Vehicle Longitudinal Dynamics Model Research Articles

Electric vehicle ramp recognition based on fusion of vehicle mass estimation

Road slope is the necessary environmental information for intelligent electric vehicles. The accuracy of slope recognition directly determines the control quality of vehicles on the hill. Aiming at the problems that the existing ramp recognition algorithms have poor adaptability to the conditions and are unable to To satisfy the application requirements of mass production vehicles, this paper proposes an electric vehicle slope recognition method based on vehicle mass estimation. Firstly, the vehicle longitudinal dynamics model is established, and the signal characteristics of the acceleration sensor under the actual vehicle conditions are analyzed. The least square vehicle mass estimation strategy with forgetting factor is constructed to obtain the vehicle mass directly under the starting condition. Ramp recognition algorithms are designed for static parking and dynamic driving scenarios. In the static scenario, the filter latch strategy is used to deal with the interference factors such as activities in the vehicle. In the dynamic scenario, the Kalman filter algorithm based on measurement noise adaptation is designed to realize the fusion estimation of dynamic observation and kinematic observation for the slope. The effectiveness of the method is verified by Simulink -CarSim joint simulation. Finally, the real vehicle test is completed on Chery new energy's mass production electric vehicle platform and domain controller. road test results show that the mass estimation error is less than ±10 kg; the static estimation error of the slope is less than 0.001 rad, and the dynamic error is within 0.005 rad. The estimation accuracy and stability are greatly improved, which ensures the environmental adaptability of the intelligent electric vehicles.

Analysis of Energy Consumption for electric motorcycles depending on vehicle configuration and driving cycles

This article presents an analysis of energy consumption for battery electric motorcycles (BEM) based on vehicle configuration and driving cycles. The study investigates the impact of two architectures: rear-wheel regenerative (RWR), and two-wheel regenerative (2WR) on consumption. For the analysis, the study considered the effect of various parameters on the longitudinal vehicle dynamics such as speed profiles (WMTC, real-world profile), longitudinal road slope (from -5° to 5° with 0.5° step increment), and 5 different brake distributions. The research employs simulation techniques using the Matlab/Simulink environment to develop a simplified longitudinal vehicle dynamics model (1 DOF) and for the implementation of a serial brake-blending control strategy. The results of the analysis provide energy potential regeneration and insight into the sensitivity of energy consumption to road slope variations, powertrain working points, and potential energy harvesting. The findings contribute to a valuation of the factors that influence energy consumption in electric motorcycles and have implications for the development of vehicle architectures through accurate range assessment on real-world riding conditions and provide valuable information for powertrain components right-sizing.

Regularized Iterative Weighted Total Least Squares for Vehicle Mass Estimation

This work addresses the challenge of vehicle mass estimation using a longitudinal vehicle dynamics model, which adopts an errors-in-variables formulation due to the presence of noise-contaminated measurements in both input and output variables. The reduced vehicle dynamics model is ill-conditioned by nature of correlated input variables and a lack of persistent excitation in the measured data. A regularized iterative weighted total least squares (RIWTLS) method is therefore developed and has the advantage of producing parameter uncertainty quantification and measurement bias estimation alongside the estimated system parameters. A complementary adaptive regularization scheme is developed and serves to control the numerical stability of the RIWTLS algorithm based on the conditioning of incoming data. Experimental tests using electric vehicle data and a batch estimation scheme highlight the performance of the proposed RIWTLS algorithm, estimating vehicle mass to within ±1% accuracy.

Research on the Prediction Model of Engine Output Torque and Real-Time Estimation of the Road Rolling Resistance Coefficient in Tracked Vehicles.

Road parameter identification is of great significance for the active safety control of tracked vehicles and the improvement of vehicle driving safety. In this study, a method for establishing a prediction model of the engine output torques in tracked vehicles based on vehicle driving data was proposed, and the road rolling resistance coefficient f was further estimated using the model. First, the driving data from the tracked vehicle were collected and then screened by setting the driving conditions of the tracked vehicle. Then, the mapping relationship between the engine torque Te, the engine speed ne, and the accelerator pedal position β was obtained by a genetic algorithm-backpropagation (GA-BP) neural network algorithm, and an engine output torque prediction model was established. Finally, based on the vehicle longitudinal dynamics model, the recursive least squares (RLS) algorithm was used to estimate the f. The experimental results showed that when the driving state of the tracked vehicle satisfied the set driving conditions, the engine output torque prediction model could predict the engine output torque T^e in real time based on the changes in the ne and β, and then the RLS algorithm was used to estimate the road rolling resistance coefficient f^. The average coefficient of determination R of the T^e was 0.91, and the estimation accuracy of the f^ was 98.421%. This method could adequately meet the requirements for engine output torque prediction and real-time estimation of the road rolling resistance coefficient during tracked vehicle driving.

