Model-Based Ambient Temperature Estimation for Control of an HVAC System
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Solution Overview
Problem
HVAC systems face challenges in accurately estimating ambient temperature due to discrepancies between measured and actual ambient temperatures, leading to poor comfort control and potential equipment instability, especially when dynamic offsets occur, affecting heating and cooling performance and relative humidity accuracy.
Innovation Solution
A model-based ambient temperature estimation method using a combination of transient models and sensor measurements, including conditioned air and return air temperature sensors, along with humidity sensors, to accurately estimate ambient temperature through regression analysis and adaptive parameter learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed correction is applied to reduce the difference between measured and ambient temperature, then the temperature measurement accuracy is improved, but the controller stability deteriorates due to dynamic offsets reversing the direction of measured temperature
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed correction approach to a dynamic, adaptive correction system. The controller continuously adjusts the correction based on real-time conditions, using feedback from temperature sensors and predictive algorithms to account for transient effects like airflow-induced temperature drops. This dynamic adaptation prevents the controller instability caused by static corrections becoming invalid under changing operating conditions.
Solution Approach 2:
The patent implements feedback mechanisms where the controller continuously monitors measured temperature, compares it with predicted ambient temperature from the predictive algorithm, and adjusts the correction dynamically. This closed-loop feedback system detects when transient effects cause measurement deviations and automatically compensates, maintaining both measurement accuracy and controller stability without requiring manual recalibration.
2Ease of operation
If the measured temperature is used directly for comfort control, then the control system is simple to operate, but the comfort control quality deteriorates due to uncorrelated temperature signals
Solution Approach 1:
The patent introduces an intermediary predictive algorithm that acts as a mediator between the simple temperature sensor and the comfort control system. This algorithm processes the raw measured temperature and generates a corrected ambient temperature estimate that accurately reflects actual thermal conditions. The intermediary layer maintains ease of operation by automatically performing corrections without user intervention while significantly improving comfort control accuracy through dynamic compensation of transient effects.
3Ease of manufacture
If relative humidity control uses measured temperature as feedback, then the control system is easy to implement, but the latent capacity control quality deteriorates due to temperature fluctuations reducing humidity accuracy
Solution Approach 1:
The patent applies feedback by continuously monitoring measured temperature and using the predictive algorithm to generate corrected ambient temperature values for humidity control. The system detects when temperature fluctuations occur and automatically compensates by using the predicted ambient temperature instead of raw measurements. This feedback mechanism maintains easy implementation while significantly improving relative humidity accuracy and latent capacity control by eliminating the correlation between temperature noise and humidity measurement errors.
Data Source
AI summary
A method is provided for controlling an HVAC system. The method includes obtaining observations of a measured temperature by at least one temperature sensor, and accessing a lumped-element model of the at least one temperature sensor in which a change in the measured temperature is expressed as a function of one or more independent variables including the measured temperature, and unknown parameter(s) including an ambient temperature of the conditioned space. A regression analysis of the lumped-element model is performed using observations of the one or more independent variables including the observations of the measured temperature, and estimates of the unknown parameter(s), to determine updated estimates of the unknown parameter(s) including an updated estimate of the ambient temperature. A value of the ambient temperature is determined from the updated estimate of the ambient temperature, and HVAC equipment is controlled using the value of the ambient temperature.