Vehicle System Research Articles

Clarifying the impact of engine operating parameters of heavy-duty diesel vehicles on NOx and CO2 emissions using multimodal fusion methods

The issue of air pollution from transportation sources remains a major concern, particularly the emissions from heavy-duty diesel vehicles, which pose serious threats to ecosystems and human health. China VI emission standards mandate On-Board Diagnostics (OBD) systems in heavy-duty diesel vehicles for real-time data transmission, yet the current data quality, especially concerning crucial parameters like NOx output, remains inadequate for effective regulation. To address this, a novel approach integrating Multimodal Feature Fusion with Particle Swarm Optimization (OBD-PSOMFF) is proposed. This network employs Long Short-Term Memory (LSTM) networks to extract features from OBD indicators, capturing temporal dependencies. PSO optimizes feature weights, enhancing prediction accuracy. Testing on 23 heavy-duty vehicles demonstrates significant improvements in predicting NOx and CO2 mass emission rates, with mean squared errors reduced by 65.205 % and 70.936 % respectively compared to basic LSTM models. This innovative multimodal fusion method offers a robust framework for emission prediction, crucial for effective vehicle emission regulation and environmental preservation.

Investigating dehumidification and heating performance in a dual evaporator heat pump system for electric vehicles

In cold, moisture-rich winter environments, window fogging represents a substantial safety hazard for drivers. Electric vehicles often incorporate heat pump systems to address challenges such as dehumidification and heating specific to cold weather. Therefore, it is essential to evaluate the dehumidification and heating efficiency of these systems through focused research. This study presents a dual-evaporator heat pump system designed specifically for electric vehicles, equipped with two distinct modes for dehumidification and heating. The research examines how factors such as inlet air volume and the degree of opening of the electronic expansion valve affect the system's dehumidification and heating performance. Experimental analyses were conducted to explore the system's response under various conditions of inlet air humidity and compressor speed in both modes. Results suggest that increasing inlet air volume improves dehumidification effectiveness but may reduce heating performance. Likewise, a wider opening of the electronic expansion valve enhances heating but could decrease dehumidification efficiency. Importantly, the study indicates that when the relative humidity of the inlet air exceeds 70 %, a single evaporator mode is more effective for dehumidification. However, when the relative humidity is below 70 %, the dual evaporator mode is more advantageous, showing better heating performance.

Study on Performance Simulation Matching of One-Dimensional Hydrogen Storage and Supply System for Hydrogen Fuel Cell Vehicles

Article Study on Performance Simulation Matching of One-Dimensional Hydrogen Storage and Supply System for Hydrogen Fuel Cell Vehicles Qi Liu 1,2, * , Biao Xiong 1,3, Yuxuan Liu 1,3, Chuanyu Zhang 1,3, Shuo Yuan 1,2, and Wenshang Ma 1,3 1 College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China 2 Research Institute of Hunan University in Chongqing, Chongqing 401120, China 3 State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China * Correspondence: author: hnuliuqi@hnu.edu.cn Received: 1 July 2024; Accepted: 12 September 2024; Published: 27 September 2024 Abstract: With the improvement of environmental protection requirements, hydrogen fuel cell vehicles are considered one of the most potential and promising new energy vehicles because of their advantages, such as pollution-free emission, long cruising range, and short hydrogenation time. However, there are still unresolved problems between the storage and supply of hydrogen and the power demand during the operation of hydrogen fuel cell vehicles. In this study, a hydrogen fuel cell vehicle is taken as the research object, and a one-dimensional model is built according to the basic performance parameters so as to explore the operation law of the power performance demand of the hydrogen fuel cell vehicle, simulate the power demand in the actual operation process, summarize the influence of different parameters on the power economic performance of the vehicle, and put forward optimization strategies to improve the power, durability, and fuel economy of the vehicle.

