Super‐heating degree responsive control strategy of variable refrigerant flow air conditioning system for energy saving and stable operation

The control strategy is important for variable refrigerant flow (VRF) air conditioning system to realize energy saving and stable operation. In this work, the super‐heating degree (SHD) responsive control strategy was developed to dynamically adjust the respective SHD target value (ΔTe,tar) of indoor units so as to adapt to the variable cooling load. The capacity code (Ecode) is introduced into the control model to quantify the reference target value of SHD. To responsively adapt to variable cooling load, the correction target values of SHD based on discharge temperature and deviation of the outlet air temperature are introduced in the SHD model for operation protection of the compressor and the consistency of refrigerant distribution. The enthalpy difference analogy method is developed to estimate the performance of the VRF system including cooling capacity, electric power, and energy efficiency ratio (EER). The performance was tested under the conditions of different ΔTe,tar. Experimental results demonstrate that the ΔTe,tar is an important factor to determine the operation frequency, which affects cooling capacity, power, EER, and operation status of the system. The cooling capacity and electric power at the ΔTe,tar of 9°C was about 55.2% and 63.5% lower than those at the ΔTe,tar of 1°C, and the corresponding EER was improved by 22.6%. To further verify the SHD control strategy, the VRF system was tested by the responsive control model under the condition that Ecode and ΔTe,tar were constantly automatically adjusted and changed with the actual indoor ambient temperature. Experimental results demonstrate that the VRF system can achieve a good and uniform cooling effect and realize energy saving and stable operation according to the responsive super‐heating degree control.


