Search Results

The performance of an organic Rankine cycle (ORC) that recovers heat from the exhaust of a heavy-duty diesel engine was simulated. The work was an extension of a prior study that simulated the performance of an experimental ORC system developed and tested at Oak Ridge National laboratory (ORNL). The experimental data were used to set model parameters and validate the results of that simulation. For the current study the model was adapted to consider a 15 liter turbocharged engine versus the original 1.9 liter light-duty automotive turbodiesel studied by ORNL. Exhaust flow rate and temperature data for the heavy-duty engine were obtained from Southwest Research Institute (SwRI) for a range of steady-state engine speeds and loads without EGR. Because of the considerably higher exhaust gas flow rates of the heavy-duty engine, relative to the engine tested by ORNL, a different heat exchanger type was considered in order to keep exhaust pressure drop within practical bounds.

Correct shifting schedule of vehicle coasting mode play a vital role in improving vehicle comfort and economy. At present, the calibration of the transmission shifting schedule ignores the impact of vehicle’s dynamic mass. This paper proposes a method for optimizing the shifting schedule of the coasting modes with gear based on the dynamic mass identification of the vehicle. This method identifies the dynamic mass of the vehicle during driving and substitute them into the process of solving the shifting schedule parameters. Then we get the optimal shifting schedule. At first, establish the Extended Kalman Filter to Pre-process the experimental data, reducing errors caused by excessive data fluctuations. Then, establishing a weighted squares estimation model based on particle swarm optimization to identify the dynamic mass of the vehicle.

Discretionary lane changing is commonly seen in highway driving. Intelligent vehicles are expected to change lanes discretionarily for better driving experience and higher traffic efficiency. This study proposed to optimize the decision-making and trajectory-planning process so that intelligent vehicles made lane changes not only with driving safety taken into account, but also with the goal to improve driving comfort as well as to meet the driver’ s expectation. The mechanism of how various factors contribute to the driver’s intention to change lanes was studied by carrying out a series of driving simulation experiments, and a Lane-Changing Intention Generation (LCIG) model based on Bi-directional Long Short-Term Memory (Bi-LSTM) was proposed.

With the increase of car ownership and the complex and crowded parking environment, it is difficult for drivers to complete the parking operation quickly and accurately, which may cause traffic accidents such as vehicle collisions and road jams because of poor parking skills. The emergence of an automatic parking system can help drivers park safely and reduce the occurrence of safety accidents. In this paper, the neural network identifier on the control method of an adaptive integral derivative of a neural network is proposed for an automatic parallel parking system with front-wheel steering is studied by using MATLAB/Simulink environment, and the simulation is carried out. Firstly, according to vehicle parameters and obstacle avoidance constraints, the minimum parking space, and parking starting position are calculated. Meanwhile, the path planning of parallel parking spaces is carried out by quintic polynomial.

On-road testing of driving performance of the urban bus was carried out, and a representative urban bus driving cycle was developed after on-road testing, according to the test results. Then, the vehicle simulation software AVL CRUISE was used to simulate the dynamic behavior of the urban bus. It involves the simulation of complete drive train system and the driver behavior. The model is validated by comparing the results of the simulation to the results of the field test. Then the developed driving cycle is evaluated by fuel consumption resulted from the simulation and engine bench test on fuel economy.

Dual-Clutch-Transmission (DCT) is one kind new automatic transmission which has double clutch structure. The most important unit of DCT is Transmission-Control-Module (TCM).In the development process of TCM, simulation is an important research tools. We have analyzed the DCT principle of work, established its mathematical model, created the charge and discharge oil models of typical wet dual clutch transmission, established the control logic to unify and separate double clutch in turn, and also designed out the shift control using fuzzy control using MATLAB/Simulink software. Utilizing engine model, driver model, the DCT model, the TCM model, the vehicle model, established the vehicle simulation model, and implemented simulation; Result indicated that, the established model can correctly reflect the torque and speed change when shifted gears and can correctly realize the automatic shift gears.

In this paper a new pressure control method of a modified accumulator-type Electro-hydraulic Braking System (EHB) is proposed. The system is composed of a hydraulic motor pump, an accumulator, an integrated master cylinder, a pedal feel simulator, valves and pipelines. Two pressurizing modes are switched between by-motor and by-accumulator to adapt different pressure boost demands. A differentiator filtering raw sensor signal and calculating pedal speed is designed. By using the pedal feel simulator, the relationship between wheel pressures and brake force is decoupled. The relationships among pedal displacement, pedal force and wheel pressure are calibrated by experiments. A model-based PI controller with predictor is designed to lower the influences caused by delay. Moreover, a self-tuning regulator is introduced to deal with the parameter’s time-varying caused by temperature, brake pads wearing and delay variation.

