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To establish battery electric vehicles as an attractive alternative to internal combustion vehicles, charging times of 15 min or less are increasingly demanded. This is especially challenging for lower battery temperatures, as this exacerbates the risk of accelerated battery degradation due to lithium plating. Therefore, active battery heating is utilized in state-of-the-art electric vehicles. To evaluate the impact of such heating strategies at vehicle level, we deployed an electrochemical battery model coupled with a longitudinal vehicle dynamics model. Using anode potential control to prevent lithium plating, we assess the time-saving potential versus the energy cost of different preconditioning and fast-charging strategies. The results reveal substantial energy saving and charge speed increase potential through optimal charge-stop planning, preconditioning timing, cost-adjusted thermal management thresholds, and considering driving behavior. This emphasizes the need for advanced operation strategies, taking into account both battery-level electrical and thermal restrictions, as well as vehicle integration and route planning. [ABSTRACT FROM AUTHOR]

In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model's prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R2 value of 0.97 for unknown conditions. [ABSTRACT FROM AUTHOR]

Direct yaw moment control (DYC) can effectively improve the yaw stability of four-wheel distributed drive electric vehicles (4W-DDEVs) under extreme conditions, which has become an indispensable part of active safety control for 4W-DDEVs. This study proposes a novel hierarchical DYC architecture for 4W-DDEVs to enhance vehicle stability during ever-changing road conditions. Firstly, a vehicle dynamics model is established, including a two-degree-of-freedom (2DOF) vehicle model for calculating the desired yaw rate and sideslip angle as the control target of the upper layer controller, a DDEV model composed of a seven-degree-of-freedom (7DOF) vehicle model, a tire model, a motor model and a driver model. Secondly, a hierarchical DYC is designed combining the upper layer yaw moment calculation and low layer torque distribution. Specifically, based on Matlab/Simulink, improved linear quadratic regulator (LQR) with weight matrix optimization based on inertia weight cosine-adjustment particle swarm optimization (IWCPSO) is employed to compute the required additional yaw moment in the upper-layer controller, while quadratic programming (QP) is used to allocate four motors' torque with the optimization objective of minimizing the tire utilization rate. Finally, a comparative test with double-lane-change and sinusoidal conditions under a low and high adhesion road surface is conducted on Carsim and Matlab/Simulink joint simulation platform. With IWCPSO-LQR under double-lane-change (DLC) condition on a low adhesion road surface, the yaw rate and sideslip angle of the DDEV exhibits improvements of 95.2%, 96.8% in the integral sum of errors, 94.9%, 95.1% in the root mean squared error, and 78.8%, 98.5% in the peak value compared to those without control. Simulation results indicate the proposed hierarchical control method has a remarkable control effect on the yaw rate and sideslip angle, which effectively strengthens the driving stability of 4W-DDEVs. [ABSTRACT FROM AUTHOR]

