Vehicle Acceleration Research Articles

Enhancing ride comfort of semi-active suspension through collaboration control using dung beetle optimizer optimized Fuzzy PID controller

The enhancement of automobile quality is a significant area of modern research and development, with a focus on improving ride comfort and driving experience. This is achieved not only through structural improvements but also through the optimization of suspension control strategies. To enhance the ability of online real-time adjustment and overcome the limitations of fuzzy control, this study combines dung beetle optimizer (DBO), Fuzzy control, and Proportional-Integral-Derivative (PID) control to propose a reduced optimization complexity control method to improve MR damper control. The control parameters generated by fuzzy control are utilized as the initial values for DBO iteration to obtain the optimal solution, which is subsequently fed into the PID controller to regulate changes in the actuator’s magnetic field. Use MATLAB/Simulink to build a quarter semi-active suspension model with magnetorheological (MR) dampers for simulation analysis. Under sinusoidal and random disturbances, compared with the passive suspension, the vehicle acceleration of the Fuzzy-DBO-PID controller is reduced by 51%, 50.82%, 47.56% and 11.42%. Compared with Fuzzy-PID, the vehicle acceleration of Fuzzy-DBO-PID is reduced by 25.98%, 32.86%, 29.41%, and 7.19% on sinusoidal and random disturbances, which improves the ride comfort and verifies the effectiveness of the proposed control strategy.

Simple Energy Model for Hydrogen Fuel Cell Vehicles: Model Development and Testing

Hydrogen fuel cell vehicles (HFCVs) are a promising technology for reducing vehicle emissions and improving energy efficiency. Due to the ongoing evolution of this technology, there is limited comprehensive research and documentation regarding the energy modeling of HFCVs. To address this gap, the paper develops a simple HFCV energy consumption model using new fuel cell efficiency estimation methods. Our HFCV energy model leverages real-time vehicle speed, acceleration, and roadway grade data to determine instantaneous power exertion for the computation of hydrogen fuel consumption, battery energy usage, and overall energy consumption. The results suggest that the model’s forecasts align well with real-world data, demonstrating average error rates of 0.0% and −0.1% for fuel cell energy and total energy consumption across all four cycles. However, it is observed that the error rate for the UDDS drive cycle can be as high as 13.1%. Moreover, the study confirms the reliability of the proposed model through validation with independent data. The findings indicate that the model precisely predicts energy consumption, with an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation compared to empirical data. Furthermore, the model is compared to FASTSim, which was developed by the National Renewable Energy Laboratory (NREL), and the difference between the two models is found to be around 2.5%. Additionally, instantaneous battery state of charge (SOC) predictions from the model closely match observed instantaneous SOC measurements, highlighting the model’s effectiveness in estimating real-time changes in the battery SOC. The study investigates the energy impact of various intersection controls to assess the applicability of the proposed energy model. The proposed HFCV energy model offers a practical, versatile alternative, leveraging simplicity without compromising accuracy. Its simplified structure reduces computational requirements, making it ideal for real-time applications, smartphone apps, in-vehicle systems, and transportation simulation tools, while maintaining accuracy and addressing limitations of more complex models.

BiLSTM-AM: A deep learning model for indirect identification of track irregularity using in-service vehicle acceleration responses

BiLSTM-AM: A deep learning model for indirect identification of track irregularity using in-service vehicle acceleration responses

Determination and description of a vehicle's maximum jerk capacity

Both Transient Optimal and Quasi-Steady-State vehicle modelling are frequently used for minimum time manoeuvre and minimum lap-time simulations for motorsport/vehicle development purposes. Quasi-Steady-State methods have been shown to perform such tasks with low computational cost but produce results with measurable deviation from equivalent Transient Optimal studies. It is hypothesised that bounding point-mass models with jerk limits will improve alignment with Transient Optimal simulations through the consideration of transient behaviours such as control application rates and inertia. This work presents a new method for the determination of a vehicle's maximum jerk capacity from a seven degree-of-freedom vehicle model. A range of results from this proposed technique are presented and show the sensitivity of jerk capacity to changes in control application rate limits and yaw inertia, as well as current vehicle velocity, acceleration, and jerk. It is proposed that further exploration of the validity of the jerk limit calculation method take place through characterisation of said limits and application to a point-mass model, after which comparison to Transient Optimal and Quasi-Steady-State equivalents can occur.

