Vehicle State Research Articles

Design and Economic Analysis of a Solar-Powered Charging Station for Personal Electric Vehicles in Indonesia

Design and Economic Analysis of a Solar-Powered Charging Station for Personal Electric Vehicles in Indonesia

State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF

The prerequisite for intelligent vehicles to achieve autonomous driving and active safety functions is acquiring accurate vehicle state parameters. Traditional Kalman filters often underperform in non-Gaussian noise environments due to their reliance on Gaussian assumptions. This paper presents the IMM-MCCKF filter, which integrates the interacting multiple model theory (IMM) and the maximum correntropy cubature Kalman filter method (MCCKF), for estimating the state parameters of intelligent vehicles. The IMM-MCCKF successfully suppresses non-Gaussian noise by optimizing a nonlinear cost function using the maximum correntropy criteria, allowing it to capture and analyze signal data outliers accurately. The filter designs various state and measurement noise submodels to adapt to the environment dynamically, thus reducing the impact of unknown noise statistical properties. Accurately measuring the velocity of a vehicle and the angle at which its center of mass drifts sideways is of utmost importance for its ability to maneuver, maintain stability, and ensure safety. These parameters are critical for implementing advanced control systems in intelligent vehicles. The study begins by constructing a nonlinear Dugoff tire model and a three-degrees-of-freedom (3DOF) vehicle model. Subsequently, utilizing low-cost vehicle sensor signals, joint simulations are conducted on the CarSim-Simulink platform to estimate vehicle state parameters. The results demonstrate that in terms of estimation accuracy and robustness in non-Gaussian noise scenarios, the proposed IMM-MCCKF filter consistently outperforms the MCCKF and CKF algorithms.

The application of CNN in Smart city management and supervision under the background of Smart city

Intelligent transportation management, as an important component of intelligent city supervision, has practical value in improving the effectiveness of urban management. To improve the efficiency of intelligent city traffic management, this study proposes to use convolutional neural networks to improve the performance of navigation and intelligent transportation networks in intelligent transportation. This network is used for vehicle recognition in navigation to predict road congestion and determine the optimal driving path. This structure is applied in the intelligent road network to monitor the driving status of vehicles on the road. It is combined with the vehicle networking system to locate abnormal vehicles to achieve intelligent traffic management and supervision. These results confirm that this algorithm achieves a vehicle recognition accuracy of 91.2 % after 96 iterations and completes the recognition process within 163 ms. This algorithm converges to a vehicle positioning accuracy of 82.4 % after 93 iterations and achieves positioning within 277 ms. This algorithm can not only improve the optimal path planning effect of navigation, but also identify abnormal vehicles within 77 ms. Therefore, the proposed algorithm has high practical value in intelligent transportation management.

Personalized driving behavior oriented autonomous vehicle control for typical traffic situations

autonomous driving systems not only provide services for human drivers, but also need to consider the personalized driving requirements of human beings. In current road traffic environments, the driving behaviors of drivers differ significantly. This suggests that the various needs of different drivers cannot be met by a single behavior mode in an autonomous driving decision-making system. This paper looks at the personalized characteristics of various drivers and considers the implications of their differences. First, a Proportional Integral Differential (PID) feedback channel is introduced in a traditional Model Predictive Control (MPC) to improve the performance of the controller, and comprehensively considering the collision risk, motion hysteresis and rule constraints, referring to the MPC idea, a collision avoidance method based on Q-ABSAS optimization is proposed. Then based on the Chance Constrained Programming, the control constraint is combined with driver personalization to reflect a variety of driving personality characteristics. Finally, the proposed method is tested using Hardware-in-the-loop (HIL) experiments. The experiment results demonstrate that the proposed method can successfully implement vehicle tracking control and make the vehicle's state of movement match the driver's expectations, which can increase driver comfort and driving safety.

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station’s performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system’s simulation results are provided and scrutinized to confirm the concept’s suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations.

