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Experimental and numerical study of air flow and temperature variations in an electric vehicle cabin during cooling and heating
Applied Thermal Engineering 137 (2018) 356–367
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Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng
Research Paper
Experimental and numerical study of air flow and temperature variations in an electric vehicle cabin during cooling and heating Yiyi Mao1, Ji Wang1, Junming Li
T
⁎
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China
H I GH L IG H T S
numerical results agreed well with the experiments. • The vehicle motion increased the thermal losses and gains from the cabin. • The thermal stored in the seat strongly affects the air temperature variations. • The • The highest velocities were near the inlets and outlets of the computational domain.
A R T I C LE I N FO
A B S T R A C T
Keywords: Passenger comfort Vehicle cabin Air conditioner
A series of experiments was conducted in an electric vehicle with four occupants while parked or being driven in the winter and the summer. The air and surface temperatures in the vehicle cabin and the heat fluxes on different closure surfaces were measured during the cooling and heating periods. With the temperature were then modeled in a transient numerical simulation of the electric vehicle cabin the air flow and temperature variations inside the cabin analyzed for the same conditions as in the experiments. The numerical model accurately predicted the temperature variations with differences of less than 10% compared with the experimental data. Analysis of the air temperature and velocity distributions from the transient model showed that the thermal storage in the seat significantly affected the thermal comfort adjustments for the transient conditions. The vehicle movement increased the convective heat transfer at the outer surface of the cabin, which increased the heat transfer between the outside and the inside air and extended the time to adjust the thermal comfort.
1. Introduction The thermal comfort in vehicle compartments is an important concern for occupants due to the considerable amount of time spent in vehicles. Vehicles have higher heat losses or gains than buildings because of their structure and materials. The thermal comfort can be greatly influenced by changes in the outside temperature, air velocity and solar radiation. Especially in extreme summer or winter conditions, the air temperatures can be quite non-uniform and variable around the occupants. For instance, the temperature inside the car can reach 72 °C in summer with an outdoor temperature of 34 °C and a solar radiation of around 800 W/m2 according to Grundstein et al. [1]. In such situations, the heater and air conditioner cannot easily control the vehicle compartment thermal environment. According to the study of Tsutsumi et al. [2], the car cabin
⁎
1
Corresponding author. E-mail address: [email protected] (J. Li). These authors contributed equally to this work and should be considered co-first authors.
https://doi.org/10.1016/j.applthermaleng.2018.03.099 Received 6 May 2017; Received in revised form 25 March 2018; Accepted 29 March 2018 Available online 29 March 2018 1359-4311/ © 2018 Elsevier Ltd. All rights reserved.
environment affects driver's comfort, performance and fatigue. Allnutt and Allan [3] pointed out that the improved climate within vehicles is critical not only to passenger comfort but also to their safety. Dannen et al. [4] found that driving performance was affected by the cold and hot ambient conditions. Therefore, thermal comfort has become one of the most important criteria for evaluating vehicle performance. The tightening fuel economy constraints and the use of environmentally safe refrigerant is driving the need to develop more efficient car air conditioning systems to improve energy savings. Efficient systems are especially significant in emerging electric vehicles (EV). Unlike conventional vehicles, which utilize engine waste heat to heat the compartment, the thermal environment in an EV is completely controlled by its electrical driven heating, ventilation and air conditioning (HVAC) systems. Thus, the design of the air flow and the temperature distributions in electric vehicle cabins during both cooling
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β δ η θ λ, κ ρ ω
Nomenclature a A h I Nu Pr q, Q Re t, T
atmospheric extinction coefficient sun azimuthal angle heat transfer coefficient (W·m−2·K−1) solar radiation Nusselt number Prandtl number heat flux (W) Reynolds number temperature (K)
Subscripts i, in o, out tol dir dif ref
Greek symbols α φ
sun altitude thickness (mm) hour angle surfaces inclination angle thermal conductivity (W·m−1·K−1) material absorptivity solar inclination on the experimental date
surfaces azimuthal angle geographical latitude
inside outside total direct diffuse ground-reflected
regions model was then developed using the commercial software FLUENT. The flow field and temperature distributions in the electric vehicle cabin were simulated during cooling and heating periods. The simulation results are compared with the experimental data to validate the model.
and heating periods is especially important for EV designs. There have been many experimental and numerical studies of the air flow and thermal characteristics of passenger compartments. Sevilgen and Kilic [5–8] did series of investigations of the unsteady conditions in the passenger compartment with 3-D transient numerical analyses of the airflow and heat transfer in a vehicle during heating. They used a virtual manikin divided into 17 parts with real dimensions and a realistic physiological shape in a 1:1 scale automobile cabin model. Later, Klilic and Sevilgen used a hex-core mesh to reduce the computational time. They found that the flow field in the vehicle cabin was mainly affected by the radiation heat transfer and little affected by the boundary conditions on the manikin surfaces. They also found that different velocity and temperature distributions were obtained with different types of inlet vents for cooling with the same heating and air condition systems’ cooling load. Zhang et al. [9,10] simulated 3-D temperature distributions and the flow field in compartments with and without passengers during cooling. They found that a good method to reduce the air-conditioner cooling load was to increase the inlet air temperature, not by reducing the volumetric inlet air flow rate. They also concluded that the outside temperature has appreciable effect on the cooling load. Chien et al. [11] also numerically simulated the thermal behavior in a vehicle. They used e-NTU theoretical equations to calculate the air conditioner cooling capacity. Their predicated temperature distributions in the vehicle cabin were in good agreement with measured values. Zhang [12] coupled a solid wall and a fluid flow heat transfer model of a vehicle cabin to simulate the ‘temperature-flow’ characteristics during air-conditioning. The results showed that changing the window glass optical properties effectively reduced the impact of the external environmental conditions on the vehicle cabin temperatures. Lorenz et al. [13] and Torregrosa-Jaime et al. [14] also found that a lower emissivity window coating can significantly reduce the heating energy demand and enhance thermal comfort. Kayiem et al. [15] carried out the measurements during the sunny time under non shaded parking environment. They found that the hottest air was accumulated in the top part of the cabin and natural circulation take place with large scale cavity due to natural heat transfer from the dashboard and the rear windshield. Levinson et al. [16] used the ADVISOR vehicle simulation tool to estimate the fuel consumption and pollutant emissions of each vehicle and calculated the fuel savings and emission reductions attainable by using a cool shell to reduce ancillary load. These studies have provided methods for improving the thermal comfort in vehicle cabins. However, most of the experiments were carried out indoors while idling, which may not accurately represent actual conditions. This paper developed an experimental study to measure the temperature distribution inside an electric vehicle cabin while moving and not moving with cooling or heating. A 3-D coupled fluid and solid
2. Experiments 2.1. Experimental setup The experimental studies were conducted in Yangjiang, Guangdong province, China (21.8500°N, 111.9667°E). The tests were performed on a 2012 model BYD-E6 Pioneer electric vehicle (Fig. 1). That was 4560 mm × 1822 mm × 1630 mm which had only an electric motor. The measures were performed in the automobile while parked (defined as the idle condition) or being driven (defined as operating condition) in both the winter and summer. The ambient conditions for the tests in these two seasons are shown in Table 1. The air temperatures in the passenger compartment were measured by thermocouples the locations shown in Fig. 2. The body surface temperatures of all the passengers were also measured by thermocouples. The human body was divided into 17 segments as shown in Fig. 3. The temperature measurement points were arranged at the center of each segment. The interior and exterior temperatures of various surfaces were also measured by thermocouples (see Fig. 4). All the temperature data was collected by a handheld data recorder. The heat fluxes through various surfaces were measured by heat flux meters to better analyze the influence of the vehicle design. Fig. 5 shows the heat flux meter measurement principle. The heat flux sensor was a thin plate with thermal conductivity, λ , and thickness, δ , which was pasted on the tested surface. The temperature difference across the plate was measured by a chromel-alumel thermocouple pile. The heat
Fig. 1. Electric vehicle. 357
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Table 1 An ambient test conditions. Ambient parameters
Summer
Winter
Temperature Relative Humidity Wind velocity Solar radiation
35 °C 65% ≤5 m/s ≥800 W/m2
5 °C 70% ≤5 m/s ≈500 W/m2
flux, Q, through the sensor and through the surface at steady state is given by: (1)
Q = ΔT λ / δ
2.2. Data reduction As shown in Fig. 6, the heat flux sensors affected the actual heat fluxes through the vehicle surfaces in the experiments. The impacts on opaque surfaces (Fig. 6a) and the glass (Fig. 6b) differed due to their different solar radiation transmittances. Fig. 6a shows the impact of the sensors on opaque surfaces such as the roof, floor and doors of the vehicle. The difference in the measured and actual heat fluxes is mainly due to the thermal conduction resistance of the sensor. The measured ″ heat flux, qmeasure , can be given by:
″ = qmeasure
tin−tout − 1 ho
+
δe λe
+
Fig. 3. Temperature measurement positions on the human body.
