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The increasingly stringent emission regulations have mandated the use of CCRT (catalyzed continuously regeneration trap) made by upstream DOC (diesel oxidation catalyst) and downstream CDPF (catalyzed diesel particulate filter) for heavy-duty diesel vehicles, which is proved to be the only way that can efficiently control the gaseous and particulate emissions. The performance of after-treatment is greatly influenced by the running conditions of the diesel vehicle and its exhaust parameters, so this paper intended to use grey relational analysis to study the correlation between running conditions (velocity, acceleration, VSP (vehicle specific power)), exhaust parameters (exhaust flow rate, DOC inlet temperature, concentrations of CO, THC, O2 and NOX) and the performance of DOC and CCRT based on chassis dynamometer test. Results showed that the effect of DOC on CO and THC is mainly affected by exhaust flow rate, exhaust temperature and THC concentration.

Regulated gaseous and particulate matter (PM) emissions in the exhaust from a heavy duty diesel engine with biodiesel fuel were studied, and the emission characteristics of PM and polycyclic aromatic hydrocarbons (PAHs) emissions in PM were highlighted. In the experiment, pure diesel fuel and B10 (a blend of diesel and biodiesel fuels with the volume ratio of 9 to 1) fuel were chosen. The study shows that, compared to the pure diesel, the emissions of PM, soluble organic fractions (SOF) and PAHs from the heavy duty diesel engine decrease when the engine burns B10 fuel, and the nitrogen oxides (NOx) emission slightly increases, while the unburned hydrocarbon (HC) and carbon monoxide (CO) emissions also decline. Among the detected 12 kinds of PAHs, emission concentrations of 10 kinds of PAHs from the engine with B10 descend. Especially Benzo(a)pyrene equivalent toxicity (BEQ) analysis results show that the BEQ of B10 fuel decreases by 15.2% compared to pure diesel.

The nonuniformity property of the temperature field distribution will not only affect on the battery charging and discharging performance but also its lifetime. In this paper the elementary structural design is implemented for Ni-Mh battery package and the corresponding test platform is constructed from the point of view of temperature difference control strategy, the test results show that the present structural design schemes can effectively restrain temperature difference enlargement among the battery stacks. Through the application of adopting the flow field uniformity method to control temperature difference, and flow field optimization inside the battery package, it is found that the flow field velocity change quantity ΔV is gradually reduced as the increase of the afflux hood angle Ak and air vent width Da, and the difference of battery temperature is relatively lower, which denoting that the corresponding relationship can be created based on test data.

The prediction of passenger flow is of great significance to facilitate the decision-making processes for local authorities and transport operators to provide an effective bus scheduling. In this work, a backpropagation neural network (BPNN) was adopted to predict the bus passenger flow. To reduce the prediction error and improve the prediction accuracy, a combined model CEEMDAN-BP, which combines CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) method and BPNN, has been proposed. CEEMDAN is an improved method based on EEMD, which has been widely applied to signal smoothing and de-noising. Experimental results show that this combined model can exactly achieve an excellent prediction effect and improve the prediction accuracy of the network greatly.

Aiming at the test safety problems in the early stage of self-driving cars development, firstly the virtual vehicle on-board CAN data acquisition module of the present project was designed based on virtual LabVIEW. Then a wireless remote control system for the self-driving car was constructed, which integrated the built virtual vehicle on-board CAN data acquisition system, the remote real-time image monitoring module and the remote upper computer control module based on ZigBee wireless transmission. It can execute the environmental awareness training and continuous and complex motion manipulation testing of the vehicle without relying on the driver, which can solve the safety problems in the tests of initial development of self-driving cars. Finally, the four-wheel independent steering electric vehicle was used as the self-driving test vehicle, and the wireless remote control system was tested on the double lane change type path and S-type path.

In recent years, one of the keys to achieving energy conservation and emission reduction and practicing sustainable development strategies is the wide-area access of large-scale electric vehicles. The charging behavior of large-scale electric vehicles has brought great challenges to the load management and adjustment capacity determination of the power system. Therefore, the prediction of adjustable ability of electric vehicle aggregator based on deep-belief-network is proposed in this paper. First of all, this paper selects the indicators related to the load of the electric bus station: including the arrival time, departure time, and daily mileage of the electric vehicle, from which the SOC variation trend and accurate charging demand of the single electric vehicle are obtained.

A tractor-trailer vehicle (TTV) consists of an actuated tractor attached with several full trailers. Because of its nonlinear and noncompleted constraints, it is a challenging task to avoid collisions for path planner. In this paper, we propose an efficient method to plan an optimal trajectory for TTV to reach the destination without any collision. To deal with the complicated constraints, the trajectory planning problem is formulated as an optimal control problem uniformly, which can be solved by the interior point method. A novel incremental optimization solving algorithm (IOSA) is proposed to accelerate the optimization process, which makes the number of trailers and the size of obstacles increase asynchronously. Simulation experiments are carried out in two scenarios with static obstacles. Compared with other methods, the results show that the planning method with IOSA outperforms in the efficiency.