Data-driven state-of-charge estimation of a lithium-ion battery pack in electric vehicles based on real-world driving data

State-of-charge (SOC) is one of the most important indicators in the battery management system (BMS) of electric vehicles (EVs). Accurate online estimation of battery pack SOC is essential for ensuring safety by preventing overcharging and overdischarging, as well as for facilitating proper operation and efficient energy utilization. This paper proposes a deep learning model, CNN-BiLSTM-Attention, to drive the battery pack SOC estimation by datasets collected from actual operating EVs. Multivariate time series (MVTS) signals such as vehicle speed, battery voltage, current and temperature are carefully selected as inputs to the model. Additionally, a three-parameter Weibull probability model is used to model the terminal voltage distribution of all cells within the investigated battery pack, and the evolution of cell terminal voltage inconsistency concerning depth of discharge (DOD) is described from the perspective of dispersion and symmetry. By adding these distribution features to the input dimension of the CNN-BiLSTM-Attention model, the estimation accuracy of battery pack SOC has been further improved. Experimental results indicate that when the window length (WL) is set to 25 sampling time steps and the window overlap rate is >72 %, the SOC estimation error is <3 % and the root mean square error (RMSE) is <1.12 % within the maximum DOD variation range experienced by the battery pack. Especially when the battery pack is in the discharge platform period of 30 %–100 % SOC, the SOC estimation error is reduced to <2 %. After adding the Weibull distribution features that reflect the cell inconsistency evolution into the model's input dimensions, the overall SOC estimation RMSE is reduced by about 31 %, especially by about 51 % at lower SOC levels (below 31 %).

A hierarchical design of complex interactive interface with multi‐perception channels for a helmet‐mounted display system of vehicle

A hierarchical design of complex interactive interface with multi‐perception channels for a helmet‐mounted display system of vehicle

Estimation of SOC for Li-ion battery-powered three-wheeled electric vehicle using machine learning methods

Abstract The Battery Management System (BMS) serves as the heart of the electric vehicle system, in which estimating the state of charge (SOC) is the crucial part of the BMS to ensure the durability, reliability, and sustainability of the battery pack. Due to its nonlinear characteristics, accurately estimating the SOC for a slow degradation of the charge is highly cumbersome. The literature provides a series of machine learning algorithms (MLA) to estimate and predict the SOC of lithium-ion (Li-ion) battery systems for electric vehicle (EV) applications. The literature has proposed various MLA, coulomb counting, and different Kalman filter methods to address this challenge and estimate the SOC of Li-ion battery systems for EV applications. This research looks at the differences and similarities between the coulomb counting method, the unscented Kalman filter method, and a number of machine learning algorithms. These include linear regression, polynomial linear regression, support vector regression, decision trees, random forests, artificial neural networks (ANN), and long short-term memory (LSTM). The goal is to assess the MLAs' accuracy in estimating battery SOC. Analyzing model errors optimizes the battery's performance parameter. We identify ANN and LSTM as the two most efficient methods for accurate SOC estimation in an EV-operated BMS system by evaluating the performance indices of the aforementioned machine learning methodologies. Once again, the LSTM model for SOC estimation has proven to be highly accurate in analyzing the discrepancy between the actual and predicted traveling ranges of the designed prototype. We design a MATLAB/SIMULINK EV powertrain by collecting realtime data from the Li-ion battery pack, analyzing the SOC variation data, and using the previously mentioned MLA in the Python platform to estimate the SOC and its accuracy. It highlights the effectiveness of advanced MLAs in improving SOC estimation, thereby enhancing the performance and reliability of EV battery systems.

Reinforcement learning-based robust formation control for Multi-UAV systems with switching communication topologies

This paper introduces a novel optimal robust formation method for quadcopter multiple unmanned aerial vehicle (multi-UAV) systems. Firstly, a reinforcement learning (RL) algorithm based on a unique gradient descent training approach is proposed to solve the Hamilton–Jacobi–Bellman (HJB) equation, which can effectively eliminate the requirement of the Persistent Excitation (PE) condition. Secondly, the robustness of the controlled system is emphasized, and an Uncertainty and Disturbance Estimator (UDE) observer is developed to suppress model uncertainty and external disturbances through filtering techniques. Furthermore, a switched sliding mode control technique according to the average dwell time (ADT) is employed to convert switching communication topology between UAVs dynamically, and the stability analysis of the corresponding closed-loop control systems is then performed by the use of Lyapunov analysis. Finally, the simulation examples are provided to verify the effectiveness of the designed control strategy.

