Vehicle Cabin Temperature Control With Predictive AI Intervention
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Solution Overview
Problem
Existing refrigerated transport vehicles face inefficiencies due to manual control and reactive measures, overburdening the power source, and lack of proactive optimization for varying cargo requirements and external factors, leading to suboptimal thermal and energy management.
Innovation Solution
An autonomous temperature and energy management system with a dedicated multi-source power supply and sensor suite, using machine learning to predict temperature changes and optimize power usage across multiple compartments, integrating solar panels and battery power to maintain target temperature ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual control and reactive measures are used to manage temperature, then the system is simple to operate, but the thermal and energy management efficiency deteriorates
Solution Approach 1:
The system uses autonomous temperature and energy management with machine learning models that automatically predict temperature changes and optimize power usage without manual intervention. The control unit autonomously draws from multiple power sources and adjusts refrigeration based on predicted thermal loads, enabling the system to manage itself efficiently.
Solution Approach 2:
The system performs preliminary actions by using machine learning models to predict future temperature changes and power requirements before they occur. This allows the control unit to proactively adjust refrigeration settings and power source selection in advance, optimizing thermal and energy management rather than reacting after temperature deviations occur.
2Use of energy by moving object
If a single power source is used for both vehicle propulsion and refrigeration, then the power supply system is simple, but the power source becomes overburdened and energy efficiency deteriorates
Solution Approach 1:
The power supply system is segmented into multiple independent sources including vehicle battery, portable power sources, and solar panels. Each power source can be independently selected and combined based on thermal load requirements and energy optimization goals, preventing any single source from becoming overburdened.
Solution Approach 2:
The system dynamically selects and combines power sources based on real-time conditions including thermal load predictions, battery state of charge, and energy optimization requirements. This dynamic power source selection allows the system to optimize energy efficiency by using the most appropriate power sources for each operating condition.
3Adaptability or versatility
If hardwired temperature parameters are used, then the control system is simple, but the adaptability to different cargo requirements and external factors deteriorates
Solution Approach 1:
The system uses machine learning models that dynamically adjust temperature parameters based on cargo type, external temperature conditions, and thermal load predictions. Instead of hardwired parameters, the system continuously optimizes temperature setpoints by changing parameters in response to varying cargo requirements and environmental factors.
Solution Approach 2:
The system incorporates continuous feedback from temperature sensors and environmental sensors to adjust control parameters in real-time. This feedback mechanism allows the system to adapt to different cargo requirements and external conditions by learning from actual temperature measurements and optimizing control strategies accordingly.
4Adaptability or versatility
If reactive temperature management is used, then the response time is short, but the ability to proactively optimize for different conditions deteriorates
Solution Approach 1:
The system performs preliminary optimization by using machine learning models to predict future temperature changes and power requirements before they occur. This allows the system to proactively adjust refrigeration settings and power source selection in advance, maintaining adaptability to different conditions while avoiding the time loss associated with reactive responses.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system efficiently manages thermal and energy loads autonomously, optimizing power usage and maintaining target temperatures across compartments, enhancing vehicle efficiency and reducing battery drain.
Implementation Method 1
The disclosed systems and methods provide an autonomous temperature and energy management system for temperature-controlled vehicles... integrating solar panels and battery power
Implementation Method 2
the control unit obtains and fuses live data from both internal and external sensors to autonomously predict a rate of temperature change for each individually temperature-controlled region
Implementation Method 3
The system efficiently manages thermal and energy loads autonomously, optimizing power usage and maintaining target temperatures across compartments
Data Source
AI summary
A control unit mounted within a vehicle having a plurality of individually temperature-controlled regions obtains a set of signals, the control unit spanning at least a portion of each region of the plurality of individually temperature-controlled regions, the control unit configured to control a temperature of each of the regions. The control unit inputs the set of signals into a machine learning model, receives, as output from the machine learning model, for a given region of the plurality of individually temperature-controlled regions, a rate of change of temperature, and determines whether the rate of change of temperature will take a temperature of the given region out of a target range. Responsive to determining that the rate of change of temperature will take a temperature of the given region out of the target range, the control unit performs an intervention on at least one hardware component within the vehicle.