The present work discusses the effects of intake manifold water injection in a six-cylinder heavy duty natural gas (NG) engine through one-dimensional simulation. The numerical study was carried out based on GT-Power under different engine working conditions. The established simulation model was firstly calibrated in detail through the whole engine speed sweep under full load conditions before the model of intake manifold water injector was involved, and the calibration was based on experimental data. The intake manifold water injection mass was controlled through adjustment of intake water/gas (water/natural gas) ratio, a water/gas ratio swept from 0 to 4 was selected to investigate the effects of intake manifold water injection on engine performance and emissions characteristics. On the other hand, the enhancement potential of intake manifold water injection in heavy duty NG engine under lean and stoichiometric condition was also investigated by the alteration of air-fuel ratio.

In this study, the real-world NOx and particle emissions of buses burning pure diesel fuel (D100), biodiesel fuel with 20% blend ratio (B20) and liquefied natural gas (LNG) were measured with portable emission measurement system (PEMS). The measurement conducted at 6 constant speed, which ranged from 10km/h to 60 km/h at 10km/h intervals, and a period of free driving condition. The relationship between vehicle specific power (VSP) and NOx/particle emissions of each bus were analyzed. The results show that the change rules of NOx, PN and PM emission factors with the increase of VSP were basically the same for the same bus, but for the bus using different fuel, the change rules may change. In VSP bin 0, the vehicles were mostly in idle condition and the emission factors of NOx, PN and PM of three buses were all in a relatively high level. In low VSP interval, which ranged from bin 0 to bin 4, the emissions of three buses first decreased and then increased with the growth of VSP.

Range extended electric vehicles achieve significant reductions in fuel consumption by employing as an energy source a small displacement combustion engine that is optimized for high efficiency at one, or a few, operating points. The present paper examines the impact of various energy management strategies on the particulate emissions from the auxiliary power unit (APU) of a range extended electric bus, including optimized auxiliary power unit (APU) on/off strategy, single-point strategy, two-point strategy, power-following strategy and equivalent fuel consumption minimization strategy (ECMS). In addition, this paper also compares the particulate emissions of single energy storage system and composite energy storage system on single-point energy management strategy.

The performances of heavy-duty natural gas engines have been limited by combustion temperature and NOx emissions for a long time. Recently, water injection technology has been widely considered as a technical solution in reducing fuel consumption and emissions simultaneously in both gasoline and diesel engines. This paper focuses on the impacts of intake manifold water injection on characteristics of combustion and emissions in a natural gas heavy-duty engine through numerical methods. A computational model was setup and validated with experimental data of pressure traces in a CFD software coupled with detailed chemical kinetics. The simulation was mainly carried out in low-speed and full-load conditions, and knock level was also measured and calculated by maximum amplitude of pressure oscillations (MAPO).

Previous studies have indicated that longer torque increase time benefits the reduction of emissions during transient process for a diesel engine. However, quantitative conclusions on reduction of emissions and effects on fuel economy have not been made clear so far. The aim of this study was to evaluate the transient process of diesel engine under different torque increase time, and to find the quantitative statement between torque increase time, fuel economy and engine-out emissions. To do this, experiment was carried out on a 7L common rail diesel engine used for commercial vehicles. Three engine speeds (1100r·min−1, 1300r·min−1 and 1500r·min−1) were chosen to represent an engine working range. For each speed, the engine torque is increased within different time (0.5s, 1s, 2s and 5s). It was shown that, in the transient process mentioned above, engine torque increase time effects fuel economy, smoke opacity and CO emission.

As the commercial vehicle increases staggeringly in China, environmental pollution and excessively fuel consumption can't be neglected anymore. Vehicle thermal management has been adopted by many vehicle manufactures as an ideal alternative to reduce fuel consumption and exhaust emission by its cost-efficient and effective merit. In addition, the components in heavy duty commercial vehicle engine hood may suffer overheat harm. Hence investigating the thermal characteristics in engine hood can be an effective way to identify and dismiss the potential overheat harm. In terms of this, the paper has adopted CFD simulation method to obtain the comprehensive thermal flow field characteristics of engine hood in a heavy commercial vehicle. Then by analyzing the thermal flow field in engine hood, concerning optimization strategies were put forward to improve the thermal environment.

The driving conditions of commercial logistics vehicles have the characteristics of combined urban and suburban roads with relatively fixed mileage and cargo load alteration, which affect the vehicular fuel economy. To this end, an adaptive equivalent consumption minimization strategy (A-ECMS) with vehicle speed and weight recognition is proposed to improve the fuel economy for a range-extender electric van for logistics in this work. The driving conditions are divided into nine representative groups with different vehicle speed and weight statuses, and the driving patterns are recognized with the use of the bagged trees algorithm through vehicle simulations. In order to generate the reference SOC near the optimal values, the optimal SOC trajectories under the typical driving cycles with different loads are solved by the shooting method and the optimal slopes for these nine patterns are obtained.

The Chinese government and industries have proposed strategic plans and policies for automotive renewable-energy transformation in response to China’s commitments to peak the national carbon emissions before 2030 and to achieve carbon neutrality by 2060. We thus analyze the evolution of carbon emissions from the vehicle fleet in China with our data-driven models based on these plans. Our results indicate that the vehicle life-cycle carbon emissions are appreciable, accounting for 8.9% of the national total and 11.3% of energy combustion in 2020. Commercial vehicles are the primary source of automotive carbon emissions, accounting for about 60% of the vehicle energy cycle. Among these, heavy-duty trucks are the most important, producing 38.99% of the total carbon emissions in the vehicle operation stage in 2020 and 52.18% in 2035.