and the thermal conductivity of sensors. The interface temperatures of the opaque surfaces, t′2 , which were also measured by the thermocouples on the surfaces can be calculated by:
ρe Itol
1 hi
ho
+
δs λs
(2)
where tin is the inside air temperature, tout is the outside air temperature, ρe is the absorption of the structures, Itol is the solar radiation, ho is the exterior convective heat transfer coefficient, hi is the interior convective heat transfer coefficient, δe, and λe are the thickness and the thermal conductivity of the structures, and, δs, and λs are the thickness
1
2
3
4
7
8
9
10
5
6
11
12
t2′ =
(
1 ho
+
δe ke 1 ho
)t
in
+
+ δe ke
tout hi
+
+
(3)
Fig. 6b shows the impact of the sensors on transparent surfaces, such as the side windows and the windshield. The measured heat flux can be
13
14 15
16
17
17
6,12
18
3,9
1,7
ho hi
1 hi
5,11
2,8
ρe Itol
15 4,10
14 13
Fig. 2. Measurement positions in the test vehicle cabin.
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Fig. 4. The raw pictures of measured points in the cabin.
The thermal resistances of the structures, δe/ ke , which will be used as boundary conditions in the simulation can be determined once the convective heat transfer coefficient and the total solar radiation are settled. According to Ref. [17], the exterior convective heat transfer coefficient, ho, and the interior convective heat transfer coefficient, hi, can be calculated from the air velocity near the surface by:
Nu = 0.0296 Re 4/5 Pr 1/3
(6)
where Re is Reynolds number, Pr is Prandtl number. The material absorptivity, ρ , was assumed to be 0.95 based the color and the material type. The solar radiation, Itol, can be divided into the direct radiation, Idir, the diffuse radiation, Idif, and the ground-reflected radiation, Iref:
Itol = Idir + Idif + Iref
Fig. 5. Heat flux meter measurement principle.
(7)
where Idir can be calculated by:
a ⎞ Idir = I0exp ⎜⎛− ⎟ sin(β + θ )cos(A + α ) sin β⎠ ⎝
given by:
″ = qmeasure
tin−tout − 1 ho
ρe Itol
+
ho δe ke
−
ξρs Itol
+
−
δe ξρs Itol
ho ke 1 δs + hi ks
where I0 = 1361 W/m2 is the solar constants, a is the atmospheric extinction coefficient, β is the sun altitude calculated according to the time and date of the experiments, θ is the surfaces inclination angle, A is the sun azimuthal angle and α is the surfaces azimuthal angle. The sun altitude is given by:
(4)
where ξ is the transmittance of the glass, ρs is the absorption of sensors. The interface temperatures can then be calculated by:
t1′ =
(
1 ho
+
δe ke 1 ho
)t
in
+
+ δe ke
tout hi
+
1 hi
+
(8)
ρe Itol
sinβ = cosφcosηcosω + sinφsinω
ho hi
(9)
where φ is geographical latitude of the experimental location, η is hour angle, ω is solar inclination on the experimental date. The sun
(5)
Fig. 6. Heat transfer through the vehicle surfaces.
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T (°C)
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28 26 24
10 0
5
10
15
20
25
22
30
Time (min)
0
5
10
15
20
25
30
Time (min)
(a) Cooling period
(b) Heating period
Fig. 7. Measured temperatures at the inlet vents.
Table 2 The raw data of Fig. 7. Time (min)
Cooling period
Heating period
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
28.35 25.95 22.425 21.75 22.075 21.2 21.15 15.125 15.425 15.025 14.625 14.55 13.85 13.425 13 13 12.775 12.925 12.8 12.625 12.7 12.65 12.775 12.5 12.85 12.775 11.975 12.025 11.875 12.2 11.625
23.05 25.475 26.5 26.85 27.125 27.45 27.65 27.8 27.5 27.675 27.725 27.975 27.7 28.075 28.275 27.825 27.9 28.1 28.325 28.05 28 28.075 28.025 28.1 28.125 28.075 28.475 28.475 28.7 28.75 29.025
Fig. 8. Graphtec GL820 mid Logger. Table 3 Measurement uncertainties.
cosωsinη cosβ
Uncertainty
J-type thermocouple
Measurement range: 200–260 °C Measurement range: 0.20–20.00 m/s Measurement range: 12–3500 W/m2
± 0.5 °C ± 0.01 m/s ± 2%
(10)
Iref = 0.5γ (1−cosω)(Idir + Idif )|α = 0
The diffuse radiation, Idif, can be calculated using the Berlage equation:
Idif = 0.5I0sinβ
Properties
Fluke 923 thermo-sensitive anemometer KEM HFM-215 heat flux meter
azimuthal angle is given by:
sinA =
Apparatus
(
a
1−exp − sinβ
2.3. Experimental procedure
)
1−1.4 ln exp(−a)
(12)
During the idling experiments the empty car was parked in the sunlight for one hour to obtain a constant inside temperature. After all the thermocouples were attached on the occupants’ bodies, the driver
(11)
The Ground-reflected radiation is given by:
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(a) CAD model of the vehicle cabin
(b) Mesh
Fig. 9. Numerical simulation model.
The air temperature variations at the vents shown in Fig. 7 were measured as the inlet boundary condition for the numerical simulations (the raw data is shown in Table 2).
Table 4 Vehicle cabin surfaces. Surface of cabin
Surface area (m2)
Surface of cabin
Surface area (m2)
Windshield Back window Left front window Right front window Left rear window Right rear window
1.382 0.592 0.311 0.311 0.312 0.312
Left door Right door Discharge vents Exit vents Ceiling Floor
1.533 1.533 0.000625 0.0025 2.479 3.383
2.4. Facilities and uncertainties The temperature data was collected by a Graphtec GL820 midi Logger (see Fig. 8, produced by DATAQ, Japan). The heat flux data was collected by a HFM-215 with 6 channels. The details and uncertainties of all the devices are given in Table 3.
Table 5 Boundary conditions.
3. Numerical simulations
Idle condition Manikin surfaces Inlet vents Outlet vents Side windows Windshield Back window Doors Ceiling
Operating condition
3.1. Computational domain and mesh generation
Constant heat flux (60 W/m ) [6] Transient temperature profile obtained from measured data Constant velocity (6.94 m/s) Gauge pressure = 0 Pa Convective (h = 15 W/m2·K) Convective (h = 30 W/m2·K) Convective (h = 20 W/m2·K) Convective (h = 50 W/m2·K) Convective (h = 15 W/m2·K) Convective (h = 30 W/m2·K) Convective (h = 1 W/m2·K) Convective (h = 3 W/m2·K) Convective (h = 3 W/m2·K) Convective (h = 5 W/m2·K) 2
The computational domain with the same dimensions at the BYD E6 electric vehicle is shown in Fig. 9(a). The main surface areas of the cabin are listed in Table 4. As can be seen in the figure, there are two separated seats in the front and a bench seat in the back. There are four rectangular air inlets located on the instrument panel. A rectangular return air outlet was also located on the front panel below the air inlets just in front of the front passenger’s seat as shown in Fig. 9. The driver and three passengers were modeled as on the experiments. The computational domain contained a fluid domain (inside the vehicle cabin) and solid domains (passengers and seats). The mesh is shown in Fig. 9(b). Unstructured grids were used with 3-D hex-core cells in the central area and tetrahedral cells near the solid surfaces of the model. The computational mesh had around 1,100,000 volume cells. The elements around the air inlets and the outlet were locally refined due to the large temperature and air velocity
34
34
32
32
30
30
28
28
Temperature( C)
26
o
o
Temperature ( C)
and three passengers (one front and two rear passengers) were seated in the cabin, the doors were closed and the heating and air conditioning was immediately turned on. All the data was collected by the data recorder every five minutes for one hour. The basic steps for tests while operating were similar to those while idling except that the vehicle was run at a constant velocity after all the passengers sat in the vehicle.
