Vehicle Model Research Articles

A machine learning based method for parametric environmental impact model for electric vehicles

Environmental managers attempt to increasingly incorporate precautionary principles into decision making. The literature lacks Machine Learning -based approaches for forewarning lifecycle environmental impacts. This paper proposes a method to support electric vehicle design. The main innovation of the work lays in merging Life Cycle Assessment (LCA) and Machine Learning foundations to provide support and awareness to designers. The present approach overcomes the present literature because it provides a method for the design phase, is based on a well-established methodology (LCA) and provides quantitative results from little inputs. The approach exploits machine Learning Methods to develop models with the design features of a generic electric vehicle (such as vehicle mass and distance traveled) in six phases (Problem definition; Data collection; Data Preparation; Modeling; Model evaluation; Model interpretation). Differently from existing environmental analyses, all stages of the product life cycle have been considered in building the database; moreover, the model provides quantitative results. Regression models and supervised algorithms were used. The obtained model can be used by product engineers, as well as those not experts on LCA. Moreover, the model guarantees the database and hypothesis's uniqueness, ensuring the results coherence and comparability. The level of accuracy obtained in the case study (error or 17%) is comparable with studies handling full environmental analysis (that should be more accurate), and outstanding, as the present case is for the design phase. Future works will focus on additional significative indicators, similar electric vehicle design and integration with prospective LCA approaches.

Hybrid modeling of multibody vehicles with partially known physics: discovering complex behaviors of tires

Hybrid modeling of multibody vehicles with partially known physics: discovering complex behaviors of tires

Optimal Scheduling of Integrated Energy System Considering Electric Vehicle Battery Swapping Station and Multiple Uncertainties

In recent years, there has been rapid advancement in new energy technologies aimed at mitigating greenhouse gas emissions stemming from fossil fuels. Nonetheless, uncertainties persist in both the power output of new energy sources and load. To effectively harness the economic and operational potential of an Integrated Energy System (IES), this paper introduces an enhanced uncertainty set. This set incorporates N-1 contingency considerations and the nuances of source–load distribution. This framework is applied to a robust optimization model for an Electric Vehicle Integrated Energy System (EV-IES), which includes Electric Vehicle Battery Swapping Station (EVBSS). Firstly, this paper establishes an IES model of the EVBSS, and then proceeds to classifies and schedules the large-scale battery groups within these stations. Secondly, this paper proposes an enhanced uncertainty set to account for the operational status of multiple units in the system. It also considers the output characteristics of both new energy sources and loads. Additionally, it takes into consideration the N-1 contingency state and multi-interval distribution characteristics. Subsequently, a multi-time-scale optimal scheduling model is established with the objective of minimizing the total cost of the IES. The day-ahead robust optimization fully considers the multivariate uncertainty of the IES. The solution employs the Nested Column and Constraint Generation (C&CG) algorithm, based on the distribution characteristics of multiple discrete variables in the model. The intraday optimal scheduling reallocates the power of each unit based on the robust optimization results from the day-ahead scheduling. Finally, the simulation results demonstrate that the proposed method effectively reduces the conservatism of the uncertainty set, ensuring economic and stable operation of the EV-IES while meeting the demands of electric vehicle users.

Brake-actuated steering control strategy for turning of articulated vehicles

The use of high-capacity vehicles has been shown to significantly reduce carbon dioxide emissions from heavy goods transport and improve the efficiency of the transport sector. Despite their benefits, current legislations regarding the dimensions and weight of the vehicles have limited the widespread use of heavier and longer vehicles. Long vehicles are less manoeuvrable and often necessitate technological interventions to pass manoeuvrability tests. Various rear-steering systems proposed to solve these issues lead to high costs and mass penalties compared to standard fixed axles. This paper presents a new steering concept which utilises the axles’ existing brake actuators to steer, eliminating the need for dedicated actuators on the axle and associated steering hardware. Brakes are individually controlled on each side of a freely steerable axle to generate the necessary torque about the kingpins causing it to steer. A validated vehicle model is used to design a feedback steering strategy based on path-following steering control for low-speed manoeuvring. The proposed system's performance is compared to conventional vehicles and current state-of-the-art active-steering systems through simulations. It demonstrates comparable steering performance to that of electro-hydraulic active-steer systems, with an estimated 50% reduction in both mass and costs.

Learning-Based Control of Autonomous Vehicles Using an Adaptive Neuro-Fuzzy Inference System and the Linear Matrix Inequality Approach.