| INTRODUCTION
With the development of the economy and the improvement of living standards, building energy consumption increases dramatically. According to statistics, it accounts for more than one-third of the total social energy consumption in China 1-3 and 40% in Europe and the United States. 4 The air conditioning and heating equipment contribute to more than 60% of the building's energy consumption. 5 The variable refrigerant flow (VRF) air conditioning system, as the most promising and convenient central air conditioning system to decrease energy consumption, has been used in an increasing number of buildings. [6][7][8] According to the statistics of Central Air Conditioning Business Information, 9 in recent 2 years, the market sale proportion of the VRF systems to the total central air conditioning units has reached more than 50% in China. The current designed work concentrates the most on energy efficiency of the VRF system. 10 The control strategy becomes the core technology to improve the energy efficiency ratio (EER) and guarantee reliable operation of the VRF system. 11,12 In the aspect of energy savings, the current research mainly focuses on control strategies and the corresponding control algorithm of compressor output capacity (COC), which became a hot topic of the VRF system recently. 13 For control strategies of COC, the operation frequency of the compressor (INV_F) is the most important parameter affecting the EER. It is adjusted according to a certain evaporation temperature in cooling mode and condensation temperature in heating mode as control objectives, respectively. [14][15][16] Under the conditions of partial load and wide ambient temperatures, the control strategy with variable or dynamic evaporation temperature and condensation temperature can improve EER (in cooling mode) and COP (in heating mode). 6,[16][17][18][19][20][21] For example, Zhao et al. 6 presented a control strategy with variable evaporation temperature under partial load conditions to adjust the INV_F. Tu et al. 17 built the control model of variable condensation temperature target for the VRF system to improve COP and ensure stable operation at a relatively higher ambient temperature. Tu et al. 18 built a dynamic control model of evaporation temperature target to adjust the INV_F of the VRF system with a sub-cooler. Yun et al. 19 developed a load determinant algorithm to evaluate the cooling loads of indoor units to increase the target evaporation temperature and decrease the COC for higher EER. Furthermore, Yun et al. 20 developed the load-responsive high-pressure control of a VRF system to decrease the high pressure from 30 to 25 kgf/cm 2 to reduce the INV_F and energy consumption under part-load conditions. Lee et al. 21 built the evaporation temperature control model according to the part-load factor. The verification results demonstrate that the built refrigerant evaporation temperature control strategy can reduce energy consumption by increasing the evaporation temperature. Zhu et al. 22 built a model-based online optimal control strategy for the air conditioning system and a cost function based on energy consumption and thermal comfort. Results show that the optimal strategy can effectively reduce the energy consumption of the system. Tu et al. 23 investigated the heating control strategy of VRF system with multimodule outdoor units, proposed the INV_F adjustment method, and built the control model of module switching and equal INV_F allocation to realize high energy efficiency.
The essence of these control strategies is to increase the evaporation temperature in cooling mode and reduce the condensation temperature in heating mode to decrease the INV_F for reducing energy consumption.
The operation stability of the system is the other important indicator for evaluating the system's performance. It not only affects the operation reliability of the VRF system but also affects the stability of the indoor ambient temperature. The super-heating degree (SHD) of the evaporator can make a great influence on the performance and operation stability of the refrigeration and heat pump system. In previous studies, the SHD is defined as the temperature difference between the outlet temperature of the evaporator and the saturation temperature corresponding to the outlet pressure of the evaporator. A suitable SHD is crucial for the stable operation of the system. If the SHD is below a certain value, it will cause a hunting phenomenon. 24,25 The hunting phenomenon has been found in refrigeration and heat pump systems controlled by thermostatic expansion valves (TEVs) 26,27 and electronic expansion valve (EEVs). 