Dongzhi Gao

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.

Off-road machinery is one of the significant contributors to air pollution due to its large quantity. In this study, a deep learning model was developed to predict the transient engine emissions of CO, NO, NO₂, and NO_x, which are the main pollutants emitted by off-road machinery. A portable emission measurement system (PEMS) was used to measure the exhaust emission features of four types of construction machinery. The raw PEMS data were preprocessed using data compensation, local linear regression, and normalization to ensure that the data could handle transient conditions. The proposed model utilizes the preprocessing PEMS data to estimate the CO, NO, NO₂, and NO_x emissions from off-road machinery using a recurrent neural network (RNN) based on a long short-term memory (LSTM) model. The experimental results show that the proposed method can effectively predict the emissions from off-road construction machinery under transient conditions and can be applied to controlling the emissions from off-road construction machinery.

The efficient application of oxygen-containing clean fuels in engines has always been a research focus. With the increase in ethanol production, the output of fusel as a co-product is also increasing. The application of fusel is also an effective way to lessen the consumption of fossil fuels. Therefore, the influences of fusel on performance and emissions were investigated in the current study on a six-cylinder heavy-duty compression-ignition engine and revolved around the WHSC test cycle. The three test fuels were diesel, F20NW (the volume proportion of anhydrous fusel is 20%, and the rest is pure diesel), and F20WW (the volume proportion of hydrous fusel is 20%). The addition of fusel improved BTE, reduced NOx and soot emissions, and thermal efficiency and emissions were further improved in combination with EGR optimization. In terms of WHSC, the improvement effect of hydrous fusel was the best. The equivalent fuel consumption, NOx, soot, and CO₂ emissions of F20WW were reduced by 1.77%, 37.49%, 17.38%, and 1.32%, respectively, with the optimization of EGR compared with pure diesel. The addition of 20% hydrous fusel combined with the introduction of EGR can be directly applied to existing diesel engines and achieve a simultaneous reduction in fuel consumption and emissions.

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.