Accurate vehicle acceleration prediction is useful for developing reliable Advanced Driving Assistance Systems (ADAS) and improving road safety. The existence of driver heterogeneity magnifies the variations in acceleration data, leading to consequential impacts on the precision of vehicle acceleration prediction. However, few studies have fully considered the driver heterogeneity when predicting vehicle acceleration. To model the characteristics of individual drivers, this study first identifies the driving behavior semantics which is defined as the underlying patterns of driving behaviors. The analysis results from the coupled hidden Markov model (CHMM) are used to evaluate the driving behavior differences between different drivers by Wasserstein distance. Then the convolutional neural network (CNN) and long short-term memory (LSTM) network are applied to predict vehicle acceleration. To validate the accuracy of the proposed prediction framework, vehicle acceleration data in car-following conditions is extracted from the safety pilot model deployment (SPMD) dataset. The segmentation results indicate that the CHMM possesses a robust capacity for modeling driving behavior. The prediction results demonstrate that the proposed framework, which incorporates driver clustering before prediction, significantly improves the accuracy of predictions. And the CNN-LSTM outperforms the LSTM in predicting vehicle acceleration during car-following scenarios. The findings from this study can enhance the development of personalized functionalities within ADAS to promote its deployment, thereby improving its acceptance and safety. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Despite its excellent performance in path tracking control, the model predictive control (MPC) is limited by computational complexity in practical applications. The neural network control (NNC) is another attractive solution by learning the historical driving data to approximate optimal control law, but a concern is that the NNC lacks security guarantees when encountering new scenarios that it has never been trained on. Inspired by the prediction process of MPC, the deviation sequence neural network control (DS-NNC) separates the vehicle dynamic model from the approximation process and rebuilds the input of the neural network (NN). Taking full use of the deviation sequence architecture and the real-time vehicle dynamic model, the DS-NNC is expected to enhance the adaptability and the training efficiency of NN. Finally, the effectiveness of the proposed controller is verified through simulations in Matlab/Simulink. The simulation results indicate that the proposed path tracking NN controller possesses adaptability and learning capabilities, enabling it to generate optimal control variables within a shorter computation time and handle variations in vehicle models and driving scenarios. [ABSTRACT FROM AUTHOR]

This paper proposed a novel direct yaw moment control (DYC) system to enhance vehicle stability and handling performance in various driving conditions and overcome the chattering problem of traditional sliding mode control. Accordingly, a DYC strategy is developed for eight-wheeled DDEVs by utilizing a super-twisting sliding mode (STSM) algorithm. Initially, a three-degrees-of-freedom model, nonlinear tire model, and motor model are established for vehicles. Subsequently, the reference yaw rate is obtained based on the reference model of the vehicle to serve as a control target. The DYC strategy is then established using the error between the actual yaw rate and the reference yaw rate as the input. Moreover, a traditional sliding mode (SM) controller is developed to enhance vehicle stability. A second-order SM controller is designed by incorporating a STSM control algorithm to address the chattering problem associated with traditional SM controllers. The algorithm adaptively adjusts the sliding surface and controls the gains based on the dynamic state of the vehicle. The effectiveness of the proposed control strategy is validated via hardware-in-the-loop simulations. [ABSTRACT FROM AUTHOR]

How to improve the driving performance of the vehicle while carrying out path tracking control has become a hot issue in current research. In this paper, an MPC (Model predictive control) path tracking control algorithm incorporating differential braking control is proposed. By establishing a vehicle dynamics model of a semi-trailer train, the model predictive control theory is adopted for path tracking. Then, the vehicle dynamics model, considering the additional yaw moment, is established to design the differential braking control strategy. Under low-speed working conditions, the PID (Proportional Integral Derivative) algorithm is used to solve the additional yaw moment with the yaw rate of the tractor traveling alone as the desired value. Under high-speed working conditions, the Fuzzy PID algorithm is used to solve the additional yaw moment with the control objective of reducing the articulation angle. Simulation models are built using MATLAB/Simulink, and TruckSim for numerical experimental validation. The numerical experimental results show that the differential braking control method proposed in this paper can improve the maneuverability of vehicles driving in low-speed conditions and the stability of vehicles driving in high-speed conditions without decreasing the precision of path tracking control. [ABSTRACT FROM AUTHOR]

In this paper, a system identification methodology based on vehicle response and test track prioritization is presented. The proposed method can be used to perform time-domain parameter estimation by simply driving the vehicle on different road profiles, thus eliminating the necessity of having a special test setup or excitation input feed. The method is based on exploiting the dependence of the identifiability of vehicle parameters on vehicle responses and test tracks. To prioritize the test tracks, the responses and test tracks are weighed based on their capabilities to estimate the vehicle parameters, and these weights are obtained in a preliminary analysis of a seven-degree-of-freedom ride model developed in MATLAB® (R2021b). An optimization framework based on a differential evolution scheme with an optional archive scheme (JADE) is used to estimate the twelve vehicle parameters. The proposed system identification scheme is verified on tests using a high-fidelity vehicle model developed in ADAMS® (29.2)using several tracks with different severities. Experimental validation is performed on a test vehicle, and it is demonstrated that the vehicle suspension system parameters can be identified accurately with fast convergence performance. [ABSTRACT FROM AUTHOR]