28,29 This phenomenon is more likely to occur in refrigeration and heat pump systems controlled by TEVs than those controlled by EEVs because the EEV can make a quicker response to the abrupt change in the refrigerant flow rate than TEV. 30 Huelle 25 first proposed the concept of minimal stable SHD (MSS), which was defined as a critical minimal SHD when a refrigeration system could exhibit unstable operation. The existence of such a MSS line was experimentally verified by Chen et al. 31 Then, Chen et al. 32 proposed a new modified MSS line with a maximum MSS value and a minimal MSS value based on experimental results. To mitigate the possible system hunting, some control strategies were proposed. Xia et al. 33 developed a new SHD controller to slow down the transfer rate of the SHD signal, which can mitigate the hunting amplitude. Some research results [34][35][36] demonstrate that increasing the thermal resistance between TEV's sensing bulb and the tube wall or increasing the time constant of TEV's sensing bulb is beneficial to reduce hunting. Similarly, increasing the time constant of EEV's temperature sensor would benefit operational stability. 37 Yan et al. 38 developed an adaptive variable speed controller to seasonably regulate the SHD and found that the method achieved stable, effective, and fast control of the SHD. Lin et al. 39 considered that the reference superheat settings can be determined by an optimization procedure to obtain the resultant steadystate superheat. The control experiments indicate that the proposed controller can successfully regulate indoor temperatures and maintain steady-state superheat temperatures at acceptable levels.
Control algorithms are the technical means to achieve high EER and stable and reliable operation. Some common control algorithms include proportional and integral (PI) 40,41 and proportional, integral, and derivative (PID), 42 fuzzy control, [43][44][45] multi-input and multioutput control (MIMO), 46,47 and artificial neural network model in the control algorithm. [48][49][50][51] For example, Chung et al. 30 adopted an artificial neural network (ANN) model to predict the amount of cooling energy consumption for the different settings of the VRF cooling system's control variables. The optimized model demonstrated its prediction accuracy and proved its potential for application in the control algorithm for creating a comfortable indoor thermal environment. Li et al. 49 developed an artificial neural network based on a dynamic model to control indoor air temperature and humidity simultaneously by varying the compressor speed and supply fan speed. Guo et al. 50 presented the optimized back propagation neural network (BPNN) method for fault diagnosis of the VRF air conditioning system in the heating mode. A feature variable set optimization approach of diagnosis models is proposed based on the data mining method. Kim et al. 52 proposed model-based multi-objective optimal control of a VRF system combined system with dedicated outdoor air system (DOAS) using a genetic algorithm under heating conditions to optimize the multiobjective functions of the thermal and humidity comfort, indoor air quality, and total energy consumption. For these control algorithms, PI and PID technologies are mature and have been applied in actual product development and design. ANN algorithm is currently only at the academic research stage and has not been applied in actual product development. For air conditioning systems with many control variables, especially VRF systems, the MIMO control algorithm is particularly important, which can take into account the energy efficiency, safety, and stability of unit operation.
Although some progress on the energy efficiency and stable operation of the refrigeration and heat pump system has been made, few literature studies on the SHD responsive control strategy of the VRF system with multimodule for energy saving and stable operation have been reported. This study is to develop a new SHD responsive control strategy of refrigerant flow amount distribution by adjusting the EEVi openings to automatically adapt to variable ambient temperatures, indoor cooling loads, and compressor operating protection conditions, so as to achieve good cooling effect, stable operation of compressor, and consistency of cooling effect. This SHD is different from traditional superheat. It is defined as the temperature difference between the outlet temperature and the inlet temperature of the evaporator.

