Energy-Efficient Dynamic Temperature Control for Plant-Based Food Cold Chains: A Model Predictive Control Approach
(2026) MTTM02 20261Production Management
Engineering Logistics
- Abstract
- Energy management in cold logistics is critical for sustainable cold chain transportation. However, conventional reactive temperature control strategies often induce excessive energy consumption and mechanical wear due to rigid air temperature tracking and inability to anticipate system thermal dynamics. Guided by the Design Science Research (DSR) methodology, this study proposes a simulation-based digital twin and Model Predictive Control (MPC) framework specifically tailored for the cold chain logistics of plant-based foods. First, a 2R2C thermal model was constructed, in which the specific thermodynamic properties of plant-based foods were rigorously mathematically derived to map the dynamic thermal coupling relationship between the... (More)
- Energy management in cold logistics is critical for sustainable cold chain transportation. However, conventional reactive temperature control strategies often induce excessive energy consumption and mechanical wear due to rigid air temperature tracking and inability to anticipate system thermal dynamics. Guided by the Design Science Research (DSR) methodology, this study proposes a simulation-based digital twin and Model Predictive Control (MPC) framework specifically tailored for the cold chain logistics of plant-based foods. First, a 2R2C thermal model was constructed, in which the specific thermodynamic properties of plant-based foods were rigorously mathematically derived to map the dynamic thermal coupling relationship between the carriage and the goods. Crucially, the proposed MPC shifts the control strategy from continuous temperature tracking to strict boundary compliance, treating the product spoilage threshold as a hard constraint rather than an optimization objective.
Through a quantitative, simulation-based comparative analysis, the robustness of the artifact is rigorously evaluated under varying parameters. Results demonstrate that the MPC can achieve significant energy reductions under various conditions, yielding a 34.47% energy reduction in the baseline scenario and a 44.18% reduction under winter conditions. Especially under high packaging and cargo thermal resistance conditions, the baseline PI controller encountered severe thermal decoupling and integral windup, resulting in the refrigeration unit being overloaded and excessive cooling. In contrast, MPC was able to proactively convert the large thermal inertia of the plant-based food into an active energy saving mechanism, significantly extending compressor coasting phases. Furthermore, MPC exhibits superior dynamic robustness in dealing with hardware degradation. As the insulation performance of the wall deteriorated, the energy saving capability compared to conventional control was further enhanced. Ultimately, this study provided a highly robust algorithm architecture for vehicular ECUs, unlocking extreme energy efficiency potential for complex cold chain scenarios. (Less) - Popular Abstract
- Giving Cold Chains a“Brain”: How Smart Algorithms Make Plant-Based Food Logistics Truly Green BY JUNYANG LI & XINYU XU (June 2026)
Division of Engineering Logistics, Lund University Faculty of Engineering (LTH)
Plant-based foods are celebrated for their remarkably low carbon footprint. However, there is a hidden cost: safely transporting them to your local supermarket requires massive amounts of energy. The complex physical structure of plant-based foods makes them extremely sensitive to temperature fluctuations, meaning refrigerated trucks must work overtime, guzzling electricity and diesel, which undermines the sustainability of these green products.
The root of the problem lies in the overly simplistic brain of current... (More) - Giving Cold Chains a“Brain”: How Smart Algorithms Make Plant-Based Food Logistics Truly Green BY JUNYANG LI & XINYU XU (June 2026)
Division of Engineering Logistics, Lund University Faculty of Engineering (LTH)
Plant-based foods are celebrated for their remarkably low carbon footprint. However, there is a hidden cost: safely transporting them to your local supermarket requires massive amounts of energy. The complex physical structure of plant-based foods makes them extremely sensitive to temperature fluctuations, meaning refrigerated trucks must work overtime, guzzling electricity and diesel, which undermines the sustainability of these green products.
The root of the problem lies in the overly simplistic brain of current refrigerated trucks. Traditional thermostats only passively monitor the air temperature inside the trailer. When a truck arrives at a store and opens its doors for delivery, warm air rushes in. The system immediately panics and blasts the compressor at full speed to compensate. This reactive approach acts only after the heat has entered, forcing the equipment to run inefficiently and waste energy.
