Vehicle State Research Articles

Shared automated mobility-on-demand system with different response, pricing and dispatching strategies

ABSTRACT The shared automated mobility-on-demand system can offer convenient ‘point-to-point’ services for travelers. However, most previous studies have not combined pricing strategies with dispatching strategies to investigate their impact on system operation results. This paper proposes two demand response strategies for the shared automated mobility-on-demand system. From the perspective of time and space, eight ridesharing pricing strategies are proposed. The preliminary vehicle matching and routing are obtained through the integer linear programming considering system profit and service level, and the vehicle status is updated by four dispatching strategies that consider whether the matching scheme and route are updated over time. The results show that the factors taken into account in the ridesharing pricing strategy are restricted by the system to maximize profit, and the most flexible dispatching strategy is not the most profitable. These detailed analyses can provide a basis for formulating an effective operation mode of the automated mobility-on-demand system.

Joint RSU and agent vehicle cooperative localization using mmWave sensing

In vehicle networks, accurate vehicle localization is crucial. This paper proposes a joint roadside unit (RSU) and agent vehicles cooperative localization framework based on dual-function radar-communication (DFRC) technology. It utilizes unscented Kalman filtering (UKF) to process DFRC signals and obtain vehicle status information. To improve the angle prediction accuracy of the agent vehicle, an angle fusion estimation scheme based on the maximum likelihood algorithm is proposed. Furthermore, a weighted method is introduced within the joint RSU and agent vehicle cooperative localization to enhance vehicle localization accuracy. Experimental results demonstrate that the proposed angle fusion scheme reduces angle estimation error, and the joint RSU and agent vehicle localization framework significantly improves vehicle localization accuracy.

Bi-level programming model of location and charging electric vehicle stations in distribution networks

Electric vehicles (EVs) are currently a focal point in distribution network research due to their potential benefits. Integrating EVs into distribution networks can enhance voltage profile stability. However, unplanned EV connection and charging can lead to increased peak load power consumption, voltage profile irregularities, and heightened network losses. Addressing the placement of charging stations in distribution networks is crucial to prevent voltage drop crises and mitigate other issues. This study proposes a Bi-level particle programming optimization programming approach, utilizing the Particle Swarm Optimization (PSO) algorithm to minimize losses and determine optimal station locations. The algorithm iteratively minimizes losses while considering location and operating costs until a stopping condition is met. Additionally, an EV charging planning algorithm is introduced to manage EV connections without exacerbating peak load or causing severe voltage drops. Simulations conducted on an IEEE 33-bus system using MATLAB analyze the proposed design's impact on power consumption, system losses, and network voltage. Simulations in an IEEE 33-bus system show that the proposed model shifts peak power consumption by about 4.6% to about 11.6% under different EV penetration scenarios. It achieves a reduction in the network losses by up to 0.39%, while the voltage deviations in critical buses are minimized to enhance stability during periods of high demand for EVs. Results demonstrate that the programmed charging algorithm effectively reduces power consumption at peak load, minimizes network losses, and smooths the daily voltage profile.

An explicit discrete-time dynamic vehicle model with assured numerical stability

Numerical stability is of great significance for discrete-time dynamic vehicle model. Among the unstable factors, low-speed singularity stands out as one of the most challenging issues, which arises from that the denominator of tire side angle term only contains the vehicle longitudinal speed. Consequently, for the common low-speed and stop-start driving scenarios, the calculated tire slip angle will approach infinity, which will further lead to the numerical explosion of other vehicle states. In response to this critical challenge, we propose a discrete-time dynamic vehicle model that effectively mitigates the low-speed singularity issue, ensuring numerical stability and maintaining the explicit form – highly favoured by model-based control algorithms. To validate the numerical stability of our model, we conduct a rigorous theoretical analysis, establishing sufficient conditions for stability, and conduct extensive empirical validation tests across a wide spectrum of speeds. Subsequent to the validation process, we conduct comprehensive simulations comparing our proposed model with both kinematic models and existing dynamic models discretised through the forward Euler method. The results demonstrate that our proposed model shows better comprehensive performance in terms of both the accuracy and numerical stability. Finally, the real vehicle experiments are carried out to support that our proposed model can closely aligns to the real vehicle trajectories showcasing its practicality and ease of use. Notably, our work stands as the pioneering endeavour in introducing an explicit discrete-time dynamic vehicle model suitable for common urban driving scenarios including low-speed and stop-start.