| System configuration
The system configuration diagram of the VRF system with two modules and three indoor units is shown in Figure 1, where the arrows indicate the direction of refrigerant flow in cooling operation mode. For the VRF system with more modules and dozens of indoor units, the principle diagram is similar to that in Figure 1.
In Figure 1, T D,TS , T def , T ei, and T eo are, respectively, the discharge temperature and suction temperature of the inverter-driven compressor, condenser coil temperature, inlet temperature, and outlet temperature of the evaporator, which are measured by the relevant temperature sensors. T ao and T ai are outdoor ambient temperature and indoor ambient temperature.
The outdoor unit of the VRF system is composed of two modules. The liquid pipes and vapor pipes of the two modules are converged by the corresponding joint pipes, and connected to the indoor units by the corresponding branch pipes. Every indoor unit is equipped with an electronic expansion valve (EEVi) at the evaporator inlet, which is used to control the refrigerant flow rate into the indoor unit. The indoor ambient temperature (T ai ) is used to evaluate the cooling effect and judge the thermo-on or thermo-off condition of the indoor unit. The difference between the inlet temperature (T ei ) and outlet temperature (T eo ) of the evaporator in the indoor unit is calculated as the current super-heating degree of the indoor unit ( T Δ e,cur ).

| Control strategy
When the outdoor unit PCB controller receives cooling startup command from any indoor unit, the system performs flexible startup operation. The compressor starts to operate at a suitable INV_F for 5 min, such as 45 rps. During the flexible startup stage of the compressor, T ei and T eo of the evaporator are unstable, so the SHD value cannot be used to control its opening. A reasonable fixed initial EEVi opening, which is generally 120 PLS according to the test experience, can be used to control the refrigerant flow. For the thermo-off indoor unit, the EEVi opening is closed.
After the flexible startup of the system for 5 min, the refrigerant throttling and flow amount distribution are carried out by adjusting the respective EEVi opening according to a certain SHD of the evaporator as the control target. Ultimately, T Δ e,cur of the actual operation reaches the target value of the SHD ( T Δ e,tar ). The model of the T Δ e,tar is expressed as Equation(1a).
Based on previous research experience, to mitigate or even eliminate hunting, the actual superheat target in the controller is expressed as follows: where T Δ e,E is a reference target value of the SHD determined by the capacity code (E code ), and T Δ d,tar and T Δ avg,tar are the correction values of the target SHD for discharge temperature protection and uniform distribution of refrigerant through every indoor unit. These two correction values are, respectively, used to prevent the compressor from overheating and wet compression and avoid the inconsistent cooling effect of the indoor unit.
E code as the dimensionless number of COC requirement was introduced to quantify T Δ e,E . The values of E code under different temperature differences ( T Δ ai,set ) between the T ai and set temperature (T set ) are shown in Figure 2. It can be explained by the mathematical model. If the calculated T Δ ai,set takes on an upward trend, the E code of an indoor unit can be expressed as Equations (2a), (2b), and (2c).
T Δ ai,set is a dynamic parameter that varies with the change of the T ai because the T ai decreases gradually as the refrigeration process goes on. Equation (2a) means that the number in the bracket is the adjustment value of E code . For Condition A, when T Δ ai,set remains within the corresponding temperature range for 5 min, E code is responsively increased by 1 to dynamically adjust T Δ ai,set to adapt to the variable cooling load. The purpose is to increase E code to reduce T Δ e,E for increasing the EEVi opening, enlarging the refrigerant flow rate, and improving the cooling effect. For Condition B, if T Δ ai,set cannot keep within the corresponding temperature range for 5 min, E code will dynamically be adjusted with the variation of T Δ ai,set . From Equation (2a) and Figure 2, it can be seen that when an indoor unit is turned on by the wire controller, if T Δ ai,set is less than 1°C, the indoor unit keeps in the thermo-off state. Only when T Δ ai,set is larger than 1°C, the indoor unit begins to be thermo-on. For the whole VRF system, when E code of the system is larger than 5, the compressor begins to run.
On the contrary, when T Δ ai,set begins to drop, the expression of E code is written as Equation (3).
The meaning of x sgn( ) in Equation (3) is similar to that in Equation (2b). It should be noted that when T Δ ai,set is between −1°C and 0°C for 5 min, the value of the E code is subtracted by 1°C. From Equation (3) and Figure 2, it can be understood that for the thermo-on indoor unit, when the current T ai drops to 1°C lower than the T set , the indoor unit will stop operation and its EEVi will be closed.
The corresponding target values of T Δ e,E under different E code are shown in Figure 3.
In Figure 3, X is the initial T Δ e,E of an evaporator when E code is larger than 13. Taking into account the safety of compressor operation and the full play of the evaporator performance, it is more appropriate to take X as 1°C. Thus, the refrigerant flows through the evaporator and undergoes sufficient heat exchange in the evaporator to form superheating vapor flowing into the compressor to avoid wet F I G U R E 2 Variation of E code with ΔT ai,set .  Figure 4.
In Figure 4, T d1 and T d6 are, respectively, the minimum and maximum values of the discharge temperatures of the compressor in thermo-on modules A and B, and T d2 -T d5 are, respectively, the average value of the discharge temperatures in thermo-on modules A and B. According to the design experience and requirements for the safe operation range of the compressor, the suitable values of T d1 -T d6 are 55°C, 60°C, 70°C, 75°C, 90°C, and 95°C, respectively. T Δ d,tar0 is an original correction value of the target SHD. Considering that when the compressor discharge temperature is between 70°C and 75°C, the operation range is reasonable, and there is no need to correct the target value of T Δ d,tar . So T Δ d,tar0 is set as 0°C.
Furthermore, the consistency control of the cooling effect of all the indoor units should be added to the SHD control model. The deviation of the outlet air temperature ( T Δ avg,tar ) can be evaluated by the corresponding outlet air temperature of a certain thermo-on indoor unit and the average outlet air temperature of all the thermo-on indoor units.
According to previous work, 7 experimental results demonstrate that the outlet air temperature is approximately equal to T eo , so ∆T avg,tar is calculated as Equation (4a) and (4b). And In Equation (4b), N is the number of thermo-on indoor units and i is the serial number of the thermo-on indoor unit.
To prevent T Δ e,tar from being overcorrected, its value should be limited between 0 and 9, which can be expressed as Equation (5).
The above SHD responsive control model can dynamically adjust the target value of SHD according to the cooling load demand, discharge temperature, and refrigerant distribution to achieve a good cooling effect, reliable compressor operation protection, and uniform cooling effect of every indoor unit. The flow chart of the super-heating degree responsive control strategy is shown in Figure 5.