Energy-based approach to the assessment of traffic flow

This article focuses on modeling vehicle acceleration noise in different road conditions, emphasizing urban, highway, and rural roads in Ukraine. Acceleration noise, which refers to the fluctuations in a vehicle's acceleration, is a critical factor in vehicle safety, fuel efficiency, and driving comfort. The research aims to improve current vehicle dynamics models by integrating multi-body dynamics and machine learning algorithms, allowing for more precise predictions of acceleration variability in real-time. The study is based on the existing literature, showing that road surface quality significantly affects acceleration noise. With frequent stop-and-go traffic, urban roads produce moderate but irregular noise patterns. Highways show stable acceleration noise at moderate speeds, but noise increases sharply as vehicles approach higher speeds due to aerodynamic forces. Rural roads, especially those in poor condition, exhibit the highest variability in acceleration noise, even at low speeds. The proposed model has been validated using real-world data. It demonstrates a strong correlation between the predictions and actual vehicle behavior on various road types. One of the key innovations in this research is the use of machine learning to adjust model parameters in real-time dynamically. This adaptive approach enhances the model’s accuracy and applicability, especially in intelligent transport systems. The model can inform traffic management strategies, allowing for real-time adjustments to speed limits, traffic signals, and routing decisions based on road conditions. This contributes to safer, more efficient, and sustainable transport systems, particularly in regions with inconsistent road infrastructure. The research concludes that integrating acceleration noise modeling into intelligent transport systems can significantly improve traffic flow and vehicle safety. Future research will expand the dataset to include a broader range of vehicle types and road conditions, further refining the model's predictive capabilities.

Vehicle Mass Estimation via Practical Supervisory Artificial Neural Networks Using Perturbed Engine Torque and Acceleration Inputs

Various model-based mass estimation approaches have been discussed for a long time. However, estimation performance often deteriorates in some driving situations and, in particular, slow convergence and excessive overshoot of estimates are a major issue for model-based approaches. Meanwhile, mass estimation approaches using ANN models have recently emerged to propose better solutions, but their usefulness has not been fully investigated. Therefore, this paper presents a vehicle mass estimation strategy using practical supervisory artificial neural networks to achieve more accurate results with better convergence. Here, the perturbed engine torque and vehicle longitudinal acceleration are selected as the inputs of the ANN (instead of the original engine torque and vehicle acceleration), which allows for the faster convergence of estimates with high accuracy; these inputs are existing sensor values already available in the vehicle system. The effectiveness of the proposed ANN approach was verified using simulation and software-in-the-loop simulation (SILS) with field test data, and it was found that the convergence speed of the proposed ANN is almost twice as fast as that of the model-based approach, the accuracy is much better, and the estimation quality is constantly stable without any excessive transient responses. This study will provide further insights into mass estimation using the ANN approach.

Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM

Quantifying and predicting driving risks for connected autonomous vehicles (CAVs) is critical to ensuring the safe operation of traffic in complex environments. This study first establishes a car-following model for CAVs based on molecular force fields. Subsequently, using a convolutional neural network and long short-term Memory (CNN-LSTM) deep-learning model, the future trajectory of the target vehicle is predicted. Risk is quantified by employing models that assess both the collision probability and collision severity, with deep-learning techniques applied for risk classification. Finally, the High-D dataset is used to predict the vehicle trajectory, from which the speed and acceleration of a target vehicle are derived to forecast driving risks. The results indicate that the CNN-LSTM model, when compared with standalone CNN and LSTM models, demonstrates a superior generalization performance, a higher sensitivity to risk changes, and an accuracy rate exceeding 86% for medium- and high-risk predictions. This improved accuracy and efficacy contribute to enhancing the overall safety of connected vehicle platoons.

