Vehicle System Model Research Articles

Modeling of Li-ion Battery Management System for Unmanned Aerial Vehicles

Nowadays, systems that use more electricity in aircraft are increasing due to harmful gas emissions. This increase has created the need to store electrical energy and accelerated the trend towards battery technologies. Since energy storage in batteries occurs as a result of chemical reactions, problems may occur that will damage the battery group or the entire system. These problems are caused by high current, voltage, and temperature, which affect the reaction rate during battery charging/discharging. A Battery Management System (BMS) is needed to prevent problems and to use the required electrical energy safely. In this study, it is aimed to meet the energy needs of the system in a controlled manner by disabling only the damaged cell in case of problems that may occur in the cells in the battery. For this purpose, a model of an Unmanned Aerial Vehicle (UAV) system been created by adding a BMS block to each cell to control the battery cells. The BMS model is realized based on the cell temperature, the State of Charge (SoC) value of the cell, and the output voltage values of the cell. The UAV system is modeled using MATLAB/Simulink environment. Thanks to the proposed BMS, in case of a problem that may occur in any cell in the battery, that cell will be disabled and the required energy will be met through the remaining cells. It is observed from the results obtained that when the cell parameters become normal, it continues to feed the system again.

Carbody swaying suppression for a high-speed rail vehicle by utilising active lateral suspension control

This study proposes the use of active lateral secondary suspension (ALS) to address low-frequency carbody swaying in high-speed rail vehicles under low wheel-rail conicity conditions. Field experiments reveal lateral acceleration exceeding 0.10 g at a dominant frequency of 1.4 Hz, with a Sperling index exceeding 3.0 during carbody swaying occurs. By integrating field-measured wheel/rail profiles and yaw damper parameters into a multibody dynamics model of vehicle system, the coupling between bogie hunting and carbody yaw and upper-center roll modes is identified as the root cause of swaying. Evaluating classic sky-hook ALS controls, damping control proves highly effective. Doubling the damping coefficient and blow-off force becomes essential when only half of the four actuators per vehicle are active. Stiffness control adjusts carbody suspension modal characteristics to prevent resonances with the bogie hunting mode, effectively suppressing swaying. In contrast, inerter control exhibits limited efficacy. Semi-active control, with optimised gain settings, can also alleviate swaying, with control system delay identified as a critical factor, suggesting a 100 ms limit for effective swaying suppression.

Auto-disturbance rejection fault-tolerant control of quadrotor based on adaptive observer

To solve the problem of fault diagnosis and adjustment of the quadrotor actuator, a model of the quadrotor vehicle system is established by using the Newton-Euler method considering the unmodeled uncertainty of the system. A fault diagnosis observer with an adaptive threshold is designed to isolate the fault and estimate the fault amplitude. By using the compensation function to improve the extended state observer, an improved active disturbance rejection control system with high order feedback is constructed to improve the robustness and security of the system. Numerical simulation results show that the proposed method is feasible.

Estimation of road adhesion coefficient and sideslip angle for electric vehicles with slip-aware constraints using strong tracking unscented Kalman filter

Accurate estimation of road adhesion coefficient and sideslip angle are crucial for safer driving and ride quality of electric vehicles (EVs). To that end, an estimation algorithm of road adhesion coefficient and sideslip angle for EVs with slip-aware constraints and strong tracking unscented Kalman filter (SSTUKF) algorithm is designed in this paper. First, a vehicle system model is established as the basis for deriving the desirable estimation algorithm. Then, a strong tracking unscented Kalman filter (STUKF) algorithm with fading factor is presented to strengthen the correction effect of error covariance matrix used in unscented Kalman filter (UKF) algorithm. Meanwhile, slip-aware constraints are considered to calculate the adaptive compensated steering angle, which is obtained by repeated iteration of the fitness function. Additionally, a data fusion estimator is established based on the extremum theory to solve the problem of inaccurate estimation of sideslip angle under complex driving conditions. Finally, the CarSim-MATLAB/Simulink simulation platform is used to simulate under different driving conditions, and the simulation results reveal that the presented SSTUKF algorithm can enhance the estimation performance.