24 22
Position Idle Operating Head Abdomen Feet
20 18 16
0
5
10
15
20
25
30
26 24 22
Occupant Idle Operating Front pasenger Driver Rear passenger
20 18
35
Time (min)
16
0
5
10
15
20
25
30
Time (min)
(b) In front of each person’s head
(a) In front of the front seat passengers
Fig. 10. Air temperature variations at different locations during heating.
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Table 6 The raw data of Fig. 10. In front of the front seat passenger
In front of each person’s head
Idle
Idle
Operating
Operating
Time (min)
Head
Abdomen
Feet
Head
Abdomen
Feet
Front passenger
Driver
Rear passenger
Front passenger
Driver
Rear passenger
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
22.2 22.2 26.7 26.4 27.5 28.3 28.7 29.2 29.5 28.1 28.5 28.6 28.3 30.2 30.6 30.8 30.4 30.5 31.2 30.6 31.2 31.3 31.7 31.7 31.8 32 32 32.2 32.1 32.2 32.4 32.5 32.4 32.5
24.7 24.1 24.1 23.9 24.2 24.8 25 24.5 25 25.3 25 25.8 28.3 27.2 28.3 28.5 27.6 26.4 27.6 28.5 28.5 29.1 28.8 30 28.8 29.9 29.5 29.3 29.9 30.6 30.2 30.3 30.4 30.5
23 23.5 24.4 23.7 23.6 24.8 26.1 26 26.3 26.6 27 26.5 26.2 26.6 26.8 27.5 28.1 27.2 27.7 28 27.8 28.2 28.3 28.3 28.8 28.8 29 29.1 29.3 29.7 30 29.6 29.8 29.8
23.6 24.7 22.7 23.2 24.6 25.1 25.3 25.1 25.4 25.4 25.6 25.3 25.6 25.7 25.6 25.5 25.9 25.7 26 25.9 25.9 25.9 26.1 26.2 26.2 26.6 26.5 26.7 27 27.3 27.5 27.7 27.9
22 23 21.7 22.7 23.6 23.1 23.8 23.8 24 24.1 24.2 24.4 24.4 24.1 24.2 23.9 24.3 24.6 24.3 24.1 24.4 24.1 24.6 24.7 24.7 25.3 25.3 25.5 25.8 26.1 26.3 26.6 26.7
23.6 22.6 21.2 22.4 23.2 23.1 23.5 23.3 24.1 23.7 23.9 23.8 24.3 23.9 23.8 23.8 24 24.2 24 24 24.3 23.9 24.4 24.6 24.4 25.1 25.1 25.3 25.5 25.8 26.1 26.6 26.6
22.2 22.2 26.7 26.4 27.5 28.3 28.7 29.2 29.5 28.1 28.5 28.6 28.3 30.2 30.6 30.8 30.4 30.5 31.2 30.6 31.2 31.3 31.7 31.7 31.8 32 32 32.2 32.1 32.2 32.4 32.5 32.4 32.5
23.6 22.6 25.7 26.4 28 28.4 29.4 29.8 29.6 29.1 29.1 29.5 28.8 30.6 30.9 31 31.3 31.7 31.7 31.4 32 32 32.3 32.4 32.6 32.7 32.7 32.6 32.8 33 33.1 33.1 32.8 33.3
23.2 22.3 24 26.1 27.2 28.3 29.1 29.3 29.6 28.5 27.4 28.5 28.1 29.7 30.3 30.2 30.3 30.7 30.8 30.4 31 30.9 31.3 31.3 31.4 31.6 31.7 31.7 31.7 31.8 31.9 32 32 32.1
23.6 24.7 22.7 23.2 24.6 25.1 25.3 25.1 25.4 25.4 25.6 25.3 25.6 25.7 25.6 25.5 25.9 25.7 26 25.9 25.9 25.9 26.1 26.2 26.2 26.6 26.5 26.7 27 27.3 27.5 27.7 27.9
22 22 22 22.1 22.1 22.3 22.4 22.6 22.6 22.7 22.8 22.8 22.9 23.1 23.2 23.3 23.5 23.6 23.7 23.9 24.1 24.1 24.1 24 24.1 24.1 24.1 24.4 24.3 24.4 24.5 24.3 24.5
22.9 23.7 23 24 23.8 24.4 25.4 25 25.8 25.6 25.9 25.6 26.1 25.9 26.1 26 26.2 26.1 26.1 26 26 26 26.3 26.2 26.2 26.5 26.5 26.5 26.5 27 26.9 27.1 27.2
temperature changes. The average specific heat of the seat was 1256 W/ kg·k and the average density was 481 kg/m3. The unsteady, incompressible governing equations were discretized by the finite volume method. The second order implicit scheme was used to discretize the transient term. The time step was set to 1 s for all the calculations. The convection term was discretized using the secondorder upwind difference method with the diffusion term using the central difference. The SIMPLE algorithm was used for the coupling between the pressure and velocity. The renormalization group (RNG) ke model was used with the standard wall function for the near wall region treatment, which is demonstrated by Jalal [18]. The convergence was assumed to be obtained when the normalized residuals of the flow equations were less than 10−4 and these of the energy and radiation equations were less than 10−7. The total simulation time was 30 min. In this study, the radiation heat transfer was computed using the discrete ordinates (DO) radiation model which solves the radiative transfer equation for a finite number of discrete solid angles, each associated with a vector direction fixed in the global Cartesian system. The DO model spans the entire range of optical thicknesses and allows the solution of the radiation at semitransparent walls. The solar load was derived from the “solar calculator” in the DO irradiation model by setting the longitude, latitude, date and time of the case and the driving direction of the vehicle according to the experimental conditions. The
gradients, with an average element near the inlets and outlet of around 1 mm. The average length of the other elements was around 50 mm. Grid independence tests showed that this grid gave a grid independent solution. 3.2. Boundary conditions and computational method The air flow in the cabin was assumed to be incompressible, turbulent, and unsteady with constant thermos physical properties and no viscous dissipation. The boundary conditions are shown in Table 5. Convective boundary conditions were used along the outer surfaces of the cabin. The convective heat transfer coefficients h:
1 1 δ = + e h ho ke
(13)
are the combination of the actual convective heat transfer coefficient on the outside and the thermal resistance of the structures and were obtained from the experimental data. The glass structures were defined as semi-transparent walls with the others defined as opaque walls. The air velocity was set to 6.94 m/s for all the inlet vents as measured in the experiments. The inlet air temperature was defined with a user-defined function (UDF) based on the experimental data shown in Fig. 5. The boundary conditions for all the manikins were constant heat flux. The temperature changes in the seats had a significant effect on the air
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40
40 Position Head Abdomen Feet
35 o
30 25 20 15
0
5
10
15
20
25
20 0
5
20
0
5
10
15
20
25
15
20
Position Exp. Driver Front passenger Rear passenger
30
o
25
15
10
25
30
35
Num.
Temperature ( C)
o
Temperature ( C)
30
Exp.
25
Time (min) (b) In front of the passengers while idling
(a) In front of the driver while idling Position Head Abdomen Feet
Num.
30
15
30
Time (min)
35
Position Exp. Driver Front passenger Rear passenger
Num.