A study of the kinematic response of launch vehicle subjected to a far-field blast impact

Abstract The launch vehicle is exposed to a far-field blast wave, leading to shock displacement and attitude change, thereby posing the risk of losing its launch condition. To analyze the motion response of the launch vehicle under blast wave load, simulations are conducted based on the results of shock experiments from the shock tube. Comparing the experiment with the simulation results reveals that utilizing an equal impulse triangular wave load instead of a blast wave load can also yield relatively correct outcomes. Furthermore, by employing dimensional analysis, a similarity relationship between blast wave load and the motion response of the launch vehicle is established. The study summarizes the effects of blast wave pressure and action time on the displacement, velocity, and acceleration of the launch vehicle. Finally, the similarity law is verified through simulation. Under conditions of geometric and physical similarity, the kinematic quantities of the original size model can be predicted through the calculation of the small model of the launch vehicle. This holds significant value for research on the kinematic response of launch vehicles when exposed to explosion blast waves and related engineering applications.

Inertial mass modeling and compensation for an energy-harvesting vehicle suspension system with limited-DOF parallel mechanisms actuator

An energy-harvesting suspension can improve the energy efficiency of vehicle. By introducing a lower-mobility parallel mechanism as motion transfer in the energy-harvesting suspension, the stiffness of the system is significantly improved. However, the overlage equivalent of inertial mass make the system is difficult to control and imped the development of this kind of suspension. To solve the problem, an equivalent linearization model to calculate the nonlinear inertial mass of low-DOF (Degree Of Freedom) parallel mechanism actuator is studied firstly. Furthermore, a 2-DOF 1/4 suspension system dynamic model that includes inertial mass is proposed to analyze the vehicle acceleration, dynamic tire load, and dynamic stroke. On this basis, the linear-quadratic Gaussian (LQG) controller is adopted to compensate the inertial mass of the suspension to improve the system performance. Results show that the proposed model accurately reflects the characteristics of the suspension inertial mass, the root-mean-square (RMS) value of the vehicle body acceleration and suspension dynamic stroke through compensation controller is reduced by 33.4% and 30.89%, respectively. Moreover, the RMS of the dynamic tire load is reduced by 3.06%, and the system stability is improved significantly.

Aging Analysis for Li Metal Batteries Exposed to Calendar and Non-Uniform Use Conditions

Lithium metal batteries have seen dramatic advancement over the last 5 years. In realistic cell designs the ability to achieve both high energy and long cycle life have been demonstrated using constant current cycling. Based on the observed performance the timing is right for more detailed analysis of some of these promising systems in more realistic use cases including calendar aging and using interspersed charge and discharge pulses which mirror vehicle acceleration and regeneration. In a combination of studies it has been found that shifts to these use cases present both significant promise for the current technical approaches as well as distinct challenges which will need to be addressed for future implementation in long range, high performing electric vehicles.This discussion will focus on a few of these key findings and present options to continue the advancement of high energy Li metal batteries.

Numerical Analysis of the Vehicle Damping Performance of a Magnetorheological Damper with an Additional Flow Energy Path

Vehicles experience various frequency excitations from road surfaces. Recent research has focused on active dampers that adapt their damping forces according to these conditions. However, traditional magnetorheological (MR) dampers face a “block-up phenomenon” that limits their effectiveness. To address this, additional flow-type MR dampers have been proposed, although revised designs are required to accommodate changes in damping force characteristics. This study investigates the damping performance of MR dampers with an additional flow path to enhance the vehicle ride quality. An optimization model was developed based on fluid dynamics equations and analyzed using electromagnetic simulations in ANSYS Maxwell software. Vibration analysis was conducted using AMESim by applying a sinusoidal road surface model with various frequencies. Results show that the optimized diameter of the additional flow path obtained from the analysis was 1.1 mm, and it was shown that the total damping force variation at low piston velocities decreased by approximately 56% compared to conventional MR dampers. Additionally, vibration analysis of the MR damper with the optimized additional flow path diameter revealed that at 30 km/h, 37.9% acceleration control was achievable, at 60 km/h, 18.7%, and at 90 km/h, 7.73%. This demonstrated the resolution of the block-up phenomenon through the additional flow path and confirmed that the vehicle with the applied damper could control a wider range of vehicle upper displacement, velocity, and acceleration compared to conventional vehicles.

