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Visual Place Recognition (VPR) excels at providing a good location prior for autonomous vehicles to initialize the map-based visual SLAM system, especially when the environment changes after a long term. Condition change and viewpoint change, which influences features extracted from images, are two of the major challenges in recognizing a visited place. Existing VPR methods focus on developing the robustness of global feature to address them but ignore the benefits that local feature can auxiliarily offer. Therefore, we introduce a novel hierarchical place recognition method with both global and local features deriving from homologous VLAD to improve the VPR performance. Our model is weak supervised by GPS label and we design a fine-tuning strategy with a coupled triplet loss to make the model more suitable for extracting local features.

With the development of economic globalization, the speed of development of container terminals is also very rapid. Under the pressure brought by the surge in throughput, the unmanned and intelligent terminals will become the future development direction of terminals. As the cornerstone of the unmanned terminal, the positioning technology provides the most basic position information for system scheduling, path planning, real-time correction, and loading and unloading. Therefore, this paper is aimed to design a low-cost, high-precision, and easy-to-maintain unmanned dock positioning system in order to better solve the problem of unmanned dock positioning. The main research content of this paper is to design a positioning algorithm for unmanned terminal Automated Guided Vehicle (AGV) based on single-line lidar, including point cloud data acquisition, background filtering, point cloud clustering, vehicle position extraction, and result optimization.

The transient characteristics of combustion and emissions during the engine start up at different higher cranking speeds for hybrid electric vehicle (HEV) applications were presented in this paper. Cycle-by-cycle analysis was done for each start up case. Intake air mass during the first several cycles decrease as the engine was cranked at higher speed. Ignition timing is delayed with higher cranking speed, which leads to an increase of exhaust temperature. For various start up cases, similar quantity of fuel is injected at the first cycle, but the ignition timing is significantly delayed to meet the acceleration requirement when cranking speed enhanced. Because of the deterioration of intake charge, the air-fuel mixture is over-enriched in the first several cycles for the cases at higher cranking speed. With cranking speed is increased, the in-cylinder residual gas fraction rises, which leads to poor combustion and decrease of mass fraction of burned fuel.

The electricity energy consumption for passenger cabin heating can drastically shorten the driving range for electric vehicles in cold climates. Mobile heat pump system is considered as an effective method to improve heating efficiency. This study investigates the system characteristics of mobile heat pump systems for electrical vehicle application. Based on KULI thermal management software, simulation models including HFC-R134a direct heat pump (DHP) and secondary loop heat pump (SLHP) were developed. The secondary loop employed in the SLHP includes a coolant pump, an indoor heater core and a plate heat exchanger, instead of an indoor condenser in the DHP. The use of a secondary loop has advantages to improve air outlet temperature uniformity. The simulation models were verified by measured data obtained from calorimeter experiments. By adopting simulation models, the effects of indoor and outdoor temperatures on system performance and cycle characteristics were discussed.

The vehicle chassis integrated control system can improve the stability of vehicles under extreme conditions using tire force allocation algorithm, in which, the nonlinearity and uncertainty of tire-road contact condition need to be taken into consideration. Thus, An MPC (Model Predictive Control) controller is designed to obtain the additional steering angle and the additional yaw moment. By using a robust optimal allocation algorithm, the additional yaw moment is allocated to the slip ratios of four wheels. An SMC (Sliding-Mode Control) controller is designed to maintain the desired slip ratio of each wheel. Finally, the control performance is verified in MATLAB-CarSim co-simulation environment with open-loop manoeuvers.

Lift-off length is defined as the distance from injector hole to the location where flame stabilized on a high injection pressure direct injection (DI) diesel spray. In this paper we used the high-speed (40 kHz) Schlieren and time-averaged OH chemiluminescence imaging technique to simultaneously measure the flame lift-off locations on a DI diesel spray in an optically accessible and constant-volume combustion vessel. The time-resolved development of the diesel spray acquired from the high-speed Schlieren imaging system enabled us to observe the instantaneous spray structure details of the spray flames. The OH chemiluminescence image obtained from a gated, intensified CCD video camera with different delay and width settings was used to determine the quiescent lift-off length. Experiments were conducted under various ambient temperatures, ambient gas densities, injection pressures and oxygen concentrations.

The wedge clutch takes advantages of small actuation force/torque, space-saving and energy-saving. However, big challenge arises from the varying self-reinforced ratio due to the varying friction coefficient inevitably affected by temperature and wear. In order to improve the smoothness and synchronization time of the slipping process of the wedge clutch, this paper proposes a self-tuning PID controller based on Lyapunov principle. A new Lyapunov function is developed for the wedge clutch system. Simulation results show that the self-tuning PID obtains much less error than the conventional PID with fixed gains. Moreover, the self-tuning PID is more adaptable to the variation of the friction coefficient for the error is about 1/5 of the conventional PID.