A Hierarchical Control Scheme for Adaptive Cruise Control System Based on Model Predictive Control

An adaptive cruise control (ACC) system can improve safety and comfort during driving by taking over longitudinal control of the vehicle. It requires the coordination between the upper-layer controller and the lower-layer actuators. In this paper, a hierarchical anti-disturbance cruise control architecture based on electronic stability control (ESC) system is proposed. The upper-layer controller outputs the desired longitudinal acceleration or deceleration to the lower-layer actuators. In order to improve the accuracy of model prediction and achieve the coordinated control of multiple objectives, an upper-layer model prediction cruise controller is established based on feedback control and disturbance compensation. In addition, based on the hydraulic control unit (HCU) model and the vehicle longitudinal dynamics model, a lower-layer nonlinear model predictive deceleration controller is proposed in order to solve the problems of pressure fluctuations and the low accuracy of small decelerations when ESC is used as the actuator for the ACC system. Finally, the simulation and experimental tests were carried out. The results show that the proposed control architecture can improve the stability and comfort of the cruise control process. Moreover, compared with the traditional PID deceleration controller, it effectively improves the deceleration control accuracy.

Resilient and Safe Platooning Control of Connected Automated Vehicles Against Intermittent Denial-of-Service Attacks

Connected automated vehicles (CAVs) serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety, and reducing fuel consumption and vehicle emissions. A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads. This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service (DoS) attacks that disrupt vehicle-to-vehicle communications. First, a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties, including diverse vehicle masses and engine inertial delays, unknown and nonlinear resistance forces, and a dynamic platoon leader. Then, a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability, attack resilience, platoon safety and scalability. Furthermore, a numerically efficient offline design algorithm for determining the desired platoon control law is developed, under which the platoon resilience against DoS attacks can be maximized but the anticipated stability, safety and scalability requirements remain preserved. Finally, extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.

Research on intelligent formation operation performance of straddle-type rapid transit vehicles in heterogeneous operating environment

This paper presents an intelligent formation operation mode (IFOM) based on straddle-type rapid transit vehicles. Taking the straddle-type rapid transit vehicles operating in IFOM as the object, the operation mode of the vehicle is defined in the control system architecture of the straddle-type rapid transit system. In addition, considering the heterogeneous operating environ-ment of formation vehicles, an evaluation index system for formation vehicles is proposed. Then, according to the vehicle longitudinal dynamics model and the artificial potential field formation algorithm, we build the formation vehicle operation controller. After that, referring to the vehicle dynamics model, a multi rigid body dynamics simulation model of formation vehicles in a heterogeneous operating environment is established. Finally, the operating performance of formation vehicles in heterogeneous operating environment is analyzed. The analysis results show that the operation performance of the formation vehicle meets the requirements of the evaluation index system, which proves the feasibility of the formation operation of straddle-type rapid transit vehicles operating in IFOM.

Joint Estimation of Mass and Center of Gravity Position for Distributed Drive Electric Vehicles Using Dual Robust Embedded Cubature Kalman Filter

The accurate estimation of the mass and center of gravity (CG) position is key to vehicle dynamics modeling. The perturbation of key parameters in vehicle dynamics models can result in a reduction of accurate vehicle control and may even cause serious traffic accidents. A dual robust embedded cubature Kalman filter (RECKF) algorithm, which takes into account unknown measurement noise, is proposed for the joint estimation of mass and CG position. First, the mass parameters are identified based on directly obtained longitudinal forces in the distributed drive electric vehicle tires using the whole vehicle longitudinal dynamics model and the RECKF. Then, the CG is estimated with the RECKF using the mass estimation results and the vertical vehicle model. Finally, different virtual tests show that, compared with the cubature Kalman algorithm, the RECKF reduces the root mean square error of mass and CG by at least 7.4%, and 2.9%, respectively.

