Search

Lightweight electric vehicles (LEVs) possess great advantages in the viewpoint of fuel consumption, environment protection, and traffic mobility. However, due to the drastic reduction of vehicle weights and body size, the effects of inertial parameter variation in LEV control system become much more pronounced and have to be systematically estimated. This paper presents a dual adaptive unscented Kalman filter (AUKF) where two Kalman filters run in parallel to synchronously estimate vehicle inertial parameters and additional dynamic states such as vehicle mass, vehicle yaw moment of inertia, the distance from front axle to centre of gravity and vehicle sideslip angle. The proposed estimation only integrates and utilizes real-time measurements of in-wheel-motor information and other standard in-vehicle sensors in LEV. The LEV dynamics estimation model including vehicle payload parameter analysis, Pacejka model, wheel and motor dynamics model is developed, the observability of the observer is analysed and derived via Lie derivative and differential geometry theory. To address nonlinearities and undesirable noise oscillation in estimation system, the dual noise adaptive unscented Kalman filter (DNAUKF) and dual unscented Kalman filter (DUKF)are also investigated and compared. Simulation with various manoeuvres are carried out to verify the effectiveness of the proposed method using MATLAB/Simulink-Carsim®. The simulation results show that the proposed DNAUKF method can effectively estimate vehicle inertial parameters and dynamic states despite the existence of payload variations. [ABSTRACT FROM AUTHOR]

This paper aims to develop an automatic miscalibration detection and correction framework to maintain accurate calibration of LiDAR and camera for autonomous vehicle after the sensor drift. First, a monitoring algorithm that can continuously detect the miscalibration in each frame is designed, leveraging the rotational motion each individual sensor observes. Then, as sensor drift occurs, the projection constraints between visual feature points and LiDAR 3-D points are used to compute the scaled camera motion, which is further utilized to align the drifted LiDAR scan with the camera image. Finally, the proposed method is sufficiently compared with two representative approaches in the online experiments with varying levels of random drift, then the method is further extended to the offline calibration experiment and is demonstrated by a comparison with two existing benchmark methods. [ABSTRACT FROM AUTHOR]

Four-wheel independently driven electric vehicles (FWID-EV) endow a flexible and scalable control framework to improve vehicle performance. This paper integrates the torque vectoring and active suspension system (ASS) to enhance the vehicle's longitudinal and vertical motion control performance. While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden. To facilitate the controller design and ease the load, a half-vehicle dynamics system is built and simplified to the linear-time-varying (LTV) model. Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety, energy-saving and comfort requirements. The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption. Finally, the effectiveness of the proposed controller is verified through the rapid-control-prototype (RCP) test. The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability. [ABSTRACT FROM AUTHOR]

Lightweight electric vehicles (LEVs) possess great advantages in the viewpoint of fuel consumption, environment protection and traffic mobility. However, due to the drastic reduction of vehicle weights and body size, the effects of payload parameter variation in LEV control and estimation system become much more pronounced and have to be systematically analysed. This paper proposes a novel payload parameter sensitivity analysis to provide quantitative insight into the sensitivity of payload parameter on the LEV system responses and state estimation. The analysis-oriented LEV dynamic model considering payload parameter variations is developed. Then, the trajectory sensitivity index of the influential parameters is defined and derived with the perturbation approach, the median method is used to improve the calculation accuracy for the trajectory sensitivity of payload parameter. Finally, the extended Kalman filter is designed to show the effect and importance of the sensitive payload parameters on the observation accuracy, the payload parameter variations along with fundamental state estimation such as vehicle sideslip angle, longitudinal velocity and vehicle roll angle are analysed. Simulation results with Matlab/Simulink-Carsim® show that the proposed method can accurately describe the relationship between the vehicle payload parameters and system state estimation, which is helpful to design and evaluate LEV controller and observer performances. [ABSTRACT FROM AUTHOR]

