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The effectiveness of after-treatment systems depends on the exhaust gas temperature, which is low during cold-start. As a result, Euro III, Euro IV and FTP75 require that the emissions tests include exhaust from the beginning of cold start. It is proved that 50%∼80% of HC and CO emissions are emitted during the cold start and the amount of unburned fuel from the crevices during starting is much higher than that under warmed engine conditions. The piston crevices is the most part of combustion chamber crevices, and results of mathematical simulations show that the piston crevice contribution to HC emissions is expected to increase during cold engine operation. Based on the transient HC test data and the double zone combustion model, this paper presents the study of the crevice HC Mechanism of the first firing cycle at cold start on an LPG SI Engine. A fast-response flame ionization detector (FFID) was employed to measure transient HC emissions of the first firing cycle.

“LIN-BOX” is designed as a development tool for simulation, implementation and test of the automotive LIN (Local Interconnect Network) control devices or entire network. The tool can be used to simulate master and/or slaves around LIN system. The configurable signal processing makes it possible to simulate and test the communication behavior. LIN-BOX monitors the bus traffic in the vehicle. The data on LIN bus can not only be shown on various windows but also written into log files. LIN-BOX has been used by several cases for debugging and validation, the result shows that it is a powerful tool for LIN cluster design, simulation and test.

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.

The range-extended electric transit bus (REEbus) equipped with the auxiliary power unit (APU) using high efficient diesel engine as power source can reduce the cost of power battery and is an ideal transitional powertrain architecture to the pure electric drive. Based on chassis tests of a 12m long REEbus, fuel consumption and emission characteristics during Charge-Sustaining (CS) stage effected by temperature of the REEbus are researched. The APU of REEbus starts to work around just one point with best efficiency and lower emission when the state of charge (SOC) is too low and stop when the SOC is high, which aims to lower fuel consumption. As a result, even during CS stage, the fuel consumption of REEbus is only 22.84 L/100km. Also almost all emissions decrease dramatically and the NOx emission is only 0.68g/km, but the ultrafine-particle number increases owing to better combustion.

Biodiesel as a renewable energy is becoming increasingly attractive due to the growing scarcity of conventional fossil fuels. Meanwhile, the development of after-treatment technologies for the diesel engine brings new insight concerning emissions especially the particulate matter pollutants. In order to study the coupling effects of biodiesel blend and CCRT (Catalyzed Continuously Regeneration Trap) on the particulate matter emissions, the particulate matter emissions from an urban bus with and without CCRT burning BD0 and BD10 respectively was tested and analyzed using electrical low pressure impactor (ELPI). The operation conditions included steady state conditions and transient conditions. Results showed that the particulate number-size distribution of BD10 and BD0 both had two peaks in nuclei mode and accumulation mode at the conditions of idle, low speed and medium speed while at high speed condition the particulate number-size distribution only had one peak.

Recently Hybrid Electric Buses (HEBs) have been widely used in China for energy saving and emission reduction. In order to study the real road emission performance of HEBs, the emission tests of an in-use diesel-electric hybrid bus (DHEB) are evaluated both on chassis dynamometer over China City Bus Cycles (CCBC) and on-road using Portable Emissions Measurement Systems (PEMS). The DHEB is powered by electric motor alone at speed of 0~20km/h. When the speed exceeds 20km/h, engine gets engaged rapidly and then works corporately with the electric motor to drive the bus. For chassis dynamometer test over CCBC, emissions of NOx, particulate number, particulate mass, and THC of the DHEB are 7.68g/km, 5.88E+11#/km, 0.412mg/km, and 0.062g/km, respectively. They have all decreased greatly compared to those of the diesel bus. But the CO emission which is 3.48g/km has increased significantly. Then the Real Driving Emissions (RDE) of the DHEB are compared with the dynamometer test results.

A segment of steel tube with the inner diameter of 60 mm and length of 100 mm was fixed between the intake manifold and cylinder head in a direct injection natural aspirated diesel engine. The surface of the tube could be heated to be above 400 °C by the heater enwrapped outside within several minutes under the power less than 600 W. The tip of an injector traditionally used for in-cylinder diesel direct injection was extended to the axis of the tube. The diesel sprays could impinge onto the hot inner surface of the tube and atomize quickly if the temperature of the tube was high enough. Then the fuel-air mixture would be sucked into the cylinder, and HCCI combustion could be fulfilled. The vaporization ratio of the impinged diesel sprays was estimated by fuel consumption, intake air flux and excess air coefficient (λ) calculated from the volumetric concentration of O2, CO2 and CO emissions. The NOx emission was always very low.

With regulatory standards for diesel engine emissions becoming stricter worldwide, integrated catalytic systems are becoming increasingly necessary. One of the better approaches is to use an after-treatment system consisting of a diesel oxidation catalyst (DOC), a catalyzed diesel particulate filter (CDPF), and a selective catalytic reduction (SCR), but many factors can affect how well this system works. This study investigates the effects of DOC and CDPF catalyst composition on emissions characteristics for DOC + CDPF + SCR systems by collecting reactor and engine data. The reactor results show that the light-off temperatures (T50) of CO and C3H6 increase with the growth of Pt:Pd ratio while the T50 of NO degrades. An engine dynamometer test was conducted on a light-duty diesel engine equipped with DOC + CDPF + SCR. The results show light-off curves of CO and THC that are smoother than the reactor data.