Temperature ( C)
o
Temperature ( C)
35
Exp.
Time (min) (c) In front of the driver while operating
25 20 15
30
Num.
0
5
10
15
20
25
30
Time (min) (d) In front of the passengers’ heads while operating
Fig. 11. Air temperature variations at different locations while cooling.
35
32
30
Simulation (
Simulation (
)
36
)
40
+15%
25 -15% 20 15 15
28
+10%
24 -10% 20
20
25
30
Experiment (
35
16 16
40
)
20
24
28
Experiment (
Idling
32
36
)
Operating
Fig. 12. Differences between the predicted and measured temperatures while cooling.
diffuse fraction of the solar radiation was also used in this model. A more detailed description of the radiation model was given earlier [19,20].
temperature was lower than the inside temperature, so the heat transfer direction was from inside to outside. The heat transfer along the outer surface of the cabin was natural convection while idling and forced convection while operating. Therefore, less heat was lost through the surfaces while idling than while operating. As a consequence, the air temperature while idling condition increases faster than that at the same location while operating, and the constant air temperature after the increase while idling was higher than which operating (see Table 6). As shown in Fig. 10a, during the heating, the air temperature in front of the front seat passenger’s head is higher than that in front of his
4. Results and discussion 4.1. Experimental results Fig. 10 shows the air temperature variations at different locations for the idling and operating conditions during heating. The outside air
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o
Temperature ( C)
o
Temperature ( C)
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28 26 Position Head Abdomen Feet
24 22 20
0
5
10
15
Exp.
Num.
28 26 Position Driver Front passengers Rear passengers
24 22
20
25
20
30
Time (min)
28
26
26
Temperature ( C)
28
5
10
15
Num.
20
25
30
o
o
0
Exp.
Time (min) (b) In front of passengers’ heads while idling
(a) In front of the front seat passenger while idling
Temperature ( C)
30
24 Position Head Abdomen Feet
22 20
0
5
10
15
Exp.
20
Num.
25
24
20
30
Position Exp. Driver Front passenger Rear passenger
22
0
5
10
Time (min)
15
20
Num.
25
30
Time (min)
(c) In front of front passenger while operating
(d) In front of passengers’ heads while operating
Fig. 13. Air temperature variations at different locations while heating.
29
34
28
+10%
+10%
)
27
30
Simulation (
Simulation (
)
32
28 -10%
26
26 25 24
-10%
23 24 22 22
22 24
26
28
Experiment (
30
32
21 21
34
)
22
23
24
25
26
27
28
29
Experiment ( )
(a) Idling
(b) Operating
Fig. 14. Differences between the measured and predicted temperature while heating.
with the experimental data for both the idling and operating conditions. The largest difference between the experimental data and the predictions is 3.5 °C. The relative differences between the experimental and simulation results during cooling are shown in Fig. 12. The predictions agree well with the measurements during the cooling with 92.4% of the differences being smaller than 15% while idling and 90.2% of the differences being smaller than 10% while operating. The numerical model tends to underestimates the results while operating. Fig. 13 shows the measured and predicted temperature variations at
abdomen and feet. This is due to the hot air from the heating inlets flowing up to the roof (as shown in Section 4.3), so the air temperature at higher positions increases faster than that at lower positions in the cabin during heating. 4.2. Comparisons of the experimental and simulation results The measurement and calculated air temperature variations at different locations in the cabin during cooling are shown in Fig. 11. The calculated decreasing air temperatures at different locations agree well
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Fig. 15. Temperature (K) distributions in a vertical plane while cooling.
different locations while heating. The increasing trends of the predicted temperatures agree well with the experimental results, with a largest difference between predicted and measured temperatures of 3.0 °C. Fig. 14 shows the relative difference between the experimental and predicted results while heating. The predicted agree well with the measured temperatures while heating with 88.8% of the difference being smaller than 10% while idling and 89.7% of the difference being smaller than 10% while operating.
are on the surfaces of the passengers and seats. The air flow from the vents makes the air temperature gradients near the vents higher than in other zones. The air temperature decreases again slows after 1000 s and stops at 30 min when the temperature reaches steady state. Fig. 17a shows the air velocity distributions on the vertical plane, and Fig. 17b on the horizontal plane. In most of the cabin the air velocities are quite low with high velocities only near the inlets (the air conditioning vents) and the outlet of the computational domain.
4.3. Temperature and air flow distribution in the vehicle cabin
5. Conclusions
Fig. 15 shows the temperature distributions in a vertical plane in the cabin while cooling. The vertical plane is in the middle of the vent on the right side of the front passenger. The cabin temperature is quite high at the beginning of the cooling period. After the air-conditioning starts working, the cabin temperature decreases with time. The lowest temperature is at the vent outlet, while the highest temperatures are at the outer surfaces of the seats, the inside surfaces of the windows and the outer surfaces of the passengers. Therefore, the heat sources are mainly the heat from the windows, the heat stored in the seats and the passengers. Thus the temperature distribution in the cabin is strongly affected by the air flow velocity and the vent directions. The air temperature decreases slows after 1000 s with steady state at around 30 min. The seats cool much more slowly than the air, which means that the effect of the heat storage in the seats on the thermal comfort changes cannot be neglected. The temperature distributions on a horizontal plane (at the same height as the passengers’ chests) are shown in Fig. 16. This horizontal plane crosses across the four air conditioning vents. The cabin air temperature decreases with time. The lowest temperature on this horizontal plane are near the vent outlets while the highest temperatures
This paper presents an experimental and numerical study of the temperature and air flow variations inside an electrical vehicle cabin with four occupants. A series of experiments were conducted in a parked and a moving vehicle during both cooling and heating periods. The transient 3-D numerical model considered both the air and the solid regions in the cabin including the passengers for the experimental conditions. The air temperature and air flow variations in the cabin were then analyzed. The following conclusions can be drawn: 1. The numerical results agreed well with the data with around 90% of the temperature differences being smaller than 10%. 2. The vehicle motion had an important effect on the heat transfer. The outside air velocity increased the convective heat transfer between the outside air and the vehicle outer surfaces, which increased the thermal losses and gains from the cabin. Thus, the heating and air conditioning systems required more time to make the cabin comfortable. 3. During the cooling period, the lowest temperatures were near the vent outlets with the highest temperatures on the surfaces of the windows, passengers and seats. Thus, the thermal stored in the seat
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Fig. 16. Temperature (K) distributions in a horizontal plane while cooling.
(a) Vertical plane
(b) Horizontal plane
Fig. 17. Air velocity (m/s) distributions in the vehicle cabin.
Acknowledgement
strongly affects the air temperature variations in the cabin. It could be a better choice to heat the seat in the winter or cool the seat in the summer to improve the thermal comfort. 4. The highest air velocities were near the inlets and outlets of the computational domain, that is the inlet vents and the return air outlet of the ventilation system. This result showed that the position of the vents has a great influence on the thermal comfort of the car.
