Vehicle Parameters Research Articles

Capabilities of compressed air energy storage in the economic design of renewable off-grid system to supply electricity and heat costumers and smart charging-based electric vehicles

The study presents the sizing of a hybrid renewable islanded system that can feed both electrical and thermal energy simultaneously. The aforementioned energies are generated through the utilization of wind systems and bio-waste units. The integration of combined heat and power technology in the bio-waste unit enables the concurrent generation of electricity and heat power. Electric vehicles consume electric energy, but function based on a smart charging. The study employs compressed air energy storage as a means to bridge the disparity between the patterns of electric power generation and consumption, with the aim of enhancing energy efficiency and reducing planning expenses. Thermal energy storage serves as an intermediary between renewable power and load profiles within the thermal sector. Objective here is find the minimum annual capital and maintenance costs of sources, storages, and power electronic converters. Problem is constrained to operational model of renewable sources, storages, and electric vehicles. The present model prioritizes the utilization of renewable sources for the purpose of supplying electrical and thermal loads. Electric vehicles regulate their active power to minimize the disparity between load and electric production profiles. Storages perform charging and discharging functions to bridge the disparity between power generation and consumption. Strategy incorporates uncertainty related to load, wind speed, bio-waste-derived gas production, and electric vehicles parameters. The study employs a point estimate method to attain a dependable solution. The numerical results indicate that incorporation of compressed air storage in the hybrid system results in a decrease of 7.7 % (12.9 %) in the planning costs relative to system with batteries (hydrogen storage). The proposed design's concurrent provision of electrical and thermal energy leads to an approximately 57 % increasing in planning costs when compared to a system that solely provides electrical energy. The implementation of a smart charging strategy for electric vehicles drop the hybrid system's overall scheduling cost by 3.9 %.

A novel re-entry trajectory design strategy enforcing inequality and terminal constraints in height-velocity plane

A novel re-entry trajectory design approach for a winged reusable launch vehicle that satisfies path and terminal state constraints in the height velocity plane is proposed in this paper. A two-phase re-entry trajectory is proposed. A height-velocity plane entry corridor is designed using all path and quasi-equilibrium glide constraints. The proposed method creates the reference trajectory inside the re-entry corridor using three piecewise polynomials, such as linear and logarithmic. A two-parameter search optimization problem minimizes terminal range-to-go error for this reference trajectory generation problem. Improved Search Space Reduction solves this optimization problem. The Improved Search Space Reduction algorithm is compared to the Grey Wolf Optimizer and Particle Swarm Optimisation algorithms to prove its efficacy and superiority. The reference bank angle is modified using feedback linearization and bank reversal logic to create an entry trajectory that meets all path and terminal constraints. The effectiveness of the proposed method has been validated through Monte Carlo simulations that incorporate uncertainties such as variations in initial state, uncertainties in aerodynamics, and atmospheric perturbations. The controller OPAL-RT OP4510 provides bank angle command at every instant based on the current state of the vehicle parameters associated with real-time simulator OP4510. The real-time hardware-in-loop simulation results show that the obtained re-entry trajectory satisfies all path and terminal constraints.

Comprehensive Study of Fuel Cell Hybrid Electric Vehicles: Classification, Topologies, and Control System Comparisons

The utilization of fuel cells (FC) in automotive technology has experienced significant growth in recent years. Fuel cell hybrid electric vehicles (FCHEVs) are powered by a combination of fuel cells, batteries, and/or ultracapacitors (UCs). By integrating power converters with these power sources, the FCHEV system can overcome the limitations of using them separately. The performance of an FCHEV is influenced by the efficiency of the power electronics converter controller, as well as the technical efficiency of the power sources. FCHEVs need intricate energy management systems (EMSs) to function effectively. Poor EMS can lead to low efficiency and accelerated fuel cell and battery degradation. The literature discusses various types of EMSs such as equivalent consumption minimization strategy, classical PI controller, fuzzy logic controller, and mutative fuzzy logic controller (MFLC). It also discusses a systematic categorization of FCHEV topologies and delves into the unique characteristics of these topologies. Furthermore, it provides an in-depth comparative study of EMSs applied in FCHEVs, encompassing rule-based, optimization-based, and advanced learning-based approaches. However, comparing different EMSs can be challenging due to the varying vehicle and system parameters, which might lead to false claims being made regarding system performance. This review aims to categorize and discuss the various topologies of FCHEVs, highlighting their pros and cons, and comparing several EMSs based on performance metrics such as state of charge (SOC) and FC deterioration. This paper seeks a deeper comprehension of the recent advancements in EMSs for FCHEVs. It offers insights that can facilitate a more comprehensive grasp of the current state of research in this field, aiding researchers in staying up to date with the latest developments.