Safe and Energy-Saving Vehicle-Following Driving Decision-Making Framework of Autonomous Vehicles

The safety and energy efficiency of autonomous vehicles in the vehicle-following driving scenario is seriously affected by the decision-making framework. This article proposes a safe and energy-saving decision-making framework for autonomous vehicles to avoid the potential rear-end collision and improve the energy efficiency in vehicle-following driving scenario. To realize this framework, the vehicle longitudinal dynamics model is first presented, and the autonomous vehicle's requirements from the safety and energy efficiency perspectives are comprehensively analyzed in the vehicle-following driving scenario. Then, a two-stage decision-making framework is designed. The first stage determines the safety region in the powertrain control domain, and the second stage determines the optimal energy efficiency in the safety region. Based on the two-stage decision-making framework, the perspective of “safety first” principle can be enforced by simultaneously considering the driving safety and energy efficiency. Finally, the experiment is conducted to demonstrate the advantages of the proposed method by comparing it with two other state-of-art methods.

Optimization Based Adaptive Cruise Control and Energy Management Strategy for Connected and Automated FCHEV

With the development of vehicle electrification, automation and connectivity, collaborative optimization among the traffic throughput, driving comfort, fuel economy and driving safety targets is still a huge challenging barrier for a connected and automated fuel cell/battery hybrid electric vehicle. Hence, this paper proposes an optimal car-following energy management strategy (EMS) that combines energy management and adaptive cruise control considering the above targets. Specifically, based on vehicle-to-vehicle and vehicle-to-infrastructure information, an optimal following distance algorithm is developed to obtain the optimal following distance considering driving safety, driving comfort and traffic throughput. Then, based on the established vehicle longitudinal dynamics model, an adaptive cruise controller using back-stepping technique is designed to accurately track optimal following distance. Meantime, combining the obtained controller, optimal EMS based on equivalent consumption minimization strategy is proposed to coordinate the output power of fuel cell and battery to improve fuel economy. The simulations of short and long-term driving cycles indicate that the proposed method can reduce hydrogen consumption by 12.12%, jerk by 61.21%, and keep the desired following distance tracking error within 0.5m.

NeuralFMU: Presenting a Workflow for Integrating Hybrid NeuralODEs into Real-World Applications

The term NeuralODE describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as the right-hand side of the ODE to be solved. This concept was further extended by a black-box model in the form of a Functional Mock-up Unit (FMU) to obtain a subclass of NeuralODEs, named NeuralFMUs. The resulting structure features the advantages of the first-principle and data-driven modeling approaches in one single simulation model: a higher prediction accuracy compared to conventional First-Principle Models (FPMs) and also a lower training effort compared to purely data-driven models. We present an intuitive workflow to set up and use NeuralFMUs, enabling the encapsulation and reuse of existing conventional models exported from common modeling tools. Moreover, we exemplify this concept by deploying a NeuralFMU for a consumption simulation based on a Vehicle Longitudinal Dynamics Model (VLDM), which is a typical use case in the automotive industry. Related challenges that are often neglected in scientific use cases, such as real measurements (e.g., noise), an unknown system state or high-frequency discontinuities, are handled in this contribution. To build a hybrid model with a higher prediction quality than the original FPM, we briefly highlight two open-source libraries: FMI.jl, which allows for the import of FMUs into the Julia programming language, as well as the library FMIFlux.jl, which enables the integration of FMUs into neural network topologies to obtain a NeuralFMU.

Model predictive control for active vibration suppression of hybrid electric vehicles during mode transition

To realize a rapid and comfortable mode transition process from pure electric mode to hybrid mode (E-H), this study proposes an innovative control strategy which combines open-loop control with model predictive control to regulate the E-H process for a P2.5 configuration hybrid electric vehicle. Firstly, a detailed vehicle longitudinal dynamic model is established and the mode transition process is divided into four phases to reveal the control problems. Based on this model, a strategy combining open-loop control with model predictive control is developed. The open-loop control is adopted before the clutch is locked in order to speed up the transition process and limit the vehicle jerk. The model predictive control (MPC) is adopted after the clutch is locked to actively suppress the vibration caused by the abrupt change of clutch torque at the moment of clutch lock-up. Finally, simulation and hardware-in-the-loop test demonstrate that the proposed strategy for mode transition can achieve both switching rapidity and riding comfort. The algorithm robustness is also discussed and the signal transmission delay influence caused by controller area network (CAN) is studied.