Prediction of the Development of China's New Energy Industry

In the past decade, China's new energy vehicle industry has developed rapidly due to the influence of national policies. In recent years, China has started planning to cease policy support for the new energy vehicle industry. Therefore, predicting the future development of China's new energy industry is of great significance for policy-making in China. This paper selects 10 factors influencing the development of new energy electric vehicles in the past ten years and 4 indicators reflecting the development of new energy electric vehicles. We combine the Entropy Weight Method with TOPSIS to score the development status of new energy electric vehicles based on 4 indicators. Then, we use Principal Component Analysis to reduce the dimensionality of the ten influencing factors to three principal components. To quantify these effects, Lasso regression is performed using 3 principal components to prevent overfitting. Therefore, this article obtained a model for predicting the development status of China's new energy vehicle industry, with a goodness of fit of over 0.98, indicating a good fit of the model. In order to predict the development score for the next decade, this paper first predict the data of ten factors for the next decade. The factors that remain unchanged are not treated. Factors that change the trend significantly are predicted using GM (1,1) and the forecast is good. For stationary time series, this paper collects data from nearly 20 years and trains on Long Short-Term Memory Networks. R2 = 0.891 and RMSE < 0.1, Therefore, the model can be used to predict data for the next 10 years. After obtaining various data, this paper obtains the development score of new energy electric vehicles in the next 10 years according to the regression equation. Finally, this paper concludes that the development and progress of China's new energy industry will slow down in the next decade, with a score increase of about 20%.

FGO-MFI: factor graph optimization-based multi-sensor fusion and integration for reliable localization

Reliable and precise information pertaining to the position, velocity, and attitude is essential for automated driving. This paper proposes FGO-MFI, a cost-effective and robust multi-sensor fusion and integration localization framework that utilizes factor graph optimization. Firstly, a tightly coupled Global Navigation Satellite Systems (GNSS)/on-board sensor fusion localization framework is established to estimate vehicle states, including position, velocity, and attitude. To address the large drift rate of the Inertial Measurement Unit (IMU), this study introduces a novel IMU/Dynamics pre-integration method based on the vehicle dynamics model. We establish a two-degree-of-freedom vehicle dynamics model utilizing measurements from the wheel speed sensor and steering wheel angle sensor. The IMU/Dynamics factor is devised through a close integration of the model output and IMU pre-integration, enabling the construction of precise odometry with low-cost on-board sensors. Then, to address the issue of the non-Gaussian distribution of GNSS pseudorange error, this paper employs a Gaussian Mixture Model (GMM) to characterize the pseudorange noise, which is then applied to further sensor fusion. Given the time-varying nature of pseudorange noise, the expectation maximization algorithm is utilized to estimate GMM parameters online, leveraging the pseudorange residuals within a sliding window. Comprehensive experiments, inclusive of challenging scenarios such as urban canyons, tunnels, and wooded areas, have been carried out. They affirm the superior performance of the proposed method. Experiments have shown that our method demonstrates reliable localization across different statuses of GNSS signal, exhibiting a 39.4% improvement in the root mean square error of position error when compared to the state-of-the-art. Additionally, this FGO-MFI is a general sensor data fusion framework and is able to incorporate diverse sensor measurements, for example, from cameras and LiDARs to provide more reliable and accurate localization information.

Learning-Based Vehicle State Estimation Using Gaussian Process Regression Combined with Extended Kalman Filter

In order to address the challenge of accurate vehicle state estimation, especially in highly nonlinear and complex operating conditions, this paper proposes a learning-based method for vehicle state estimation. To achieve this, a novel theoretical framework is constructed that combines the model-based Extended Kalman Filter (EKF) with Gaussian process regression (GPR). By establishing a dynamic model of the vehicle and describing the errors using machine learning techniques, the sources of state estimation errors are analyzed. Furthermore, by considering the model uncertainty, an error prediction model is developed through GPR learning from real-world vehicle data. Combined with EKF, the proposed method enables high-precision online estimation of vehicle state. To validate the proposed method, tests are performed on a real vehicle test platform equipped with high-precision sensors and data acquisition equipment, under two different operating conditions: emergency and normal. The results are compared with those obtained using EKF and State Observer, which demonstrates a more accurate and adaptable vehicle state estimation, and provides a method for quantitatively describing the confidence interval of vehicle state estimation result.