This work was supported by Guangdong Industry-AcademiaResearch Project (No. 2011A090200018), the new energy vehicles industry project (2011) of Guangdong Special Funds for Strategic Emerging Industries, and the National Natural Science Foundation for Creative Research Groups of China (No. 51621062). 366
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References
Simulation results and discussion, Appl. Therm. Eng. 29 (2009) 2028–2036. [11] C.-H. Chien, J.-Y. Jang, Y.-H. Chen, 3-D numerical and experimental analysis for airflow within a passenger compartment, Int. J. Automot. Technol. 9 (4) (2008) 437–445. [12] W.C. Zhang, Study on the Key Technology of Thermal Environment and Occupant’s Thermal Comfort Analysis in Vehicle Cabins, South China University of Technology, 2013. [13] M. Lorenz, D. Fiala, M. Spinnler, et al., A Coupled Numerical Model to Predict Heat Transfer and Passenger Thermal Comfort in Vehicle Cabins. No. 2014-01-0664. SAE Technical Paper, 2014. [14] B. Torregrosa-Jaime, F. Bjurling, J.M. Corberán, et al., Transient thermal model of a vehicle's cabin validated under variable ambient conditions, Appl. Therm. Eng. 75 (2015) 45–53. [15] H.H.A. Kayiem, M.F.B.M. Sidik, Y.R.A.L. Munusammy, Study on the thermal accumulation and distribution inside a parked car cabin, Am. J. Appl. Sci. 7 (6) (2010) 784–789. [16] R. Levinson, H. Pan, G. Ban-Weiss, et al., Potential benefits of solar reflective car shells: cooler cabins, fuel savings and emission reductions, Appl. Energy 88 (12) (2011) 4343–4357. [17] S.M. Yang, W.Q. Tao, Heat Transfer, fourth ed., Higher Education Press, 2006. [18] J. Jalil, H. Alwan, CFD simulation for a road vehicle cabin, J. King Abdulaziz Univ. Eng. Sci. 18 (2) (2007) 129–148. [19] E.H. Chui, G.D. Raithby, Computation of radiant heat transfer on a non-orthogonal mesh using the finite-volume method, Numer. Heat Transfer B 23 (1993) 269–288. [20] G.D. Raithby, E.H. Chui, A finite-volume method for predicting a radiant heat transfer in enclosures with participating media, J. Heat Transfer 112 (1990) 415–423.
[1] A. Grundstein, V. Meentemeyer, J. Dowd, Maximum vehicle cabin temperatures under different meteorological conditions, Int. J. Biometeorol. 53 (2009) 255–261. [2] H. Tsutsumi, Y. Hoda, S.I. Tanabe, et al., Effect of Car Cabin Environment on Driver's Comfort and Fatigue, SAE Technical Papers, 2007. [3] M.F. Allnutt, J.R. Allan, The effects of core temperature elevation and thermal sensation on performance, Ergonomics 16 (2) (1973) 189–196. [4] H.A.M. Daanen, E. van de Vliert, X. Huang, Driving performance in cold, warm and thermoneutral environments, Appl. Ergonom. 34 (2003) 597–602. [5] Gokhan Sevilgen, Muhsin Kilic, Transient numerical analysis of airflow and heat transfer in a vehicle cabin during heating period, Int. J. Veh. Des. 52 (2010) 144–159. [6] Muhsin Kilic, Gokhan Sevilgen, Evaluation of heat transfer characteristics in an automobile cabin with a virtual manikin during heating period, Numer. Heat Transfer, Part A 56 (2009) 515–539. [7] Gokhan Sevilgen, Muhsin Kilic, Investigation of transient cooling of an automobile cabin with a virtual manikin under solar radiation, Therm. Sci. 17 (2) (2013) 397–406. [8] Muhsin Kilic, Gokhan Sevilgen, The effects of using different type of inlet vents on the thermal characteristics of the automobile cabin and the human body during cooling period, Int. J. Adv. Manuf. Technol. 60 (2012) 799–809. [9] H.J. Zhang, G.Q.Xu. Lan Dai, et al., Studies of air-flow and temperature fields inside a passenger compartment for improving thermal comfort and saving energy. Part I: Test/numerical model and validation, Appl. Therm. Eng. 29 (2009) 2022–2027. [10] H.J. Zhang, Lan Dai, G.Q. Xu, et al., Studies of air-flow and temperature fields inside a passenger compartment for improving thermal comfort and saving energy. Part II:
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Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng
Research Paper
Experimental and numerical study of air flow and temperature variations in an electric vehicle cabin during cooling and heating Yiyi Mao1, Ji Wang1, Junming Li
T
⁎
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China
H I GH L IG H T S
numerical results agreed well with the experiments. • The vehicle motion increased the thermal losses and gains from the cabin. • The thermal stored in the seat strongly affects the air temperature variations. • The • The highest velocities were near the inlets and outlets of the computational domain.
A R T I C LE I N FO
A B S T R A C T
Keywords: Passenger comfort Vehicle cabin Air conditioner
A series of experiments was conducted in an electric vehicle with four occupants while parked or being driven in the winter and the summer. The air and surface temperatures in the vehicle cabin and the heat fluxes on different closure surfaces were measured during the cooling and heating periods. With the temperature were then modeled in a transient numerical simulation of the electric vehicle cabin the air flow and temperature variations inside the cabin analyzed for the same conditions as in the experiments. The numerical model accurately predicted the temperature variations with differences of less than 10% compared with the experimental data. Analysis of the air temperature and velocity distributions from the transient model showed that the thermal storage in the seat significantly affected the thermal comfort adjustments for the transient conditions. The vehicle movement increased the convective heat transfer at the outer surface of the cabin, which increased the heat transfer between the outside and the inside air and extended the time to adjust the thermal comfort.
1. Introduction The thermal comfort in vehicle compartments is an important concern for occupants due to the considerable amount of time spent in vehicles. Vehicles have higher heat losses or gains than buildings because of their structure and materials. The thermal comfort can be greatly influenced by changes in the outside temperature, air velocity and solar radiation. Especially in extreme summer or winter conditions, the air temperatures can be quite non-uniform and variable around the occupants. For instance, the temperature inside the car can reach 72 °C in summer with an outdoor temperature of 34 °C and a solar radiation of around 800 W/m2 according to Grundstein et al. [1]. In such situations, the heater and air conditioner cannot easily control the vehicle compartment thermal environment. According to the study of Tsutsumi et al. [2], the car cabin
⁎
1
Corresponding author. E-mail address: [email protected] (J. Li). These authors contributed equally to this work and should be considered co-first authors.
https://doi.org/10.1016/j.applthermaleng.2018.03.099 Received 6 May 2017; Received in revised form 25 March 2018; Accepted 29 March 2018 Available online 29 March 2018 1359-4311/ © 2018 Elsevier Ltd. All rights reserved.
environment affects driver's comfort, performance and fatigue. Allnutt and Allan [3] pointed out that the improved climate within vehicles is critical not only to passenger comfort but also to their safety. Dannen et al. [4] found that driving performance was affected by the cold and hot ambient conditions. Therefore, thermal comfort has become one of the most important criteria for evaluating vehicle performance. The tightening fuel economy constraints and the use of environmentally safe refrigerant is driving the need to develop more efficient car air conditioning systems to improve energy savings. Efficient systems are especially significant in emerging electric vehicles (EV). Unlike conventional vehicles, which utilize engine waste heat to heat the compartment, the thermal environment in an EV is completely controlled by its electrical driven heating, ventilation and air conditioning (HVAC) systems. Thus, the design of the air flow and the temperature distributions in electric vehicle cabins during both cooling
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β δ η θ λ, κ ρ ω
Nomenclature a A h I Nu Pr q, Q Re t, T
atmospheric extinction coefficient sun azimuthal angle heat transfer coefficient (W·m−2·K−1) solar radiation Nusselt number Prandtl number heat flux (W) Reynolds number temperature (K)
Subscripts i, in o, out tol dir dif ref
Greek symbols α φ
sun altitude thickness (mm) hour angle surfaces inclination angle thermal conductivity (W·m−1·K−1) material absorptivity solar inclination on the experimental date
surfaces azimuthal angle geographical latitude
inside outside total direct diffuse ground-reflected
regions model was then developed using the commercial software FLUENT. The flow field and temperature distributions in the electric vehicle cabin were simulated during cooling and heating periods. The simulation results are compared with the experimental data to validate the model.
