Fig 1.
Implementation process of the vehicle HVAC pre-conditioning system.
The system was designed for internet-enabled vehicles; therefore, passengers send network commands to activate parked vehicle systems for ventilation. The pre-conditioning system runs 10 minutes before passenger demand. The vehicle system specifies the optimal air conditioning parameters based on the current environmental conditions to the satisfy the passengers.
Fig 2.
Research method of automobile cabin pre-conditioning.
The system levels and classifies thermal environment. The database develops solutions for each thermal environment level. Cabin pre-conditioning invokes solutions based on the identified thermal environment level.
Fig 3.
The data flow within the data-driven decision model.
A classification model identifies and matches schemes based on the cabin temperature and humidity, seat and wall temperature and climate conditions.
Fig 4.
The physical relationship of cabin thermal transfer.
Solar radiation (ΔQsolar −w, ΔQsolar −s) is an important factor to the cabin temperature, the convection of external airflow (ΔQatm−s) as well. As the uneven airflow from HVAC, the cabin is divided into two thermal zones: the front row’s region (zone 1) and the rear row’s region (zone 2). During cooling, thermal zones are affected by energy and mass exchanges: 1) the convection (ΔQa−w, ΔQa−s) and cooling mass flow (Δmac) from air-conditioning; 2) the solar radiation load (ΔQsolar −w, ΔQsolar −s) received; 3) air circulation between zones (Δma1−a2) and in HVAC (Δmcir); 4) thermal exchanges with outside (ΔQatm−s).
Fig 5.
Arrangement of thermocouples in the cabin.
The temperature measurement points on the car body were set to analyze the thermal load of the vehicle. (a): Measurement points on the car body. (b): Measurement points in the cabin.
Fig 6.
Experimental scenario and related layout.
A solar radiation meter was set to obtain the amount of solar radiation entering the cabin through the windows. Air temperature and humidity were measured by a sensor suspended from the roof. (a): Dashboard’s thermocouples and radiometer. (b): Air temperature and humidity sensor.
Fig 7.
Cabin air temperature Tca obtained by experiment.
The frequency in (a) is described as ; while average cabin temperature f(tm) = 48.08 sin(0.172tm + 0.257)+ 11.06 sin(0.86tm + 1.65). tm is the month. (a): Frequency of occurrence of cabin air temperature. (b): Change in cabin air temperature by month.
Fig 8.
Thermal load of main components during parking.
During the radiation increasing, the relationship between the solar radiation and the components temperature follows as: atmosphere, Tatm = 0.006086I1.117 + 25.95; cabin air, Tca = 0.006086I1.117 + 25.95; the steering wheel, TSw = 0.1161I0.9124 + 20.69; front seat, Ts1 = 2.475I4 + 0.3333I3−9.094I2 + 10.64I + 50.25; rear seat, Ts2 = −0.1648I2 + 10.35I + 40.8; During the radiation decreasing, the correlation follows as: atmosphere ; steering wheel TSw = 1.739I0.4881 + 33.89; front seat Ts1 = −1.886I4 + 0.8018I3 + 4.752I2 + 6.391I + 53.98; rear seat Ts2 = −0.8943I4 + 1.891I3 + 1.865I2 + 1.704I + 51.24. In (b), the atmospheric temperature is described as
and
; cabin air temperature are
and Tacmin =
; atmospheric RH are described as RHatm max =
and
; cabin RH are described as
and
6.219tm + 28.13. (a): The influence of changes in solar radiation. (b): Maximum and minimum of air temperature and relative humidity.
Fig 9.
Temperature and humidity distribution on July 15.
The car suffered from the worst heat load at noon, with a maximum Tca of 68.66°C.
Fig 10.
Characteristics of scenarios for different thermal environment levels.
Tca and RHca are respectively the air temperature and relative humidity of the cabin, measured at the front and rear rows. Ts1 and Ts2 are the temperatures of the front and rear seat cushion, while Tw is derived from the average wall temperature in the thermal zone, including car door interior trim panel, pillars, the car roof, and floor.
Table 1.
The typical scenarios for different thermal environment levels.
Table 2.
The typical scenarios for different thermal environment levels.
Table 3.
The typical scenarios for different thermal environment levels.
Fig 11.
Influence of different parameter combinations on scenario 1.
Compared with others, the parameter scheme [6, 7.5, 100] wins the highest score, with a CEI being -0.099.
Fig 12.
Influence of different parameter combinations in 7.5 m/s on scenario 2.
The parameter combination [8, 7.5, 100] wins the higher score.
Fig 13.
Tca and schemes distribution of the sample point.
The 300 samples selected consider the uniformity and representativeness of the sample points in the thermal environment state.
Fig 14.
Confusion matrix for classification models training.
Classification mistakes mainly happen between scheme 1 and scheme 2 and between scheme 4 and scheme 5. (a): Cubic SVM. (b): RF. (c): Weighted KNN.
Table 4.
Accuracy of different classification models.
Table 5.
Case study for pre-conditioning analysis.
Fig 15.
The pre-conditioning results for case study.
All case studies achieve a CEI rating of 0 or higher, bringing cabin heat down to an acceptable value.