Gang Li

The real driving emission (RDE) test is going to be a necessary and effective evaluation method in the next-stage heavy-duty vehicle (HDV) emission standards, the rulemaking of which is under way worldwide (e.g., EPA 2027, Euro 7 and China 7). In this work, a time-based method (TBM) was proposed for future HDV RDE calculation. In TBM, cold-start and hot-run emissions are evaluated separately with moving average windows, yet no type-approval test results are needed so that it can also be used as a remote monitoring algorithm. This study analyzes the emissions of NO_x. The value of 0.1 times maximum engine power is utilized to determine the cold-start window, while a 2-bin window structure is adopted for hot-run analysis. In order to further illustrate and validate this method, 16,629.4 h of remote monitoring data with a sampling rate of 1 Hz from 36 China 6 HDVs and 4 different months were analyzed for driving and NO_x emission characteristics with TBM. The average duration of the 21,466 trips analyzed in this work was found to be 0.68 h, and the average ratio of trip work to WHTC (world harmonized transient driving cycle) work was around 1.38, indicating that lower duration and work requirements are needed in future RDE test. Moreover, the average cold-start length was approximately 912.4 s (15.2 min), and long cold starts could be found in cases with low ambient temperatures, low driving speeds and frequent stops. As for hot-run analysis, the proportion of Bin 1 (low-load windows) and Bin 2 (high-load windows) is directly related to the driving scenarios. The calculation results of TBM are comparable to the 2-bin method in EPA 2027. In addition, the optimization of NO_x emissions under cold start and idle conditions are challenging for future HDV updates.

Carbon emissions are currently a hot topic in the international community. CO₂ reduction from heavy-duty commercial vehicles plays a significant role in slowing down the global greenhouse effect and promoting sustainable development. To control carbon emissions, many countries have tightened CO₂ emission regulations and policy requirements for heavy-duty commercial vehicles in recent years. Various CO₂ emission simulation models have been developed, such as the Greenhouse Gas Emissions Model (GEM) in the United States and the Vehicle Energy Consumption Calculation Tool (VECTO) in the European Union, to evaluate the real CO₂ emission levels of commercial vehicles and provide a scientific basis for formulating corresponding emission reduction policies and control measures. This paper systematically analyzes the CO₂ emission regulations and policy requirements for heavy-duty commercial vehicles in the United States, the European Union, China, and other developed countries. It also analyzes the GEM software in the United States, the VECTO software used in Europe, and the energy consumption simulation software for commercial vehicles in China. The influencing factors of CO₂ emission simulation are explored in detail. This study found that, although GEM and VECTO software are recognized for their high accuracy, their applications are still dependent on local policies. In other countries and regions, VECTO software has broader applicability. On the other hand, China’s commercial vehicle energy consumption simulation software and other reported studies have only been validated for specific vehicle types. The accuracy and generalizability of these models should be further promoted and verified.

Global warming is directly related to heavy-duty vehicle fuel consumption and greenhouse gas (CO₂ mainly) emissions, which, in China, are certified on the vehicle chassis dynamometer. Currently, vast amounts of vehicle real-road data from the portable emission measurement system (PEMS) and remote monitoring are being collected worldwide. In this study, a binning-reconstruction calculation model is proposed, to predict the chassis dynamometer fuel consumption and CO₂ emissions with real-road data, regardless of operating conditions. The model is validated against chassis dynamometer and PEMS test results, and remote monitoring data. Furthermore, based on the proposed model, the fuel consumption levels of 1408 heavy-duty vehicles in China are analyzed, to evaluate the challenge to meet the upcoming China fourth stage fuel consumption limits. For accumulated fuel consumption based on the on-board diagnostic (OBD) data stream, a predictive relative error less than 5% is expected for the present model. For bag sampling results, the proposed model’s accuracy is expected to be within 10%. The average relative errors between the average fuel consumption and the China fourth stage limits are about 3%, 8%, and 0.7%, for current trucks, tractors, and dump trucks, respectively. The urban operating condition, with lower vehicle speeds, is the main challenge for fuel consumption optimization.