Research on the crossing obstacle ability of wheeled vehicle on soft beach ground

Given the crossing-obstacle problems of wheeled vehicle on soft beaches, the changing rules with driving speeds of the crossing-obstacle ability of wheeled vehicle, soil moisture content, and soil type on soft beach ground were innovatively studied in this paper, with the hook traction force taken as the evaluating index. It showed that the RMS values of wheeled vehicle hook traction force, which was 9484N at 5 km/h and 8859N at 20 km/h, decreased with the increase in driving speed when the wheeled vehicle crossed the horizontal ditch. It collected soft beach ground soil samples and tested the parameters of mechanical properties. By considering specific structural parameters of the wheeled vehicle, this paper constructed the simulating model of the crossing obstacle ability of wheeled vehicle on the soft beach, analyzed the differences between the simulation and test result through full-scale vehicle tests. It discussed the influential factors of the crossing obstacle ability of wheeled vehicle on soft beach ground, indicated that the crossing obstacle ability of wheeled vehicle on soft beach increased with the increase of equipment’s driving speed and soil moisture content. This paper creatively proposed a method to evaluate the equipment’s off-road trafficability based on the combination of soil in-situ collection, soil tank test, dynamic simulation and experimental verification, and proposed the influencing factors and changing rules of equipment’s off-road trafficability on soft beach ground, which laid a theoretical foundation for the research on the off-road trafficability of wheeled vehicle.

Design of a robust controller for the VEGA TVC using the L1-Adaptive Control

The purpose of this study is to Design of a robust controller for the VEGA TVC using the L1-Adaptive Control. The performance of the system with an L1 adaptive controller has been compared with that of a classical controller. The L1 adaptive controller optimization is an efficient as well as an effective approach for the design of a robust controller. The L1 adaptive controller ensures the robustness of the system against uncertainties, noise, and disturbances[10]. In a launch vehicle parameters like mass, thrust, and aerodynamic properties are time-varying. Due to the time-varying nature of these parameters gain scheduling is necessary with the classical control design methods otherwise the closed-loop system might become unstable for some values of these parameters. This procedure is costly and has no robustness. In addition to this, there are external disturbances also which can make the control system unstable. This is the reason why robust control is necessary. There is a trade-off between achievable performance and robustness. So, we need to compromise between required performance and robustness as per the requirement.

A secure multifactor-based clustering scheme for internet of vehicles

The development of the Internet of Vehicles (IoV) has been made possible through a variety of communication technologies and advanced AI techniques. In this context, ensuring stable and efficient communication for IoV is extremely important. It addresses several challenges related to security issues, high dynamism, constant connection outages, and the expected high traffic density. To overcome these challenges, vehicle clustering is a viable strategy for a reliable communication environment. The majority of current research focuses on solving the problem of cluster stability and efficiency by utilizing one or multiple factors, particularly vehicle location, mobility, and behavior. This article introduces an efficient Multifactor Clustering Scheme for IoV (MFCS-IoV). MFCS-IoV includes two stages: cluster formation and cluster head selection. The cluster formation is based on the improved K-means algorithm, considering both the vehicle mobility and final destination within the driving zone. While, a weighted cluster fitness function that includes mobility, behavior, dynamic location, and security is used to optimally select the Cluster Head (CH). Blockchain technology has been integrated into the model to safeguard the privacy of information like destination and other vehicle parameters. Simulation results demonstrate the success of MFCS-IoV in partitioning the vehicles into stable clusters and selecting the optimal cluster heads based on the proposed parameters. The effectiveness of MFCS-IoV is demonstrated by simulating different scenarios of 50 to 300 vehicles in the driving area. A comparison with related works shows that MFCS-IoV outperforms other schemes regarding average node-to-node delay, packet delivery rate, and throughput. Additionally, the proposed MFCS-IoV increases communication reliability by providing stable clusters while maintaining security measures.