| Experimental device
The schematic diagram of the VRF system is shown in Figure 6.
The VRF system is composed of two outdoor modules and 11 indoor units. The rated cooling capacity of every module was designed to be 33.5 kW (12HP). Every module mainly consists of an inverter-driven compressor (mode: E655DHD), a finned condenser, a 750 W fan motor, and an EEV in the outdoor unit (EEVo). The model of the indoor unit is a low static pressure duct unit, whose rated cooling capacity is 2.8 kW. The fan motor power of an indoor unit is about 50 W.
The system was installed in the office building of a factory in Zhongshan City, China. Eight indoor units were mounted on both sides of the large office on the second floor. Other three indoor units as studied objects were installed in the offices on the third floor, shown in Figure 7.

| Estimation method of Q c , EP, and EER
The enthalpy difference analogy method is used to calculate the Q c , EP, and EER.
According to the cooling cycle principle, the cooling capacity (Q c,0 ) and electric power of the compressor (EP c,0 ) under the standard test conditioning can be expressed as Equations (6a) and (6b).
In Equations (6a) and (6b), h 1 , h 2, and h 4 are, respectively, the specific enthalpy at the suction port, the discharge port of the compressor, and the inlet point of the evaporator, V 1 and γ 1 are, respectively, the volumetric flow rate and specific volume of the refrigerant into the suction port of the compressor. The subscript "0" means the standard test condition.
The standard test conditions indicate that the evaporation temperature (T eva ) and the condensation temperature (T con ) are 7.2°C and 54.4°C, respectively, and the suction super-heating degree (ΔT s ) and the sub-cooling degree (ΔT sc ) are 11.1°C and 8.3°C, respectively. The T eva and T con are the saturation temperatures corresponding to the suction pressure (P s ) and the discharge pressure (P d ), respectively. The ΔT s is defined as the temperature difference between the suction temperature (T s ) and the T eva . The ΔT sc is defined as the temperature difference between the T con and the liquid pipe temperature (T liq ).
Under other test conditions, the cooling capacity (Q c ) and electric power of the compressor (EP c ) can be written as Equations (7a) and (7b).
Under the condition of the same INV_F, the volume flow rate can be considered unchanged, which is expressed as Equation (8).
So the Q c and EP c can be written as Equations (9a) and (9b). Under the different INV_F and standard test conditions, Q c,0 and EP c,0 can be calculated according to the following expression, which were provided by the compressor manufacturer.

Q A A T A T A T A T T A T A T A T T A T T A T
= + + + + + + + + + , c,0 0 1 eva 2 con 3 eva 2 4 eva con 5 con 2 6 eva 3 7 con eva 2 8 eva con 2 9 con 3 (10a)

B B T B T B T B T T B T B T B T T B T T B T
c,0 0 1 eva 2 con 3 eva 2 4 eva con 5 con 2 6 eva 3 7 con eva 2 8 eva con 2 9 con 3 (10b) Taking the INV_F of 30, 60, and 90 rps as an example, these coefficients including A 0 -A 9 and B 0 -B 9 in Equation (10a) and (10b) are shown in Tables 1 and 2. This calculation method about the Q c,0 and EER is called the compressor 10 coefficient model method. The calculation method and parameters in the compressor 10 coefficient model shown in Tables 1 and 2 in the manuscript were provided by Guangzhou Hitachi Compressor Company.
For VRF with multimodule, the Q c and EP of the system are the sum of the cooling capacity and electronic power of two modules, which can be expressed as Equations (11a) and (11b).
So the EER of the system can be written as Equation (12).
where EP f is the total electric power of the fan motors in the outdoor units and indoor units.

| Test method
The experimental purpose was to test the effects of the responsive control strategy of the SHD on the performance of the VRF system and verify the rationality and feasibility of the control strategy. The effects of different T Δ e,tar on the performance of the VRF system were tested. The experiment was tested during the process of continuous operation without thermo-off operation. To prevent the indoor units from shutting down during the process of test, the T set is preset to be the lowest value of 16°C. The experiment was carried out for about 1 h under the condition of different T Δ e,tar , such as 1°C, 3°C, 5°C, 7°C, and 9°C.
To verify the SHD control strategy, the VRF system was tested by the responsive control model. the E code and T Δ e,tar were constantly automatically adjusted and changed with the actual T Δ ai,set . The experiment was made to verify the responsive control strategy of the SHD for energy savings and stable operation. T Δ e,tar was responsively controlled by the main control program according to the control model.