and heating periods is especially important for EV designs. There have been many experimental and numerical studies of the air flow and thermal characteristics of passenger compartments. Sevilgen and Kilic [5–8] did series of investigations of the unsteady conditions in the passenger compartment with 3-D transient numerical analyses of the airflow and heat transfer in a vehicle during heating. They used a virtual manikin divided into 17 parts with real dimensions and a realistic physiological shape in a 1:1 scale automobile cabin model. Later, Klilic and Sevilgen used a hex-core mesh to reduce the computational time. They found that the flow field in the vehicle cabin was mainly affected by the radiation heat transfer and little affected by the boundary conditions on the manikin surfaces. They also found that different velocity and temperature distributions were obtained with different types of inlet vents for cooling with the same heating and air condition systems’ cooling load. Zhang et al. [9,10] simulated 3-D temperature distributions and the flow field in compartments with and without passengers during cooling. They found that a good method to reduce the air-conditioner cooling load was to increase the inlet air temperature, not by reducing the volumetric inlet air flow rate. They also concluded that the outside temperature has appreciable effect on the cooling load. Chien et al. [11] also numerically simulated the thermal behavior in a vehicle. They used e-NTU theoretical equations to calculate the air conditioner cooling capacity. Their predicated temperature distributions in the vehicle cabin were in good agreement with measured values. Zhang [12] coupled a solid wall and a fluid flow heat transfer model of a vehicle cabin to simulate the ‘temperature-flow’ characteristics during air-conditioning. The results showed that changing the window glass optical properties effectively reduced the impact of the external environmental conditions on the vehicle cabin temperatures. Lorenz et al. [13] and Torregrosa-Jaime et al. [14] also found that a lower emissivity window coating can significantly reduce the heating energy demand and enhance thermal comfort. Kayiem et al. [15] carried out the measurements during the sunny time under non shaded parking environment. They found that the hottest air was accumulated in the top part of the cabin and natural circulation take place with large scale cavity due to natural heat transfer from the dashboard and the rear windshield. Levinson et al. [16] used the ADVISOR vehicle simulation tool to estimate the fuel consumption and pollutant emissions of each vehicle and calculated the fuel savings and emission reductions attainable by using a cool shell to reduce ancillary load. These studies have provided methods for improving the thermal comfort in vehicle cabins. However, most of the experiments were carried out indoors while idling, which may not accurately represent actual conditions. This paper developed an experimental study to measure the temperature distribution inside an electric vehicle cabin while moving and not moving with cooling or heating. A 3-D coupled fluid and solid
2. Experiments 2.1. Experimental setup The experimental studies were conducted in Yangjiang, Guangdong province, China (21.8500°N, 111.9667°E). The tests were performed on a 2012 model BYD-E6 Pioneer electric vehicle (Fig. 1). That was 4560 mm × 1822 mm × 1630 mm which had only an electric motor. The measures were performed in the automobile while parked (defined as the idle condition) or being driven (defined as operating condition) in both the winter and summer. The ambient conditions for the tests in these two seasons are shown in Table 1. The air temperatures in the passenger compartment were measured by thermocouples the locations shown in Fig. 2. The body surface temperatures of all the passengers were also measured by thermocouples. The human body was divided into 17 segments as shown in Fig. 3. The temperature measurement points were arranged at the center of each segment. The interior and exterior temperatures of various surfaces were also measured by thermocouples (see Fig. 4). All the temperature data was collected by a handheld data recorder. The heat fluxes through various surfaces were measured by heat flux meters to better analyze the influence of the vehicle design. Fig. 5 shows the heat flux meter measurement principle. The heat flux sensor was a thin plate with thermal conductivity, λ , and thickness, δ , which was pasted on the tested surface. The temperature difference across the plate was measured by a chromel-alumel thermocouple pile. The heat
Fig. 1. Electric vehicle. 357
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Table 1 An ambient test conditions. Ambient parameters
Summer
Winter
Temperature Relative Humidity Wind velocity Solar radiation
35 °C 65% ≤5 m/s ≥800 W/m2
5 °C 70% ≤5 m/s ≈500 W/m2
flux, Q, through the sensor and through the surface at steady state is given by: (1)
Q = ΔT λ / δ
2.2. Data reduction As shown in Fig. 6, the heat flux sensors affected the actual heat fluxes through the vehicle surfaces in the experiments. The impacts on opaque surfaces (Fig. 6a) and the glass (Fig. 6b) differed due to their different solar radiation transmittances. Fig. 6a shows the impact of the sensors on opaque surfaces such as the roof, floor and doors of the vehicle. The difference in the measured and actual heat fluxes is mainly due to the thermal conduction resistance of the sensor. The measured ″ heat flux, qmeasure , can be given by:
″ = qmeasure
tin−tout − 1 ho
+
δe λe
+
Fig. 3. Temperature measurement positions on the human body.
and the thermal conductivity of sensors. The interface temperatures of the opaque surfaces, t′2 , which were also measured by the thermocouples on the surfaces can be calculated by:
ρe Itol
1 hi
ho
+
δs λs
(2)
where tin is the inside air temperature, tout is the outside air temperature, ρe is the absorption of the structures, Itol is the solar radiation, ho is the exterior convective heat transfer coefficient, hi is the interior convective heat transfer coefficient, δe, and λe are the thickness and the thermal conductivity of the structures, and, δs, and λs are the thickness
1
2
3
4
7
8
9
10
5
6
11
12
t2′ =
(
1 ho
+
δe ke 1 ho
)t
in
+
+ δe ke
tout hi
+
+
(3)
Fig. 6b shows the impact of the sensors on transparent surfaces, such as the side windows and the windshield. The measured heat flux can be
13
14 15
16
17
17
6,12
18
3,9
1,7
ho hi
1 hi
5,11
2,8
ρe Itol
15 4,10
14 13
Fig. 2. Measurement positions in the test vehicle cabin.
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Fig. 4. The raw pictures of measured points in the cabin.
The thermal resistances of the structures, δe/ ke , which will be used as boundary conditions in the simulation can be determined once the convective heat transfer coefficient and the total solar radiation are settled. According to Ref. [17], the exterior convective heat transfer coefficient, ho, and the interior convective heat transfer coefficient, hi, can be calculated from the air velocity near the surface by:
Nu = 0.0296 Re 4/5 Pr 1/3
(6)
where Re is Reynolds number, Pr is Prandtl number. The material absorptivity, ρ , was assumed to be 0.95 based the color and the material type. The solar radiation, Itol, can be divided into the direct radiation, Idir, the diffuse radiation, Idif, and the ground-reflected radiation, Iref:
Itol = Idir + Idif + Iref
Fig. 5. Heat flux meter measurement principle.
(7)
where Idir can be calculated by:
a ⎞ Idir = I0exp ⎜⎛− ⎟ sin(β + θ )cos(A + α ) sin β⎠ ⎝
given by:
″ = qmeasure
tin−tout − 1 ho
ρe Itol
+
ho δe ke
−
ξρs Itol
+
−
δe ξρs Itol
ho ke 1 δs + hi ks
where I0 = 1361 W/m2 is the solar constants, a is the atmospheric extinction coefficient, β is the sun altitude calculated according to the time and date of the experiments, θ is the surfaces inclination angle, A is the sun azimuthal angle and α is the surfaces azimuthal angle. The sun altitude is given by:
(4)
where ξ is the transmittance of the glass, ρs is the absorption of sensors. The interface temperatures can then be calculated by:
t1′ =
(
1 ho
+
δe ke 1 ho
)t
in
+
+ δe ke
tout hi
+
1 hi
+
(8)
ρe Itol
sinβ = cosφcosηcosω + sinφsinω
ho hi
(9)
where φ is geographical latitude of the experimental location, η is hour angle, ω is solar inclination on the experimental date. The sun
(5)
Fig. 6. Heat transfer through the vehicle surfaces.
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25
30
T (°C)
30
20
o
T ( C)
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15
28 26 24
10 0
5
10
15
20
25
22
30
Time (min)
0
5
10
15
20
25
30
Time (min)
(a) Cooling period
(b) Heating period
Fig. 7. Measured temperatures at the inlet vents.