Fuel-saving-oriented collaborative driving is a highly promising yet challenging endeavor that requires satisfying the driver's operational intentions while surpassing the driver's fuel-saving performance. In light of this challenge, the paper introduces an innovative collaborative driving strategy tailored to the objective of fuel conservation in the context of commercial vehicles. An enhancement to this strategy involves the development of a network prediction model for vehicle speed, leveraging insights from driver style recognition. Employing the predicted speed as a reference, a model-predictive-control-based optimal controller is designed to track the reference while optimizing fuel consumption. Furthermore, a straightforward yet effective collaborative rule is proposed to ensure alignment with the driver's intention. Subsequently, the proposed control scheme is validated through simulation and real-world driving data, revealing that the human–machine cooperative driving controller saves 4% more fuel than human drivers. [ABSTRACT FROM AUTHOR]

The driving smoothness of vehicles is heavily influenced by their suspension system, and implementing active suspension control can effectively minimize the vibration movement of the vehicle and ensure a comfortable driving experience. An 8-DoF active suspension model of the full vehicle is established, and a fuzzy-PID controller is designed to autonomously regulate the parameters of the PID controller. Using the MATLAB/Simulink environment, a simulation model for suspension is created, and the vibration characteristics of passive, PID control, and fuzzy-PID control suspensions are compared with the help of the continuous crossing road hump model and C-level road model as road inputs. The results show that the utilization of fuzzy-PID control considerably diminishes the vertical, pitch, and roll oscillations of the suspension body and modifies the suspension dynamic deflection and tire dynamic load in contrast to the other two scenarios, thus enhancing ride comfort. Fuzzy-PID control led to a decrease of approximately 40% in acceleration, 25% in suspension workspace, and 30% in tire deflection compared to passive suspension. In addition, the reduction in acceleration is about 20%, the reduction in suspension workspace is approximately 10%, and the reduction in tire deflection is about 15% compared to the PID control suspension system. [ABSTRACT FROM AUTHOR]

When an intelligent vehicle changes lanes, the state of other vehicles may change, which increases the risk of collision. Therefore, real-time local path replanning is needed at this time. Based on model predictive control (MPC), a lane-change trajectory replanning strategy was proposed, which was divided into a lane-change trajectory correction strategy, a lane-change switchback strategy and forward active collision avoidance strategy according to collision risk. Based on the collision risk function of the rectangular safety neighborhood, the objective functions were designed according to the specific requirements of different strategies. The vehicle lateral controller based on MPC and the vehicle longitudinal motion controller were established. The longitudinal velocity was taken as the joint point to establish the lateral and longitudinal integrated controller. The trajectory planning module, trajectory replanning module and trajectory tracking module were integrated in layers, and the three trajectory replanning strategies of lane-change trajectory correction, lane-change switchback and forward active collision avoidance were respectively simulated and verified. The simulation results showed the trajectory replanning strategy achieves collision avoidance under different scenarios and ensures the vehicle's driving stability. The trajectory tracking layer achieves accurate tracking of the conventional lane-change trajectory and has good driving stability and comfort. [ABSTRACT FROM AUTHOR]

