Effects of Payloads on Non-exhaust PM Emissions from A Hybrid Electric Vehicle during A Braking Sequence

Received: March 28, 2022
Revised: May 14, 2022
Accepted: May 25, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Cite this article:

Songkitti, W., Sa-ard-iam, S., Plengsa-Ard, C., Wirojaskunchai, E. (2022). Effects of Payloads on Non-exhaust PM Emissions from A Hybrid Electric Vehicle during A Braking Sequence . Aerosol Air Qual. Res. 22, 220150. https://doi.org/10.4209/aaqr.220150

HIGHLIGHTS

• Each additional payload can yield up to 25% increase in non-exhaust PM emissions.
• With 6 passengers comparing to 2 passengers, PM emission is increased by 3 times.
• The relationship between PM_2.5/PM₁₀ emissions and payloads are linearly dependent.
• Variations of payloads have a limited effect on PM₁

Keywords: Non-exhaust PM emissions, Hybrid electric vehicle, Onboard PM measuring device

1 INTRODUCTION

PM has been known as one of the most important air pollutants harming human health. It is mainly divided into PM₁₀ and PM_2.5, which represent particles with a diameter of less than 10 µm and 2.5 µm, respectively. PM can be found mostly in cities and urban areas where vehicles equipped with internal combustion engines are used. Many countries have introduced the use of EV to cope with this PM problem. Governments consider EV as a promising way since it is believed that EV produces zero emissions and, therefore, should not create air pollutants. However, when EV are being more and more used, it has become evident that PM₁₀ emissions remain (Soret et al., 2014; Kuenen et al., 2014). In fact, both conventional vehicles and EV emit PM, such as tyre wear, brake wear, road surface wear/abrasion, and resuspension of road dust (Timmers and Achten, 2016), which are considered as non-exhaust emissions. PM emitted by EV are mostly PM₁₀ with a significant amount of PM_2.5 containing heavy metals such as zinc (Zn), copper (Cu), iron (Fe) and lead (Pb), among others (Thorpe and Harrison, 2008). Road dust (from surface wear/abrasion) and tyre wear are caused by the friction between the tyre thread and road surface, while brake wear is caused by the friction between the brake pad and disc brake. Resuspension of road dust is caused by the diffusion of air current underneath and behind vehicles and mostly considered as PM₁₀ (Simons, 2016).

Among these non-exhaust PM emissions, brake wear is considered as a major contribution because of high frequencies of its usage. The contribution of brake wear emissions can be as high as 42% of total non-exhaust PM emissions (Simons, 2016). It was already hypothesized that PM emissions from brake wear were highly influenced by vehicle weight, as stated in past studies (Barlow, 2014; Garg et al., 2000). They focused on measuring non-exhaust PM emissions between passenger cars and light duty vehicles (LDV). Their result showed that LDV emitted more brake wear PM than passenger cars (Luekewille et al., 2001). It was also mentioned that the inertia weight while the vehicle being stopped could be one of the most important factors contributing to brake wear rates. However, no test has been done to absolutely confirm this hypothesis and verify the observation on various vehicle weights from the same vehicle.

Therefore, this research focuses on investigating of non-exhaust PM emissions emitted from a hybrid electric vehicle (HEV) during a braking sequence. The test is done by using a PM Mobile onboard measuring equipment attached directly onto a moving vehicle at the spot nearby the centre cap bore of the front wheel. Payloads on EV are varied to study the effects of weights on non-exhaust PM emissions during braking sequences.

 
2 METHODS

An experimental setup is done by attaching real-time PM monitoring equipment near the left front brake of a hybrid midsize passenger car as shown in Fig. 1. The specification of the tested vehicle is shown in Table 1. All hardware of this real-time PM monitoring equipment is shown in Fig. 2. Table 2 shows the specifications of the dust sensor used in the current study.

Fig. 1. Measurement setup with instruments.

Fig. 2. Real-time PM monitoring equipment.

The measurement concept is as the follows. PM is detected by a dust sensor connecting to ESP32 board (WIFI + Blutooth), that is run by Arduino IDE, for sending and receiving commands. ESP32 needs to upload a code program, namely, Bluetooth32 to connect a Bluetooth, PMS_MCU to EMS32 for command sensor, and Plantower PMS5003 for detecting PM₁, PM_2.5, and PM₁₀. A schematic of all equipment connection is shown in Fig. 3.

Fig. 3. A schematic of PM measuring devices.

PM readings from the current setup are compared to the PM standard measuring tool, namely, Tapered Element Oscillating Microbalance (TEOM) as seen in Fig. 4. Results show good agreements between the current PM measuring device and TEOM.

Fig. 4. A comparison of PM concentrations measuring by the current setup and TEOM.

In the current study, all tests are performed on a hybrid electric vehicle. The braking and resuspension systems are Original Equipment Manufacturer, and the vehicle is always in a routine regular maintenance. All tests are done on the same road within a closed road to minimize PM diffusion from another vehicle. The vehicle velocities are increased from 0 to 40 km h^–1 to ensure that the vehicle is in the electrified mode and all PM that are emitted from the tested vehicle come from non-exhaust sources. The stopping distance from the velocity of 40 km h^–1 is set within 5 meters until the vehicle is fully stopped. Tests are repeated by increasing various payloads on the tested vehicle. The payload is increased by adding approximately 70 kg, ranging from 2 to 6 passengers in the vehicle. The analysis of relationships between non-exhaust PM emissions and payloads are shown in the next section.