Table 2 The raw data of Fig. 7. Time (min)
Cooling period
Heating period
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
28.35 25.95 22.425 21.75 22.075 21.2 21.15 15.125 15.425 15.025 14.625 14.55 13.85 13.425 13 13 12.775 12.925 12.8 12.625 12.7 12.65 12.775 12.5 12.85 12.775 11.975 12.025 11.875 12.2 11.625
23.05 25.475 26.5 26.85 27.125 27.45 27.65 27.8 27.5 27.675 27.725 27.975 27.7 28.075 28.275 27.825 27.9 28.1 28.325 28.05 28 28.075 28.025 28.1 28.125 28.075 28.475 28.475 28.7 28.75 29.025
Fig. 8. Graphtec GL820 mid Logger. Table 3 Measurement uncertainties.
cosωsinη cosβ
Uncertainty
J-type thermocouple
Measurement range: 200–260 °C Measurement range: 0.20–20.00 m/s Measurement range: 12–3500 W/m2
± 0.5 °C ± 0.01 m/s ± 2%
(10)
Iref = 0.5γ (1−cosω)(Idir + Idif )|α = 0
The diffuse radiation, Idif, can be calculated using the Berlage equation:
Idif = 0.5I0sinβ
Properties
Fluke 923 thermo-sensitive anemometer KEM HFM-215 heat flux meter
azimuthal angle is given by:
sinA =
Apparatus
(
a
1−exp − sinβ
2.3. Experimental procedure
)
1−1.4 ln exp(−a)
(12)
During the idling experiments the empty car was parked in the sunlight for one hour to obtain a constant inside temperature. After all the thermocouples were attached on the occupants’ bodies, the driver
(11)
The Ground-reflected radiation is given by:
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(a) CAD model of the vehicle cabin
(b) Mesh
Fig. 9. Numerical simulation model.
The air temperature variations at the vents shown in Fig. 7 were measured as the inlet boundary condition for the numerical simulations (the raw data is shown in Table 2).
Table 4 Vehicle cabin surfaces. Surface of cabin
Surface area (m2)
Surface of cabin
Surface area (m2)
Windshield Back window Left front window Right front window Left rear window Right rear window
1.382 0.592 0.311 0.311 0.312 0.312
Left door Right door Discharge vents Exit vents Ceiling Floor
1.533 1.533 0.000625 0.0025 2.479 3.383
2.4. Facilities and uncertainties The temperature data was collected by a Graphtec GL820 midi Logger (see Fig. 8, produced by DATAQ, Japan). The heat flux data was collected by a HFM-215 with 6 channels. The details and uncertainties of all the devices are given in Table 3.
Table 5 Boundary conditions.
3. Numerical simulations
Idle condition Manikin surfaces Inlet vents Outlet vents Side windows Windshield Back window Doors Ceiling
Operating condition
3.1. Computational domain and mesh generation
Constant heat flux (60 W/m ) [6] Transient temperature profile obtained from measured data Constant velocity (6.94 m/s) Gauge pressure = 0 Pa Convective (h = 15 W/m2·K) Convective (h = 30 W/m2·K) Convective (h = 20 W/m2·K) Convective (h = 50 W/m2·K) Convective (h = 15 W/m2·K) Convective (h = 30 W/m2·K) Convective (h = 1 W/m2·K) Convective (h = 3 W/m2·K) Convective (h = 3 W/m2·K) Convective (h = 5 W/m2·K) 2
The computational domain with the same dimensions at the BYD E6 electric vehicle is shown in Fig. 9(a). The main surface areas of the cabin are listed in Table 4. As can be seen in the figure, there are two separated seats in the front and a bench seat in the back. There are four rectangular air inlets located on the instrument panel. A rectangular return air outlet was also located on the front panel below the air inlets just in front of the front passenger’s seat as shown in Fig. 9. The driver and three passengers were modeled as on the experiments. The computational domain contained a fluid domain (inside the vehicle cabin) and solid domains (passengers and seats). The mesh is shown in Fig. 9(b). Unstructured grids were used with 3-D hex-core cells in the central area and tetrahedral cells near the solid surfaces of the model. The computational mesh had around 1,100,000 volume cells. The elements around the air inlets and the outlet were locally refined due to the large temperature and air velocity
34
34
32
32
30
30
28
28
Temperature( C)
26
o
o
Temperature ( C)
and three passengers (one front and two rear passengers) were seated in the cabin, the doors were closed and the heating and air conditioning was immediately turned on. All the data was collected by the data recorder every five minutes for one hour. The basic steps for tests while operating were similar to those while idling except that the vehicle was run at a constant velocity after all the passengers sat in the vehicle.
24 22
Position Idle Operating Head Abdomen Feet
20 18 16
0
5
10
15
20
25
30
26 24 22
Occupant Idle Operating Front pasenger Driver Rear passenger
20 18
35
Time (min)
16
0
5
10
15
20
25
30
Time (min)
(b) In front of each person’s head
(a) In front of the front seat passengers
Fig. 10. Air temperature variations at different locations during heating.
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Table 6 The raw data of Fig. 10. In front of the front seat passenger
In front of each person’s head
Idle
Idle
Operating
Operating
Time (min)
Head
Abdomen
Feet
Head
Abdomen
Feet
Front passenger
Driver
Rear passenger
Front passenger
Driver
Rear passenger
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
22.2 22.2 26.7 26.4 27.5 28.3 28.7 29.2 29.5 28.1 28.5 28.6 28.3 30.2 30.6 30.8 30.4 30.5 31.2 30.6 31.2 31.3 31.7 31.7 31.8 32 32 32.2 32.1 32.2 32.4 32.5 32.4 32.5
24.7 24.1 24.1 23.9 24.2 24.8 25 24.5 25 25.3 25 25.8 28.3 27.2 28.3 28.5 27.6 26.4 27.6 28.5 28.5 29.1 28.8 30 28.8 29.9 29.5 29.3 29.9 30.6 30.2 30.3 30.4 30.5
23 23.5 24.4 23.7 23.6 24.8 26.1 26 26.3 26.6 27 26.5 26.2 26.6 26.8 27.5 28.1 27.2 27.7 28 27.8 28.2 28.3 28.3 28.8 28.8 29 29.1 29.3 29.7 30 29.6 29.8 29.8
23.6 24.7 22.7 23.2 24.6 25.1 25.3 25.1 25.4 25.4 25.6 25.3 25.6 25.7 25.6 25.5 25.9 25.7 26 25.9 25.9 25.9 26.1 26.2 26.2 26.6 26.5 26.7 27 27.3 27.5 27.7 27.9
22 23 21.7 22.7 23.6 23.1 23.8 23.8 24 24.1 24.2 24.4 24.4 24.1 24.2 23.9 24.3 24.6 24.3 24.1 24.4 24.1 24.6 24.7 24.7 25.3 25.3 25.5 25.8 26.1 26.3 26.6 26.7
23.6 22.6 21.2 22.4 23.2 23.1 23.5 23.3 24.1 23.7 23.9 23.8 24.3 23.9 23.8 23.8 24 24.2 24 24 24.3 23.9 24.4 24.6 24.4 25.1 25.1 25.3 25.5 25.8 26.1 26.6 26.6
22.2 22.2 26.7 26.4 27.5 28.3 28.7 29.2 29.5 28.1 28.5 28.6 28.3 30.2 30.6 30.8 30.4 30.5 31.2 30.6 31.2 31.3 31.7 31.7 31.8 32 32 32.2 32.1 32.2 32.4 32.5 32.4 32.5
23.6 22.6 25.7 26.4 28 28.4 29.4 29.8 29.6 29.1 29.1 29.5 28.8 30.6 30.9 31 31.3 31.7 31.7 31.4 32 32 32.3 32.4 32.6 32.7 32.7 32.6 32.8 33 33.1 33.1 32.8 33.3
23.2 22.3 24 26.1 27.2 28.3 29.1 29.3 29.6 28.5 27.4 28.5 28.1 29.7 30.3 30.2 30.3 30.7 30.8 30.4 31 30.9 31.3 31.3 31.4 31.6 31.7 31.7 31.7 31.8 31.9 32 32 32.1
23.6 24.7 22.7 23.2 24.6 25.1 25.3 25.1 25.4 25.4 25.6 25.3 25.6 25.7 25.6 25.5 25.9 25.7 26 25.9 25.9 25.9 26.1 26.2 26.2 26.6 26.5 26.7 27 27.3 27.5 27.7 27.9
22 22 22 22.1 22.1 22.3 22.4 22.6 22.6 22.7 22.8 22.8 22.9 23.1 23.2 23.3 23.5 23.6 23.7 23.9 24.1 24.1 24.1 24 24.1 24.1 24.1 24.4 24.3 24.4 24.5 24.3 24.5
22.9 23.7 23 24 23.8 24.4 25.4 25 25.8 25.6 25.9 25.6 26.1 25.9 26.1 26 26.2 26.1 26.1 26 26 26 26.3 26.2 26.2 26.5 26.5 26.5 26.5 27 26.9 27.1 27.2
temperature changes. The average specific heat of the seat was 1256 W/ kg·k and the average density was 481 kg/m3. The unsteady, incompressible governing equations were discretized by the finite volume method. The second order implicit scheme was used to discretize the transient term. The time step was set to 1 s for all the calculations. The convection term was discretized using the secondorder upwind difference method with the diffusion term using the central difference. The SIMPLE algorithm was used for the coupling between the pressure and velocity. The renormalization group (RNG) ke model was used with the standard wall function for the near wall region treatment, which is demonstrated by Jalal [18]. The convergence was assumed to be obtained when the normalized residuals of the flow equations were less than 10−4 and these of the energy and radiation equations were less than 10−7. The total simulation time was 30 min. In this study, the radiation heat transfer was computed using the discrete ordinates (DO) radiation model which solves the radiative transfer equation for a finite number of discrete solid angles, each associated with a vector direction fixed in the global Cartesian system. The DO model spans the entire range of optical thicknesses and allows the solution of the radiation at semitransparent walls. The solar load was derived from the “solar calculator” in the DO irradiation model by setting the longitude, latitude, date and time of the case and the driving direction of the vehicle according to the experimental conditions. The
