More intelligent and efficient thermal environment management: A hybrid model for occupant-centric thermal comfort monitoring in vehicle cabins

He, Xinglei; Zhang, Xiaohan; Zhang, Rui; Liu, Jiaxin; Huang, Xiaoyu; Pei, Jinchen; Cai, Jingyang; Guo, Fen; Wang, Yichun

doi:10.1016/j.buildenv.2022.109866

Cited by 13 publications

(4 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides autonomous driving in AI, researchers now focus on using AI to improve in-cabin comfort. However, most efforts prioritize the impact of temperature and humidity on occupant comfort ( 49 ), while neglecting VOCs. In view of this, we built an LSTM-A-E model to predict the in-cabin VOC concentrations (the principle of the developed model is introduced in the “Deep learning models” section), together with the fully connected neural network (FNN), RNN, GRU, LSTM and LSTM-A models for comparison.…”

Section: Resultsmentioning

confidence: 99%

“…Goh et al ( 48 ) utilized three machine learning models to predict the in-cabin air quality, with support vector regression emerging as the top-performing predictor. He et al ( 49 ) established a data-driven model for predicting thermal comfort of passengers in each seating position, which helps to the development of personalized thermal comfort in cars. However, the aforementioned studies primarily focus on either atmospheric environments, or specific pollutants such as particulate matter and CO 2 , or thermal comfort in vehicle cabins.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cabin air dynamics: Unraveling the patterns and drivers of volatile organic compound distribution in vehicles

Zhang,

Zhao,

Wang

et al. 2024

PNAS Nexus

Self Cite

View full text Add to dashboard Cite

Volatile organic compounds (VOCs) are ubiquitous in vehicle cabin environments, which can significantly impact the health of drivers and passengers, whereas quick and intelligent prediction methods are lacking. In this study, we firstly analyzed the variations of environmental parameters, VOC levels and potential sources inside a new car during 7 summer workdays, indicating that formaldehyde had the highest concentration and about one third of the measurements exceeded the standard limit for in-cabin air quality. Feature importance analysis reveals that the most important factor affecting in-cabin VOC emission behaviors is the material surface temperature rather than the air temperature. By introducing the attention mechanism and ensemble strategy, we present an LSTM-A-E deep learning model to predict the concentrations of 12 observed typical VOCs, together with other five deep learning models for comparison. By comparing the prediction–observation discrepancies and five evaluation metrics, the LSTM-A-E model demonstrates better performance, which is more consistent with field measurements. Extension of the developed model for predicting the 10-day VOC concentrations in a realistic residence further illustrates its excellent environmental adaptation. This study probes the not-well-explored in-cabin VOC dynamics via observation and deep learning approaches, facilitating rapid prediction and exposure assessment of VOCs in the vehicle micro-environment.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cabin air dynamics: Unraveling the patterns and drivers of volatile organic compound distribution in vehicles

Zhang,

Zhao,

Wang

et al. 2024

PNAS Nexus

Self Cite

View full text Add to dashboard Cite

show abstract

“…All physical quantities discussed below are considered as continuous fields depending on three-dimensional coordinates. As a mathematical model for determining the pressure outside and inside the cabin, we can consider the system of Navier-Stokes equations, consisting of the mass conservation Equation (3), momentum Equation (4), the energy Equation ( 5), and the Clapeyron-Mendeleev relation (6) of an ideal gas to take into account compressibility environment [38][39][40][41][42]:…”

Section: Governing Equationsmentioning

confidence: 99%

“…In view of the active electrification and automation of transport, recently, more attention has been paid to optimizing the operation of climate control systems [1][2][3][4][5][6][7], which allows not only the obtaining of comfortable microclimate indicators, but it also reduces the energy consumption of the climate control system. The standard HVAC (heating, ventilation, and air conditioning) parameters studied include temperature and air speed [8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Improving the Energy Efficiency of Vehicles by Ensuring the Optimal Value of Excess Pressure in the Cabin Depending on the Travel Speed

Panfilov,

Beskopylny,

Meskhi

2024

Fluids

View full text Add to dashboard Cite

This work is devoted to the study of gas-dynamic processes in the operation of climate control systems in the cabins of vehicles (HVAC), focusing on pressure values. This research examines the issue of assessing the required values of air overpressure inside the locomotive cabin, which is necessary to prevent gas exchange between the interior of the cabin and the outside air through leaks in the cabin, including protection against the penetration of harmful substances. The pressure boost in the cabin depends, among other things, on the external air pressure on the locomotive body, the power of the climate system fan, and the ratio of the input and output deflectors. To determine the external air pressure, the problem of train movement in a wind tunnel is considered, the internal and external fluids domain is considered, and the air pressure on the cabin skin is determined using numerical methods CFD based on the Navier–Stokes equations, depending on the speed of movement. The finite-volume modeling package Ansys CFD (Fluent) was used as an implementation. The values of excess internal pressure, which ensures the operation of the climate system under different operating modes, were studied numerically and on the basis of an approximate applied formula. In particular, studies were carried out depending on the speed and movement of transport, on the airflow of the climate system, and on the ratio of the areas of input and output parameters. During a numerical experiment, it was found that for a train speed of 100 km/h, the required excess pressure is 560 kPa, and the most energy-efficient way to increase pressure is to regulate the area of the outlet valves.

show abstract

An Experimental Study on the Effects of Temperature and Humidity Levels on Human Thermal Comfort During Running

Yuan,

Zou,

et al. 2024

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

More intelligent and efficient thermal environment management: A hybrid model for occupant-centric thermal comfort monitoring in vehicle cabins

Cited by 13 publications

References 42 publications

Cabin air dynamics: Unraveling the patterns and drivers of volatile organic compound distribution in vehicles

Cabin air dynamics: Unraveling the patterns and drivers of volatile organic compound distribution in vehicles

Improving the Energy Efficiency of Vehicles by Ensuring the Optimal Value of Excess Pressure in the Cabin Depending on the Travel Speed

An Experimental Study on the Effects of Temperature and Humidity Levels on Human Thermal Comfort During Running

Contact Info

Product

Resources

About