This paper proposes a learning-based control approach for autonomous vehicles. An explicit Takagi-Sugeno (TS) controller is learned using input and output data from a preexisting controller, employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. At the same time, the vehicle model is identified in the TS model form for closed-loop stability assessment using Lyapunov theory and LMIs. The proposed approach is applied to learn the control law from an MPC controller, thus avoiding the use of online optimization. This reduces the computational burden of the control loop and facilitates real-time implementation. Finally, the proposed approach is assessed through simulation using a small-scale autonomous racing car.

How we deal with transition planning as we approach the e-bus era

With the rapid development of new energy, the widespread adoption of electric buses is significant. This paper delves into the environmental impact, economic implications, and strategic planning solutions associated with the proliferation of electric buses. We initiate this process by using bus data from Qingdao, China. Subsequently, using the fuel life cycle method, we carefully examined CO and the pollutant emission characteristics of Qingdao bus fleet before and after electrification. The research results reveal that CO, nitrogen oxides, and VOC emissions have all increased. An economic analysis of electric vehicle adoption in public transportation was conducted in response to the second question. Considering government subsidies, a cost-saving model for electric vehicles per 100 kilometers was constructed. Furthermore, the study found that as the electrification rate increases, the operating costs of electric buses in Qingdao gradually decrease. We developed a comprehensive linear integer programming model to address the third question. The model, aimed at achieving the lowest comprehensive cost while considering various constraints, was solved through the branch and bound method. We applied this model to analyze the optimal plans for completing the conversion of electric buses in Qingdao, Zhengzhou, and Xi’an within a 10-year timeframe. For the fourth question, we have put forward policy recommendations.

CL-FDAPF trajectory planner and FO-LADRC motion controller for autonomous sweeper vehicle

Aiming at keeping safe in time and addressing disturbance of uncertainty, an closed loop forward simulation filtering double-layer artificial potential field (CL-FDAPF) trajectory planner and first order linear active disturbance rejective control (FO-LADRC) motion controller are proposed for autonomous sweeper vehicle. Firstly, the double-layer artificial potential field, which consists of traditional potential cost layer and safe level layer, is adopted here to keep planning realtime, meet safe limitations and satisfy operational requirements, and the postprocessing of mean filtering and closed loop forward simulation is for vehicle dynamic constraints. Secondly, it is worth developing active disturbance rejection control strategy, which has the ability to accommodate uncertainty, since an accurate mathematical model of autonomous sweeper vehicle is unavailable as there being inevitable uncertainties in the system state observation and unavoidable environmental disturbances. Thirdly, several typical scenarios are designed in order to verify the real-time and reliability of the proposed algorithm. The results illustrate that the CL-FDAPF planner has highly real-time and stability as the peak time less than 0.045 s and mean time being about 0.02 s in 1000 cycles, and FO-LADRC controller has robust both at uncertainty of wheelbase and steering ratio, since the FO-LADRC have smaller lateral errors compared with two existing methods.

Two-Layer Optimization Strategy of Electric Vehicle and Air Conditioning Load Considering the Benefit of Peak-to-Valley Smoothing

To satisfy the interests of multiple agents and those of comprehensive indicators such as peak-to-valley differences and load fluctuations occurring on the network side, this paper presents a flexible load demand-side response optimization method that considers the benefits of peak-to-valley smoothing. First, load aggregation modelling of air conditioning and electric vehicles was conducted, and the complementarity of the power consumption behavior of different types of flexible loads was used to improve the responsiveness of the load aggregator. Second, considering demand-side responses and taking into account the interests of both supply and demand, the load fluctuation and peak-to-valley difference on the network side are reduced, and a flexible load double-layer optimization model incorporating the peak-to-valley smoothing benefit is established. Finally, the effectiveness of the proposed optimization model is verified by using the KKT condition and the big M method to evaluate this two-layer optimization problem as a single-layer optimization problem. Comparative examples show that the proposed two-layer optimization method can take advantage of the complementarity of air conditioning and electric vehicles to improve the income of load aggregators. Moreover, the proposed method can effectively reduce the load peak-to-valley difference and load fluctuation of the distribution network by introducing the peak-to-valley smoothing benefit model.