String stable control design for automated vehicles under cyberattacks

Emerging automated vehicle (AV) technologies open a door for cyberattacks, where a select number of AVs are compromised to drive in an adversarial manner, degrading the performance of transportation systems. Hence, designing stable controls for AVs that can mitigate the impact of attacks on traffic flow is important and necessary as AVs gradually become a reality. In this study, we first present a general framework for describing mixed-autonomy traffic involving AVs and human-driven vehicles (HVs) based on car-following dynamics. Under this framework, a class of malicious attacks on AV control commands (vehicle acceleration) is mathematically characterized. To deal with the lack of knowledge of the attacks, we model attacks as an unstructured process, allowing for the inclusion of both deterministic and stochastic attack behaviors, bounded by a known bound (to remain stealthy) without being subject to any specific statistical distribution. Moreover, we analytically derive a set of sufficient conditions for string stable control design of individual AVs which can help mitigate the disturbances to traffic flow caused by unknown attacks on AVs. Furthermore, for any given market penetration rate of AVs, a set of conditions is also derived for selecting appropriate feedback gains of AVs to ensure string stability of heterogeneous traffic, reducing the undesired impact of attacks on traffic flow. These sufficient conditions provide important criteria for string stable control design of AVs without requiring much knowledge of the attacks. A series of numerical results is presented to show effectiveness of the proposed approach on mitigating attack disruption.

Reconstructing Road Roughness Profiles Using ANNs and Dynamic Vehicle Accelerations

Road networks are crucial infrastructures that play a significant role in the progress and advancement of societies. However, roads deteriorate over time due to regular use and external environmental factors. This deterioration leads to discomfort for road users as well as the generation of noise and vibrations, which negatively impact nearby structures. Therefore, it is essential to regularly maintain and monitor road networks. The International Roughness Index (IRI) is commonly used to quantify road roughness and serves as a key indicator for assessing road condition. Traditionally, obtaining the IRI involves manual or automated methods that can be time-consuming and expensive. This study explores the potential of using artificial neural networks (ANNs) and dynamic vehicle accelerations from two simulated car models to reconstruct road roughness profiles. These models include a simplified quarter-car (QC) model with two degrees of freedom, valued for its computational efficiency, and a more intricate full-car (FC) model with seven degrees of freedom, which replicates real-life vehicle behavior. This study also examines the ability of ANNs to predict the mechanical properties of the FC model from dynamic vehicle responses to obstacles. We compare the accuracy and computational efficiency of the two models and find that the QC model is almost 10 times faster than the FC model in reconstructing the road roughness profile whilst achieving higher accuracy.

An Improved Adaptive Sliding Mode Control Approach for Anti-Slip Regulation of Electric Vehicles Based on Optimal Slip Ratio

To optimize the acceleration performance of independently driven electric vehicles with four in-wheel motors, this paper proposes an anti-slip regulation (ASR) strategy based on dynamic road surface observer for more efficient tracking of the optimal slip ratio and enhanced vehicle acceleration. The method uses the Unscented Kalman Filter (UKF) observer to estimate vehicle speed and calculate the actual slip ratio, while a fuzzy controller based on the Burckhardt tire model identifies road surfaces. The road’s peak adhesion coefficient and optimal slip ratio curve are fitted using a Back Propagation Neural Network (BPNN) optimized by Particle Swarm Optimization (PSO). The control strategy further refines torque management through an adaptive sliding mode control (ASMC) that integrates adaptive laws and a super-twisting sliding mode approach to track the optimal slip ratio. Joint simulations with MATLAB/Simulink and Carsim on low-adhesion, joint, and split road surfaces demonstrate that the strategy quickly and accurately identifies the optimal slip ratio across various road surfaces. This enables the tire slip ratio to approach the optimal value in minimal time, significantly improving vehicle dynamic performance. Compared to conventional sliding mode controllers, the optimized ASMC reduces chattering and improves control precision.