This research is based on the Controller Area Network (CAN) bus, and briefly analyzed its communication protocol with reference to the layered model of Open System Interconnect Reference Model (OSI). Subsequently, a data acquisition system was designed and developed including a Vehicle Communication Interface (VCI) and a laptop. After the overall architecture was built, the communication mechanism of the VCI was studied. Furthermore, the lap top app was built using the layered design followed by the implementation of a scheme for data collection and experimentation involving the test driving of a real car on road. Finally, the driving style was identified by means of fuzzy reasoning and solving ambiguity based on fuzzy theory; via training the acceleration sample and forecast using the excellent learning and generalization ability of Support Vector Machine (SVM) for high-dimensional, finite samples.

It’s not easy to start the engine in winter, especially in frigid highlands, because the low temperature increases the fuel’s viscosity, decreasing the lubricating oil flow ability and the storage performance of battery. Current electrical heating method can improve the engine starting performance in low temperature condition, but this method adds an external power to the engine, leading to the engine cannot maintain an efficient energy utilization. A warming device using the solar energy is designed to conserve the energy during the daytime, and directly warm up the engine at the time when the engine turns off for a long time, especially during the night. A solar collector installed on the top of the vehicle is used to convert the solar energy to the thermal energy, which is then transferred to the heat accumulator that contain the phase-change medium which can increase the heat storage performance.

This paper presents a novel application of hydraulic electromagnetic energy-regenerative shock absorber (HESA) into commercial vehicle suspension system and vehicle road performance are simulated by the evaluating indexes (e.g. root-mean-square values of vertical acceleration of sprung mass, dynamic tire-ground contact force, suspension deflection and harvested power; maximum values of pitch angle and roll angle). Firstly, the configuration and working principle of HESA are introduced. Then, the damping characteristics of HESA and the seven-degrees-of-freedom vehicle dynamics were modeled respectively before deriving the dynamic characteristics of a vehicle equipped with HESA. The control current is fixed at 7A to match the similar damping effect of traditional damper on the basis of energy conversion method of nonlinear shock absorber.

Overloading of trucks can easily cause damage to roads, bridges and other transportation facilities, and accelerate the fatigue loss of the vehicles themselves, and accidents are prone to occur under overload conditions. In recent years, various countries have formulated a series of management methods and governance measures for truck overloading. However, the detection method for overload behavior is not efficient and accurate enough. At present, the method of dynamic load identification is not perfect. No matter whether it is the dynamic weight measurement method of reconstructing the road surface or the non-contact dynamic weight measurement method, little attention is paid to the difference of different vehicles. Especially for different vehicles, there should be different load limits, and the current devices are not smart enough.

The turbine blade is a key component in the operation of the steam turbine hence the design and manufacturing level of the blade will directly affect the performance and efficiency of the turbine. CAD/CAM has been the foundation and important part of advanced manufacturing technology. It is important to study the steam turbine blade integrated CAD/CAM system to improve the design and manufacture of the blade. In this paper, the structure of CAD/CAM system for steam turbine blade is studied and the extensible framework structure including user layer, functional layer and system layer is proposed. Based on the control points of the turbine blade profile design method, and based on the expression between the three-dimensional parametric vector modeling method in UG-based platform, the use of UG/Open development tools and Visual C # developed a turbine blade CAD system.

Road traffic accidents resulting from alcohol-impaired driving are increasing globally despite several measures, currently in place, to curb the trend. For this reason, recent research aims at integrating alcohol early-detection systems and driving simulator experiments to identify intoxicated drivers. However, driving simulator experiments on drunk driving have focused mostly on male participants than female drivers whose characteristics have scarcely been explored. Hence in this paper, vehicle dynamic control inputs on steering, braking, and acceleration performance of 75 licensed female drivers with an upshot of alcohol at four different blood alcohol concentration (BAC) levels (0%, 0.03%, 0.05%, and 0.08%) were investigated. The participants completed simulated driving in a fixed-based simulator experiment coupled with real-time ecological scenarios to extract discrete responses.