A new model of friction brake efficiency of urban vehicles on unpaved roads using an analytical and numerical approach

• The approach estimates the efficiency of disc braking systems of urban vehicles. • The proposed approach takes into account the irregularities of the road surface. • In addition to the surface irregularities, the slip rate is taken into consideration. • The effect of wheel deformation during braking is considered. • The influence of the pressure on the brake disc is considered. This paper supports the implementation of a braking efficiency estimator of urban vehicle on rural roads, based on analytical and numerical approach. A statistical study is defined by the Association Road Managers and Partners (AGEPAR) estimates the rate of unpaved roads in Africa around 72%, which approximately 90% of passengers and goods transport that generally doesn’t respect loading standards. This is a major reason for increase traffic accidents in these countries. Hence the need to define vehicle speed limitations that takes in vehicle parameters and irregularities road. This can be done by defining a predicting model of dynamic behaviour for small vehicles, which constitute the majority of African vehicle fleet. To achieve this, we numerically solve the longitudinal dynamic equations of a vehicle body combine with front and rear axles on a road which has irregularities, opposed to existing models and Field data that are defined on surface roads which have little or no irregularities. The input parameters of the model are the initial vehicle speed, the road profile, inclination angle of road, peak wheel cylinder, wheelbase, centre of gravity height, rising time, brake pads contact area, final drive gear ratio, gearbox gear ratio, moment of inertia of engine, slip ratio, vehicle weight, moment of inertia at on axle and dynamic tyre radius which are assumed similarly and the braking system which is a disc braking system. The suspension is assumed identical and rigid since our study focuses on longitudinal and not vertical dynamics. The results produced by our modelling on paved and unpaved roads are compared with those obtained experimentally by J. R. Choa and the unpaved road data we obtained. The relative differences in braking times are respectively 6.5% and 3.1%. For the stopping distance, these relative differences are 11.5% and 11.9%.

Dual-Pulse Repeated Frequency Waveform Design of Time-Division Integrated Sensing and Communication Based on a 5G New Radio Communication System

With the development of 5G communication systems, it is a hot topic to embed integrated sensing and communication (ISAC) based on the existing 5G base station by sharing the hardware and the same frequency spectrum. In this paper, we propose a dual pulse repeated frequency (dual-PRF) waveform design of time-division ISAC (TD-ISAC) based on a 5G new radio (NR) communication system using downlink communication slots. We choose time-division mode to design waveform to avoid the interference between sensing and communication. Embedding sensing functions in a 5G NR system, we design a dual-PRF sensing slot to satisfy the constraints of common channel and uplink communication. Considering two uplink modes, namely flexible and fixed, we design two dual-PRF waveforms and illustrate the sensing theory performance of the designed waveform by the ambiguity function. Then, we exploit the designed waveform to the vehicle parameter estimation. To verify that the designed waveform has good adaptability to different signal processing methods, we realize the parameter estimation by two types of methods: the discrete Fourier transformation-based method and the compressed sensing-based method. At last, we verify the effectiveness of the designed waveform system by simulation experiments and real traffic scenarios.

Method for the Conceptual Design of a New Mode of Urban Delivery Vehicle

The transportation industry is undergoing a radical transformation driven by global trends and technological developments. Its main challenges include meeting the increasing demand for urban mobility and delivery services, while simultaneously addressing issues such as the requirement to reduce traffic congestion, limited parking space, and environmental pollution. This paper proposes a method for the conceptual design of innovative urban delivery vehicles meeting these challenges. The methodology is demonstrated in the proposed vehicle concept: a narrow-track leaning vehicle with the road signature of a motorscooter and the cargo capacity of a small four-wheeled vehicle aimed for low to high range and speed -mile delivery. The study presents a comprehensive methodology to optimize the vehicle parameters based on total annual costs of delivery and energy consumption. The result parameters are later used to demonstrate the benefits from a range in these vehicles, in terms of efficiency and cost-effectiveness, compared to traditional four-wheeled delivery vehicles. It compares the performance of the proposed vehicle with a four-wheeled vehicle in delivery operations, in both midmile city use and in megacity scenarios. The findings underscore that the proposed vehicle has lower total annual costs of delivery, lower energy consumption, and higher delivery efficiency than the traditional vehicle. This paper concludes that the methodology guided the design to present a narrow-track leaning vehicle concept with high potential for reducing financial costs, mitigating negative environmental impact, and improving urban mobility.