In this paper, a non-pneumatic tire (NPT) based on the bionic petal structure has been proposed to have high load carrying capacity in low stress level. Firstly, according to GB/T 2977-2008 truck tire specifications, the numerical models of bionic petal NPTs (BP-NPTs) for five different spokes are developed. A set of third-order Bezier curves is introduced to control the geometric configuration. Then, the static and dynamic characteristics of BP-NPTs are investigated numerically, and compared with typical NPTs like Tweel and Hexagonal Honeycomb non-pneumatic wheel (NPW). The results indicate that BP-NPTs have better performance by taking all of these factors into consideration. Also, the influence law of key parameters including the curvature and thickness distribution on the load carrying capacity, the maximum local stresses of spokes and tread under static or dynamic load are obtained. Finally, a multi-objective optimization design method by involving the Hammersley test design, moving least-square agent model and particle swarm optimization is proposed to optimize BP-NPTs. The results demonstrate that the performance of optimized structure is significantly improved. The mass of tire and the maximum stress of spokes under dynamic loads are reduced by 9.3% and 12.3%, respectively, and the load carrying capacity is improved by 10.1%. It is indicated that the BP-NPTs have a bright future in the automobile application. [ABSTRACT FROM AUTHOR]

Distributed drive electric vehicles (DDEVs) possess great advantages in the viewpoint of fuel consumption, environment protection and traffic mobility. Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size, and inertial parameter has seldom been tackled and systematically estimated. This paper presents a dual central difference Kalman filter (DCDKF) where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters, such as vehicle sideslip angle, vehicle mass, vehicle yaw moment of inertia, the distance from the front axle to centre of gravity. The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs. The four-wheel nonlinear vehicle dynamics estimation model considering payload variations, Pacejka tire model, wheel and motor dynamics model is developed, the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory. To address system nonlinearities in vehicle dynamics estimation, the DCDKF and dual extended Kalman filter (DEKF) are also investigated and compared. Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-Carsim®. The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions. This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability. [ABSTRACT FROM AUTHOR]

Autonomous-rail rapid transit (ART) is a new medium-capacity rapid transportation system with punctuality, comfort and convenience, but low-cost construction. Combined velocity planning is a critical approach to meet the requirements of energy-saving and punctuality. An ART velocity pre-planning and re-planning strategy based on the combination of punctuality dynamic programming (PDP) and pseudospectral (PS) method is proposed in this paper. Firstly, the longitudinal dynamics model of ART is established by a multi-particle model. Secondly, the PDP algorithm with global optimal characteristics is adopted as the pre-planning strategy. A model for determining the number of collocation points of the real-time PS method is proposed to improve the energy-saving effect while ensuring computation efficiency. Then the enhanced PS method is utilized to design the velocity re-planning strategy. Finally, simulations are conducted in the typical scenario with sloping roads, traffic lights, and intrusion of the pedestrian. The simulation results indicate that the ART with the proposed velocity trajectory optimization strategy can meet the punctuality requirement, and obtain better economy efficiency compared with the punctuality green light optimal speed advisory (PGLOSA). [ABSTRACT FROM AUTHOR]

At present, the impact of cuproptosis-related genes in the study of osteosarcoma is largely unknown. Genome-wide data of osteosarcoma and controls were downloaded from 3 different databases, and specific diagnostic models associated with cuproptosis in osteosarcoma were constructed by support vector machines with artificial intelligence, random forest trees and LASSO regression. Differential analysis of immune cell infiltration was examined using routine blood data from 25,665 cases. Differential expression was examined using immunohistochemistry and PCR. PDHA1 and CDKN2A were obtained as specific cuproptosis-related biomarkers for osteosarcoma after artificial intelligence analysis. PDHA1, CDKN2A and neutrophils were differentially expressed in OS and control groups. PDHA1 and CDKN2A are significantly dysregulated in OS and are able to serve as biomarkers of OS. [ABSTRACT FROM AUTHOR]

Noise, vibration and harshness (NVH) problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles. Although high accuracy manufacturing, modern structural roads and advanced suspension technology have already significantly reduced NVH problems and their impacts; off-road condition, obstacles and extreme operating condition could still trigger NVH problems unexpectedly. This paper proposes a vehicular electronic image stabilization (EIS) system to solve the vibration problem of the camera and ensure the environment perceptive function of vehicles. Firstly, feature point detection and matching based on an oriented FAST and rotated BRIEF (ORB) algorithm are implemented to match images in the process of EIS. Furthermore, a novel improved random sampling consensus algorithm (i-RANSAC) is proposed to eliminate mismatched feature points and increase the matching accuracy significantly. And an adaptive Kalman filter (AKF) is applied to improve the adaptability of the vehicular EIS. Finally, an experimental platform based on a gasoline model car was established to validate its performance. The experimental results show that the proposed EIS system can satisfy vehicular performance requirements even under off-road condition with obvious obstacles. [ABSTRACT FROM AUTHOR]