Data-Driven Enhancements for MPC-Based Path Tracking Controller in Autonomous Vehicles

The accuracy of the control model is essential for the effectiveness of model-based control methods. However, factors such as model simplification, parameter variations, and environmental noise can introduce inaccuracies in vehicle state descriptions, thereby compromising the precision of path tracking. This study introduces data-driven enhancements for an MPC-based path tracking controller in autonomous vehicles (DD-PTC). The approach consists of two parts: firstly, Kolmogorov–Arnold Networks (KANs) are utilized to estimate tire lateral forces and correct tire cornering stiffness, thereby establishing a dynamic predictive model. Secondly, Gaussian Process Regression (GPR) is deployed to accurately capture the unmodeled dynamics of the vehicle to form a comprehensive control model. This enhanced model allows for precise path tracking through steering control. The superiority of DD-PTC is confirmed through extensive testing on the Simulink-CarSim simulation platform, where it consistently surpasses normal MPC and Linear Quadratic Regulator (LQR) strategies, especially in minimizing lateral distance errors under challenging driving conditions.

Sustainable Charging Stations for Electric Vehicles

In this work, we develop a detailed analysis of the current outlook for electric vehicle charging technology, focusing on the various levels and types of charging protocols and connectors used. We propose a charging station for electric cars powered by solar photovoltaic energy, performing the analysis of the solar resource in the selected location, sizing the photovoltaic power plant to cover the demand completely, and exploring different configurations such as grid connection or physical and virtual electric energy storage. Despite the current development applying for specific working conditions, operating voltage, charging rate, power demand, etc., the proposed configuration is modular, adaptable, and resilient. The simulated system operates within the 360 V to 800 V range of direct current for charging the electric vehicles, with a selectable power range between 20 and 180 kW. The basic layout includes four charging poles, each servicing all working voltages. An oversized PV plant powers the charging station at any time of the year, saving money compared to the alternative of the electric storage unit. In addition, we build simulation tools and algorithms that optimize the design of future projects, providing a solid basis for sustainable energy infrastructure planning and design.

Adaptive event-triggered dynamic output feedback control for networked autonomous vehicles affected by signal quantization and actuator failures

In this paper, an event-triggered sliding-mode output feedback control (SMOFC) strategy with adaptive fault-tolerance is proposed to achieve path tracking for networked autonomous vehicles, considering the challenges posed by network communication and electric steering systems. Initially, signal quantization, external disturbances, and actuator faults are incorporated into the vehicle model. This incorporation enhances the designed controller’s robustness against a broader and more demanding range of driving scenarios. Subsequently, in situations where only output feedback is available, a dynamic output compensator is designed to reconstruct the unmeasurable vehicle state. Utilizing the reconstructed vehicle state, an event-triggered strategy is devised to alleviate the network burden and determine the minimum time between triggering events to prevent Zeno behavior. Furthermore, an adaptive mechanism is employed to estimate the actuator fault boundaries. The performance of the designed controller is evaluated through simulation instances.

Three-point adaptive preview steady-state compensation driver model based on articulated heavy vehicle

This article proposes a three-point adaptive preview steady-state compensation driver model based on articulated heavy vehicles (AHVs). Although AHVs have extremely high road transportation efficiency, they have poor driving stability and high accident risk during high-speed transportation due to their large weight, high center of mass and multi-body characteristics. However, there are few studies on intelligent driving control of AHVs. In order to improve the active safety performance of AHVs, a three-point adaptive preview driver model is designed in this article. Based on the driver model, a sliding mode steady-state compensation controller is added. The three-point adaptive preview driver is different from other driver models. Its characteristic is that the preview distance can be automatically adjusted according to the vehicle speed and road curvature. A driver model with three-point preview weighted deviation, preview time, current vehicle speed and vehicle state as input and front wheel angle as output is established. Considering the trailer state, the front wheel angle is compensated and corrected by sliding mode steady-state compensation controller. The simulation results show that the model can reasonably judge the relationship between the target path and the vehicle position, adapt to the dynamic adjustment mechanism of the preview distance, and has good tracking accuracy. The front wheel compensation angle provided by the sliding mode controller under high speed and extreme conditions can ensure that the AHVs can track the target trajectory while maintaining driving stability.