With the development of the Internet for vehicles, the Car-sharing has been developed rapidly in recent years. This paper focuses on the network programming and distribution for Car-sharing, which helps to clarify the characteristics and basic law of Car-sharing network development, as well as the main approaches to construct it. Firstly, by analyzing the effect factors and expanding ways of Car-sharing network, characteristics of the development of Car-sharing industry and its network, as well as main Car-sharing users and services, the influence factors of Car-sharing demand and the main demand points in a city are summarized. Secondly, in order to better evaluate the network programming and distribution for Car-sharing, this paper proposes an optimization decision method of the car-sharing network planning by evaluating the possible alternatives in a same scale. The assessment index of Car-sharing network planning is constructed.

With the development of highway transportation and automobile industry technology, highway truck overload phenomenon occurs frequently, which poses a danger to road safety and personnel life safety. So it is very important to identify the overload phenomenon. Traditionally, static detection is adopted for overload identification, which has low efficiency. Aiming at this phenomenon, a dynamic overload identification method is proposed. Firstly, the coupled road excitation model of vehicle speed and speed bump is established, and then the 4-DOF vehicle model of half car is established. At the same time, considering that the double input vibration of the front and rear wheels will be coupled when vehicle passes through the speed bump, the model is decoupled. Then, the vertical trajectory of the body in the front axle position is obtained by Carsim software simulation.

In recent years, research on car-like robots has received more attention due to the rapid development of artificial intelligence from diverse disciplines. As essential parts, path planning and lateral path tracking control are the basis for car-like robots to complete automation tasks. Based on the two-degree-of-freedom vehicle dynamic model, this study profoundly analyzes the car-like robots’ path planning and lateral path tracking control. Three objectives: path length, path smoothness, and path safety, are defined and used to construct a multi-objective path planning model. By introducing an adaptive factor, redefining the selection of reference points, and using the cubic spline interpolation for path determination, an improved NGSA-III is proposed, which is mostly adapted in solving the multi-objective path planning problem.

In the Formula Student Electric China (FSEC), the body structure is generally divided into two types, truss steel tube body and carbon fiber load-bearing body (monocoque). The monocoque is loved by Formula Student teams around the world because it has a higher stiffness and lighter weight than the truss steel tube body. With the widespread application of monocoque, it also brings more problems. Due to the use of the monocoque, the connection between each component and the body was changed from the welding of the original truss steel pipe frame to a bolted connection. However, the bolted connection will provide a large preload force to the monocoque, resulting in the monocoque easily crushed in the local, so it is necessary to pre-bury an enhanced part in the monocoque to ensure the connection strength, that is, the embedded part. At present, aluminum plug-ins after topological hollow processing are being used.

Intelligent sweeper vehicle is gradually applied to human life, in which the accuracy of garbage identification and classification can improve cleaning efficiency and save labor cost. Although Deep Learning has made significant progress in computer vision and the application of semantic network segmentation can improve waste identification rate and classification accuracy. Due to the loss of some spatial information during the convolution process, coupled with the lack of specific datasets for garbage identification, the training of the network and the improvement of recognition and classification accuracy are affected. Based on the Unet algorithm, in this paper we adjust the number of input and output channels in the convolutional layer to improve the speed during the feature extraction part. In addition, manually generated datasets are used to greatly improve the robustness of the model.

The vehicle overload seriously jeopardizes traffic safety and affects traffic efficiency. At present, the static weighing station and weigh-in-motion station are both relatively fixed, so the detection efficiency is not high and the traffic efficiency is affected; the on-board dynamic weighing equipment is difficult to be popularized because of the problem of being deliberately damaged or not accepted by the purchaser. This paper proposes an efficient, accurate, non-contact vehicle overload identification method which can keep the road unimpeded. The method can detect the vehicle overload by the relative distance (as the characteristic distance) between the dynamic vehicle's marking line and the road surface. First, the dynamics model of the vehicle suspension is set up. Then, the dynamic characteristic distance of the traffic vehicle is detected from the image acquired by the calibrated camera based on computer vision and image recognition technology.

In the Plug-in hybrid electric bus, the power train parameter and control strategy significantly affect the economy and dynamic performance. Thus, the simultaneous optimization of power train parameter and control strategy is designed for the trade-off between the dynamic and economic performance. Depending on the parallel electric auxiliary control strategy in a plug-in hybrid electric bus, a vehicle dynamic simulation model is built with the software AVL Cruise. Aiming at the minimization of equivalent gas consumption and acceleration time from 0 to 50kmph, the gear ratio, final drive ratio, gear shifting strategy and control strategy are chosen as optimal variable, which significantly impact power performance and fuel economy. The driving performance and the driving range with full battery are considered as constraints. Based on the software Isight, multi-objective optimization model is built by adopting non-dominated sorting genetic optimization algorithm (NSGA-II).