A Nonlinear Suspension Road Roughness Recognition Method Based on NARX-PASCKF.

Road roughness significantly impacts vehicle safety and dynamic responses. For nonlinear suspension systems, the nonlinear characteristics often make it challenging for estimators to identify the actual road roughness accurately. This paper proposes a hybrid road roughness identification algorithm based on nonlinear auto-regressive with exogenous inputs (NARX) and a process noise adaptive square root cubature Kalman filter (PASCKF) to address this issue. Driven by vehicle acceleration data, an NARX-based road roughness identification system is constructed to mitigate the model uncertainties. Furthermore, a hybrid strategy is proposed. On the one hand, the accurate road roughness estimated by the NARX is converted into process noise covariance, enhancing the estimator's accuracy and convergence rate. Another switching strategy is proposed to optimize the non-convergence issues of the PASCKF. Finally, simulation and actual vehicle experiment data demonstrate that this approach offers superior identification accuracy and adaptability compared to the standalone SCKF algorithm.

Congested traffic patterns of mixed lattice hydrodynamic model combining the perceptual range differences with passing effect

With the aid of Vehicle-to-everything (V2X) technologies for network connectivity, connected autonomous vehicles (CAVs) can broaden the drivers' perceptual boundaries and receive a greater quantity of exogenous vehicle information, thereby governing the vehicle's acceleration information of the next moment. Nonetheless, constrained by contemporary communication networks and sophisticated vehicle control technology, the process of promoting CAVs is long-lasting, and throughout this stage of transition, both CAVs and human-driven vehicles (HDVs) will coexist on the road. Moreover, passing behaviour has received limited attention in the research on the traffic flow models despite being a fundamental microscopic driving behaviour. To bridge these gaps, we introduce the percentage ratios of CAVs into the lattice hydrodynamic model by integrating the perceptual range differences between two different types of vehicles with passing effects. Subsequently, the stability norm associated with the new model is ascertained by performing the linear stability analysis. When the stability condition is not achieved, we investigate the complex behaviour of the new model. The associated existing conditions and the modified Korteweg-de Vries (mKdV) equation are determined simultaneously. When the passing ratio is inadequate, no jam and kink jam make up the whole phase region; when the passing ratio surpasses the minimum, the initial unstable region can be segregated into two extra segments: the chaotic sub-region and the kink jam sub-region, and the density wave progressively transitions from being a kink-Bando traffic wave to a chaotic phase. The findings of the numerical experiment are consistent with with the theoretical derivation.

Indirect measurement of bridge surface roughness using vibration responses of a two-axle moving vehicle based on physics-constrained generative adversarial network

This study addresses the challenge of indirectly measuring bridge surface roughness through the vibration responses of a moving vehicle, which is crucial for pavement maintenance and bridge safety assessment. A physics-constrained generative adversarial network (PC-GAN) was proposed for the probabilistic estimation of surface roughness. The method consists of two steps: initially, a GAN informed by physics-based knowledge extracts combined information of bridge vibration deflection and surface roughness from vehicle accelerations. Subsequently, a feed-forward network isolates the bridge surface roughness from the combined data. Numerical examples validate the PC-GAN method, demonstrating sustained high accuracy under challenging conditions, including ISO 8608 level C road roughness, vehicle speeds up to 8 m s-1, 10 % deviation in vehicle parameters, 10 % environmental noise, and 10 % vehicle damping ratio. Laboratory tests further confirmed the method's efficacy, with the successful detection of artificial barriers on the bridge surface and a mean relative error of 3.33 % in height estimation. The PC-GAN method is demonstrated to be a robust tool for estimating bridge surface roughness under various numerical and laboratory conditions. These findings provide valuable insights for the rapid inspection of bridge pavement conditions using vibration responses from moving test vehicles.