GNSS and LiDAR Integrated Navigation Method in Orchards with Intermittent GNSS Dropout

Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method aims to enhance the research and application of autonomous operational equipment in orchards. Firstly, we design and integrate robot vehicles; secondly, we unify the positioning information of GNSS/INS and laser odometer through coordinate system transformation; next, we propose a dynamic switching strategy, whereby the system switches to LiDAR positioning when the GNSS signal is unavailable; and finally, we combine the kinematic model of the robot vehicles with PID and propose a path-tracking control system. The results of the orchard navigation experiment indicate that the maximum lateral deviation of the robotic vehicle during the path-tracking process was 0.35 m, with an average lateral error of 0.1 m. The positioning experiment under satellite signal obstruction shows that compared to the GNSS/INS integrated with adaptive Kalman filtering, the navigation system proposed in this article reduced the average positioning error by 1.6 m.

Design of Active Suspension Controller for Ride Comfort Enhancement and Motion Sickness Mitigation

This paper presents a method for designing an active suspension controller for ride comfort enhancement and motion sickness mitigation. For this, it is necessary to design an active suspension controller, which aims to reduce the vertical acceleration and pitch rate of a sprung mass in a vehicle. A half-car vehicle model was selected. For the controller design, a static output feedback (SOF) control was selected instead of a full-state feedback control because it is hard to measure all state variables in real vehicles. With the available signals, three types of SOF controller were proposed. To determine the gains of the SOF controllers, a linear quadratic optimal control methodology and a simulation-based optimization method were adopted. To validate the proposed method, a simulation was carried out using vehicle simulation software. The simulation results show that the proposed method is quite effective for ride comfort enhancement and motion sickness mitigation.

Suitability Study of Biofuel Blend for Light Commercial Vehicle Application under Real-World Transient Operating Conditions

<div>Driving schedule of every vehicle involves transient operation in the form of changing engine speed and load conditions, which are relatively unchanged during steady-state conditions. As well, the results from transient conditions are more likely to reflect the reality. So, the current research article is focused on analyzing the biofuel-like lemon peel oil (LPO) behavior under real-world transient conditions with fuel injection parameter MAP developed from steady-state experiments. At first, engine parameters and response MAPs are developed by using a response surface methodology (RSM)-based multi-objective optimization technique. Then, the vehicle model has been developed by incorporating real-world transient operating conditions. Finally, the developed injection parameters and response MAPs are embedded in the vehicle model to analyze the biofuel behavior under transient operating conditions. The results obtained for diesel-fueled light commercial vehicle (LCV) have shown better fuel economy than LPO biofuel with their developed fuel injection parameter MAP. The maximum BTE obtained was 29.7% for diesel and 29.5% for LPO at 2100 rpm and 20 Nm torque. The mean HC emissions were identified as 0.02046 g/km for diesel and 0.03488 g/km for LPO fuel over the modified Indian driving cycle (MIDC). Except for NOx emission, LPO biofuel exhibited diesel-like performance and emission characteristics under the MIDC.</div>

A Forecast of CO2 Emissions Based on the Fuel Consumption Rating of a Particular Vehicle

The accumulation of carbon dioxide will not only cause global warming but also cause a series of catastrophic consequences and cause irreversible damage to the earth's ecology. The prediction of carbon dioxide emissions from light-duty vehicles can provide decision-making support for consumers when purchasing cars and is beneficial to consumers in purchasing environmentally friendly cars. This paper constructs a vehicle carbon dioxide emission model based on a multiple linear regression model, using the four indicators of vehicle engine size, vehicle fuel consumption, Hwy (L/100km), and Comb (L/100km) as output variables. The quantity is the predictor variable. The correlation coefficient of this study is 0.9675. The regression equation of this linear regression model is Y=22.2344+2.7157X1+32.9843X2+26.6259X3-39.1429X4. The order of impact of the four indicators studied on carbon emissions is Comb (L/100km), vehicle fuel consumption, Hwy (L/100km), and vehicle engine size. The determination of carbon emissions of specific models of new light vehicles can refer to the multiple linear regression model proposed in this study.

Simplified prediction models for acoustic installation effects of train-mounted equipment

Acoustic models of railway vehicles in standstill and pass-by conditions can be used as part of a virtual certification process for new trains. For each piece of auxiliary equipment, the sound power measured on a test bench is combined with measured or predicted transfer functions. It is important, however, to allow for installation effects due to shielding by fairings or the train body. In the current work, fast-running analytical models are developed to determine these installation effects. The model for roof-mounted sources takes account of diffraction at the corner of the train body or fairing, using a barrier model. For equipment mounted under the train, the acoustic propagation from the sides of the source is based on free-field Green’s functions. The bottom surfaces are assumed to radiate initially into a cavity under the train, which is modelled with a simple diffuse field approach. The sound emitted from the gaps at the side of the cavity is then assumed to propagate to the receivers according to free-field Green’s functions. Results show good agreement with a 2.5D boundary element model and with measurements. Modelling uncertainty and parametric uncertainty are evaluated. The largest variability occurs due to the height and impedance of the ground, especially for a low receiver. This leads to standard deviations of up to 4 dB at low frequencies. For the roof-mounted sources, uncertainty over the location of the corner used in the equivalent barrier model can also lead to large standard deviations.