Dynamic Obstacle Avoidance Strategy for High-Speed Vehicles Via Constrained Model Predictive Control and Improved Artificial Potential Field

Abstract The paper proposes an automatic planning and control strategy to keep the automatic driving high-speed vehicles collision-free based on the improved artificial potential field (APF) and constrained model predictive control (MPC). Firstly, the road potential field satisfying the constraints of vehicle safe driving is constructed according to the driving characteristics of vehicles and road boundary conditions. Focusing on the influence of the speed and direction of the obstacle vehicle on the potential field, the dynamic obstacle vehicle potential field is established. Secondly, the road potential field and the dynamic obstacle vehicle potential field are incorporated into the objective function of the path planning module to establish a real-time path planner. Thirdly, the yaw stability constraints of the vehicle are established, which are added to the QP solver and updated in real time according to the current vehicle states, so as to establish the constrained MPC controller. In the end, the safety of this planning and control strategy for obstacle avoidance overtaking and the effectiveness of constrained MPC in improving vehicle stability are verified by comparative simulation analysis in multi-obstacle vehicles scenarios.

RBCKF-Based Vehicle State Estimation by Adaptive Weighted Fusion Strategy Considering Composite-State Tire Model

The acquisition of vehicle driving status information is a key function of vehicle dynamics systems, and research on high-precision and high-reliability estimation of key vehicle states has significant value. To improve the state observation effect, a vehicle sideslip angle estimation method adopting a robust bias compensation Kalman filter and adaptive weight fusion strategy is proposed. On the basis of the extended Kalman filter algorithm, and with the goals of estimation exactitude and robustness, considering the potential signal deviation, a vehicle state robust deviation compensation Kalman filter estimation algorithm considering bias compensation and residual covariance matrix weighting is proposed. Meanwhile, considering the adaptive and dynamic adjustment capabilities of the observation system in complex state-change scenarios, an estimation strategy based on adaptive weight fusion and a model-based estimator is proposed. The results confirm that the robust bias compensation Kalman filter can ensure estimation exactitude and robustness when the vehicle state fluctuates greatly, and the proposed fusion strategy can ensure that the vehicle maintains optimal estimation performance during operating condition switching.

Collaborative control framework at isolated signalized intersections under the mixed connected automated vehicles environment

AbstractThis study proposes a collaborative control framework under the mixed traffic environment of connected and automated vehicles and connected human‐driven vehicles, which can simultaneously optimize the signal timing, lane settings, and vehicle trajectories at isolated intersections. Initially, considering the dynamics of traffic demand and incompatible signals, we analyze the vehicle delay of each lane. Based on the delay analysis, the spatiotemporal resource collaborative optimization model of lane setting and signal timing is established to minimize the average delay. Subsequently, in the buffer zone, a graph‐theoretic‐based sorting and platooning model provides a clear and concise representation of the transformation process from the initial state to the target state of vehicles, enabling the platoon formation. Additionally, trajectory optimization is integrated into the collaborative control framework by the optimal control model and car‐following model in the passing zone. Simulation experiments and sensitivity analyses demonstrate the effectiveness of the proposed framework in reducing average vehicle delay, improving fuel consumption, and coping with changing traffic demand at intersections.

Research on intelligent optimal allocation control strategy for stability and braking of heavy tyre blowout vehicle

The traditional control strategy mainly compensates and corrects the driving state of heavy vehicles with a tyre blowout, and rarely considers the need for emergency braking. It is also difficult to effectively coordinate the problems of lateral yaw, slip and tailspin, and longitudinal braking performance degradation. For this, an intelligent optimization allocation control strategy combining stability control and braking control is innovatively designed in this article. The joint simulation model for heavy tyre blowout vehicles is built based on the Dugoff tyre blowout model and the TruckSim vehicle model. A linear quadratic form regulator stable control based on the particle swarm optimization algorithm optimization is used to dynamic equilibrium of the additional yaw moment generated by the vehicle tyre blowout. A logic threshold braking control based on adaptive adjustment is adopted to achieve an ideal braking effect for the vehicle. In order to simultaneously consider the stability and braking performance of vehicles with blowout tyre, a braking force distribution strategy and a fuzzy optimization allocation algorithm are proposed to achieve coordination of lateral and longitudinal control strategies. The results indicate that intelligent optimized allocation control can effectively correct the deviation trajectory of a heavy tyre blowout vehicle, enabling it to achieve rapid braking while maintaining stability.