Finite-time formation trajectory tracking control for unmanned underwater vehicles with time delays and Markov-switch topology

ABSTRACT The formation trajectory tracking control strategy of unmanned underwater vehicles is proposed for communication time delay and communication topology random switching. The methods ensure that formation systems are finite-time bounded under discrete time. Firstly, based on the consistency theory and the exact feedback linearised dynamics model of unmanned underwater vehicle, a control strategy is proposed for discrete formation system with communication time delay. Secondly, a cooperative control method is designed for communication problems of Markovian switching topology and time delay. Then, definition of finite-time bounded and linear matrix inequality technique are applied to sufficient conditions of system stability for the above problem. Finally, the formation motion of discrete unmanned underwater vehicle system in three-dimensional space is simulated, fully demonstrating the feasibility of the method.

A Robust Factor Graph Optimization Method of GNSS/INS/ODO Integrated Navigation System for Autonomous Vehicle

Abstract The GNSS/INS/ODO integrated navigation system provides accurate positioning for autonomous vehicles. However, the observation quality of GNSS and ODO can decrease in complex environments and various driving conditions, leading to a decline in the accuracy and robustness of the integrated navigation system. An FGO-Robust method is proposed to resolve this challenge by fusing the INS/ODO-Slip factor and adaptive GNSS factor. The INS/ODO-Slip factor compensates for ODO measurement values by estimating the wheel slip ratio in real-time during optimization, thereby reducing ODO measurement errors' impact on factor graph optimization. Additionally, the adaptive GNSS factor minimizes the impact of GNSS outliers in the optimization process by designing a dynamic observation weight function. The vehicle experiments are conducted to evaluate the performance of the proposed method in different simulated conditions with ODO and GNSS measurement failures. The experimental results demonstrate that the FGO-Robust method can effectively improve the positioning accuracy of the integrated navigation system under various measurement failure conditions, with improvements of 83.4%, 66.1%, and 68.1% relative to FGO. Moreover, the FGO-Robust method improves the robustness of the integrated navigation system in complex environments and various driving conditions.

Development of a Vertical Submerging and Emerging Bat-Ray-Inspired Underwater Vehicle.

In this article, the development of a bat-ray-inspired underwater vehicle is presented; although the propulsion of the vehicle is based on traditional thrusters, the shape of the ray's fins was used as a model to design the body of the vehicle; this architecture allows the independent control of the forward velocity and the full attitude of the vehicle using only two thrusters and two articulated fins. The compact design of the robot, along with the high dexterity of the architecture, allows the vehicle to submerge and emerge vertically as well as navigate horizontally. The mathematical model of the proposed vehicle, including dynamics and propulsion system, is presented and validated using numerical simulations. Finally, experimental tests are presented to demonstrate the capabilities of the proposed design.

Fuzzy adaptive finite-time formation control of unmanned ground vehicles with performance and feasibility constraints

This paper investigates the problem of fuzzy adaptive finite-time formation tracking control for unmanned ground vehicles (UGVs) systems with two different constraints. By utilizing the distance tracking error between the actual trajectory and the desired trajectory of each UGV, as well as the constraints of bearing angle, to achieve the performance and feasibility constraints, respectively. Furthermore, the fuzzy logic systems (FLSs) are used to approximate unknown nonlinear functions. Due to the limitations of the field of view and communication distance of UGVs, the controller is designed by using finite time stability theory and universal barrier function. Under the proposed control scheme, the stability of the closed-loop system can be ensured, feasibility and performance constraints are also achieved. Finally, the effectiveness of the proposed control design is verified through simulation results.