Graph Convolutional Network Surrogate Model for Mesh-Based Structure-Borne Noise Simulation

This study presents a unique method of building a surrogate model using a graph convolutional network (GCN) for mesh-based structure-borne noise analysis of a fluid–structure coupled system. Structure-borne noise generated from irregular shape panel vibration and sound pressure was measured in a closed-volume cavity coupled with the panel. The proposed network was trained to predict the sound pressure level with three steps. The first step is predicting the natural frequency of panels and cavities using the graph convolutional network, the second step is to predict the averaged vibration and acoustic response of the panel and cavity, respectively, in a given excitation condition using a triangular wave-type inference function based on the natural frequency predicted from the first step, and the third step is to predict the sound pressure in a cavity using a panel and cavity average response as an input to a 2D convolutional neural network (CNN). This method is an efficient way to build a surrogate model for predicting the response of a system which consisted of several sub-systems, like a full vehicle system model. We predicted the response of each sub-system and then combined this to obtain the response of the whole system. Using this method, an average 0.86 r-square value was achieved to predict the panel-induced structure-borne noise in a cavity from 10 to 500 Hz range in 1/12 octave band. This study is the first step towards creating a surrogate model of an engineering system with various sub-systems by changing it into a heterogeneous graph.

Developing an official program to calculate heavy-duty vehicles CO2 emissions in Korea

In 2020, Korean government introduced a CO2 monitoring regulation that heavy-duty vehicle (HDV) manufactures are required to submit CO2 calculation data using Heavy-duty vehicle Emission Simulator (HES). Based on HES program, the mandatory CO2 regulation for HDVs is expected to be implemented from 2026. This study addresses the development of HES program, which is a vehicle dynamics-based vehicle system model that estimates fuel consumption and CO2 emissions of HDVs. The CO2 prediction accuracy of HES was rigorously validated with 19 experimental data, and the average CO2 emission error between the experimental values and HES results was 4.6%. For comparative analysis between HES and European HDV simulation program VECTO, 21 simulations were performed using the same input data for both programs, and the average CO2 emission error is 1.59%. Using the HES program, we calculated CO2 emissions from HDVs from 2015 to 2020 in Korea.

Vehicle Control Strategy Evaluation Based on the Driving Stability Region

Vehicle stability control strategies can improve driving safety effectively; however, there is still a lack of unified evaluation criteria for different control strategies. This paper proposes a vehicle control strategy evaluation method based on the driving stability region and is analyzed by using direct yaw moment control (DYC) and four-wheel steering (4WS) as examples. Firstly, the five-degree-of-freedom (5DOF) vehicle system models including DYC and 4WS are established, and the effectiveness of the control strategies is verified by nonlinear analysis methods; the dynamic characteristics of the system are also analyzed. Following this, a hybrid algorithm combining the Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) methods is used to solve the system equilibrium points, and the driving stability regions under different control strategies are obtained. Finally, the driving stability regions are tested based on the CarSim and Simulink simulations, and the control performance is evaluated. The results indicate that DYC and 4WS can improve vehicle stability and expand the range of driving stability regions. When the initial longitudinal velocity is below 30 m/s, the driving stability regions under DYC and 4WS expand to different extents compared to the original driving stability region. The expanded driving stability regions show that the stability region of the vehicle with DYC is larger than that of 4WS; thus, the control effect of DYC is better than that of 4WS. The proposed method can be used to evaluate the effective range of different control strategies.

A Multi-Disciplinary Approach for the Electrical and Thermal Characterization of Battery Packs—Case Study for an Electric Race Car

A novel, multi-disciplinary approach is presented where experiments, system simulation and Computational Fluid Dynamics are combined for the electrical and thermal characterization of an air-cooled battery pack. As a case study, a Formula Student race car is considered and the procedure proposed consists of three steps: (1) experimental characterization of the battery cells under several thermal conditions; (2) thermal and electrical modeling of the battery stack with system simulation; (3) three-dimensional, time-dependent Conjugate Heat Transfer simulation of the whole battery pack to investigate the cooling performance of the chosen design, and to access fundamental quantities of the batteries, such as state of charge, temperature and ohmic heating. Future improvements of the current work are discussed, including the extension to a liquid-cooled design, battery aging consideration and model integration into a full vehicle system model.

