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Multi-Regime GRU-Scheduled PID Control for Greenhouse VPD Regulation with Real-Time PLC Deployment
(2026-01-01)
Wanna, Natthanan
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Nabudda, Kriengkrai
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Sitthaphanit, Suphasit
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Thasnas, Natakorn
Vapor pressure deficit is a physiologically meaningful climate variable governing plant transpiration and water transport in greenhouse cultivation. However, accurate regulation in evaporative-cooled greenhouses is challenging due to nonlinear temperature-humidity coupling, time-varying dynamics, and rapidly changing solar disturbances. Conventional fixed-gain proportional-integral-derivative controllers, tuned for a single operating condition, often exhibit performance degradation under regime transitions. This study proposes a multi-regime admissible-region-constrained gain-scheduled control framework in which a gated recurrent unit network serves as a supervisory scheduler to generate smooth, near-optimal controller gains for real-time vapor pressure deficit regulation. Control-oriented first-order-plus-dead-time models are identified under representative operating regimes, and an interval-bounded plant ensemble is constructed to capture dynamic variability. Regime-wise optimal gains are obtained offline using hybrid evolutionary optimization and used as supervisory training labels. To support safe deployment, all scheduled gains are constrained within a pre-validated admissible stability region, such that each deployed gain configuration remains inside an offline-validated frozen-time stabilizing set. The controller is implemented on an industrial programmable logic controller and experimentally validated in a full-scale evaporative greenhouse under real environmental disturbances. Comparative multi-day experiments demonstrate improved tracking accuracy, reduced overshoot, smoother actuator behavior, and enhanced disturbance attenuation relative to fixed-gain strategies, while maintaining deterministic real-time execution. The results establish an industrially deployable learning-based gain-scheduling framework for practical greenhouse climate control.
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The State of Health Estimation of Retired Lithium-Ion Batteries Using a Multi-Input Metabolic Gated Recurrent Unit
(2025-03-01)
He, Yu
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Pattanadech, Norasage
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Sukemoke, Kasiean
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Pan, Minling
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Chen, Lin
With the increasing adoption of lithium-ion batteries in energy storage systems, accurately monitoring the State of Health (SoH) of retired batteries has become a pivotal technology for ensuring their safe utilization and maximizing their economic value. In response to this need, this paper presents a highly efficient estimation model based on the multi-input metabolic gated recurrent unit (MM-GRU). The model leverages constant-current charging time, charging current area, and the 1800 s voltage drop as input features and dynamically updates these features through a metabolic mechanism. It requires only four cycles of historical data to reliably predict the SoH of subsequent cycles. Experimental validation conducted on retired Samsung and Panasonic battery cells and packs under constant-current and dynamic operating conditions demonstrates that the MM-GRU model effectively tracks SoH degradation trajectories, achieving a root mean square error of less than 1.2% and a mean absolute error of less than 1%. Compared to traditional machine learning algorithms such as SVM, BPNN, and GRU, the MM-GRU model delivers superior estimation accuracy and generalization performance. The findings suggest that the MM-GRU model not only significantly enhances the breadth and precision of SoH monitoring for retired batteries but also offers robust technical support for their safe deployment and asset optimization in energy storage systems.
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Intelligent Battery Management System for Electric Vehicles: AI-Driven Voltage Cell Prediction Using GRU and K-Means Clustering
(2025-01-01)
Pahaisuk, Narawit
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Pourbunthidkul, Supavee
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Phasukkit, Pattarapong
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Houngkamhang, Nongluck
The advancement of Electric Vehicles (EVs) requires intelligent Battery Management Systems (BMS) for accurate voltage prediction, ensuring battery reliability, longevity, and efficiency. Traditional BMS architectures rely on rule-based monitoring, which lacks predictive capabilities for early fault detection and proactive maintenance. This study presents an AI-driven BMS framework, integrating K-Means clustering and Gated Recurrent Unit (GRU) networks to enhance real-time voltage forecasting. Unlike conventional approaches that focus solely on clustering or deep learning, this research combines both methodologies to create a robust predictive system. K-Means clustering segments battery voltage data into operational groups, improving predictive accuracy by organizing similar voltage behaviors. GRU networks then capture sequential voltage dependencies, enabling precise voltage fluctuation predictions. Experimental validation was conducted using voltage data from 120 lithium-ion battery cells, recorded at 20 km/h under three load conditions (Load 0, Load 10, and Load 20) over a 5-minute duration. The findings demonstrate that K-Means clustering effectively categorizes battery voltage states, while GRU-based forecasting achieves high accuracy, reinforcing the practicality of AI-powered predictive maintenance. This study advances AI-driven BMS frameworks by demonstrating the integration of clustering and deep learning for voltage prediction, providing a foundation for real-time battery diagnostics and predictive analytics.

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