Robust Static Output Feedback Control of a Semi-Active Vehicle Suspension Based on Magnetorheological Dampers

This paper proposes a novel design method for a magnetorheological (MR) damper-based semi-active suspension system. An improved MR damper model that accurately describes the hysteretic nature and effect of the applied current is presented. Given the unfeasibility of installing sensors for all vehicle states, an MR damper current controller that only considers the suspension deflection and deflection rate is proposed. A linear matrix inequality problem is formulated to design the current controller, with the objective of enhancing ride safety and comfort while guaranteeing vehicle stability and robustness against any road disturbance. A series of experiments demonstrates the enhanced performance of the proposed MR damper model, which exhibits greater accuracy than other state-of-the-art damper models, such as Bingham or bi-viscous. An evaluation of the vehicle behavior under two simulated road scenarios has been conducted to demonstrate the performance of the proposed output feedback MR damper-based semi-active suspension system.

Sustainable Smart Parking Site Selection with Charging Stations for Electric and Hybrid Vehicles

Sustainable Smart Parking Site Selection with Charging Stations for Electric and Hybrid Vehicles

Operational vehicle state of health estimation framework based on local-global attention mechanism

The state of health (SOH) of electric vehicles directly correlates with operational safety and driving range. Accurate assessment of SOH is crucial for improving the safety and reliability of electric vehicles. This article proposes a novel SOH estimation framework based on the local-global attention mechanism (L-G-A) and real-world incomplete charging processes. Firstly, an analysis of the charging modes is conducted. Specifically, the average value of capacity is calculated for multiple constant current segments as the reference capacity, and the effect of ambient temperature on charging efficiency and battery capacity is considered. Subsequently, aging characteristics of interval capacities are extracted through incremental capacity analysis. To investigate the impact of different usage patterns on battery degradation during daily use by vehicle owners, the historical cycle charging segments are categorized based on segment similarity. Then, the L-G-A model is established by utilizing convolutional layers to extract local-scale temporal information and computing global attention scores based on degradation information between each segment and across classes. The experimental results demonstrate that the proposed framework has the highest estimation accuracy with mean absolute error and root mean square error of 0.069 % and 0.084 %, respectively, which outperforms the traditional LSTM, CNN, and SVR models in terms of accuracy of SOH evaluation.

Research on Weighted Network Model Construction and Layout Structure of New Energy Vehicle Charging Station

(1) Background: Spatial layout is the key to the construction and development of new energy vehicle charging stations; (2) Methods: A network analysis method is used to build the new energy vehicle charging station network, design network indicators, analyze the structural characteristics of new energy vehicle charging stations based on the local nodes and the overall structure of the network, and identify the key nodes and important parts of the network; (3) Results: Taking the new energy vehicle charging station in Jinan City of Shandong Province as an example, the empirical analysis is carried out. The empirical results show that the new energy vehicle charging station network constructed in this paper and the network indicators designed can effectively describe the layout of new energy vehicle charging stations, identifying key stations and important parts of the network; (4) Conclusions: This provides a theoretical basis for further improving the utilization rate of charging stations and the reasonable planning of new energy vehicle charging stations. It provides an idea and method for the research on the layout of charging stations for new energy vehicles.