Enhancing electromagnetic compatibility and energy efficiency of electric vehicle charging stations

Problem. The article proposes a single-link structure for an electric vehicle charging station utilizing an active four-square rectifier with power factor correction. A Matlab model of the proposed charging station is developed, taking into account parameters such as the power network, the switches of the active rectifier, its automatic control system, and an equivalent model of the battery compartment. Additionally, a mathematical model for calculating static and dynamic losses is created based on polynomial approximation of the energy dependencies of IGBT modules. The analysis investigates power quality parameters, components of energy losses, and efficiency of the charging station across various charge currents and PWM frequencies during a full battery charge interval. Goal. The aim of this study is to propose a single-link structure for an electric vehicle charging station using an active four-square rectifier with power factor correction. It includes an analysis of power quality parameters, components of energy losses, and efficiency of the charging station at different charge currents and PWM frequencies during a full battery charge interval. Methodology. To achieve the goal, several key steps are considered. These include theoretical substantiation of the scheme of the electric microgrid charging station for electric vehicles with one-stage energy conversion, analysis of the battery connection scheme in the Tesla Model S electric car, research and calculation of efficiency, modeling of the charging station, development of a Matlab model of a microgrid system for the charging station, SAC analysis of battery charge voltage and current of a three-phase AV with PWM, modeling of losses in IGBT modules by polynomial approximation of dependencies, distribution of losses in the charging station system, and analysis of energy efficiency parameters. Results. The study presents the energy efficiency parameters of an external DC EV charging station using an active rectifier. It reveals that maximum efficiency of the system is achieved at minimum charge current. However, decreasing the charge current prolongs the charge process and slightly affects power quality parameters. Originality. A mathematical model for calculating static and dynamic losses was developed based on polynomial approximation of the energy dependencies of IGBT modules. The analysis encompasses power quality parameters, components of energy losses, and efficiency of the charging station across various charge currents and PWM frequencies during a full battery charge interval. Practical value. This study contributes to the further development of electric vehicles by improving the energy indicators of electric vehicle batteries and converters of electric vehicle charging stations, enabling fast charging modes. Active development is observed in each of these directions.

Distributed-Drive Vehicle Lateral-Stability Coordinated Control Based on Phase-Plane Stability Region

The lateral stability control of vehicles is one of the most crucial aspects of vehicle safety. This article introduces a coordinated-control strategy designed to enhance the handling stability of distributed-drive electric vehicles. The upper controller uses active front steering and direct yaw moment-control controllers designed based on sliding-mode control theory. The lower controller optimally allocates control inputs to the upper controller, considering factors such as load transfer and tire load rate. It divides the stability region by relying on the phase plane and develops a coordinated-control strategy based on the degree of deviation of the vehicle state from the stability region. The results of the simulation experiments demonstrate that the proposed control strategy effectively improves handling stability under extreme working conditions.

Wireless Charging Station for Electric Vehicles

The project mainly focuses on the model of wireless charging station for electric vehicles. In this charging station the user no need to use physical cables for charging their electric vehicles. When the car arrives on the particular slot for charging the IR sensor detects the vehicle and sends signal to the Arduino Board, then the required voltage is transferred to the transmitter coil which is placed in the parking slot, with the help of magnetic induction principle the energy from transmitter coil is transmitted to the receiver coil which is placed in the bottom of the vehicle.

Robust cooperative control strategy for a platoon of connected and autonomous vehicles against sensor errors and control errors simultaneously in a real-world driving environment

In a real-world driving environment, a platoon of connected and autonomous vehicles (CAVs) is subject to many internal and external disturbances, resulting in uncertain vehicle dynamics. In general, the disturbances can be categorized into two types: disturbances due to vehicle sensor errors (e.g., GPS error) and disturbances due to vehicle control errors (e.g., actuator delay). In the literature, many control strategies have been proposed to improve the robustness of the CAV platoon against uncertain vehicle dynamics induced by these disturbances. However, most of these strategies only consider one type of disturbance and cannot tackle both types of disturbances simultaneously. Furthermore, they are designed to maximize the benefits of each vehicle in the platoon independently, which can deteriorate the performance of the platoon. To address these problems, this study proposes a robust cooperative control (RCC) strategy to maneuver the vehicles in the platoon cooperatively to counteract the impacts of both types of disturbances. The RCC strategy is developed based on a minimax problem, where the maximization subproblem seeks to find the worst inputs for the uncertainty terms in the vehicle dynamics equation to minimize the platoon performance, while the minimization subproblem seeks to find the optimal control decisions for all subsequent vehicles to maximize the platoon performance in the worst case. To solve the minimax problem, this study proposes a globally convergent solution algorithm. It can solve the minimax problem very efficiently to enable real time deployment of the RCC strategy. Numerical application indicates that compared to the existing methods, the RCC strategy can dramatically improve the robustness of the CAV platoon against the uncertain vehicle dynamics induced by both vehicle state detection errors and vehicle control errors. Therefore, it can maneuver the CAV platoon safely and efficiently in a real-world driving environment.