To solve this, an Model Predictive Control (MPC) algorithm was introduced into cold logistics, giving refrigerated trucks the ability to see into the future. Instead of blindly tracking air, the algorithm builds a virtual model of the truck that calculates and protects the core temperature of the food itself. More importantly, it plans ahead like a chess player. If the system knows the truck doors will open in an hour, it proactively stores extra cold in the vegan mince when the compressor is most efficient, turning the cargo itself into a massive “thermal battery”. When outside temperatures peak, the system can let the frozen food slowly buffer the heat without triggering the compressor, avoiding operation when its Coefficient of Performance (COP), or overall cooling efficiency, drops to its lowest level.
Through rigorous computer simulations, this predictive smart algorithm could reduce the refrigeration unit energy consumption by 34.47% while strictly guaranteeing food safety, keeping the core temperature of the foods below -18 °C. When faced with severe external thermal shocks, such as prolonged door openings for unloading or the scorching midday sun, the system remains remarkably resilient. It has already anticipated these harsh conditions. The controller confidently relies on the pre-stored cold energy rather than panicking and forcing the compressor to work overtime at its least efficient moments.
MPC algorithm actively balances the delicate line between absolute thermodynamic safety and economic cost, refusing to waste power on blind overcooling. This research proves that by upgrading the software brain of cold logistics, companies can break the energy-efficiency bottleneck, ensuring that green food is delivered in a truly green way.
This popular scientific article is derived from the master thesis: Energy-Efficient Dynamic Temperature Control for Plant-Based Food Cold Chains: A Model Predictive Control Approach, written by Junyang Li and Xinyu Xu (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/student-papers/record/9234193
- author
- Xu, Xinyu LU and Li, Junyang LU
- supervisor
- organization
- course
- MTTM02 20261
- year
- 2026
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Model Predictive Control (MPC), Cold Chain Logistics, Plant-Based Foods, Energy Efficiency, Dynamic Temperature Control, Thermal Modeling
- other publication id
- 6056
- language
- English
- id
- 9234193
- date added to LUP
- 2026-06-11 15:27:01
- date last changed
- 2026-06-11 15:27:01
@misc{9234193,
abstract = {{Energy management in cold logistics is critical for sustainable cold chain transportation. However, conventional reactive temperature control strategies often induce excessive energy consumption and mechanical wear due to rigid air temperature tracking and inability to anticipate system thermal dynamics. Guided by the Design Science Research (DSR) methodology, this study proposes a simulation-based digital twin and Model Predictive Control (MPC) framework specifically tailored for the cold chain logistics of plant-based foods. First, a 2R2C thermal model was constructed, in which the specific thermodynamic properties of plant-based foods were rigorously mathematically derived to map the dynamic thermal coupling relationship between the carriage and the goods. Crucially, the proposed MPC shifts the control strategy from continuous temperature tracking to strict boundary compliance, treating the product spoilage threshold as a hard constraint rather than an optimization objective.
Through a quantitative, simulation-based comparative analysis, the robustness of the artifact is rigorously evaluated under varying parameters. Results demonstrate that the MPC can achieve significant energy reductions under various conditions, yielding a 34.47% energy reduction in the baseline scenario and a 44.18% reduction under winter conditions. Especially under high packaging and cargo thermal resistance conditions, the baseline PI controller encountered severe thermal decoupling and integral windup, resulting in the refrigeration unit being overloaded and excessive cooling. In contrast, MPC was able to proactively convert the large thermal inertia of the plant-based food into an active energy saving mechanism, significantly extending compressor coasting phases. Furthermore, MPC exhibits superior dynamic robustness in dealing with hardware degradation. As the insulation performance of the wall deteriorated, the energy saving capability compared to conventional control was further enhanced. Ultimately, this study provided a highly robust algorithm architecture for vehicular ECUs, unlocking extreme energy efficiency potential for complex cold chain scenarios.}},
author = {{Xu, Xinyu and Li, Junyang}},
language = {{eng}},
note = {{Student Paper}},
title = {{Energy-Efficient Dynamic Temperature Control for Plant-Based Food Cold Chains: A Model Predictive Control Approach}},
year = {{2026}},
}