The Impact of the Configuration of a Hydrogen Refueling Station on Risk Level

The paper discusses potential hazards at hydrogen refueling stations for transportation vehicles: cars and trucks. The main hazard analyzed here is an uncontrolled gas release due to a failure in one of the structures in the station: storage tanks of different pressure levels or a dispenser. This may lead to a hydrogen cloud occurring near the source of the release or at a given distance. The range of the cloud was analyzed in connection to the amount of the released gas and the wind velocity. The results of the calculations were compared for chosen structures in the station. Then potential fires and explosions were investigated. The hazard zones were calculated with respect to heat fluxes generated in the fires and the overpressure generated in explosions. The maximum ranges of these zones vary from about 14 to 30 m and from about 9 to 14 m for a fires and an explosions of hydrogen, respectively. Finally, human death probabilities are presented as functions of the distance from the sources of the uncontrolled hydrogen releases. These are shown for different amounts and pressures of the released gas. In addition, the risk of human death is determined along with the area, where it reaches the highest value in the whole station. The risk of human death in this area is 1.63 × 10−5 [1/year]. The area is approximately 8 square meters.

Adaptive control of plug-in hybrid electric vehicles based on energy management strategy and dynamic programming algorithm

This study mainly analyses the fuel consumption of plug-in hybrid vehicles during operation. A new control method for automobiles based on energy management strategy and dynamic programming algorithm is proposed. The new method plans and analyses the minimum electricity consumption, and then uses dynamic programming algorithms to analyse this parameter. The research results indicated that the vehicle state was constantly changing with the variation of SOC value during driving. The energy mobilization of the vehicle was more obvious after adding dynamic programming strategy. The efficiency of the vehicle was relatively high in driving state 1, with a minimum value of 70%, which was about 20% higher than in driving state 4. The average fuel consumption in driving state 2 was 1.8L higher than in other driving states. The overall efficiency of automobiles after incorporating dynamic programming was improved, with a shorter time to reach the lowest efficiency point compared with not incorporating dynamic programming algorithms. The highest efficiency value was 7.86% higher than that of not incorporating dynamic programming models. The new control method can reduce energy consumption and improve the energy management and control effect. The study provides a better research direction for energy management and control of hybrid electric vehicles in the future.

Efficient Wireless Power Transmission System

The work focuses on wireless power transfer for electric vehicle charging stations. The aim of the study is to analyse the processes in the system of wireless magnetic resonance transmission of electrical energy using magnetic fields and to develop approaches to improve the efficiency of such systems. The set objectives were achieved by solving the following problems: the operation of the wireless power transmission system with a resonance-tuned transmission coil, the determination of the optimal energy parameters of wireless power transmission on the example of a magnetically coupled system with a series-sequential resonant circuit. The main results of the study are as follows: it is shown that the necessary condition for obtaining the maximum power in the receiver is not only the tuning to the resistance of the receiving coil, but also the inclusion of the receiving circuit to a certain active load resistance, depending on the coupling coefficient between the transmitting and receiving coils, and analytical expressions are obtained, by means of which it is possible to calculate the load resistance at which the considered power transmission system has the maximum efficiency; it is proved that the transmission efficiency of the series-sequential resonant circuit is maximum in the single resonance mode and depends on the coupling coefficient. Significance of the obtained results: It is proved that magnetic resonance transmission of electric energy in the case of series-sequential scheme of wireless power transmission is more effective in comparison with series-parallel, at frequencies not exceeding 100 kHz, which has a positive effect on the operation of charging stations for electric vehicles in an urban industrial environment and, as a result, on solving the problem of compliance of such systems with electromagnetic compatibility requirements.

Adaptive and soft constrained vision-map vehicle localization using Gaussian processes and instance segmentation

The accurate and reliable localization is crucial for the safe performance of autonomous vehicles in uncertain and complex environments such as urban areas. Thus, this paper contributes by presenting a feature-based localization framework for autonomous vehicles where segmented map features are used for designing soft and adaptive constraints for Particle Filtering. After obtaining features of landmarks (light poles) in instance-based segmented images acquired from a monocular camera, vehicle-to-landmark distances are predicted using Gaussian Process Regression models (GPR). Both mean and variance (uncertainty) outputs of Gaussian Process models were used to implement adaptive soft constraints. Experimental results over the WATonoBus from the University of Waterloo confirm that the use of the proposed constraints improves the vehicle states estimation. Using instance segmentation increases the vehicle-to-landmark distance prediction accuracy by 27% and keeps the real-time processing capability of the proposed approach. Besides, the proposed approach reliably localizes the vehicle in very noisy conditions using only a monocular camera, even with a reduced number of landmarks, reducing the localization Euclidean error from 2.55 m to 0.97 m when compared to an unconstrained Particle Filter.