The Influence of CRTS II Slab Ballastless Track Upper Arch Deformation on the Wheel Jumping Law of High-Speed Vehicle

To study the impact of upwarp deformation in the ballastless track on the jumping behavior of the high-speed vehicle, utilizing UM and ANSYS joint simulation, a vertical vibration model of high-speed vehicle on CRTS II slab ballastless track was developed based on the non-Hertz wheel–rail contact model of virtual penetration theory. By using the single-wave cosine curve simulating the characteristics of upwarp deformation in the track slab, we calculated the whole process of wheel jumping. This allowed us to analyze how the amplitude and wavelength of the track slab upward deformation influence the vibration response of the vehicle–track system. Our findings indicate that when a wheel passes through the arch section of the track slab, the entire wheel jumping process consists of distinct stages: “wheel–rail bonding, wheel–rail separation, wheel–rail impact (one or more times), and wheel–rail bonding.” As the amplitude of upwarp deformation increases and the wavelength decreases, significant changes occur in several parameters, including the vertical force between the wheel and rail, wheel unloading rate, wheel jump height, frequency, duration, and vertical displacement of the rail. Additionally, when the wavelength is between 2 and 6[Formula: see text]m and the amplitude is 8[Formula: see text]mm, the vertical force between the wheel and rail becomes zero, the wheel load reduction rate is one, and the wheel jumps. When the wavelength is less than 3[Formula: see text]m, the wheel jump height exceeds the flange height, increasing the risk of derailment. Meanwhile, during the first wheel–rail impact, the wheel–rail vertical force and the rail vertical displacement reach their maximum, potentially impacting rail service performance negatively. Finally, compared to the amplitude of the track slab camber deformation, its wavelength has a greater impact on the entire process of wheel jumping. It is recommended that attention be paid to the change in the wavelength of the track slab camber during the maintenance and repair of the ballastless track.

An Analysis of Vehicle-to-Grid in Sweden Using MATLAB/Simulink

With more electric vehicles introduced in society, there is a need for the further implementation of charging infrastructure. Innovation in electromobility may result in new charging and discharging strategies, including concepts such as smart charging and vehicle-to-grid. This article provides an overview of vehicle charging and discharging innovations with a cable connection. A MATLAB/Simulink model is developed to show the difference between an electric vehicle with and without the vehicle-to-grid capabilities for electricity grid prices estimated for Sweden for three different electric vehicle user profiles and four different electric vehicle models. The result includes the state-of-charge values and price estimations for the different vehicles charged with or without a bidirectional power flow to and from the electric grid. The results show that there is a greater difference in state-of-charge values over the day investigated for the electric vehicles with vehicle-to-grid capabilities than for vehicles without vehicle-to-grid capabilities. The results indicate potential economic revenues from using vehicle-to-grid if there is a significant variation in electricity prices during different hours. Therefore, the vehicle owner can potentially receive money from selling electricity to the grid while also supporting the electric grid. The study provides insights into utilizing vehicle-to-grid in society and taking steps towards its implementation.

Extended Kalman Filter-Based Vehicle Tracking Using Uniform Planar Array for Vehicle Platoon Systems.

We develop an extended Kalman filter-based vehicle tracking algorithm, specifically designed for uniform planar array layouts and vehicle platoon scenarios. We first propose an antenna placement strategy to design the optimal antenna array configuration for precise vehicle tracking in vehicle-to-infrastructure networks. Furthermore, a vehicle tracking algorithm is proposed to improve the position estimation performance by specifically considering the characteristics of the state evolution model for vehicles in the platoon. The proposed algorithm enables the sharing of corrected error transition vectors among platoon vehicles, for the purpose of enhancing the tracking performance for vehicles in unfavorable positions. Lastly, we propose an array partitioning algorithm that effectively divides the entire antenna array into sub-arrays for vehicles in the platoon, aiming to maximize the average tracking performance. Numerical studies verify that the proposed tracking and array partitioning algorithms improve the position estimation performance.