gradients, with an average element near the inlets and outlet of around 1 mm. The average length of the other elements was around 50 mm. Grid independence tests showed that this grid gave a grid independent solution. 3.2. Boundary conditions and computational method The air flow in the cabin was assumed to be incompressible, turbulent, and unsteady with constant thermos physical properties and no viscous dissipation. The boundary conditions are shown in Table 5. Convective boundary conditions were used along the outer surfaces of the cabin. The convective heat transfer coefficients h:
1 1 δ = + e h ho ke
(13)
are the combination of the actual convective heat transfer coefficient on the outside and the thermal resistance of the structures and were obtained from the experimental data. The glass structures were defined as semi-transparent walls with the others defined as opaque walls. The air velocity was set to 6.94 m/s for all the inlet vents as measured in the experiments. The inlet air temperature was defined with a user-defined function (UDF) based on the experimental data shown in Fig. 5. The boundary conditions for all the manikins were constant heat flux. The temperature changes in the seats had a significant effect on the air
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Fig. 11. Air temperature variations at different locations while cooling.
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diffuse fraction of the solar radiation was also used in this model. A more detailed description of the radiation model was given earlier [19,20].
temperature was lower than the inside temperature, so the heat transfer direction was from inside to outside. The heat transfer along the outer surface of the cabin was natural convection while idling and forced convection while operating. Therefore, less heat was lost through the surfaces while idling than while operating. As a consequence, the air temperature while idling condition increases faster than that at the same location while operating, and the constant air temperature after the increase while idling was higher than which operating (see Table 6). As shown in Fig. 10a, during the heating, the air temperature in front of the front seat passenger’s head is higher than that in front of his
4. Results and discussion 4.1. Experimental results Fig. 10 shows the air temperature variations at different locations for the idling and operating conditions during heating. The outside air
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with the experimental data for both the idling and operating conditions. The largest difference between the experimental data and the predictions is 3.5 °C. The relative differences between the experimental and simulation results during cooling are shown in Fig. 12. The predictions agree well with the measurements during the cooling with 92.4% of the differences being smaller than 15% while idling and 90.2% of the differences being smaller than 10% while operating. The numerical model tends to underestimates the results while operating. Fig. 13 shows the measured and predicted temperature variations at
abdomen and feet. This is due to the hot air from the heating inlets flowing up to the roof (as shown in Section 4.3), so the air temperature at higher positions increases faster than that at lower positions in the cabin during heating. 4.2. Comparisons of the experimental and simulation results The measurement and calculated air temperature variations at different locations in the cabin during cooling are shown in Fig. 11. The calculated decreasing air temperatures at different locations agree well
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Fig. 15. Temperature (K) distributions in a vertical plane while cooling.
different locations while heating. The increasing trends of the predicted temperatures agree well with the experimental results, with a largest difference between predicted and measured temperatures of 3.0 °C. Fig. 14 shows the relative difference between the experimental and predicted results while heating. The predicted agree well with the measured temperatures while heating with 88.8% of the difference being smaller than 10% while idling and 89.7% of the difference being smaller than 10% while operating.
are on the surfaces of the passengers and seats. The air flow from the vents makes the air temperature gradients near the vents higher than in other zones. The air temperature decreases again slows after 1000 s and stops at 30 min when the temperature reaches steady state. Fig. 17a shows the air velocity distributions on the vertical plane, and Fig. 17b on the horizontal plane. In most of the cabin the air velocities are quite low with high velocities only near the inlets (the air conditioning vents) and the outlet of the computational domain.
4.3. Temperature and air flow distribution in the vehicle cabin
5. Conclusions
Fig. 15 shows the temperature distributions in a vertical plane in the cabin while cooling. The vertical plane is in the middle of the vent on the right side of the front passenger. The cabin temperature is quite high at the beginning of the cooling period. After the air-conditioning starts working, the cabin temperature decreases with time. The lowest temperature is at the vent outlet, while the highest temperatures are at the outer surfaces of the seats, the inside surfaces of the windows and the outer surfaces of the passengers. Therefore, the heat sources are mainly the heat from the windows, the heat stored in the seats and the passengers. Thus the temperature distribution in the cabin is strongly affected by the air flow velocity and the vent directions. The air temperature decreases slows after 1000 s with steady state at around 30 min. The seats cool much more slowly than the air, which means that the effect of the heat storage in the seats on the thermal comfort changes cannot be neglected. The temperature distributions on a horizontal plane (at the same height as the passengers’ chests) are shown in Fig. 16. This horizontal plane crosses across the four air conditioning vents. The cabin air temperature decreases with time. The lowest temperature on this horizontal plane are near the vent outlets while the highest temperatures
This paper presents an experimental and numerical study of the temperature and air flow variations inside an electrical vehicle cabin with four occupants. A series of experiments were conducted in a parked and a moving vehicle during both cooling and heating periods. The transient 3-D numerical model considered both the air and the solid regions in the cabin including the passengers for the experimental conditions. The air temperature and air flow variations in the cabin were then analyzed. The following conclusions can be drawn: 1. The numerical results agreed well with the data with around 90% of the temperature differences being smaller than 10%. 2. The vehicle motion had an important effect on the heat transfer. The outside air velocity increased the convective heat transfer between the outside air and the vehicle outer surfaces, which increased the thermal losses and gains from the cabin. Thus, the heating and air conditioning systems required more time to make the cabin comfortable. 3. During the cooling period, the lowest temperatures were near the vent outlets with the highest temperatures on the surfaces of the windows, passengers and seats. Thus, the thermal stored in the seat
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Fig. 16. Temperature (K) distributions in a horizontal plane while cooling.
(a) Vertical plane
(b) Horizontal plane
Fig. 17. Air velocity (m/s) distributions in the vehicle cabin.
Acknowledgement
strongly affects the air temperature variations in the cabin. It could be a better choice to heat the seat in the winter or cool the seat in the summer to improve the thermal comfort. 4. The highest air velocities were near the inlets and outlets of the computational domain, that is the inlet vents and the return air outlet of the ventilation system. This result showed that the position of the vents has a great influence on the thermal comfort of the car.
This work was supported by Guangdong Industry-AcademiaResearch Project (No. 2011A090200018), the new energy vehicles industry project (2011) of Guangdong Special Funds for Strategic Emerging Industries, and the National Natural Science Foundation for Creative Research Groups of China (No. 51621062). 366
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