic settings.
This paper presents a unified analytical design theory for multi-port polyphase transformers, targeting scalable and isolated high-power Electric Vehicle (EV) fleet charging systems with power multiplexing capability. As fleet electrification accelerates, conventional one-to-one charger architectures face significant challenges in infrastructure cost, peak power demand, and low utilization of installed power electronics. Powermultiplexed charging architectures, which dynamically distribute power from a shared pool of converter modules across multiple vehicles, have emerged as a promising solution. However, such architectures require scalable, isolated multi-port power interfaces capable of routing energy among multiple inputs and outputs, whose design remains complex and dependent on iterative modeling. To address this gap, the proposed theory provides closed-form expressions for self-inductance, leakage inductance, and mutual coupling terms for arbitrary multi-phase, multi-port transformer structures. The formulation enables direct synthesis of isolated multi-input and multi-output resonant converter systems without reliance on geometry-specific finite-element analysis or extensive parameter extraction. This capability is particularly critical for power-multiplexed systems, where modular converter structures must interface with multiple vehicles while maintaining galvanic isolation and flexible power allocation. The effectiveness of the proposed framework is demonstrated through the design of a 360 kW multi-phase system operating over a $700-900 \mathrm{VDC}$ input and $400-1250$ VDC output range. PLECS simulation results confirm accurate prediction of system behavior and validate the applicability of the approach to multi-port, power-multiplexed charging scenarios. The proposed method significantly reduces design complexity while enabling scalable, cost-effective, and fully utilized EV fleet charging infrastructure.