The remote online system of heavy-duty vehicles is composed of vehicle terminal and monitoring platform based on the specified communication protocol, data format and data transmission frequency, which is an important embodiment of heavy-duty vehicle pollution control in the application for Internet of Vehicles. The influence of different collection frequencies and compute cycles on vehicle energy consumption and emission calculation is studied in this paper. The calculation errors of mileage, vehicle fuel consumption per 100 kilometers and emission factors of 14 collection frequencies and 7 compute cycles were calculated by continuously monitoring the emission and energy consumption data for 10 heavy-duty vehicles uploaded in 1 month. The result shows that decreasing the data collection frequency will lead to the increase of error and the decrease of correlation, while increasing the computing cycle can reduce the error. When the calculation error is 1%, 5% and 10%, the collection frequency shall be at least 0.5Hz, 0.2Hz and 0.1Hz, and the computing cycle shall be greater than 1800 seconds, 3600 seconds and 7200 seconds. The study content of this paper provides the theoretical foundation for the application and storage of remote monitoring data of heavy-duty vehicles, and provides a solution to the waste of data storage space caused by the problem of remote monitoring big data set.

Real driving emissions of a heavy-duty vehicle with a maximum total mass of 18000 kg were investigated by the engine-in-the-loop (EIL) methodology. Virtual vehicle model, driver model and real road model was established, then to combined with the actual engine and emission test equipment to fulfill the vehicle emission evaluation on engine test bed. The results indicate the road driving can be well reproduced on engine laboratory with good vehicle speed followability, engine speed and torque consistence. However, the emission results show a larger difference with a 3.6 gap for CO2, a 72.3% gap for NOx and a 40% gap for PN., respectively. Analysis shows that the intercooler temperature, the exhaust temperature and equipment difference has a combined effect on the emission accuracy.

Pollutants and carbon dioxide (CO2) under different vehicle loading were investigated on a dump truck with a maximum designed total mass of 18000 kg. An engine-in-the-loop (EIL) methodology was applied to achieve the test consistency because EIL can use the same road, driver and vehicle model. The results show that the total CO2 emission increases while the brake specific CO2 emission decrease as the vehicle loading increases. Both the total and specific emissions of PN increase when vehicle loading increases. The effect regularity of increasing vehicle loading on NOx, THC and CO is not significant. It is necessary to comprehensively consider the changes in the raw exhaust due to the engine operation points changes and in the SCR catalytic efficiency derived from the SCR inlet temperature variation. The DPF generation is also need to be concerned because the CO2, PN and NOx emissions will rapidly increase during this period.

Real driving emission characteristics of heavy-duty diesel vehicles with different technical routes were conducted by portable emission measurement system (PEMS) tests at different altitudes of 1400 meter and 2300 meter. Results show that the use of EGR will lead to increased particulate matter emissions but has little effect of NOx emissions. The EGR application exhibits a large effect on the PN emission but a little effect on NOx emission. PN emissions were strongly correlated with engine load during the PEMS test. The higher the engine load, the higher the PN emissions even at lower altitudes. There are large differences in PEMS test results even at the same altitude.

With the requirement of reduced carbon emissions and air pollution, it has become much more important to monitor the oil quality used in heavy-duty vehicles, which have more than 2/3 transportation emissions. Some gas stations may provide unqualified fuel, resulting in uncontrollable emissions, which is a big challenge for environmental protection. Based on this focus, a gas station recognition method is proposed in this paper. Combining the CART algorithm with the DBSCAN clustering algorithm, the locations of gas stations were detected and recognized. Then, the oil quality analysis of these gas stations could be effectively evaluated from oil stability and vehicle emissions. Massive real-world data operating in Tangshan, China, collected from the Heavy-duty Vehicle Remote Emission Service and Management Platform, were used to verify the accuracy and robustness of the proposed model. The results illustrated that the proposed model can not only accurately detect both the time and location of the refueling behavior but can also locate gas stations and evaluate the oil quality. It can effectively assist environmental protection departments to monitor and investigate abnormal gas stations based on oil quality analysis results. In addition, this method can be achieved with a relatively small calculation effort, which makes it implementable in many different application scenarios.