Reflective Adversarial Attacks against Pedestrian Detection Systems for Vehicles at Night

Reflective Adversarial Attacks against Pedestrian Detection Systems for Vehicles at Night

Understanding the Actuation Point Stiffness of Electromechanical Drum Brakes

&lt;div&gt;Electromechanically actuated drum brakes are one interesting option for the realization of brake-by-wire systems for future electric vehicles. A key characteristic for the design and control of electromechanical brake actuators is the actuation point stiffness, as this quantity relates the actuation force to the required actuator position. The various known approaches for the control of electromechanical brakes, which primarily focus on disc foundation brakes, typically rely on the stiffness curve at least to some extent. A transfer of these approaches to drum brakes is not straightforward, because the actuation point stiffness for drum brakes is much more complex compared to disc brakes. In particular, a strong hysteretic behavior is observed for the standing drum and a considerable change of the stiffness and hysteresis can be observed for the rotating drum. Although drum brakes have been used for decades these effects have not been thoroughly discussed in literature, yet. Hence, this article proposes a minimal model, which gives a fundamental understanding of the stiffness characteristics of drum brakes. The relation to measured stiffness curves is discussed in detail to provide an in-depth understanding of the drum brake behavior. Additionally, prospect is given to a reduced complexity model that is suitable for online identification and control.&lt;/div&gt;

Distributed deadband event-triggered observer-based containment control for unmanned surface vehicles with channel fading

This article investigates the distributed deadband event-triggered containment control problem for multiple underactuated surface vehicles (USV) systems whose communication networks are subject to channel fading. The first distributed deadband event-triggered observer (DDETO) was proposed to observe the target positions of the followers in the convex hulls formed by the leaders. It also manages the internal communication between the followers in the presence of channel fading. In addition, the deadband event-triggered mechanism (DETM) in DDETO guarantees a strictly positive minimum inter-event times (MIET), which can be accurately calculated and adjusted according to the design parameters. Afterward, an adaptive controller is designed by backstepping based on the desired position information observed by DDETO. It is worth noting that the containment control scheme proposed in this paper is based on relative position measurement only, which reduces the amount of information transfer compared to transmitting position and velocity information when wireless signals are fading in the channel, improves the control accuracy, and also reduces the system’s requirements for sensors. Theoretical analysis leads to the conclusion that the error signals are uniformly ultimately bounded (UUB). Finally, the effectiveness of the control scheme is verified by numerical simulation.

Design and Experimental Validation of an Adaptive Multi-Layer Neural Network Observer-Based Fast Terminal Sliding Mode Control for Quadrotor System

This study considers the numerical design and practical implementation of a new multi-layer neural network observer-based control design technique for unmanned aerial vehicles systems. Initially, an adaptive multi-layer neural network-based Luenberger observer is designed for state estimation by employing a modified back-propagation algorithm. The proposed observer’s adaptive nature aids in mitigating the impact of noise, disturbance, and parameter variations, which are usually not considered by conventional observers. Based on the observed states, a nonlinear dynamic inversion-based fast terminal sliding mode controller is designed to attain the desired attitude and position tracking control. This is done by employing a two-loop control structure. Numerical simulations are conducted to demonstrate the effectiveness of the proposed scheme in the presence of disturbance, parameter uncertainty, and noise. The numerical results are compared with current approaches, demonstrating the superiority of the proposed method. In order to assess the practical effectiveness of the proposed method, hardware-in-loop simulations are conducted by utilizing a Pixhawk 6X flight controller that interfaces with the mission planner software. Finally, experiments are conducted on a real F450 quadrotor in a secured laboratory environment, demonstrating stability and good tracking performance of the proposed MLNN observer-based SMC control scheme.

Automotive Cybersecurity: A Survey on Frameworks, Standards, and Testing and Monitoring Technologies.

Modern vehicles are increasingly interconnected through various communication channels, which requires secure access for authorized users, the protection of driver assistance and autonomous driving system data, and the assurance of data integrity against misuse or manipulation. While these advancements offer numerous benefits, recent years have exposed many intrusion incidents, revealing vulnerabilities and weaknesses in current systems. To sustain and enhance the performance, quality, and reliability of vehicle systems, software engineers face significant challenges, including in diverse communication channels, software integration, complex testing, compatibility, core reusability, safety and reliability assurance, data privacy, and software security. Addressing cybersecurity risks presents a substantial challenge in finding practical solutions to these issues. This study aims to analyze the current state of research regarding automotive cybersecurity, with a particular focus on four main themes: frameworks and technologies, standards and regulations, monitoring and vulnerability management, and testing and validation. This paper highlights key findings, identifies existing research gaps, and proposes directions for future research that will be useful for both researchers and practitioners.