Sampling-based methods are general but time consuming for solving a Reliability-Based Design Optimization (RBDO) problem. In order to alleviate the computation burden, score function together with the Monte Carlo method was used to compute the stochastic sensitivities of reliability functions. In literature, re-weighting schemes were shown to converge faster than the regular Monte Carlo method. In this paper, a reweighting scheme together with score function is employed to perform sampling-based stochastic sensitivity analysis to improve the computational efficiency and accuracy. An analytical example is used to show the advantages of the proposed method. Comparisons to the conventional methods are made and discussed. Two RBDO problems are solved to demonstrate the use of the proposed method.

Motor speed synchronization is important in gear shifting of emerging clutchless automated manual transmissions for battery electric vehicles (BEV) and other kinds of parallel shaft-based powertrains for hybrid electric vehicles (HEV). Difficulties of the problem mainly come from random delay induced by network communication and unknown load torques from air drag, oil drag, and friction torques, etc. To deal with these two factors, this paper proposes a robust speed synchronization controller based on act-and-wait control and disturbance observer. The former is a kind of periodical controller specially for regulating problems with feedback delay while the latter is a technique for active disturbance rejection. Firstly, the dynamic model of the motor shaft is formulated, and the system parameters are offline identified. The speed tracking problem is then transformed into a regulating one.

Aiming at the high altitude operation problems for piston-type aero-engines and to improve the practical ceiling and high altitude dynamic performance, this thesis analyzes a controllable three-stage composite supercharging system, using a two-stage turbocharger coupled supercharger method. The GT-Power simulation model of a four-cylinder boxer engine was established, and the control strategy of variable flight height was obtained. The simulation research of engine performance from 0 to 20,000 meters above sea level has been carried out, which shows that the engine power is at the same level as the plain condition, and it could still maintain 85.28 percent of power even at the height of 20,000 meters, which meets the flight requirements of the aircraft.

A new kind of integrated semi-track air-cushion pitch controller is proposed in this paper. The controller first compute the target working point based on a weighed function, which is the combination of optimal power consumption and pitch angle control demand. Then the sequential quadratic programming algorithm distributes the general target values to specific control values. The performance of the controller is verified through co-simulation between Matlab/Simulink and ADAMS/View. The simulation results show the effectiveness of the control algorithm and the correctness of the choice in physical configuration with two air cushions for vehicle body pitch control.

Electric vehicles (EVs) have become a hot research topic due to the petroleum crisis and air pollution issues, and Hybrid EVs (HEVs) equipped with engines and motors are popular nowadays due to their advantage over Pure EVs. The energy distribution between the engine and the motor is the major task of the control strategy or energy management for HEVs. Rule-based and optimization-based approaches are developed in this area, but not much work has been done for large-size super-capacitor (SC) equipped HEVs, like Hybrid buses. In this paper, a new optimization-based control strategy for a hybrid bus equipped with SCs as the energy regeneration system is presented. Considering the driving patterns of a bus that is of frequent accelerations and decelerations, it is proposed to characterize each time instant by its speed and acceleration, and the energy distribution is optimized based on these two state variables.

The numerical simulation of ice melting process on an iced helicopter rotor blade is presented. The ice melting model uses an enthalpy-porosity formulation, and treats the liquid-solid mushy zone as a porous zone with porosity equal to the liquid fraction. The ice shape on the blade section is obtained by the icing code with a dynamic mesh module. Both of the temperature change and the ice-melting process on the rotor blade section surface are analyzed. The phenomenon of ice melting is analyzed through the change of temperature and liquid fraction on the abrasion/ice interface. The liquid fraction change as with time on the abrasion/ice surface is observed, which describes the ice-melting process well. The numerical results show that the ice melting process can be simulated effectively by the melting model. The de-icing process can be monitored by observing the change of the liquid fraction of the area around the abrasion/ice interface.

The heat pump with low global warming potential (GWP) refrigerants is imperative for the electric vehicle (EV) to slow down global warming and extend the driving range while meeting passengers' thermal comfort in low ambient temperatures. However, there are no appropriate refrigerants. To provide long-term and environmental-friendly refrigerants in the heat pump for EVs, herein, we reported newly developed low-GWP refrigerant mixtures, i.e., DL3B, whose GWP is lower than 140, the flammability (lower flammability limit and burning velocity), saturation pressure, lubricant miscibility, material compatibility were experimentally tested. A test bench that can investigate the performance of an R410A prototype was built. The drop-in tests of the DL refrigerant were carried out to evaluate the capacities and COPs for both cooling and heating modes in the EV heat pump system.