Recognition and interpretation of aggressive driving behavior for heavy‐duty vehicles based on artificial neural network and SHAP

AbstractAggressive driving significantly impacts traffic safety, and heavy‐duty vehicle drivers are more liable for causing serious crashes. This paper analyzes drivers' aggressive driving behavior from the vehicle type perspective and identifies the influencing factors of aggressive driving behavior through artificial neural network (ANN) and Shapley additive explanations (SHAPs). Using Kaggle's open‐source aggressive driving data, we establish an ANN model to identify driving styles, where road conditions, environmental conditions, and vehicle parameters are independent variables and driving style is a dependent variable. The following measurements, including accuracy, recall, precision, and F1 score, are used to evaluate the model's performance, and the neural network got 85.33%, 82.32%, 84.16%, and 0.8308, respectively. To illustrate the influence of independent variables, the SHAP algorithm is used to analyze the model's feature importance. It was found that illumination and weather conditions influenced the model's performance along with the vehicle length. The number of lanes relates to driving style, and there were more aggressive driving behaviors on two‐lane roads than on single‐lane roads. Besides, heavy‐duty vehicle drivers were more likely to drive aggressively in wet road conditions and indulge in aggressive driving behaviors at night. Particularly, drivers of heavy‐duty vehicles were more likely to drive aggressively, provided that the vehicle in front was also a heavy‐duty vehicle. These findings inform heavy‐duty vehicle drivers to reduce aggressive driving behavior. The information is suitable for inclusion in driver education programs, thus improving traffic safety.

Identification of Dynamic Vibration Parameters of Partial Interaction Composite Beam Bridges Using Moving Vehicle

The vibration response of a partial composite beam bridge under the influence of moving vehicular loads was investigated. Due to the coupling effect between the vehicle and the bridge, the vibration information of the vehicle encompassed the vibration information of the bridge. Consequently, the dynamic response of the vehicle could be utilized to extract the dynamic information of the composite beam. A moving mass-spring-damping system and composite beam elements considering interfacial slips were used for the interaction vibration of a vehicle-composite bridge. A finite element program for the interaction vibration analysis of the vehicle-composite beam bridge was developed. The program was used to extract the vibration information of the composite beam bridge by analyzing the vehicle displacement, velocity, and acceleration in the interaction vibration of the beam and the vehicle. Taking the Hangzhou Jiubao Bridge as the engineering background, the influences of structural parameters such as shear stiffness of connections, prestress magnitude, as well as vehicle parameters, including vehicle stiffness, damping, and mass, on frequency identification were analyzed. Furthermore, the influences of road roughness, disturbance force generated by vehicle random vibrations, and interference signals generated by signal transmission on frequency identification of the bridge were investigated.

Weigh-in-Motion Site for Type Approval of Vehicle Mass Enforcement Systems in Poland.

The need to protect road infrastructure makes it necessary to direct the mass enforcement control of motor vehicles. Such control, in order to fulfil its role, must be continuous and universal. The only tool currently known to achieve these goals are weigh-in-motion (WIM) systems. The implementation of mass enforcement WIM systems is possible only if the requirements for their metrological properties are formulated, followed by the implementation of administrative procedures for the type approval of WIM systems, rules for their metrological examination, and administrative regulations for their practical use. The AGH University of Krakow, in cooperation with the Central Office of Measures (Polish National Metrological Institute), has been conducting research in this direction for many years, and, now, as part of a research project financed by the Ministry of Education and Science. In this paper, we describe a unique WIM system located in the south of Poland and the results of over two years of our research. These studies are intended to lead to the formulation of requirements for metrological legalisation procedures for this type of system. Our efforts are focused on implementing WIM systems in Poland for direct mass enforcement. The tests carried out confirmed that the constructed system is fully functional. Its equipment with quartz and bending plate load sensors allows for the comparison of both technologies and the measurement of many parameters of the weighed vehicle and environmental parameters affecting weighing accuracy. The tests confirmed the stability of its metrological parameters. The GVW maximal measurement error does not exceed 5%, and the single axle load maximal measurement error does not exceed 12%. The sensors of the environmental parameters allow for the search for correlations between weighing accuracy and the intensity of these parameters.

Vehicle state and parameter estimation based on adaptive anti-outlier unscented Kalman filter and GA-BPNN method

A multi-machine-learning improved adaptive Kalman filtering method is proposed to address the problem of handling abnormal data encountered in the vehicle state estimation. Firstly, the unscented Kalman filter (UKF) algorithm is improved by introducing a BP neural network improved by the genetic algorithm (GA-BPNN) to regulate and correct the global error of the UKF method. Then, the anti-outlier technique is applied to fully eliminate isolated and speckled outliers in the measurement, achieving further improvement on GA-BPNN-UKF and significantly improving the robustness of the filtering process. Finally, a simulation is applied to verify the effectiveness of the proposed new algorithm, and then its results are analyzed to obtain a firm substantiation of its effectiveness for further practical applications. The simulation results indicate that the estimation performance of the GA-BPNN algorithm is significantly better than that of Extended Kalman filter (EKF) method.