Vertical dynamics analysis and multi-objective optimization of electric vehicle considering the integrated powertrain system

Integrated powertrain system, mainly composed of driving motor and reducer, is one of the excitation sources to electric vehicle vibration but is limited in coupling with the vertical vehicle dynamic in the existing research, which restricts the reliability of dynamics investigation of vehicle and integrated powertrain system. This work proposes a novel vertical dynamic model of the electric vehicle considering the integrated powertrain system, enabling the way to access the coupled vibration among the chassis, unsprung mass, and integrated powertrain system. The road irregularity, unbalanced magnetic pull, and gear mesh excitation are developed and are further applied to the novel vehicle dynamic model with ten degrees of freedom (DOFs). Then, the dynamic response between the novel model and traditional one is compared. Further, the effects of the stiffness and damping of suspension and mounting on the vibration of chassis and integrated powertrain system are explored by numerical calculation. Finally, a multi-objective optimization model is proposed and is driven by the dragonfly algorithm to minimize the vibration of chassis and integrated powertrain system. The research results shown that there will be present the new complex dynamic characteristics with considering the excitation from the integrated powertrain system. The suspension and mounting parameters have the nonlinear effects on the vehicle vibration. And the system vibration can be globally optimized with the optimization algorithm.

Optimization strategy for connected automated vehicles to reduce energy consumption on freeway in rainy weather

Energy consumption on freeway significantly contributes to environmental pollution. Rainy weather, as a common adverse condition, will exert a negative impact on car-following behavior of vehicles on freeway and further affect their energy consumption. The emergence of connected automated vehicles (CAVs) has created an opportunity to mitigate these impacts. This paper aims to propose an optimization strategy for CAVs that can reduce energy consumption during car-following behavior on freeway under different rainy weather conditions. To begin with, a calibrated car-following model for regular vehicles (RVs) on freeway in rainy weather was used to derive an optimization strategy for CAVs that have vehicle-to-vehicle communication capability to stabilize traffic flow with smoothed speed fluctuations. The proposed optimization strategy for CAVs was then subjected to simulation experiments to validate its effectiveness. Results indicate that energy consumption on freeway in rainy weather is closely linked to speed fluctuations. Frequent speed fluctuations during car-following behavior could cause more energy consumption. The proposed optimization strategy for CAVs is capable of reducing energy consumption in rainy weather by smoothing speed fluctuations. CAVs equipped with this optimization strategy shows an energy-saving of 34.69%–61.11% compared to RVs under various rainy weather conditions.

Evaluation of the strength performance of the steering system of hybrid off-road vehicles

In response to the strength performance requirements of the new energy Off road Vehicle steering system, this article analyzes and studies the strength performance and failure modes of the new energy Off road Vehicle steering rod based on a certain new energy Off road Vehicle model through simulation, bench experiments, and vehicle strength testing teams. Based on various vehicle misuse conditions, the force law of the steering rod under different vehicle driving conditions was analyzed, and a load spectrum for the steering rod strength bench test was developed to meet the actual vehicle usage conditions. The results indicate that the steering rod strength failure is mainly manifested as bending failure of the compression rod under alternating tension and compression conditions. Finally, based on the stability principle of the compression rod, the requirements for the strength performance index of the steering rod are proposed. The research results can provide certain technical support for the strength assessment of the vehicle steering rod.

Study on Driver Behavior Pattern in Merging Area under Naturalistic Driving Conditions

To reduce the risk of traffic conflicts in merging area, driver’s behavior pattern was analyzed to provide a theoretical basis for traffic control and conflict risk warning. The unmanned aerial vehicle (UAV) was used to collect the videos in two different types of merging zones: freeway interchange and service area. A vehicle tracking detection model based on YOLOv5 (the fifth version of You Only Look Once) and Deep SORT was constructed to extract traffic flow, speed, vehicle type, and driving trajectory. Acceleration/deceleration distribution and vehicle lane-changing behavior were analyzed. The influence of different vehicle models on vehicle speed and lane-changing behavior was summarized. Based on this data, the mean and standard deviation of velocity, acceleration, and variable acceleration were selected as the characteristic variables for driving style clustering. To avoid redundant information between features, principal component dimensionality reduction was performed, and the dimensionality reduction data was used for K-means and K-means++ clustering to obtain three driving styles. The results show that there are obvious differences in the driving behaviors of vehicles in different types of merging areas, and the characteristics of different areas should be fully considered when conducting traffic conflict warnings.