A novel vehicle dynamic model considering the coupling mechanism between the front wheel shimmy and the vehicle lateral handling motions is established to investigate the interacting effect between the steering shimmy instability and the general motion instability, that focuses especially on the influence of the tyre force saturation induced by lateral-longitudinal coupling when different degrees and allocation strategy of braking force are applied. The typical 3-degree-of-freedom front wheel shimmy structure of dependent suspension is dynamically modelled by using the Lagrange method, alongside the equations for plane vehicle handling motions introduced. The stretched-string-based tyre kinematic equations, that describe the transient tyre-road contact status, are proposed by analytically deriving the coupling relations between the angular shimmy variables and vehicle motion status. A theoretically approximated linear tyre model, based on the coupling between vertical, longitudinal, and lateral directional forces, is analytically derived for stability analysis. Meanwhile, a complete PAC-2002 tyre model, with experimentally identified parameters, is employed for simulation verification. The stability chart of linear stability analysis shows the variation of the system bifurcation curves and stable parameter region under different parametric configurations, which gives that the degrees and allocations of braking force largely affect the stable parameter domain and the global stability. The variations of the system characteristic roots indicate that both Hopf and S-N bifurcation could happen due to shimmy oscillations and destabilization of motion, respectively, and their co-existence at certain parameter domains make the dynamic behaviour further unpredictable and complex. Numerical simulation verifies the dynamical characteristics of shimmy, and further analysis shows that three types of vehicle motion instability which coexisted with shimmy instability could be triggered by the coupling effect. In addition, the variation law of limit-cycle amplitude under varying vehicle driving inputs is summarized, and finally, a preliminary experiment is conducted for validations. [ABSTRACT FROM AUTHOR]

In order to improve the lateral stability and handling performance of in-wheel motor drive electric vehicles, a coordinated control method considering lateral stability and differential power-assisted steering performance is proposed. A vehicle dynamics model with two degrees of freedom is established, in which the influence of system disturbance is considered. At the same time, the effect of differential torque on vehicle power-assisted steering control was analyzed, and a differential power-assisted steering control method of vehicle was designed based on referenced steering wheel torque. In response to the coupling relationship and dynamic game problem between the lateral stability control system and the differential power-assisted steering control system, a coordinated control system with a vehicle lateral stability module and a differential power-assisted steering module was designed based on the Nash equilibrium game theory, achieving comprehensive optimization of multi-objective performance. Corresponding simulation tests were conducted in the co-simulation vehicle model, and the results show that the proposed coordinated control method can achieve the differential power-assisted steering control function of vehicle while ensuring lateral stability. [ABSTRACT FROM AUTHOR]

Planning for autonomous vehicles to merge into high‐density traffic flows within limited mileage is quite challenging. Specifically, the driving trajectory will inevitably have intersections with other vehicles whose driving intentions can't be directly observed. Herein, a two‐stage algorithm framework that is decomposed into the longitudinal and lateral planning processes for online merging planning is proposed. An improved particle filter is used to estimate the driving models of surrounding vehicles for predicting their future driving intentions. Based on Monte Carlo tree search (MCTS), different action spaces are evaluated for longitudinal merging gap selection and lateral interactive merging operation, while heuristic pruning is used to reduce the computation cost. Moreover, the coefficients related to the driving styles are introduced, and their influences on merging performance are analyzed. Finally, the proposed algorithm is implemented in a two‐lane simulation environment. The results show that the proposal has outperformed other baseline methods. [ABSTRACT FROM AUTHOR]

The motion planning and control method of automated vehicles, as the key technology of automated vehicles, directly affects the safety, comfort, and other technical indicators of vehicles. The planning module is responsible for generating a vehicle driving path. The control module is responsible for driving the vehicle. In this study, we review the main methods and achievements in motion planning and motion control for automated vehicles. The advantages and disadvantages of various planning and control methods are comparatively analyzed. Finally, some predictions and summaries based on the existing research results and trends are proposed. Through this analysis, it is believed that various types of algorithms will be further integrated in the future to complement each other's strengths and weaknesses. The next area of research will be to establish more accurate vehicle models to describe vehicle motion, improve the generalization-solving ability of algorithms, and enhance the planning and control of integrated 'human-vehicle-road' traffic systems. [ABSTRACT FROM AUTHOR]