Lightweight design of leaf springs is conducive to reducing fuel consumption and improving vehicle comfort. The weight of leaf spring can be reduced obviously by using composite material. Stiffness and damping are the key factors that affect the properties of the leaf spring. The influence of the glass fiber laying angle and volume content on the stiffness and damping of the composite leaf spring was analyzed through experiment and simulation. The results show that the stiffness and damping properties of the leaf springs are related to the fiber laying angle and the fiber volume content. When the volume content and the number of layers are constant, the stiffness shows a nonlinear decreasing relationship with the laying angle, and the damping coefficient increases linearly with the laying angle. When the laying angle and the number of layers are constant, the stiffness increases linearly with the fiber volume content; the damping coefficient has a nonlinear decreasing relationship with the fiber volume content. The type of research can provide theoretical basis and reference for the design, analysis and optimization of composite leaf spring. [ABSTRACT FROM AUTHOR]

Many surveys on vehicle traffic safety have shown that the tire road friction coefficient (TRFC) is correlated with the probability of an accident. The probability of road accidents increases sharply on slippery road surfaces. Therefore, accurate knowledge of TRFC contributes to the optimization of driver maneuvers for further improving the safety of intelligent vehicles. A large number of researchers have employed different tools and proposed different algorithms to obtain TRFC. This work investigates these different methods that have been widely utilized to estimate TRFC. These methods are divided into three main categories: off-board sensors-based, vehicle dynamics-based, and data-driven-based methods. This review provides a comparative analysis of these methods and describes their strengths and weaknesses. Moreover, some future research directions regarding TRFC estimation are presented. [ABSTRACT FROM AUTHOR]

Most researches focus on the regenerative braking system design in vehicle components control and braking torque distribution, few combine the connected vehicle technologies into braking velocity planning. If the braking intention is accessed by the vehicle-to-everything communication, the electric vehicles (EVs) could plan the braking velocity for recovering more vehicle kinetic energy. Therefore, this paper presents an energy-optimal braking strategy (EOBS) to improve the energy efficiency of EVs with the consideration of shared braking intention. First, a double-layer control scheme is formulated. In the upper-layer, an energy-optimal braking problem with accessed braking intention is formulated and solved by the distance-based dynamic programming algorithm, which could derive the energy-optimal braking trajectory. In the lower-layer, the nonlinear time-varying vehicle longitudinal dynamics is transformed to the linear time-varying system, then an efficient model predictive controller is designed and solved by quadratic programming algorithm to track the original energy-optimal braking trajectory while ensuring braking comfort and safety. Several simulations are conducted by jointing MATLAB and CarSim, the results demonstrated the proposed EOBS achieves prominent regeneration energy improvement than the regular constant deceleration braking strategy. Finally, the energy-optimal braking mechanism of EVs is investigated based on the analysis of braking deceleration, battery charging power, and motor efficiency, which could be a guide to real-time control. [ABSTRACT FROM AUTHOR]

Current works of environmental perception for connected autonomous electrified vehicles (CAEVs) mainly focus on the object detection task in good weather and illumination conditions, they often perform poorly in adverse scenarios and have a vague scene parsing ability. This paper aims to develop an end-to-end sharpening mixture of experts (SMoE) fusion framework to improve the robustness and accuracy of the perception systems for CAEVs in complex illumination and weather conditions. Three original contributions make our work distinctive from the existing relevant literature. The Complex KITTI dataset is introduced which consists of 7481 pairs of modified KITTI RGB images and the generated LiDAR dense depth maps, and this dataset is fine annotated in instance-level with the proposed semi-automatic annotation method. The SMoE fusion approach is devised to adaptively learn the robust kernels from complementary modalities. Comprehensive comparative experiments are implemented, and the results show that the proposed SMoE framework yield significant improvements over the other fusion techniques in adverse environmental conditions. This research proposes a SMoE fusion framework to improve the scene parsing ability of the perception systems for CAEVs in adverse conditions. [ABSTRACT FROM AUTHOR]