A Prognosis Technique Based on Improved GWO-NMPC to Improve the Trajectory Tracking Control System Reliability of Unmanned Underwater Vehicles

The dynamics model of the unmanned underwater vehicle (UUV) system is highly nonlinear, multi-degree-of-freedom, strongly coupled, and time-varying. Its motion control has been a complex problem due to the unknown information about and the uncertainty of the working environment. To improve the performance and reliability of UUV trajectory tracking control, a trajectory tracking method based on nonlinear model predictive control is designed, and an improved gray wolf optimization (IGWO) is proposed for the optimization of nonlinear model predictive control. The convergence factor of IGWO is designed as a nonlinear attenuation function, and the memory function is added to the position update equation to enhance the effect of trajectory tracking control. Through the simulation in the ROS environment, the influence of the convergence factor on the convergence rate of trajectory tracking error and tracking control performance is obtained. By comparing the tracking effects of several groups of reference trajectories, it is shown that the proposed method is universally applicable and effective to the trajectory tracking control of UUV. Compared with traditional gray wolf optimization (GWO), SQP, and other optimization algorithms, the reliability of the proposed method for UUV trajectory tracking control is demonstrated.

Nonlinearly Parametrized Modeling and Adaptive Control for a Generic Hypersonic Vehicle

This paper constructs a nonlinearly parametrized hypersonic cruise vehicle model on the basis of the existing on-orbit flight data using a curve-fitting technique. The hypersonic cruise vehicle system is separated into two interconnected subsystem: an attitude subsystem with rotational dynamics and a velocity subsystem with engine dynamics. The continuous adaptive controllers are designed for two subsystems using a novel function bounding technique and appropriate coordinate transformations, respectively, which ensure the global boundedness of all signals and achieve the non-zero equilibrium point regulation of nonlinearly parametrized hypersonic vehicle systems. One of the implications of this result is that growing nonlinearities in the uncertain model of the hypersonic vehicle system may be allowed for global stabilization. A simulation result verifies the effectiveness of the proposed adaptive control scheme.

Robust Adaptive Autonomous Braking Control for Intelligent Electric Vehicles*

This paper presents a two-stage autonomous braking control for electric vehicles with preview information acquired by intelligent transportation systems (ITS) or connected and autonomous vehicle (CAV) technologies. The goal of the control method is firstly to plan a speed profile to comply with a target speed and a target residual distance to an upcoming deceleration event and then to track the planned speed profile by generating tracking-driven braking control torques, which maximally employ regenerative braking. Once deceleration circumstances are perceived, the intelligent reference speed planner computes a smooth speed profile by using a current vehicle speed, a target speed, and a target residual distance. For the braking control to robustly track the planned speed profile, a chattering-free sliding mode controller is designed on the interconnected vehicle and braking system model with braking system uncertainties and external disturbances, and a sufficient condition for the sliding mode gain is introduced. Also, adaptive update laws to compensate for the braking system uncertainties provide feed-forward control efforts for the tracking control inputs. To verify the effectiveness of the proposed control approach, the control methods are applied to a real commercial vehicle and the consecutive driving experiments with various deceleration circumstances illustrate a successful tracking-driven cooperative braking performance.

Modeling and adaptive control of air vehicles with partial nonlinear parametrization

This paper studies the control of a generic longitudinal air vehicle dynamic model during the cruise phase with nonlinearly parametrized uncertain aerodynamic functions. Verified by the improved coefficient precision of the aerodynamic forces and moments, using a curve fitting technique on available flight data, it is demonstrated that a nonlinearly parametrized model can more accurately capture the vehicle dynamics than a linearly parametrized model. For control design, the air vehicle model is decomposed into a flight-path angle subsystem with rotational dynamics and a speed subsystem with engine actuation dynamics. An adaptive control scheme is developed using the combination of a function bounding technique and a modified backstepping control method for two interconnected subsystems. The designed adaptive control laws ensure both global stabilization and adaptive non-zero equilibrium point regulation of the closed-loop control system. A thorough simulation study is presented to show the effectiveness of the developed adaptive control scheme for a nonlinearly parametrized air vehicle system model.

