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Transport Refrigeration Units (TRUs) in vans consume a significant amount of energy, hence an energy-efficient controller is essential. Traditional MPC controllers are computationally demanding, and onboard vehicle controllers often lack the necessary processing power.
To address this, Hierarchical MPC (H-MPC) is proposed, which separates planning and operating control with different sampling times and prediction horizons.
From our simulation results, we found: Up to 10% energy savings compared to traditional MPC. And up to 90% reduction in computation time, making it competitive for real-time applications.
Future work includes further optimizing H-MPC parameters. Integrating machine learning to improve predictive accuracy and adaptability. Predicting door-opening events to enable pre-cooling and enhance system efficiency.
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IEEE/ICACT20250244 Slide.20
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