Physics-Based Use Case Analysis: Route Planning and Dynamic Load Prediction for Battery-Electric Buses

The electrification of transportation and goods delivery networks, including ground, marine, and air, is becoming a reality around the world. These networks are logistically complex, and with this transition there is an added knowledge gap in the understanding of the energy storage system, such as lithium-ion battery packs. In Seattle, the local transit agency King County Metro is a national leader in transitioning to a fully electrified bus fleet and is the case study for this work. As transit agencies, such as King County Metro, adopt electrified vehicles, there are many new parameters that must be considered in route planning, fleet maintenance, and charging logistics. The literature shows that a longitudinal vehicle dynamics model can be used to estimate energy and load requirements along a route [1,2]. This work utilizes these known vehicle models, vehicle specifications, and battery information in a software tool. Software inputs include geographical information system (GIS) data, ridership data, and vehicle acceleration profiles. Model results allow the user to rank routes using metrics designed to consider route efficiency, vehicle performance, and known battery degradation events, such as regenerative braking and high-power discharge. This work also introduces how the battery state of charge, overpotential, current window, and deviations from equilibrium influence the route rankings. These results provide both systems-level and module-level insights beyond conventional vehicle and logistics models, allowing the user to make decisions that are predictive rather than reactive and can improve the lifetime of the batteries on-board.[1] Gallet, M., Massier, T. & Hamacher, T. Estimation of the energy demand of electric busesbased on real-world data for large-scale public transport networks. Appl. Energy 230,344–356 (2018).[2] Asamer, J., Graser, A., Heilmann, B. & Ruthmair, M. Sensitivity analysis for energydemand estimation of electric vehicles. Transp. Res. Part D Transp. Environ. 46, 182–199(2016).

MPC-based longitudinal control strategy considering energy consumption for a dual-motor electric vehicle

To improve the energy economy and speed tracking qualities of an unmanned electric vehicle (EV) having a dual-motor powertrain, this paper proposes a model predictive control (MPC) based longitudinal control strategy considering energy consumption. Firstly, an enhanced vehicle longitudinal dynamic model considering powertrain response performance is built as predictive model to guarantee the high precision and robustness of speed prediction. Secondly, pedal command is solved by an online activity set method aiming at minimizing speed tracking errors to realize fast and reliable real-time solving. Finally, an efficient energy management strategy (EMS) is developed to optimize the demand torque distribution and gear shifting. Acquiring these two quantities with an offline global optimization method, the strategy addresses frequent gear shifting problems by online adjusting gear shifting lines. The real-time performance of the proposed strategy is validated in a HIL test. Results show that the proposed MPC-based strategy improves the speed tracking accuracy by 58.93% and expands the high efficiency range of powertrain by 40.93%. The equivalent electric consumption of the EV is reduced by 9.29%. This study provides a foundation for the practical application of longitudinal control algorithms on EVs in the future.

Performance Comparison Between Predictive Functional Control and PID Algorithms for Automobile Cruise Control System

This work presents a performance comparison between a Predictive Functional Control (PFC) and a traditional Proportional Integral Derivative (PID) controller specifically for a cruise control application. The tuning efficacy, constraints handling, and disturbance rejection features of both controllers are analysed by comparing their closed-loop response. A simplified nonlinear vehicle longitudinal dynamics model is derived and utilised as a plant to simulate the control response from a real car. For a fair comparison, both PFC and PID are tuned to achieve the similar desired closed-loop time response. Qualitatively, the results show that PFC provides a better closed-loop response, constraints handling, and disturbance rejection compared to PID. Besides, it is also found that the tuning approach of PFC is more intuitive and practical in nature which can be very handy for the future development of an autonomous cruise control application.

Fracture mechanism of transmission spindle system for high-speed tracked vehicle

According to the traits of engine output torque and vehicle longitudinal dynamic model, considering the influence of the vehicle, the dynamic model of the transmission spindle system is established by using the lumped mass model, and the Runge–Kutta is used to solve the model under sinusoidal excitation and step excitation in starting stage, the time–frequency of transmission spindle under two kinds of excitation is analyzed. Modal analysis is used to decouple differential equations and solve the response of a single-degree-of-freedom system. The analytical approach and numerical solution are compared to each other to verify the correctness of the analysis method, the impact of rotating shaft torsional stiffness on the natural characteristics and dynamic load are analyzed, and the fracture mechanism of transmission spindle is further explored. The analysis results show that when the spindle stiffness is asymmetric, the sinusoidal excitation causes the dynamic load on both sides of the spindle and the driving force on both sides of the vehicle track to be inconsistent, the step excitation induces the second-order low-frequency resonance. Under the small damping, the vibration attenuation is slow, the right end of the spindle is broken under two combined excitations. When the stiffness of the spindle is symmetrical, the influence of sinusoidal excitation on both sides of the spindle is undifferentiated, and the vibration caused by step excitation decays quickly. Finally, the theoretical analysis results were confirmed by crack morphology, vehicle test data.