Research on Vehicle Active Steering Stability Control Based on Variable Time Domain Input and State Information Prediction

The controller design of vehicle systems depends on accurate reference index input. Considering information fusion and feature extraction based on existing data settings in the time domain, if reasonable input is selected for prediction to obtain accurate information of future state, it is of great significance for control decision-making, system response, and driver’s active intervention. In this paper, the nonlinear dynamic model of the four-wheel steering vehicle system was built, and the Long Short-Term Memory (LSTM) network architecture was established. On this basis, according to the real-time data under different working conditions, the information correction calculation of variable time-domain length was carried out to obtain the real-time state input length. At the same time, the historical state data of coupled road information was adopted to train the LSTM network offline, and the acquired real-time data state satisfying the accuracy was used as the LSTM network input to carry out online prediction of future confidence information. In order to solve the problem of mixed sensitivity of the system, a robust controller for vehicle active steering was designed with the sideslip angle of the centroid of 0, and the predicted results were used as reference inputs for corresponding numerical calculation verification. Finally, according to the calculated results, the robust controller with information prediction can realize the system stability control under coupling conditions on the premise of knowing the vehicle state information in advance, which provides an effective reference for controller response and driver active manipulation.

GP-based MPC with updating tube for safety control of unknown system

The Gaussian process model inferred from the Bayesian framework is a powerful data modeling method. It provides not only the predictive value but also the uncertainty measure for the predictive result. In this paper, we combine the online-updating GP models with Tube MPC to achieve safety control for the system of unknown dynamics, and especially use its capability of uncertainty quantification to assist in the safety guarantee under certain probability. Tightened constraints for safety used to determine the center of the Tube are computed according to the designed method based on the mean and variance predictive function of GP models. The constraints are updated at every control period based on the updated GP models. Meanwhile, a specific updating mechanism of the data set is adopted to accomplish effective updating. Finally, an example of a vehicle system model is used to verify the proposed method.

Control allocation technology based on fault diagnosis for the unmanned aerial vehicle system subject to physical constraints and fault reconfiguration mismatch

AbstractThe problem of the system robustness subject to physical constraints and mismatched fault reconstruction is discussed in this paper. In order to facilitate the design, a four‐rotor unmanned aerial vehicle (UAV) system model was selected for research. First, the control allocation model of the nonlinear UAV system with disturbances is shown in the paper. Secondly, a weighted pseudo‐inverse method based on adaptive weights is proposed, which reduces the impact of physical constraints on the system. After that, a dynamic weight control allocation method based on the fault efficiency matrix is designed. The weight matrix can dynamically adjust the control distribution law according to the fault estimation value provided by the observer. Then, a dynamic adaptive control allocation method for faults and physical constraints is carried out by combining adaptive weights and dynamic weights. Finally, a simulation example is presented to further illustrate the effectiveness of the algorithm proposed in this paper.

Research on Takagi-Sugeno Fuzzy-Model-Based Vehicle Stability Control for Autonomous Vehicles

Human–machine cooperative driving is an important stage in the development of autonomous driving technology. However, in emergencies, the problem of vehicle stability control for human–computer cooperative autonomous vehicles is still worthy of attention. This paper mainly realizes the stability control of the human–machine cooperative driving vehicle through active steering and considers the influence of the change of vehicle speed on the vehicle stability control performance. Therefore, a vehicle stability control method based on the superposition of steering torque is proposed, in which the Takagi-Sugeno fuzzy model is used to solve the variable parameter problem. Firstly, a vehicle system model with steering moment as input is established to ensure that the driver can participate in the steering control. Secondly, the nonlinear T-S fuzzy model is established by fuzzifying the local linear model. Then, the parallel-distributed-compensation (PDC) method is used to design the vehicle stability controller, and the asymptotic stability of the system in the range of variable parameters is proved by using the Lyapunov stability principle. Finally, the simulation and experimental results show that the control method can improve the handling stability of the human–machine cooperative driving vehicle under the condition of vehicle speed variation.

