Using Charge Determination Design of Experiments to Develop A Refrigerant Charge Health Status Model for Heat Pump Systems
Bibliographic record
Abstract
Refrigerant based heat pump systems are becoming an integral system in electric vehicle architectures due to their high efficiencies in providing heating and cooling to people and components within the car. An important component in heat pump systems that determines optimal efficiency is the amount of refrigerant. As such, the capability to model refrigerant charge helps quantify the health status of the heat pump system, whereby the lack or over abundance of refrigerant in a heat pump refrigerant system leads to various other component failures, e.g., liquid slugging, compressor overheating, material fatigue in heat exchanger, and degraded/stuck expansion valves. In designing a heat pump system, engineers need to perform a set of design of experiments to determine an optimal refrigerant charge based on a set of performance metrics in the presence of certain noise factors. This optimal refrigerant charge provides conditions where the heat pump system operates efficiently in both heating and cooling, in addition to facilitating operational conditions that will not lead to secondary component degradation or damage. The search for optimal refrigerant charge is classified as refrigerant charge determination, whereby engineers incrementally increase the refrigerant in the heat pump system in operation of heating/cooling and collect data about performance metrics. Some of the key performance metrics used to determine efficiency of a heat pump system include i) compressor inlet superheat temperature, ii) condenser outlet subcool temperature, iii) compressor high side pressure, iv) compressor low side pressure, v) condenser outlet pressure, and vi) condenser quality estimate. Furthermore, this process follows design of experiments concepts and is performed for both heating and cooling modes of operation. In this paper, we leverage refrigerant charge determination as a training data source to develop refrigerant charge models, where several performance metrics are health indicators used as model inputs and the amount of refrigerant added to the heat pump system are ground truth refrigerant charges used as model outputs. In this paper we develop regression models to estimate the total refrigerant charge, which is used to classify different health states of refrigerant based on levels of performance degradation corresponding to specific refrigerant charge thresholds. We trained a robust linear regression model using this charge determination data and found that the worst case estimation error was less than 10% with respect to the refrigerant charge grouth truth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".