Research on prediction of water target motion status of unmanned surface vehicle based on lidar

To improve the perception ability of unmanned boats on water targets and improve the autonomous collision avoidance function of unmanned boats, the problem of target motion state prediction is studied and a method for predicting the motion state of unmanned water targets based on lidar is proposed. Firstly, a three-dimensional point cloud space model is established and the down-sampling and clutter filtering are completed through various filters. Secondly, based on European clustering target segmentation, a target tracking algorithm combined with 3D-IOU metric is proposed to achieve target tracking. Finally, the Kalman filter is introduced for denoising, combined with the iterative closest point algorithm (Iterative Closest Point, ICP) and the centroid calculation algorithm to realize the prediction of the target speed and course. After the test on the lake, the method can meet the real-time requirements, complete the accurate prediction of the target motion state and control the error at a low level. At the same time, this method can also lay the foundation for the development of subsequent autonomous navigation tasks of unmanned vehicles.

Hierarchical extension H2/H∞ control approach for active front steering system of vehicle

Aiming at improving the vehicle handling stability and vehicle control performance by conventional H2/H∞ control, this paper investigates the active front steering (AFS) system by proposing a hierarchical extension H2/H∞ control strategy. Considering uncertainties and the vehicle handling stability, a mixed H2/H∞ control method is presented to generate the additional front wheel angle through tracking the desired vehicle yaw rate in the upper level. Furthermore, to enhance the H2/H∞ control effect, the novel extension control is proposed to adjust the H2/H∞ control signal dynamically according to different domains defined by the vehicle states. To track front wheel angle from the upper level, this study puts forward the fractional-order proportional-integral-derivative (FOPID) controller for driving the electro-hydraulic steering actuators. The hardware-in-the-loop experiments are performed to demonstrate the hierarchical control theory. The test results indicate that the proposed hierarchical extension H2/H∞ control strategy improves the vehicle cornering stability well, as well as can make the vehicle have better handling stability than conventional H2/H∞ and sliding mode control.

Review of Fuel-Cell Electric Vehicles

This paper presents an overview of the status and future prospects of fuel-cell electric vehicles (FC-EVs). As global concerns about emissions escalate, FC-EVs have emerged as a promising substitute for traditional internal combustion engine vehicles. This paper discusses the fundamentals of fuel-cell technology considering the major types of fuel cells that have been researched and delves into the most suitable fuel cells for FC-EV applications, including comparisons with mainstream vehicle technologies. The present state of FC-EVs, ongoing research, and the challenges and opportunities that need to be accounted for are discussed. Furthermore, the comparison between promising proton-exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) technologies used in EVs provides valuable insights into their respective strengths and challenges. By synthesizing these aspects, the paper aims to provide a comprehensive understanding and facilitate decision-making for future advancements in sustainable FC-EV transportation, thereby contributing to the realization of a cleaner, greener, and more environmentally friendly future.

Lateral Driver‐Automation Driver Authority Decision Considering Safety of the Intended Functionality

AbstractThe driver authority decision for driver‐initiated takeover is closely related to vehicle driving safety. In this paper, a lateral driver‐automation driver authority decision method considering the safety of the intended functionality is proposed to improve vehicle driving safety. Based on systems‐theoretic processes analysis, functional safety analysis of driver initiative takeover is carried out to clarify the safety of the intended functionality issues caused by the unreasonable decision. Then, a method for defining the safe driving area is developed to assess the safety level of the driving area around the ego vehicle. Taking the driver‐vehicle state parameters and vehicle–vehicle state parameters as input, a deep neural network model is constructed to determine the intention of the driver to take over the vehicle. The lateral driver‐automation driver authority decision method is designed to make the driver‐machine take‐over decisions, which considers the safety of the intended functionality. The effectiveness of the proposed method is evaluated via numerical simulation and hardware‐in‐the‐loop experiments. The results show that the designed method not only improves the control ability of the driver to the maximum extent but also integrates the perception ability of the driver into the vehicle control system to improve further vehicle driving safety.

