Under low-load cycles (LLCs), the issue of high NOx emissions from diesel engines is attracting widespread attention. Through a combination of experimental and simulation approaches, the NOx emission behavior under LLC conditions was investigated. Furthermore, the optimization strategies for reducing NOx emissions was studied based on a dual selective catalytic reduction (SCR) after-treatment system. The results indicate that emissions at load rates below 30% during LLCs account for more than 67.5% of the total cycle emissions, particularly under idling and start-stop conditions. Moreover, it was found that NOx emissions decrease significantly by using a pre-positioned dual SCR after-treatment system. And, the closer the SCR is to the engine, the higher the NOx conversion efficiency becomes. As the SCR’s position is adjusted, the rate of NOx removal stabilizes, achieving a maximum reduction in NOx concentration of up to 60.1%.

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.

Faults of the selective catalytic reduction (SCR) significantly exacerbate nitrogen oxide (NOx) emissions from heavy-duty vehicles, thereby posing a severe hazard to atmospheric environmental quality. Currently, the paucity of systematic studies on NOx emission degradation induced by typical SCR faults has severely hindered the advancement of precise emission regulation for heavy-duty vehicles in China. To address this critical gap, this study investigates the impact of typical SCR faults on NOx emission deterioration from heavy-duty vehicles. Initially, leveraging the China heavy-duty commercial vehicle test cycle as the benchmark, heavy-duty vehicle emission tests were designed and conducted under typical SCR faults. Emission datasets were acquired for three typical SCR faults—namely nozzle circuit disconnected fault, upstream temperature sensor inaccuracy fault, and urea-water replacement fault—as well as under normal operating conditions. Building upon these data, three representative scenarios were established by integrating vehicle operating condition, fuel consumption levels, and vehicle specific power states, enabling systematic quantification of the extent of NOx emission deterioration caused by each SCR fault. The findings reveal that the NOx emissions deterioration caused by urea-water replacement fault is the most severe, followed by nozzle circuit disconnected fault, and the impact of upstream temperature sensor inaccuracy fault is the least. This research provides crucial support for identifying key targets in emission control and enhancing the precision of heavy-duty vehicle emission regulation. Relevant authorities should prioritize cracking down on intentional non-compliant practices such as urea water substitution to safeguard a healthy and sustainable atmospheric environment.

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.

Auxiliary energy consumption rises sharply at low temperatures, reducing the accuracy of driving range prediction and vehicle energy management. Most previous studies have focused on battery electric vehicles, whose operating characteristics do not fully represent the powertrain architecture of range-extended electric vehicles (REEVs). This study analyzes REEV auxiliary energy consumption and develops prediction models for operation at low temperatures. Approximately 3600 km of actual road driving data were collected. Auxiliary energy consumption was examined across four dimensions: power mode, trip scale, thermal management load, and range-extender operating share. Engine waste heat reduced auxiliary energy consumption by more than 59% in range-extended mode compared with pure-electric mode. Random Forest (RF), Least-Squares Boosting (LSBoost), and Multilayer Perceptron (MLP) methods were used to develop a trip-scale auxiliary energy consumption prediction model (trip-scale model) and a second-scale auxiliary energy consumption prediction model (second-scale model). The best test-set R² was 0.826 for the trip-scale model. For the second-scale model, R² increased from 0.857 at 30 s to a maximum of 0.872 at 60 s; considering that doubling the sample duration yielded an R² improvement of only 0.015, the 30 s LSBoost model was selected for subsequent integrated prediction. In the integrated application, the selected model predicted the mean auxiliary power over the remaining trip to estimate the remaining auxiliary energy. Although the departure estimate had a 9.55% error, iterative updates kept the entire estimate close to the measured value, with a maximum absolute residual of 0.022 kWh.