 
3 RESULTS AND DISCUSSIONS

Fig. 5 shows an example of raw data from PM measurements during a brake sequence. At 0 second, the onboard PM measuring device is started while the vehicle is in a park mode. PM emissions are read as the background level (approximately 22 µg m^–3). At 10 second, the vehicle is started, and the vehicle speed is increased. PM readings during this period are due to resuspension of road dust effect (approximately 33 µg m^–3). Until the speed reaches 40 km h^–1, the brake is applied (approximately at 20 second). PM emissions increases rapidly. The vehicle is completely stopped at 26 second. However, PM emissions continuously increase for 5 seconds and slowly decreases until PM readings are equal to the background level again. Note that during this sequence, as shown in Fig. 5, the vehicle is still in the electrified mode. The levels of non-exhaust PM emissions are found to correspond to the trend observed in the past literature (Mathissen et al., 2018).

Fig. 5. An example of non-exhaust PM measurement during a braking sequence.

Prior to variation of payload tests, the effect of braking behavior is on trial. Figs. 6 and 7 show a comparison between soft (slowly decrease the vehicle velocity) and hard (rapidly decrease the vehicle velocity) brake tests. As we clearly observe from both figures, the hard brake generates more PM emissions than the soft one. This corresponds to results demonstrated in some literatures (for example, Hagino et al., 2016). However, both methods of testing yield similar PM emissions’ trend. For the current study, the hard brake test is chosen for investigating the payload effect.

Fig. 6. PM emissions during soft brake test.

Fig. 7. PM emissions during hard brake test.

Past literatures indicated that the moisture level of the road surface might affects the retention of dust on the road (Amato et al., 2012). Experiments on PM measurements on various temperature and humidity are done and an example of PM_2.5 measuring data are shown in Table 3. The first column indicates time, temperature, and humidity levels. The second column shows the average values of PM measuring data between the vehicle starts and when the brake is applied. The third column represents the maximum value of measuring data. The fourth column shows the difference between the third and second column representing the range of PM emissions. Based on these results seen in Table 3, there is no substantial impact of moisture level on non-exhaust PM measurement found in the current study.

Fig. 8 shows the time-averaged values of PM₁, PM_2.5, and PM₁₀ emissions on various payloads. Payloads are increased with additional passengers whose weight is approximately 60–70 kg each. PM data in each payload and size are presented in two columns. Data from 0–10 seconds represent the time-averaged values of PM emissions in background (before the vehicle starts). Data from 21–40 seconds represent the time-averaged values of PM emissions after the brake is applied until PM diffusions stop. Time duration of the braking sequence described here is referred to what is previously shown in Fig. 5. Error bars indicate the variability of PM measuring data.

Fig. 8. Time-averaged emissions of PM₁, PM_2.5, and PM₁₀ on various payloads.

Fig. 9 shows the difference between two columns in Fig. 8 for each PM size in each payload. By considering 2 passengers as the base line case (+0 kg), results show that by increasing payloads on the vehicle, the amounts of PM₁₀ emissions are greater during the braking sequence. This result corresponds to literatures found in Amato (2018). However, past results were only focused on different sizes of the tested vehicles, that is, larger size vehicles emit more non-exhaust PM₁₀ than smaller size ones. In the current study, it is observed that each passenger can yield almost up to 25% increase in PM₁₀ emissions. With 6 passengers in the vehicle comparing to 2 passengers, non-exhaust PM₁₀ can emit more than 3 times. Same trends are found for non-exhaust PM_2.5 emissions as shown in Fig. 10. The linear relationship between the payload and PM_2.5/PM₁₀ emissions are observed.

Fig. 9. Effects of payloads on non-exhaust PM₁, PM_2.5, and PM₁₀ emissions.

Fig. 10. Non-exhaust PM_2.5 emissions under various payloads, with the dotted line indicating a linear relationship.

On the contrary, when considering PM₁ emissions, results from Fig. 9 demonstrate that a certain cut point is observed between 3 and 4 passengers. With 2 and 3 passengers, PM₁ emissions are mostly the same. Once there are 4 passengers in the vehicle, PM₁ emissions are doubled and remain the same for 4 to 6 passengers. PM₁ emissions are mostly from the brake wear (Songkitti et al., 2022) whereas PM_2.5 and PM₁₀ emissions are due to resuspension and road dust effects (Simons, 2016). This indicates that the effects of payloads significantly impact PM emissions from resuspension and road dust. However, they have limited effects on the brake wear from a hybrid electric vehicle. This speculation will be investigated thoroughly in the future work.

 
4 CONCLUSIONS

This research study focuses on the effect of payloads on non-exhaust PM emissions from the hybrid electric vehicle. The onboard PM monitoring device is attached nearby the wheel on the vehicle for real-time PM measurement. Payload is varied from 2 to 6 passengers and data are collected during braking sequences. Results show that by increasing the payload at approximately 60–70 kg for each test, PM_2.5/PM₁₀ emissions can be increased up to 25%. With 6 passengers in the vehicle comparing to 2 passengers, the non-exhaust PM_2.5/PM₁₀ is found to increase by 3 times. The linear relationship can also be found between PM_2.5/PM₁₀ emissions and increased payloads. In the case of PM₁ emissions, variations of payloads have a limited effect. A certain cut point of PM₁ emissions increase can be found with additional mass of 130 kg.

 
ACKNOWLEDGMENTS

This research study is financially supported by Faculty of Engineering, Kasetsart University via Graduate Research Scholarship Contract No. 62/09/ME/D.ENG and Thailand Toray Science Foundation (TTSF). Special thanks go to Mr. Thana Jongcharoensiri, Mr. Phathit Sungthong, and Mr. Vitsarut Namsiriyothin for data collection and the tested vehicle.