Operational safety assessment of straddle-type monorail vehicle system based on cloud model and improved CRITIC method

This paper proposes an improved critic method and establishes an operational safety assessment method for straddle-type monorail vehicle system based on cloud model and improved critic method. Firstly, according to the structure and function of straddle- type monorail vehicle system, the reliability index system of straddle-type monorail vehicle is established, and the weight value of reliability evaluation index is determined by improved critic method. Then, the cloud model expression of reliability evaluation index of straddle-type monorail vehicle system is obtained by virtual cloud. finally, similarity analysis is conducted between the new cloud of straddle-type monorail vehicle system reliability comprehensive index and the cloud model of reliability evaluation set, so as to judge that the comprehensive cloud model of straddle-type monorail vehicle system can be used to evaluate the operation status of “main functions” of the system. The analysis shows that the reliability evaluation method of straddle-type monorail vehicle system based on cloud model and improved critic method can correctly describe the daily operation of straddle-type monorail vehicle system in Chongqing, which provides a method reference for reliability evaluation of straddle-type monorail vehicle system.

Tire multiphysical modeling for the analysis of thermal and wear sensitivity on vehicle objective dynamics and racing performances

The handling behavior of a vehicle is one of its most important properties because of its relation to performance and safety and to its deep link with concepts such as “over-steer” or “under-steer”. Tire–road interaction models play a fundamental role in the vehicle system modeling, since tires are responsible for the generation of forces arising within contact patches, fundamental for both handling and ride/comfort. Among the models used to reproduce such forces, Pacejka’s Magic Formula (MF) is undoubtedly one of the most used ones in real-time automotive simulation environments because of its ability to fit quite easily a large amount of experimental data, but its original formulation did not take into account of the tire thermodynamics and wear conditions, which clearly affect tire and vehicle dynamics and are not negligible, especially for high level applications, such as motorsport competitions. Exploiting a multiphysical tire model, which consists in an evolved version of the standard MF model (MF-evo), and a vehicle model properly validated throughout experimental data acquired in outdoor testing sessions carried out with an industrial partner, the current work presents a study on vehicle behavior variation induced by thermodynamic and wear parameters, defining a series of metrics to analyze and show results. One of the elements of interest on which the focus is placed is the possibility to highlight how under-over-steering behavior of a car changes according to different thermodynamic states of tires; to do this, a commercial software VI CarRealTime has been used to perform a series of objective steady-state maneuvers and long runs, exploiting the logic of a lap time optimizer.

A Robust Sensor and Actuator Fault Tolerant Control Scheme for Nonlinear System

In this article, a new fault-tolerant control (FTC) method is presented for the Lipschitz nonlinear systems that is capable of handling the actuator faults, sensor faults, unknown external disturbances, and system uncertainties. An augmented system is first constructed by treating the sensor fault as an auxiliary state. An adaptive fault estimation scheme with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> performance criterion is then developed to simultaneously estimate the actuator and sensor faults. To achieve the tracking control, a nonlinear sliding mode-based state feedback control law is proposed depending on the estimated states and information about fault from the fault estimating unit. The efficacy of the suggested technique is evaluated using a nonlinear model of the multirotor unmanned aerial vehicle (UAV) system with six degrees-of-freedom (DoF) motion. The proposed method is implemented in the inner loop subsystem in order to obtain the attitude and altitude tracking while the outer-loop control is simply a PID controller. Several simulations on the nonlinear system are performed to prove the effectiveness of the proposed method compared with the existing methods.

Vibration Isolation of Railway Vehicle Car body using Semi-active Suspension

In the past, the magnetorheological (MR) suspension for railway vehicle has obtained great attention for the isolation of vibrations. This work presents a numerical approach to analyse skyhook and ground hook semi-active control methods for railway vehicle suspension. A 10 DoF model of the railway vehicle system is formulated for the comparative analysis between conventional passive and semi-active suspension control in the present study. The non-linear analysis is investigated in time and frequency domain for the sinusoidal excitations from the track.