Approach and landing guidance using constrained model predictive static programming

The paper proposes a guidance scheme during the approach and landing phase of an unpowered vehicle to achieve high precision at touchdown by limiting the control inputs within allowable bounds using constrained Model Predictive Static Programming. The advantages of a single flight phase are incorporated into the design since the guess control history is obtained from a single segment based guidance approach. The requirement of ensuring continuity in states and guidance commands are hence avoided leading to minimization of control deviations using a simple cost function. Constrained input commands are used for determining control surface deflection based on dynamic inversion considering the longitudinal pitch rate dynamics. Comparison is carried out between the results obtained for constrained as well as unconstrained schemes for the nominal trajectory. Comparisons of load factor, dynamic pressure and flight path angle for three segment, two segment, single segment guidance strategies have been carried out with MPSP based guidance to evaluate the relative advantages offered by the scheme. Performance analysis of the proposed scheme is done based on numerous simulations with simultaneous variations in initial states, vehicle parameters and aerodynamic parameter variations. Simulation results indicate that the proposed scheme is suitable for landing on runways of shorter lengths due to minimal touchdown errors.

Research on precise route control of unmanned aerial vehicles based on physical simulation systems

In order to understand the precise route control system of unmanned aerial vehicles, the author proposes a research on precise route control of unmanned aerial vehicles based on wireless sensor networks and physical simulation. The author first introduces the definitions of various coordinate systems and related motion parameters of unmanned aerial vehicles. On this basis, the nonlinear dynamic equations and kinematic equations of the unmanned aerial vehicle were obtained through force analysis. Secondly, based on wireless sensor networks, a route control system for unmanned aerial vehicles was designed. There may be deviations between the actual route and the planned route of drones during flight, which not only reduces the quality of operations but also affects operational efficiency. Wireless sensor network is a technology closely integrated with unmanned aerial vehicles, which can be used to control the route of unmanned aerial vehicles. The system consists of four parts: unmanned aerial vehicle platform, sensor nodes, convergence nodes, and control center. The control of the route is completed through a two-dimensional coordinate system following algorithm. Finally, the drone will undergo flight tests before and after the installation of this route control system. The drone before the installation of this system will use traditional route control methods. There are two types of planned routes: straight and curved, with the curve being a circular shape with a radius of 5 km; Record the deviation value between the actual route and the planned route every 150 s during the flight, and compare the control accuracy of the two methods on the route. The experimental results show that the wireless sensor network is more stable in tracking straight and curved routes, and has high accuracy in route control.

Research on new energy vehicle charging prediction based on Monte Carlo algorithm and its impact on distribution network

With the vigorous promotion of new energy policies, the large-scale charging of new energy vehicles has put forward higher requirements for the safety and stability of the distribution network. Based on the daily driving habits and charging patterns of new energy vehicles, a Monte Carlo sampling algorithm was used to establish a charging load model for new energy vehicles. The model analyzed the driving range, charging load, and time related parameters of new energy vehicles. By analyzing the law of daily charging power of new energy vehicles, the overall trend of charging load of new energy vehicles is obtained. Combined with the daily electricity consumption law of the distribution network, the total load of the distribution network is obtained, and the degree of impact on the distribution network is analyzed. This provides direction for the scheduling of future electric vehicle charging behavior and the construction of related supporting facilities, and provides strong guidance for the optimization and upgrading of the distribution network.