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.

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.

Based on a 125cm3 single cylinder SI engine, the designated idle speed was controlled by adjusting of cycle ignition advance angle. By analyzing the effects of different idle speed and throttle open position on three way catalyst (TWC) light-off time and conversion efficiency of HC and CO emissions, combined with the corresponding total HC and CO emissions level, the optimum idle speed and throttle open position at engine's warm-up phase were found by the matching optimum. The present method for engine control strategy is helpful to optimize the warm-up phase emission levels in SI engine with LPG fuel.

As one of the key technologies in autonomous driving, the lateral planning module guides the lateral movement during the driving process. An integrated lateral planning module should consider the non-holonomic constraints of a vehicle, the optimization of the generated trajectory and the applicability to various scenarios. However, the current lateral planning methods can only meet parts of these requirements. In order to satisfy all the performance requirements above, a novel Potential Field (PF) based lateral planning method is proposed in this paper. Firstly, a PF model is built to describe the potential risk of the traffic entities, including the obstacles, road boundaries and lines. The potential fields of these traffic entities are determined by their properties and the traffic regulations. Secondly, the planning algorithm is presented, which comprises three modules: state prediction, state search and trajectory generation.

Chinese National projects “13th Five Year Plan” and “Made in China 2025” have both put forward a goal of developing Intelligent and Connected Vehicles(ICV). Shanghai is a typical city of automobile industry which spearhead the development of China’s ICV industry. After the adjustment and transition of industrial structure, Shanghai has initially formed the industrialization layout of ICV covering core areas including environmental perception, intelligent decision-making, actuator, human-computer interaction and vehicle integration. However, currently Shanghai is still in the beginning stage and there exists a large gap with world advanced level in both the core technology and marketization. This article is based on former qualitative survey combined with quantitative analysis which uses the Analytic Hierarchy Process(AHP) method to objectively evaluate the status quo and development trend of Shanghai’s ICV.

An integrated automatic driving system consists of perception, planning and control. As one of the key components of an autonomous driving system, the longitudinal planning module guides the vehicle to accelerate or decelerate automatically on the roads. A complete longitudinal planning module is supposed to consider the flexibility to various scenarios and multi-objective optimization including safety, comfort and efficiency. However, most of the current longitudinal planning methods can not meet all the requirements above. In order to satisfy the demands mentioned above, a new Potential Field (PF) based longitudinal planning method is presented in this paper. Firstly, a PF model is constructed to depict the potential risk of surrounding traffic entities, including obstacles and roads. The shape of each potential field is closely related to the property of the corresponding traffic entity.

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.

The past decade has witnessed the rapid development of autonomous parking technology, since it has promising capacity to improve traffic efficiency and reduce the burden on drivers. However, it is prone to the trap of self-centeredness when each vehicle is automated parking in isolation. And it is easy to cause traffic congestion and even chaos when multiple autonomous vehicles require of parking into the same lot. In order to address the multiple vehicle parking problem, we propose a parking planning method with genetic algorithm. Firstly, an optimal mathematic model is established to describe the multiple autonomous vehicle parking problem. Secondly, a genetic algorithm is designed to solve the optimization problem. Thirdly, illustrative examples are developed to verify the parking planner. The performance of the present method indicates its competence in addressing parking multiple autonomous vehicles problem.

In recent years, intelligent driving technology is being extensively studied. This paper proposes a path planning method for perpendicular parking based on vehicle kinematics model using MPC optimization, which aims to solve the perpendicular parking task. Firstly, in the case of any initial position and orientation of the vehicle, judging whether the vehicle can be parked at one step according to the location of the parking place and the width of the lane, and then calculating the starting position for parking, and use the Bezier curve to connect the initial position and the starting position. Secondly, reference parking path is calculated according to the collision constraints of the parking space. Finally, because the parking path based on the vehicle kinematics model is composed of circle arcs and straight lines, the curvature of the path is discontinuous. The reference parking path is optimized using Model Predictive Control (MPC).

Although autonomous valet parking technology can replace the driver to complete the parking operation, it is easy to cause traffic chaos in the case of lacking scheduling for multiple parking agents, especially when multiple cars compete for the same parking slot at the same time. Therefore, in order to ensure orderly traffic and parking safety, it is necessary to allocate parking slots reasonably for multiple autonomous valet parking vehicles. The parking slots allocation model is built as an optimal problem with constraints. Both parking mileage cost and parking difficult cost are considering at the objective function in the optimization problem. There are three types of constraints. The first is the capacity limit of a single parking slot, the second is the space limit occupied by a single vehicle, and the third is the total capacity limit of the parking lot. After establishing parking slots allocation model, the immune algorithm is coded to solve the problem.

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.