In the future, considering the expansion of the autonomous driving society, autonomous driving systems that can drive safely and quickly will be required for the purpose of saving lives and transporting goods even on rough road such as snowy, icy, and unpaved roads. In such unknown environments, technologies that combine model-based control and artificial intelligence (AI) are attracting attention for the purpose of ensuring operational stability and reliability. The second author has proposed a vehicle driving model that is robust to road geometry and ever-changing environmental disturbances. This model is based on a two-wheel model, and expresses the error in the position of the center of gravity of the vehicle by the front wheel steering angle deviation, and adaptively estimates this deviation. However, this model has large modeling errors when driving at high velocity on slippery roads. In this study, we extend this model proposed in previous study, and propose a new vehicle driving model that can handle situations such as driving with large acceleration changes and steering on bad roads such as snowy and wet roads. Then, we demonstrate the usefulness of the proposed method in a simulation using vehicle motion analysis software. [ABSTRACT FROM AUTHOR]

Advanced active safety systems play a crucial role in ensuring the safe driving of vehicles in critical conditions such as an obstacle avoidance manoeuvre. However, conventional techniques relying mainly on braking interventions may not result in the desired vehicle response in such situations. Over-actuation through the control of individual motion actuators could potentially improve the safety performance of vehicles. This study evaluates various configurations of motion actuators for path following and yaw stability control of vehicles in critical driving scenarios. The configurations include active front steering (S), active front steering + torque vectoring (ST), active front steering + active camber (SC) and active front steering + torque vectoring + active camber (STC). The evaluation is achieved based on a nonlinear model predictive control formulation, which considers yaw stability and the physical limits of motion actuators. This problem formulation uses a double-track vehicle model, combined with the Dugoff tyre model and its variant with the camber effect, to model the vehicle dynamics. The actuator configurations are evaluated regarding the passing velocity, tracking accuracy, safety distance and robustness to reference trajectory variation. The results indicate that the integrated control of STC performs the best among all the four configurations while S performs the worst. Furthermore, SC is generally superior to ST. [ABSTRACT FROM AUTHOR]

The vehicle particle model was built to compare and analyze the effectiveness of three different collision avoidance methods. The results show that during vehicle high-speed emergency collision avoidance, lane change collision avoidance requires a smaller longitudinal distance than braking collision avoidance and is closer to that with a combination of lane change and braking collision avoidance. Based on the above, a double-layer control strategy is proposed to avoid collision when vehicles change lanes at high speed. The quintic polynomial is chosen as the reference path after comparing and analyzing three polynomial reference trajectories. The multiobjective optimized model predictive control is used to track the lateral displacement, and the optimization objective is to minimize the lateral position deviation, yaw rate tracking deviation, and control increment. The lower longitudinal speed tracking control strategy is to control the vehicle drive system and brake system to track the expected speed. Finally, the lane changing conditions and other speed conditions of the vehicle at 120 km/h are verified. The results show that the control strategy can track the longitudinal and lateral trajectories well and achieve effective lane change and collision avoidance. [ABSTRACT FROM AUTHOR]

This study presents a dynamic model of a passenger vehicle and analyzes its dynamic characteristics and responses. A dynamic vehicle model with seven degrees of freedom was established to analyze the behavior of a driving vehicle. The vehicle model had three degrees of freedom for the sprung mass's motion and four degrees for the unsprung masses. For this model, the equations of motion were derived using Lagrange's equation. To verify the model, the suspension deformations computed using the model were compared with those measured through three actual vehicle driving tests: the slalom, double lane change, and step steer tests. Furthermore, we investigated the effects of suspension stiffness, suspension damping, and anti-roll bar torsional stiffness on the dynamic characteristics and responses of the vehicle model. This study presented a new full-car model that can analyze a turning vehicle's behavior in response to changes in the steering angle input. The developed dynamic vehicle model may help vehicle designers predict the dynamic responses of a vehicle through simulation without performing a driving test. [ABSTRACT FROM AUTHOR]