Research on Longitudinal Control of Electric Vehicle Platoons Based on Robust UKF–MPC

In a V2V communication environment, the control of electric vehicle platoons faces issues such as random communication delays, packet loss, and external disturbances, which affect sustainable transportation systems. In order to solve these problems and promote the development of sustainable transportation, a longitudinal control algorithm for the platoon based on robust Unscented Kalman Filter (UKF) and Model Predictive Control (MPC) is designed. First, a longitudinal kinematic model of the vehicle platoon is constructed, and discrete state–space equations are established. The robust UKF algorithm is derived by enhancing the UKF algorithm with Huber-M estimation. This enhanced algorithm is then used to estimate the state information of the leading vehicle. Based on the vehicle state information obtained from the robust UKF estimation, feedback correction and compensation are added to the MPC algorithm to design the robust UKF–MPC longitudinal controller. Finally, the effectiveness of the proposed controller is verified through CarSim/Simulink joint simulation. The simulation results show that in the presence of communication delay and data loss, the robust UKF–MPC controller outperforms the MPC and UKF–MPC controllers in terms of MSE and IAE metrics for vehicle spacing error and acceleration tracking error and exhibits stronger robustness and stability.

Regional vehicle energy consumption evaluation framework to quantify the benefits of vehicle electrification in plateau city: A case study of Xining, China

Vehicle electrification holds significant potential for driving both decarbonization and improved energy efficiency within the realm of road transportation. However, accurate quantification of energy consumption advantages offered by electric vehicles (EVs) has remained elusive, thus impeding their long-term development due to variations across cities and vehicle types. To address this challenge, we propose a novel regional vehicle energy consumption assessment framework that integrates a region-specific driving cycle (DC) database and advanced high-precision energy consumption models. The regional DC database is constructed using a Markov chain approach. The database is stochastic and diverse, while taking into account real driving features and regional geographic environments. Energy consumption models for internal combustion engine vehicle (ICEV), plug-in hybrid electric vehicle (PHEV) and battery electric vehicle (BEV) are constructed based on machine learning approach. These models have good generalization ability and prediction accuracy, with R2 of 0.86, 0.86 and 0.77 on the test set, respectively. According to the interpretability of the models, vehicle acceleration and vehicle specific power are considered to be the most important features affecting vehicle energy consumption. According to the evaluation results, compared to using ICEV, the promotion of PHEV and BEV in Xining, a plateau city, can reduce energy consumption by 27.83 % and 32.05 %, respectively. The framework not only unifies the scale of energy consumption evaluation among different vehicle types, but also its good generalization ability can be migrated and extended to the evaluation of vehicle energy consumption in various regions. The establishment of this framework will contribute to the promotion and popularization of electric vehicles, as well as the advancement of energy-saving initiatives in the transportation sector.

Development of a feed-forward audio-visual notification system for reducing motion sickness in autonomous vehicles

To mitigate motion sickness occurring in seat positions where driving conditions cannot be predicted due to the changed interior structure of autonomous vehicles, a feed-forward audio-visual notification system was developed. The design, implemented in the driving environment, provides information through ambient light and notifying sound by predicting signal processing information, including steering angle and turn signals, to address the audio-visual impact of changes in the vehicle's direction, speed, and acceleration with feed-forward alerts. The correlation model between psychological and physiological responses was developed through measurements such as MSQ, MISC, EEG, fNIRS, and HRV during driving, quantifying the degree of motion sickness and improvement situations. The effect of visual variation by ambient light and the customized effect of notifying sound were combined to compare and analyze the degree of motion sickness reduction following the installation of the notification system.