Eco-driving strategies for vehicles with conventional powertrains have been studied for years and attempt to reduce fuel consumption by optimizing the driving velocity profile. For electric vehicles (EVs) with regenerative braking, the speed profile with the best energy efficiency should be different from conventional vehicles. This paper proposes an energy-oriented cruising control strategy for EVs with a hierarchical structure to realize eco-cruising on highways with varying slopes. The upper layer plans the energy-optimized vehicle velocity, and the lower layer calculates the torque allocation between the front and rear axles. However, the resulting speed profile with varying velocity may cause a high charge and discharge rate of the battery, resulting in rapid battery fading. To extend the battery life, we make a tradeoff between the energy consumption and wear of the battery by formulating an optimal control problem, where driving comfort and travel time are also considered. An indirect optimal control method is implemented to derive the optimal control rule. As an extension, the control rule for avoiding rear-end collisions is presented and simulated for driving in the real world. [ABSTRACT FROM AUTHOR]

Vehicle dynamics plays a significant role on the formation of a vehicle platoon, especially at high speed condition. This paper proposes a distributed platoon formation framework for homogeneous autonomous vehicles which takes the vehicle dynamics into consideration. The boundaries of vehicle stability are firstly calculated by considering both lateral dynamics and actuator saturation. By combining the stability boundaries with multi-objective flocking (MOF) algorithm, a distributed formation controller is designed, which can form the vehicle platoon with high cruising speed steadily and rapidly. In addition, a low-level tracking controller is proposed to constitute a hierarchical control architecture which can achieve stable and accurate formation of platoon. Simulation results show the proposed platoon formation controller achieves safe, stable and rapid platoon formation than the traditional MOF-based formation controller which does not consider the vehicle dynamics. [ABSTRACT FROM AUTHOR]

Two pentafluorinated β-diketone ligands, 4,4,5,5,5-pentafluoro-1-(4-methoxyphenyl)pentane-1,3-dione (PFMP) and 4,4,5,5,5-pentafluoro-1-(4-dimethyl amino-phenyl)pentane-1,3-dione (PFAP), had been employed to synthesize six novel europium (III) complexes with ancillary ligands 2,2-dipyridine, 1,10-phenanthroline and 4,7-diphenyl-1,10-phenanthroline. The synthesized europium (III) complexes were characterized by FTIR, 1H NMR, UV-Vis, luminescence spectroscopy, elemental analysis and thermogravimetric analysis. The photoluminescence spectra of these complexes showed the typical europium (III) red emissions in solid state and chloroform solution, assigned to 5D0 → 7Fj (j = 0-4) transitions. The higher values of intensity parameter Ω2 indicated that the europium ion was in a highly polarizable ligand field in these complexes. Europium (III) complexes with the β-diketone PFMP exhibited much better photoluminescence properties than complexes with the β-diketone PFAP. Especially, the europium (III) complex of the β-diketone PFMP with the auxiliary ligand 2,2-dipyridine displayed the longest lifetime value, the highest quantum yield and good CIE color coordinates matching the pure red color (x = 0.67, y = 0.33) in these complexes. In addition, the proposed energy transfer mechanisms and the thermal stability of these complexes were also investigated and analyzed. [ABSTRACT FROM AUTHOR]

Fuel economy is pursued by hybrid electric bus (HEB). However, a key challenge for a hybrid bus is to achieve near-optimality while keeping the energy management strategy (EMS) simple. In this paper, an EMS based on the Pontryagin’s minimum principle (PMP) is developed and implemented in ADVISOR. The designed EMS can successfully calculate the fuel consumption of the parallel HEB under the specific driving cycle. The simulation result suggests that the fuel economy of the hybrid electric bus with PMP-based control strategy is close to the benchmarking optimal solution calculated through DP, which is much better than the fuel economy of the parallel HEB with the default control strategy in ADVISOR. To sum up, the EMS based on PMP can improve the fuel economy by adjusting the engine operating point to reduce the exhaust emissions. Simultaneously, it can also extend the battery life by maintaining the battery state of charge at an appropriate level. [ABSTRACT FROM AUTHOR]

Estimation of the lateral stability region and torque distribution on steering is very important to improve stability in lateral handling for all wheel drive electric vehicles. Based on the built-nonlinear vehicle dynamic model, the lateral stability region of the vehicle related to steering is estimated using Lyapunov function. We obtained stable equilibrium points of non-straight driving according to the estimated lateral stability region and also reconstructed the Lyapunov function matrix, which proved that the closed-loop system composed of yaw rate and lateral velocity is satisfied with negative definite property. In addition, the designed controller dynamically allocates the drive torque in terms of the vertical load and slip rate of the four wheels. The simulation results show that the estimated lateral stability region and the designed controller are satisfactory in handling stability performance against different roads and vehicle parameters. [ABSTRACT FROM AUTHOR]