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.
How to Cite
Charge Determination, Heat Pump System, Health Status Model
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