arXiv subjects

Zhankun Sun

Publications and source records attributed to Zhankun Sun.

3 recordsLinked to original sources

arXiv2026

Battery-Swapping Station Operation Under Forecast Uncertainty: A Scenario-Based Stochastic MPC Framework

Battery-swapping stations (BSSs) can shorten electric-vehicle energy replenishment while using centrally managed battery inventories as flexible grid-connected storage. Realizing both benefits requires the station to schedule charging, grid discharge, and swapping service before future customer demand and electricity prices are known. This paper develops a forecast-aware rolling-horizon operating framework for this problem. A lightweight DLinear model predicts 24-hour price and demand trajectories, Stein variational gradient descent quantifies their uncertainty through representative scenarios, and a two-stage stochastic model predictive controller converts those scenarios into station decisions. The controller accounts for service shortfall, terminal readiness, a protected service buffer, and electrochemical degradation without assuming perfect future information. The application contribution is an implementable controller that coordinates the station's mobility-service and energy-storage roles. The methodological contribution is a modular forecast-to-control interface that separates the operational value of mean-forecast accuracy from that of uncertainty representation. In a 120-day closed-loop evaluation, DLinear-SVGD SMPC achieves the lowest cost among the implementable controllers. Relative to deterministic DLinear MPC, it reduces final cost by 1.2\% and service-shortfall hours by 80.7\%, with 99.10\% of the evaluated hours free of shortfall.

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arXiv2025

Degradation-Aware Model Predictive Control for Battery Swapping Stations under Energy Arbitrage

Battery swapping stations (BSS) offer a fast and scalable alternative to conventional electric vehicle (EV) charging, gaining growing policy support worldwide. However, existing BSS control strategies typically rely on heuristics or low-fidelity degradation models, limiting profitability and service level. This paper proposes BSS-MPC: a real-time, degradation-aware Model Predictive Control (MPC) framework for BSS operations to trade off economic incentives from energy market arbitrage and long-term battery degradation effects. BSS-MPC integrates a high-fidelity, physics informed battery aging model that accurately predicts the degradation level and the remaining capacity of battery packs. The resulting multiscale optimization-jointly considering energy arbitrage, swapping logistics, and battery health-is formulated as a mixed-integer optimal control problem and solved with tailored algorithms. Simulation results show that BSS-MPC outperforms rule-based and low-fidelity baselines, achieving lower energy cost, reduced capacity fade, and strict satisfaction of EV swapping demands.

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arXiv2019

Semi-orthogonal Non-negative Matrix Factorization with an Application in Text Mining

Emergency Department (ED) crowding is a worldwide issue that affects the efficiency of hospital management and the quality of patient care. This occurs when the request for an admit ward-bed to receive a patient is delayed until an admission decision is made by a doctor. To reduce the overcrowding and waiting time of ED, we build a classifier to predict the disposition of patients using manually-typed nurse notes collected during triage, thereby allowing hospital staff to begin necessary preparation beforehand. However, these triage notes involve high dimensional, noisy, and also sparse text data which makes model fitting and interpretation difficult. To address this issue, we propose the semi-orthogonal non-negative matrix factorization (SONMF) for both continuous and binary design matrices to first bi-cluster the patients and words into a reduced number of topics. The subjects can then be interpreted as a non-subtractive linear combination of orthogonal basis topic vectors. These generated topic vectors provide the hospital with a direct understanding of the cause of admission. We show that by using a transformation of basis, the classification accuracy can be further increased compared to the conventional bag-of-words model and alternative matrix factorization approaches. Through simulated data experiments, we also demonstrate that the proposed method outperforms other non-negative matrix factorization (NMF) methods in terms of factorization accuracy, rate of convergence, and degree of orthogonality.

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