Air vehicle system health and asset management: modeling, simulation, and decision support

AbstractEffective Asset Management (AM) is essential to achieve operational excellence in the context of large complex Cyber-Physical Systems (CPSs). It involves coordinated activities across all life-cycle stages, including information and service exchange within and between adjacent air operations domains, for effective air operations and collaboration. For enterprises and military air operations, AM presuppose information and service exchange between the adjacent domains of air operations. Important aspects of aviation AM are that CPSs include Integrated Vehicle Health Management (IVHM), CPS models, and Digital Twin (DT), as central concepts used to predict and optimize asset performance. This article provides an overview of the aviation AM phenomenology, environment, and challenges, and how they may impact envisioned realisations of effective AM solutions for support to enterprise air vehicle operations. The article serves as an extension of the paper Cyber-physical Asset Management of Air Vehicle Systems presented at the International Congress and Workshop of Industrial AI and Maintenance (Candell et al., in Proceedings of the IAI2023–7th international congress and workshop on industrial AI and eMaintenance, Luleå, Sweden, 2023). A comprehensive approach is presented, comprising interdependent dimensions of the problem domain, and how they may be integrated into a framework and a platform concept addressing aviation AM needs.

Battery Thermal Management System in Electric Two-Wheelers

This work documents the design of a battery thermal management system for an electric vehicle in which a liquid cooling system was designed for a 24V Li-ion battery pack. Then, a thermal simulation was performed by using one-dimensional heat transfer modelling using Solidworks. This model also consists of the design of a heater, pump and evaporator to determine the total energy consumption of BTMS. The results indicate that the battery temperature is maintained between 20-30 °???? for various driving profiles with a maximum energy consumption of 0.3%.This work documents the design of a battery thermal management system for an electric vehicle in which a liquid cooling system was designed for a 24V Li-ion battery pack. Then, a thermal simulation was performed by using one-dimensional heat transfer modelling using Solidworks. This model also consists of the design of a heater, pump and evaporator to determine the total energy consumption of BTMS. The results indicate that the battery temperature is maintained between 20-30 °???? for various driving profiles with a maximum energy consumption of 0.3%.

Connected and Autonomous Vehicle Scheduling Problems: Some Models and Algorithms

In this paper, we consider some problems that arise in connected and autonomous vehicle (CAV) systems. Their simplified variants can be formulated as scheduling problems. Therefore, scheduling solution algorithms can be used as a part of solution algorithms for real-world problems. For four variants of such problems, mathematical models and solution algorithms are presented. In particular, three polynomial algorithms and a branch and bound algorithm are developed. These CAV scheduling problems are considered in the literature for the first time. More complicated NP-hard scheduling problems related to CAVs can be considered in the future.

Modeling and control system optimization for electrified vehicles: A data-driven approach

Learning-based intelligent energy management systems for plug-in hybrid electric vehicles (PHEVs) are crucial for achieving efficient energy utilization. However, their application faces system reliability challenges in the real world, which prevents widespread acceptance by original equipment manufacturers (OEMs). This paper begins by establishing a PHEV model based on physical and data-driven models, focusing on the high-fidelity training environment. It then proposes a real-vehicle application-oriented control framework, combining horizon-extended reinforcement learning (RL)-based energy management with the equivalent consumption minimization strategy (ECMS) to enhance practical applicability, and improves the flawed method of equivalent factor evaluation based on instantaneous driving cycle and powertrain states found in existing research. Finally, comprehensive simulation and hardware-in-the-loop validation are carried out which demonstrates the advantages of the proposed control framework in fuel economy over adaptive-ECMS and rule-based strategies. Compared to conventional RL architectures that directly control powertrain components, the proposed control method not only achieves similar optimality but also significantly enhances the disturbance resistance of the energy management system, providing an effective control framework for RL-based energy management strategies aimed at real-vehicle applications by OEMs.