| RESULTS AND DISCUSSION
Considering the fact that eight indoor units were installed in a large hall on the second floor, and the outlet air and return air of indoor units interfered with each other, No. 1, 5, and 11 indoor units in the separate office of the third floor were taken as the analysis objects.

| Influence of T
Δ tar e,

on energy saving and stable operation
The test was carried out when T set was 16°C and T Δ e,tar of every indoor unit was set to be 1°C, 3°C, 5°C, 7°C, and 9°C, respectively. The experimental results of variable trends of the INV_F with time under different T Δ e,tar are shown in Figure 8.
In Figure 8, the INV_F of the compressor in module A is the same as that in module B because the COC is distributed according to the control principle of equal output capacity ratio allocation. The output capacity ratio is defined as the ratio of the actual output capacity of the compressor to its maximum output capacity. Figure 8 shows that the INV_F reduced with the increase of T Δ e,tar after the flexible startup of the system. It can be understood that when T Δ e,tar increased, the EEVi openings of the indoor units reduced to satisfy T Δ e,tar , resulting in the decrease of the suction pressure (P s ). To achieve the target pressure, INV_F decreased T A B L E 1 Coefficients in calculating Q c,0 . accordingly. On the other hand, at the same T Δ e,tar , INV_F reduced as the test went on. It can be explained that with the progress of the experiment, the actual operation SH ( T Δ e,act ) gradually increased to achieve T Δ e,tar , which led to the decrease of the SEEVi opening, P s , and the INV_F. As an example of ΔT e,tar = 7°C, the test results of EEVi opening, T Δ e,act and P s,A at ΔT e,tar = 7°C with time is shown in Figure 9.

INV_F (rps)
Considering the fact that the master module of the VRF system adjusts the total COC of two modules based on its P A s, and distributes the COC to the slave module according to the control principle of the equal output ratio, only the low pressure of the main module of the VRF system is shown in Figure 9.
The calculated results of the Q c and total EP consumed by the compressor and all fan motors are shown in Figures 10 and 11. The corresponding results of EER are shown in Figure 12. Figures 10 and 11 show that the Q c and EP took on a decreasing trend under the same T Δ e,tar with the progress of the experiment. On the other hand, the Q c and EP reduced with an increase of T Δ e,tar . But the variation trends of the EER were opposite to those of the Q c and EP. The EER increased with the increase of T Δ e,tar . From Figures 10-12, it can be seen that INV_F is an important factor affecting the Q c , EP, and EER of the system. For example, when T Δ e,tar was, respectively, 9°C On the other hand, from the perspective of system operation status, there is a weak hunting phenomenon during the transition stage from soft start to normal operation controlled by the responsive SHD control. The VRF system could operate stably during the entire operation process except for this stage.
Furthermore, the experimental results of variable trends of the T ai with time under different T Δ e,tar are shown in Figure 13.
From Figure 13, it can be seen that during the process of a flexible startup within a few minutes, the cooling effect was good, T ai dropped obviously, and then changed slowly, especially when T Δ e,tar was set at 7°C and 9°C. After an hour of refrigeration operation, T ai was decreased by 5 limited extent, otherwise, the cooling effect cannot be guaranteed. Therefore, it is necessary to introduce E code and determine T Δ e,tar to control the appropriate INV_F and obtain a good cooling effect.

