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.

As the test cycle used for evaluating the fuel consumption and emission characteristics of the heavy-duty commercial vehicle in China will be replaced from C-WTVC to CHTC in a round 2020, the investigation on the variation of fuel consumption and emission test results after the replacement is well needed for further vehicle’s development and calibration. In this paper, the fuel consumption and emission characteristics of a China VI coach under these two test cycles have been discussed and compared. Results showed that fuel consumption, CO, HC and NOx emissions of the test coach all increased after changing the test cycle from C-WTVC to CHTC, which were due to the low rotation speed and low torque operating points, and the aggressive and frequent acceleration under CHTC. In addition, Acceleration driving condition contributed most to the deterioration of fuel consumption, CO, HC and NOx emissions under CHTC, which attention should be especially paid in the further vehicle calibration.

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.

As the increasing complains of the electric passenger car owners about the lower pure electric mileage under real-road driving condition than the regulation standard test result, the annual real-road energy consumption characteristics of 20 MPV electric passenger cars in Chinese city, Shenzhen, have been investigated to analyse the effect factors causing the energy consumption difference between these two results. Results showed that the annual variations of real-road energy consumption rate of each month were agreed with the variations of air conditioning using duration. The highest and lowest energy consumption rates were obtained in July and March, which increased the energy consumption rate of whole vehicle about 24% in the maximum extent. The difference of the energy consumption between real-road and regulation standard test was attributed to the effects of air conditioning using and driving condition (driving velocity and acceleration distribution), which have been qualified in this paper.