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Blower is one of the main noise sources of fuel cell vehicle. In this paper, a narrowband active noise control (ANC) model is established based on adaptive notch filter (ANF) to control the high-frequency noise produced by the blower. Under transient conditions, in order to reduce the frequency mismatch (FM) of ANC for blower, a new Frequency Mismatch Filtered-Error Least Mean Square algorithm (FM-FELMS) is proposed to attenuate blower noise under transient conditions. According to the theoretical analysis and simulation, the proposed algorithm has an excellent noise reduction performance at relatively high blower speed. While for the low speed working condition, the Normalized Least Mean Square (NLMS) algorithm is applied to attenuate noise. The two algorithms could be jointly utilized to control the blower noise actively.

Recent years, electric vehicles (EVs) have been widely used as urban transit buses in China, but high costs and a dwindling driving distance caused mainly by relatively frequent usage rate have put the electric bus in a difficult position. Range-extended electric bus (REEbus) is taken as an ideal transitional powertrain configuration, but its efficiency is not so high. Besides, with less batteries to endure more frequently charging and discharging, the lifecycle of battery pack can also be shorten. Aiming at it, range-extended electric powertrains with diverse energy storage systems (ESSs) and proper auxiliary power unit (APU) control strategies are matched and compared to find most proper ESS configuration for REEbus through simulation, which is based on a 12 meter-long urban bus.

Natural gas is a promising alternative fuel for internal combustion engines due to its rich reserves and low price, as well as good physical and chemical properties. Its low carbon structure and high octane number are beneficial for CO2 reduction and knock mitigation, respectively. Diesel and natural gas dual fuel combustion is a viable pathway to utilize natural gas in diesel engines. To achieve high efficiency and low emission combustion in a practical diesel engine over a wide range of operating conditions, understanding the performance responses to engine system parameter variations is needed. The controllability of two combustion strategies, diesel pilot ignition (DPI) and single injection reactivity controlled compression ignition (RCCI), were evaluated using the multi-dimension CFD simulation in this paper.

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.

This study investigated the effects of underhood structure parameters (two types of air ducts, two types of inlet grilles and the opening angle of inlet grilles) on the cooling characteristics of the rear-engine bus; then, the optimum design scheme of the underhood was determined. The air-side resistance load of the cooling system, which is based on fan performance, was selected as the optimization objective. Simulations were created based on a porous media model and standard a k-ε model. The next step was to build a 1D/3D coupling simulation to utilize the advantages of 1D simulation’s fast convergence speed and 3D simulation’s extensive research range. Besides, the use of 1D/3D coupling simulation can efficiently avoid the errors of simulation results which arise from the non-uniform airflow on the cooling module. Results show that the airflow rate of the rectangular air duct increased by 7 to 11percent.

Engine experiments were carried out on a heavy-duty single-cylinder engine to investigate the effects of Gasoline Compression Ignition on emissions and performance of a heavy-duty engine operating at a high load condition. Comparisons between gasoline fueled operation and diesel fueled operation are presented using a single, near top dead center injection. Although the fuel’s cetane numbers are very different, the combustion characteristics of the two fuels at high load are similar, with the gasoline-fueled case showing less than two crank angle degree longer ignition delay. Gasoline operation showed lower soot production at similar levels of NOx, initiating study of the impact of exhaust gas recirculation which spanned a range of NOx levels covering the range from minimal urea dosing to high urea dosing. A conventional soot-NOx tradeoff was found to exist with gasoline as exists with diesel.

Premixed charge compression ignition (PCCI) is a new combustion mode to reduce NOX and soot emission. It requires the optimization of the injection timing and pressure, fuel mass in pilot injection and EGR rate. A 6-cylinder, turbocharged, common rail heavy-duty diesel engine was used in this study. The effect of multiple injection strategies on diesel fuel combustion process, heat release rate, emission and economy of diesel engine is studied. The multiple injection strategies include different EGR level, pilot injection timing, pilot injection mass and post injection timing to achieve the homogeneous compression ignition and lower temperature combustion of diesel engine. Based on endoscope technology, the two-color method was applied to take the flame images in the engine cylinder and obtain soot concentration distribution, to understand the PCCI combustion in diesel engines.

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.

Lane marking detection plays a crucial role in Autonomous Driving Systems or Advanced Driving Assistance System. Vision based lane marking detection technology has been well discussed and put into practical application. LiDAR is more stable for challenging environment compared to cameras, and with the development of LiDAR technology, price and lifetime are no longer an issue. We propose a lane marking detection algorithm based on solid-state LiDARs. First a series of data pre-processing operations were done for the solid-state LiDARs with small field of view, and the needed ground points are extracted by the RANSAC method. Then, based on the OTSU method, we propose an approach for extracting lane marking points using intensity information.

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 this paper, an equivalent conversion method is proposed to apply the six-dimensional force road spectrum of the four-axle vehicle on the same platform to the three-axle through the axle load comparison. Further, the feasibility of the devolved equivalent conversion method is verified, and the fatigue performance improvement of the wishbone support structure of a commercial vehicle is finally achieved. Specifically, firstly, the load spectrum at each attachment point of the suspension for the three-axle vehicle is obtained through the iteration of the multi-body dynamic model. Furthermore, the finite element model of the suspension for the three-axle vehicle is established; the analysis of fatigue life for the suspension structure is performed by extracting stress amplitude through the multi-axis cyclic counting method and calculating equivalent force amplitude through McDiarmid’s criterion, combined with the SN curve of the material.

The displacement of the shaft head fails to be accurately measured while the three-axle heavy-duty truck is driving on the reinforced pavement. In order to obtain accurate fatigue load spectrum of the suspension bracket, the acceleration signals of the shaft heads of the suspension obtained by the reinforced pavement test measurement are virtually iterated as responses. A more accurate model of the rigid-flexible coupled multi-body dynamics (MBD) of the whole vehicle is established by introducing a flexible frame based on the comprehensive modal theory. Furthermore, the vertical displacements of the shaft heads are obtained by the reverse solution of the virtual iterative method with well-pleasing precision. The accuracy of the virtual iteration is verified by comparing the simulation results with the vertical acceleration of the shaft head under the reinforced pavement in the time domain and damage domain.

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.

With the growing prosperity of the long-distance freight and urban logistics industry, the demand for cargo trucks is gradually increasing. The connecting bracket is the critical connecting part of the truck chassis, which bears the load transmitted by the road excitation and reduces the damage to the frame caused by the load. However, the occurrence of rough road conditions is inevitable in heavy-duty transportation. In this paper, road durability tests and fatigue life analysis are carried out on the original structure to ensure the safety of the vehicle. Based on the known boundary and load constraints, a lightweight and high-performance structure is obtained through size optimization, as the original structure cannot meet the performance requirements. Firstly, the road test was conducted on the truck where the original bracket structure is located.

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.

Electric vehicles are considered as one of the most popular way to decrease the consumption of petroleum resources and reduce environmental pollutions. Motor control system is one of the most important part of electric vehicles. It includes power supply module, IGBT driver, digital signal processing (DSP) controller, protection adjustment module, and resolver to digital convertor. To implement the control strategies on motor control system, a lot of practical aspects need to be taken into accounts. It includes setup of the initial excitation current, consistency of current between motor and program code, over-modulation, field weakening control, current protection, and so on. In this paper, an induction motor control system for electric vehicles is developed based on DSP. The control strategy is based on the field-oriented control (FOC) and space vector pulse width modulation (SVPWM).

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.