Monitoring of carbon dioxide (CO&lt;inf&gt;2&lt;/inf&gt;) accumulation in vehicle cabin

Kamarudin, Latifah Munirah; Shukri, S.; Abdullah, Nooraihan; Zakaria, A.

doi:10.1109/iced.2016.7804682

Cited by 12 publications

(10 citation statements)

References 9 publications

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“…In our experiment, the temperature and humidity remained constant and we analyzed the CO 2 concentration. In the literature, we find works where the CO 2 concentration inside the vehicle cabin is analyzed [ 80 ]. However, we have not found works which study how a high CO 2 concentration influences driving stress and driving behavior.…”

Section: Methodsmentioning

confidence: 99%

The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

Magaña

Scherz²,

Seepold

et al. 2020

Sensors

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Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver’s mental state, as well as other external factors such as the CO2 concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO2 level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations.

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Section: Methodsmentioning

confidence: 99%

The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

Magaña

Scherz²,

Seepold

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…A large pressure will be created against the vehicle’s body when the vehicle travels at a high speed and leakages will occur between the joints [ 44 ]. The higher the vehicle speed, the more outdoor air will be penetrating the vehicle cabin [ 10 ]. Thus, other parameters such as temperature, humidity, and vehicle speed may also affect the AQI inside the vehicle cabin.…”

Section: Data Processing For Time Series Datamentioning

confidence: 99%

“…These are dangerous consequences to the occupants as well as potentially to other road users. Moreover, the vehicle speed might affect the gas concentration inside the vehicle cabin [ 10 ]. The undesirable effect of this condition is not limited to gas concentration only.…”

Section: Introductionmentioning

confidence: 99%

Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm

Kamarudin

Zakaria

Nishizaki

et al. 2021

Sensors

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This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.

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“…The heat flow rate between the coolant and the air in the HX2 is described by (6), what is the most simple expression of heat exchanger thermal flow rate, which is dependent on media temperatures and thermal conductance G h7 . The rest of the equations above are based on the Simplified Steady-Flow Energy Equation (SSFEE) from [30].…”

Section: Fig 3: Fev Hvac Modelmentioning

confidence: 99%

“…Nevertheless, this approach can not be used, as no fresh air will be supplied to the cabin and thus the air quality will be deteriorated by means of a higher carbon dioxide (CO 2 ) concentration. High concentrations of CO 2 can lead to driver (and also passenger) fatigue [6]. Moreover, the cabin air humidity can increase and under some conditions windows can get fogged, leading to a limited driver view.…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Model Predictive Control of Cabin Temperature and Air Quality in Fully Electric Vehicles

Glos

Otava

Václavek

2021

IEEE Trans. Veh. Technol.

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This article describes an application of Non-linear Model Predictive Control algorithms on energy efficient control of fully electric vehicle cabin temperature and air quality. Since fully electric vehicles can not utilize waste heat from a powertrain (or there is not enough waste heat) as ICE vehicles do, it is necessary to employ advanced control approaches (especially for cabin heating) due to the possible mileage lost by using energy from the batteries for cabin conditioning. The basic idea behind this is to avoid the heat losses caused by excessive air exchange and to ensure a satisfactory air quality in combination with a user defined temperature. The Non-linear Model Predictive control algorithms were successfully implemented into an Infineon AURIX Tricore microcontroller and tested within a Processor in the Loop simulation. Index Terms-non-linear model predictive control, fully electric vehicle, battery electric vehicle, vehicle cabin model, Extended Kalman filter, air quality control, temperature control NOMENCLATURE COP Coefficient

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Monitoring of carbon dioxide (CO<inf>2</inf>) accumulation in vehicle cabin

Cited by 12 publications

References 9 publications

The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm

Non-Linear Model Predictive Control of Cabin Temperature and Air Quality in Fully Electric Vehicles

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