Vehicle lateral states such as lateral distance at a preview point and heading angle are indispensable for lane keeping control systems, and such states are normally estimated by fusing signals from an onboard vision system and inertial sensors. However, the sampling rates and measurement delays are different between the two kinds of sensing devices. Most of the conventional methods simply neglect measurement delay and reduce sampling rate of the estimator to adapt to the slow sensors/devices. However, the estimation accuracy is deteriorated, especially considering the delay of visual signals may not be constant. In case of electric vehicles, the actuators for steering and traction are motors that have high control frequency. Therefore, the frequency of vehicle state feedback may not match the control frequency if the estimator is infrequently updated. In this paper, a multi-rate estimation algorithm based on Kalman filter is proposed to provide lateral states with high frequency.

With the rapid development of series-parallel hybrid electric vehicles (SPHEVs), mode transition from pure electrical drive to hybrid drive has attracted considerable attention. The presence of time delay due to response capacity of actuators and signal transmission of communication may cause decrease of speed tracking accuracy, even instable dynamics. Consequently, drivability of the SPHEV is unacceptable, and durability of the components is reduced. So far, plenty of control strategies have been proposed for mode transition, however, no previous research has been reported to deal with the time delay during mode transition. In this paper, a dynamic model with time delay of hybrid electric system is established. Next, a mode transition time-delay controller is proposed based on a two degree of freedom internal model controller (2-DOF-IMC).

Using highly efficient powertrain is one of the most important and effective approaches to increase the driving distance of electric vehicles (EVs). In this paper, a novel two-speed dual-clutch transmission (DCT) is proposed. The transmission is comprised of two traditional friction clutches and two-stage planetary gear sets. One clutch connects the input sun gear and the other connects the input carrier. The Simulink models including an electric motor and two-speed DCT are established. Gearshift schedule based on fuzzy logic which reflects the driver’s intensions is adopted to improve the dynamic and economic performance of the novel transmission. The simulation model is built using MATLAB/Simulink® to validate the effectiveness of the proposed gearshift schedule compared with the conventional two-parameter gearshift schedule. Simulation results show that both the dynamic and economic performance of the novel DCT for EVs are improved with the proposed fuzzy logic gearshift schedule.

In this paper an estimation method on location of peak pressure (LPP) employing flame ionization measurement, with the spark plug as a sensor, was discussed to achieve combustion parameters estimation at quick start of HEV engines. Through the cycle-based ion signal analysis, the location of peak pressure can be extracted in individual cylinder for the optimization of engine quick start control of HEV engine. A series of quick start processes with different cranking speed and engine coolant temperature are tested for establishing the relationship between the ion signals and the combustion parameters. An Artificial Neural Network (ANN) algorithm is used in this study for estimating these two combustion parameters. The experiment results show that the location of peak pressure can be well established by this method.

Excellent energy consumption performance of a plug-in hybrid electric vehicle (PHEV) is usually attributed to its hybrid drive mode. However, the factors including vehicle performance, driver behavior and traffic status have been shown to cause unsatisfactory performance. This phenomenon leads to a necessity of study on energy consumption control strategies under real-world driving conditions. This paper proposes a new approach for energy management optimization of plug-in hybrid electric vehicles based on real-world driving data for two purposes. One is for improving the energy consumption of PHEV under real-world driving conditions and the other is for reducing the computational complexity of optimization methods in simulation models. In this process, the paper collected real-world driving record data from 180 drivers within 6 months. Then the principal component analysis (PCA) was employed to extract and define the hidden factors from the initial real-world driving data.

An EV prototype, with all the wheels respectively driven by 4 inwheel motors, is developed, and undergoes a series of practical measurements and road tests. Based on the obtained vehicle parameters, a multi-body dynamics model is built by using SolidWorks and Adams/Car, and then validated by track test data. The virtual prototype is served as the control plant in simulation. An adaptive fractional order PID (A-FO-PID) controller is designed to enhance the handling and stability performance of the EV. Considering the model uncertainties, e.g. the variation in body mass distribution and the consequent change in yaw moment of inertial, a Parameter Self-Adjusting Differential Evolution (PSA-DE) algorithm is adopted for tuning the controller parameters, i.e. KP, KI, KD, λ and μ. As a modification of traditional DE algorithm, the so-called Variance of Population’s Fitness is utilized to evaluate the diversity of the population.