arXiv:2606. 30397v1 Announce Type: cross Abstract: The increasing integration of Electric Vehicles (EVs) has imposed a growing harmonic challenge on the power grid.
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The paper introduces EBL, an Efficient Broad Learning framework designed for distributed adaptive harmonic estimation in power grids affected by electric vehicle charging. It leverages a quantised FPGA implementation to provide high‑accuracy, half‑cycle input harmonic predictions with ultra‑low latency, outperforming existing FPGA methods by 17.4×. The online transfer learning component enables rapid adaptation across multiple charging scenarios, while bespoke quantisation and sparsity reduce resource usage to just 5.9% of the LUTs on a Zynq Ultrascale+ FPGA, compared to 82% of the state‑of‑the‑art accelerator.
Accurate remaining discharge time (RDT) prediction is challenging in real-world battery applications because future load profiles are unknown and highly dynamic. To address the uncertainty of continuo...
The paper introduces Relative Discharge Stage (RDS), a battery‑management indicator that classifies remaining discharge condition into five interpretable classes—Normal, Good, Moderate, Low, and Recharge Required—without needing future load information. It combines physics‑based SOC estimation with a lightweight temporal convolutional network that processes measured current, voltage, temperature, and SOC over a sliding window. Experiments on two public lithium‑ion datasets show RDS classification accuracy above 80% across varied load and thermal conditions.
This study evaluates four control strategies—rule-based, model predictive control (MPC), reinforcement learning without forecasts (RL‑NF), and reinforcement learning with forecasts (RL‑F)—for a renewable‑powered hydrogen supply chain. Using a unified, physically realistic simulation that includes electrolyzer constraints, storage dynamics, and grid limits, the authors find that MPC delivers the best economic performance by leveraging short‑term forecasts, while RL‑NF performs robustly without future information. RL‑F does not consistently outperform RL‑NF, indicating that forecast uncertainty and added state complexity can hinder forecast‑augmented learning.
arXiv:2607. 12775v1 Announce Type: cross Abstract: Scenario-based model predictive control (SBMPC) is a variant of model predictive control (MPC) that explicitly accounts for uncertainty by optimizing control actions over multiple predicted scenarios.
Scenario-based model predictive control (SBMPC) is a variant of model predictive control (MPC) that explicitly accounts for uncertainty by optimizing control actions over multiple predicted scenarios. However, its computational complexity increases rapidly with the number of scenarios and prediction horizon, limiting is applicability to real-time planning and control.