Pitch motion suppression of electric vehicle active suspensions based on multibody dynamics

Active suspension control strategies are proposed to suppress pitch motion and address the issue of motion sickness among electric vehicle passengers. In this work, we investigate the performance of the linear quadratic regulator (LQR), variable universe fuzzy control, adaptive-network-based fuzzy inference system (ANFIS), and fuzzy PID control under continuous acceleration and deceleration and bumpy road conditions. First, a 14-degrees-of-freedom (DOF) dynamics model of an electric vehicle is developed based on a semirecursive multibody dynamics method, which was originally proposed by Javier García de Jalón. The vehicle dynamics simulation is performed to accurately model the vehicle states and responses. Second, the LQR, variable universe fuzzy control, ANFIS, and Fuzzy PID control algorithms are tailored via vehicle states, including pitch, rate, and acceleration. Their performances are investigated and compared in detail. Finally, the robustness of the proposed control strategies is further discussed based on various speed bump parameters and initial velocities. The results indicate that the proposed control strategies are capable of suppressing the pitch motion of electric vehicles, enhancing the riding comfort under diverse operating conditions, thereby reducing the likelihood of motion sickness among passengers of electric vehicles.

A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy

Convoys composed of autonomous vehicles could improve the transportation and freight industries in several ways. One of the avenues of improvement is in fuel efficiency, where the vehicles maintain a close following distance to each other in order to reduce air resistance by way of the draft effect. While close following distances improve fuel efficiency, they also reduce both the margin of safety and the system’s tolerance to disturbances in relative position. The system’s tolerance to disturbances is known as string stability, where the error magnitude either grows or decays as it propagates rearward through the convoy. One of the major factors in a system’s string stability is its delay in sending state updates to other vehicles, the most pertinent being a hard braking maneuver. Both external sensors and vehicle-to-vehicle communication standards have relatively long delays between peer vehicle state changes and the information being actionable by the ego vehicle. The system presented here, called the Convoy Vehicular Ad Hoc Network (Convoy VANET), was designed to reliably propagate emergency event messages with low delay while maintaining reasonable channel efficiency. It accomplishes this using a combination of several techniques, notably relative position-based retransmission delays. Our results using Network Simulator 3 (ns3) show the system propagating messages down a 20-vehicle convoy in less than 100 ms even with more than a 35% message loss between vehicles that are not immediately adjacent. These simulation results show the potential for this kind of system in situations where emergency information must be disseminated quickly in low-reliability wireless environments.

When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

LoRa technology contributes to green energy by enabling efficient, long-range communication for the Internet of Things (IoT). This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa, where the network performance is affected by the density of gateways (GWs) and end devices (EDs), as well as environmental conditions. To mitigate interference, data throughput losses, and high-power consumption, the proposed spreading factor (SF) and hybrid (data rate|SF) models dynamically adjust the transmission parameters. The orchestration of concurrent data modifications within the network server (NS) is crucial for uninterrupted communication between GWs and EDs, especially in monitoring electric vehicle (EV) stations to reduce traffic congestion and pollution. Employing K-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms optimizes ED allocation, averts data congestion, and improves the signal-to-interference noise ratio (SINR). These methods ensure seamless information reception by meticulously allocated EDs across various GW combinations. To estimate the free-space losses (FSL), a log-distance path loss model (log-PL) is used. Exploring various bandwidths (BWs), bidirectional communications, and duty cycles (DCs) helps to prevent saturation, thus prolonging the operational lifespan of EDs. Empirical findings reveal a notable packet rejection rate (PRR) of 0% for the DBSCAN (hybrid model). In contrast, the K-means exhibits a PRR ranging from 5% (hybrid model) to 35.29% (SF model) for the ten GWs combination. Notably, the network saturation is reduced to 10.185% and 9.503%, respectively, highlighting an improvement in the average efficiency of slotted ALOHA (91.1%) and pure ALOHA (90.7%). These enhancements increase the lifespan of EDs to 15,465.27 days.

Design, Construction, and Validation of an Experimental Electric Vehicle with Trajectory Tracking.

This research presents an experimental electric vehicle developed at the Tecnológico Nacional de México Celaya campus. It was decided to use a golf cart-type gasoline vehicle as a starting point. Initially, the body was removed, and the vehicle was electrified, meaning its engine was replaced with an electric one. Subsequently, sensors used to measure the vehicle states were placed, calibrated, and instrumented. Additionally, a mathematical model was developed along with a strategy for the parametric identification of this model. A communication scheme was implemented consisting of four slave devices responsible for controlling the accelerator, brake, steering wheel, and measuring the sensors related to odometry. The master device is responsible for communicating with the slaves, displaying information on a screen, creating a log, and implementing trajectory tracking techniques based on classical, geometric, and predictive control. Finally, the performance of the control algorithms implemented on the experimental prototype was compared in terms of tracking error and control input across three different types of trajectories: lane change, right-angle curve, and U-turn.