Development of Vehicle Emission Model Based on Real-Road Test and Driving Conditions in Tianjin, China

Zhang, Yi; Zhou, Ran; Peng, Shitao; Mao, Hongjun; Yang, Zhiwen; André, M.; Zhang, Xin

doi:10.3390/atmos13040595

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…When the vehicle speed was larger than 5 m/s and the acceleration was greater than 0.5 m/s 2 , the CO 2 emission rate increased significantly, and the acceleration further increased the CO 2 emission rate as the vehicle speed was high. This result was similar to the finding reported by Zhang with a slightly higher threshold of speed [39].…”

Section: Impact Of Driving Factors On Co 2 Emissionssupporting

confidence: 92%

A Deep Learning Micro-Scale Model to Estimate the CO2 Emissions from Light-Duty Diesel Trucks Based on Real-World Driving

et al. 2022

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On-road carbon dioxide (CO2) emissions from light-duty diesel trucks (LDDTs) are greatly affected by driving conditions, which may be better predicted with the sequence deep learning model as compared to traditional models. In this study, two typical LDDTs were selected to investigate the on-road CO2 emission characteristics with a portable emission measurement system (PEMS) and a global position system (GPS). A deep learning-based LDDT CO2 emission model (DL-DTCEM) was developed based on the long short-term memory network (LSTM) and trained by the measured data with the PEMS. Results show that the vehicle speed, acceleration, VSP, and road slope had obvious impacts on the transient CO2 emission rates. There was a rough positive correlation between the vehicle speed, road slope, and CO2 emission rates. The CO2 emission rate increased significantly when the speed was >5 m/s, especially at high acceleration. The correlation coefficient (R2) and the root mean square error (RMSE) between the monitored CO2 emissions with PEMS and the predicted values with the DL-DTCEM were 0.986–0.990 and 0.165–0.167, respectively. The results proved that the model proposed in this study can predict very well the on-road CO2 emissions from LDDTs.

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Section: Impact Of Driving Factors On Co 2 Emissionssupporting

confidence: 92%

A Deep Learning Micro-Scale Model to Estimate the CO2 Emissions from Light-Duty Diesel Trucks Based on Real-World Driving

et al. 2022

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“…The reason is that the 23 nm SPN PEMS testing has been required for a few years (2021), and the 10 nm SPN PEMS are not regulated yet. Thus, until recently, researchers have been using different techniques to measure on-board the vehicle not only for heavy-duty vehicles (e.g., [35][36][37][38]) but also for light-duty ones (e.g., [39,40]). While research studies with SPN PEMS exist (e.g., [41,42]), the assessment of PEMS is limited.…”

Section: Introductionmentioning

confidence: 99%

Solid Particle Number (SPN) Portable Emission Measurement Systems (PEMS) for Heavy-Duty Applications

Giechaskiel,

Melas,

Broekaert

et al. 2024

Applied Sciences

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A heavy-duty engine is homologated in a test cell. However, starting with Euro VI regulation, the in-service conformity is controlled with the engine installed in the vehicle using portable emission measurement systems (PEMS). In Europe, the application of solid particle number (SPN) PEMS started in 2021 for compression ignition (diesel) vehicles and in 2023 for positive ignition vehicles, thus including those operating with compressed natural gas (CNG). Even though today only particles with sizes > 23 nm are regulated, the Euro 7 proposal includes particles > 10 nm. There are not many studies on the accuracy of the SPN PEMS, especially for heavy-duty applications. In this study, PEMS measuring > 23 and >10 nm from two instrument manufacturers were compared with laboratory-grade instruments. The particle detector of one PEMS was a condensation particle counter (CPC), and of the other a the diffusion charger (DC). The results showed the robustness and good accuracy (40% or 1 × 1011 #/kWh) of the PEMS for ambient temperatures from −7 °C to 35 °C, active regeneration events, different fuels (Diesel B7, HVO, and CNG), different test cycles, cold start or hot engine operations, and high exhaust gas humidity content. Nevertheless, for the DC-based PEMS, sensitivity to pre-charged urea particles was identified, and for the CPC-based PEMS, sensitivity to pressure changes with one vehicle was nnoticed. Nevertheless, the results of this study confirm that the PEMS are accurate enough to measure even the stricter Euro 7 limits.

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“…Vehicle emission models estimate the amount of exhaust emissions based on various input data. These data are differentiated according to the scale of the model used [15,16]. Examples of input data can be detailed vehicle data, for example, engine specifications, driving conditions, and fuel properties [17,18].…”

Section: Introductionmentioning

confidence: 99%

Vehicle Emission Models and Traffic Simulators: A Review

Mądziel

2023

Energies

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Accurate estimations and assessments of vehicle emissions can support decision-making processes. Current emission estimation tools involve several calculation methods that provide estimates of the exhaust components that result from driving on urban arterial roads. This is an important consideration, as the emissions generated have a direct impact on the health of pedestrians near the roads. In recent years, there has been an increase in the use of emission models, especially in combination with traffic simulator models. This is because it is very difficult to obtain an actual measurement of road emissions for all vehicles travelling along the analysed road section. This paper concerns a review of selected traffic simulations and the estimation of exhaust gas components models. The models presented have been aggregated into a group with respect to their scale of accuracy as micro, meso, and macro. This paper also presents an overview of selected works that combine both traffic and emission models. The presented literature review also emphasises the proper calibration process of simulation models as the most important factor in obtaining accurate estimates. This work also contains information and recommendations on modelling that may be helpful in selecting appropriate emission estimation tools to support decision-making processes for, e.g., road managers.

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Development of Vehicle Emission Model Based on Real-Road Test and Driving Conditions in Tianjin, China

Cited by 7 publications

References 19 publications

A Deep Learning Micro-Scale Model to Estimate the CO2 Emissions from Light-Duty Diesel Trucks Based on Real-World Driving

A Deep Learning Micro-Scale Model to Estimate the CO2 Emissions from Light-Duty Diesel Trucks Based on Real-World Driving

Solid Particle Number (SPN) Portable Emission Measurement Systems (PEMS) for Heavy-Duty Applications

Vehicle Emission Models and Traffic Simulators: A Review

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