Estimating the effectiveness of electric vehicles braking when determining the circumstances of a traffic accident

In the vast majority of cases, the braking process is used to prevent traffic accidents. The effectiveness of this process depends on the design and functionality of vehicle braking systems (presence of anti-lock braking system, emergency braking system, preventive safety systems, etc.) and is limited by the amount of frictional forces in contact of tires with the road. The improvement of methodical approaches to evaluating the effectiveness of braking of cars contributes to increasing the accuracy and objectivity of establishing the circumstances of the occurrence of emergency situations. The paper analyses existing methods of evaluating the braking parameters of vehicles (including those with an electric drive) and modern methods of evaluating electric vehicle braking parameters and conducting auto-technical investigations of traffic accidents, which relate to using different methodological approaches and digital technologies at all stages of expert research. In contrast to existing models, the proposed mathematical model for estimating the trajectory of two-axle cars during braking allows for considering various types of input parameter uncertainty, reducing the range of possible modeling errors by 39%. Comparing simulation results and experimental data showed that the average relative error is 4.58%, and the maximum error did not exceed 7.82%. The performed study of the stability of the electric vehicles' movement during emergency braking with the help of developed mathematical models in the Mathcad software environment reveals the content of the algorithm of a similar calculation in specialized computer programs of auto technical examination. Conducting such calculations is relevant in the analysis of real accident situations, where specific circumstances and features that cannot be considered during modeling in specialized software must be taken into account. Simultaneously, the probability of type I errors is reduced by 2–19%, and type II errors are reduced by 43–68%.

Vehicle-induced aerodynamic lateral loads on cyclists in overtaking maneuvers - Field measurements and universal model description

Vehicle-induced lateral aerodynamic loads on cyclists during overtaking were measured in a field campaign. The study comprised five common vehicle types in combination with four common cyclist types at overtaking maneuvers with vehicle velocities from 30 to 100 km/h and overtaking distances from 0.5 to 2.0 m. The obtained load time histories show distinct phases with the lateral load acting to push the cyclist away followed by a phase with the lateral load acting to pull the cyclist towards the vehicle. Key characteristics of these phases are a peak pressure and a peak suction load and their separation in time. Mathematical relations for the key characteristics based on vehicle velocity, overtaking distance, and on readily available vehicle and cyclist basic geometry parameters are presented. Finally, the so called peak-to-peak load rate, a measure which combines the magnitude of the load direction change from the peak pressure to the peak suction load and its celerity is introduced. It is argued that the peak-to-peak load rate can serve as a quantitative measure to assess cyclist safety or at least be supportive in this regard.

Analysis of torque controlling strategies of interior permanent magnet synchronous machine in hybrid electric vehicle

Significance of this research is based on torque controlling strategies in hybrid electric vehicle. The traction motor of a hybrid electric commercial vehicle must have a high power density-to-weight ratio and excellent torque at extremely low speeds. A proportional-integral controller estimates the high-resolution rotor position based on the difference between actual as well as reference output power. This technique can correct for hall position sensor installation error and also accurately estimate rotor position. In this study, a current proportional integral controller is also used to determine the permanent magnetic flux linkage. Experiments are used to test other key metrics such as d-axis and q-axis inductances, stator resistance, and energy loss. The measured parameters are kept in lookup tables that span the whole operating range at various current levels. A maximum torque per ampere control methodology, paired with the feed forward parameter iteration method, may be used to produce accurate and efficient torque control based on these precise parameters. The standard driving cycle performance requirements of the modelled Federal Test Procedure vehicle is analysed in the article for understanding the real model requirements of the vehicle. The efficacy of the proposed methodology using Matlab/Simulink environment is analysed. Both modelling and experimental findings validate the efficacy, torque ripple, stator and rotor flux, sensitivity of the vehicle parameter as well as the computational complexity of the proposed methodology.

Real-time detection of wheel-rail conditions in a monorail vehicle-track nonlinear system based on vehicle vibration signals

This paper aims to propose a real-time detection method of wheel-rail conditions in a monorail vehicle-track nonlinear system based on vehicle vibration signals. Monorail rubber tires exhibit strong nonlinearity and three-dimensional elasticity, resulting in highly coupled vertical and lateral vibrations. As a result, traditional methods applied in railway system such as transfer function analysis are not suitable for back-calculation of road irregularities for this nonlinear system. Iterative simulation with numerous parameters is time-consuming, thus this paper proposes a parallel simulation approach fused with the genetic algorithm to shorten the calculation time and facilitate big data processing. The multi-rigid body model’s simulation result can closely match the test data by intelligently modifying the vehicle parameters. This method overcomes the transfer function’s limitations in nonlinear systems and the significant errors introduced by the simplified mathematical derivation method. It also overcomes the shortcoming of significant errors in the mathematical derivation method and disperses errors caused by simplifying the multi-rigid body dynamics and ensures calculation accuracy. Additionally, this paper highlights the application of artificial intelligence techniques in intelligent wheel eccentricity detection. It improves the traditional algorithm to shorten the calculation time and benefit big data processing.