| Verification of responsive control model
The ambient temperatures of the indoor rooms with No. 1, 5, and 11 indoor units were, respectively, 29.0°C, 29.5°C, and 29.2°C before the VRF was operated. The target indoor ambient temperature of the three indoor units was set to be 26°C for 20 min and then 25°C. The VRF system was tested by the responsive control model for 90 min.
As an example of the No. 1 indoor unit, the test results about variable trends of operation parameters of the indoor unit with time are shown in Figure 14. Figure 14 shows that E code and T Δ e,tar were constantly adjusted and changed with the actual T Δ ai,set . In detail, as the test progressed, T Δ ai,set decreased gradually, the and T B d, were stable at about 80-84°C, T Δ e,tar was no longer corrected by the discharge temperatures of the two compressors. On the other hand, the T Δ eo,avg of the No. 1 indoor unit was kept at about 1-3°C, which does not meet the condition of correcting ∆T e,tar . Finally, ∆T e,tar was stabilized at 7°C, shown in Figure 14.
The experimental results of the operation parameters including the P d , suction pressure (P s ), T d, and INV_F of two modules are shown in Figure 15, where the compressor frequencies of two modules (INV_F A and INV_F B ) are exactly the same.
From Figure 15, it can be seen that the VRF system operated stably and reliably. The INV_F rapidly increased to the maximum frequency of 100 rps after about 5 min of the startup operation to realize rapid refrigeration. When T ai approached T set , the INV_F decreased from 100 to 70 rps, which is conducive to reducing energy consumption.
The uniformity of the refrigeration effect and refrigerant distribution is reflected in Figure 16. Figure 16 shows that the indoor ambient temperatures of three offices gradually approached the set temperature of  Moreover, the deviations between the outlet air temperature of No. 1, 5, and 11 indoor units and the average outlet air temperature of all the indoor units were between −3°C and 3°C, which meets the requirements of uniform distribution of refrigerant and consistency of refrigeration effect. The air temperature difference between rooms No. 1, 5, and 11, which can reflect the consistency of the refrigeration effect, was less than 0.5°C.
The calculated Q c , EP, and EER are shown in Figure 17. Figure 17 demonstrates that under the responsive SHD model, EP decreased, but EER increased with the progress of the test because the INV_F reduced from 100 to 70 rps. After half an hour of the system operation, the EER at the responsive SHD of 7°C reached about 3.4, which was equivalent to that at the SHD setting value of 7°C, shown in Figure 12. It can be concluded that the VRF can achieve high energy efficiency and realize energy saving under the condition of responsive SHD control.
Furthermore, in Figure 17, although the Q c shows a downward trend, combined with Figure 16, it can be seen that the Q c can satisfy the demand for indoor thermal load and obtain a good cooling effect.
From the above test results and analysis, it can be seen that T Δ e,tar can be adjusted according to the thermal load requirement, discharge temperature, and refrigerant distribution through the indoor units. The responsive control model can make the VRF system run stably, obtain a good cooling effect, and achieve energy saving.

| CONCLUSIONS
The super-heating degree responsive control strategy is developed to dynamically adjust refrigerant flow amount distribution so as to adapt to the variable cooling load. The capacity code (E code ) is introduced into the control model to characterize the target SHD of the indoor unit ( T Δ e,tar ). The following conclusions and control measures are helpful to reduce energy consumption and ensure the stability and reliability of the VRF system operation.
1. For the responsive control model, the E code characterized by temperature difference ( T Δ ai,set ) between the indoor ambient temperature (T ai ) and set temperature (T set ) can be used to define the reference target SHD ( T Δ e,E ). To ensure the reliable operation of the compressor and the consistency of indoor temperature, the target SHD correction values based on the discharge temperature and consistency of cooling effect are introduced into the control model of T Δ e,tar . The responsive control model comprehensively considers energy efficiency, cooling effect, and protection for the compressor. 2. T Δ e,tar is an important control parameter for the operation state of the system, energy efficiency, cooling effect, and consistency of refrigeration effect. Under the condition of the responsive control, the EER can reach 3.4, and the indoor ambient temperatures of three offices can be stabilized at the set temperature of 25°C after about 20 min of the operation. The indoor air temperature deviation between these three rooms was less than 1°C. 3. The responsive control strategy can make EEVi adapt to variable cooling loads and achieve rapid cooling effects and high energy efficiency. It is very beneficial for the VRF system to realize cooling energy savings under partial cool load conditions.