Research on Torque Distribution of Four-Wheel Independent Drive Off-Road Vehicle Based on PRLS Road Slope Estimation

In view of the high difficulty in coupling of various electric vehicle parameters, intractable parameter estimation, and unreasonable distribution of vehicle driving torque, the four-wheel hub motor is applied to drive electric vehicles, which can instantly obtain the torque and speed of the hub motor and achieve precise control of the torque of each wheel. According to the vehicle longitudinal dynamics model, a progressive RLS (PRLS) algorithm for real-time estimation of vehicle mass and road gradient is proposed. Meanwhile, by means of taking the longitudinal acceleration of the vehicle and the road gradient obtained from the estimation algorithm as the parameter of the torque distribution at the front and rear axles, a dynamic compensation and distribution control strategy of the front and rear axle torques is designed. Moreover, based on hardware-in-the-loop real-time simulation and real-vehicle tests, the effectiveness of the proposed estimation algorithm and the rationality of the real-time distribution control strategy of driving torque are verified.

Braking intensity recognition with optimal K-means clustering algorithm

Recognizing a driver’s braking intensity plays a pivotal role in the development of modern driver assistance and energy management systems; and therefore, it is especially important to autonomous and electric vehicles. This paper aims at developing a system for recognizing a driver’s braking intensity based on the pressure produced in the brake master cylinder. In this regard, a model-based, synthetic data generation concept is used to generate the training dataset. This technique involves two closed-loop controlled models: an upper-level longitudinal vehicle dynamics model, and a lower-level brake hydraulic, dynamic model. The adaptive particularly tunable fuzzy particle swarm optimization algorithm is recruited to solve the optimal K-means clustering problem and to determine the best number of clusters and centroids into which the brake pressure data are to be divided. The obtained results reveal that the brake pressure data for a vehicle traveling the new European driving cycle can be best partitioned into two clusters. A driver’s braking intensity may, therefore, be clustered as moderate or intensive. With the ability to automatically recognize a driver’s pedal feel, the system developed in this research could be implemented in intelligent driver assistance systems as well as in electric vehicles equipped with intelligent, electromechanical brake boosters.

Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation

In this paper, vehicle mass estimation problem was researched by the Kalman Filtering process to investigate the effectiveness of Extended Kalman Filter technique on mass estimation, which has widely been studied on the related literature with all the successful results. For the purpose, a longitudinal vehicle dynamics model was developed, and the equations were transformed into the well-known state space form. Nonlinear problem in its nature was discretised and linearized by using Euler method. In addition, road slope was calculated by a road slope inclinometer logic. IPG CarMaker® was utilized to overcome the nonlinearities, design the test environment and the conditions, as it was observed that the simulation results were not met the expectations with the logged vehicle physical test data. Several test scenarios were run to simulate level and wavy road with different vehicle speeds. The results showed that the Extended Kalman Filter could provide a reasonable convergence when the initial values were correctly selected, no nonlinear issue occurred and certain tuning parameters were defined with stable boundary conditions for mass estimation.

Analysis of Motion Characteristics of a Controllable Ventilated Supercavitating Vehicle Under Accelerations

Abstract The development of a maneuverable underwater high-speed vehicle is worthy of attention and study using supercavitation drag reduction theory and technology. The supercavity shape determines the hydrodynamics of the vehicle, and especially during a maneuver, its unsteady characteristics have a significant impact on the motion stability of the vehicle. The three-dimensional dynamic model of a ventilated supercavitating vehicle is established using the unsteady supercavity dynamic model based on the rigid body dynamics theory as an extension of the vehicle's longitudinal dynamic model in our recent work. The vehicle's accelerating and decelerating motions are simulated in the straight flight state using a self-developed numerical method based on the vehicle's dynamic model with the designed control law. Motion characteristics are analyzed on the evolution laws of the vehicle's motion state variables and control variables and the supercavity's characteristic parameters (i.e., ventilation cavitation number, supercavity maximum diameter, and supercavity length) in the acceleration motions. The evolution laws in the accelerating and decelerating motions are compared, and the effects of the acceleration on the laws are further analyzed. This study lays the foundation for the in-depth study of the hydrodynamic characteristics and motion stability of ventilated supercavitating vehicles in maneuvering states.