The rapid development of the automobile industry has made life easier for people, but traffic accidents have increased in frequency in recent years, making vehicle safety particularly important. This paper proposes an improved YOLOv5s algorithm for vehicle identification and detection to reduce vehicle driving safety issues based on this problem. In order to solve the problems of a disappearing model training gradient in the YOLOv5s algorithm, difficulty in recognizing small objects and poor recognition accuracy caused by the boundary frame regression function, it is necessary to implement a new function. These aspects have been enhanced in this article. On the basis of the traditional YOLOv5s algorithm, the ELU activation function is used to replace the original activation function. The attention mechanism module is then added to the YOLOv5s algorithm's backbone network to improve the feature extraction of small and medium-sized objects. The CIoU Loss function replaces the original regression function of YOLOv5s, thereby enhancing the convergence rate and measurement precision of the loss function. In this paper, the constructed dataset is utilized to conduct pertinent experiments. The experimental results demonstrate that, compared to the previous algorithm, the mAP of the enhanced YOLOv5s is 3.1% higher, the convergence rate is 0.8% higher, and the loss is 2.5% lower. [ABSTRACT FROM AUTHOR]

Accurate acquisition of vehicle dynamics state information is essential for vehicle active safety control systems. However, these states cannot be easily measured, and the measurement is expensive. Conventional Kalman filters perform well for vehicle state estimation in Gaussian environments but exhibit low accuracy and robustness under practical non-Gaussian noise. Vehicle model parameter ingestion, inaccurate tire force calculation, and non-Gaussian noise from on-board sensors cause great challenges to the estimation of vehicle driving states. Therefore, this paper presents a robust hierarchical estimation scheme for vehicle driving state based on the maximum correntropy square-root cubature Kalman filter (MCSCKF) using easily measurable on-board sensor information. First, the vehicle mass is dynamically updated based on the recursive least squares (FRLS) method with a forgetting factor. Then, an adaptive sliding mode observer (ASMO) is designed to estimate the longitudinal and lateral tire forces. Ultimately, the vehicle states are estimated based on the MCSCKF under non-Gaussian noise. Two typical operating situations are carried out to verify the validity of the proposed estimation scheme. The results prove that the proposed estimation scheme can estimate the vehicle's driving state accurately compared to other common methods. And the MCSCKF algorithm has better accuracy and robustness than the traditional Kalman filters for vehicle state estimation in non-Gaussian situations. [ABSTRACT FROM AUTHOR]

This paper proposes a test scenario design method that reflects the longitudinal characteristics of reality for effective verification of advanced driver assistance systems (ADAS) and autonomous driving systems (ADS). Since the target systems interact with the external environment differently from the existing vehicle control system, realistic and various verification scenarios are required for verification. The proposed method consists of a vehicle model for simulating the vehicle behavior and a driver model to actively respond to the driving environment. In particular, the driver model used a model predictive control (MPC) algorithm to reflect the characteristic of human drivers. The longitudinal driving characteristics of human drivers were derived through a large-scale driving database analysis and considered in the driver model. The proposed method was compared with an existing car-following model using computer simulations. It was confirmed that its longitudinal driving behavior is similar to that of human drivers and that various scenarios can be designed by changing the model parameters. [ABSTRACT FROM AUTHOR]

The optimization of operations of subway systems has critical importance in terms of energy efficiency and costs. Therefore, driving management of subway vehicles has been gaining more importance day by day. Optimal Driving Management (ODM) is the optimization of the velocity trajectory of a subway vehicle by considering operating conditions and travel time. In this study, the driving of a subway vehicle has been modeled dynamically with all parameters that affect driving. So, a realistic model has been prepared. Then, a new objective function has been proposed to reduce energy consumption by using the subway vehicle’s acceleration and braking forces parameters for ODM. The Artificial Bee Colony algorithm (ABC) and Genetic algorithm (GA) have been used on the prepared model to determine the driving dynamics of the subway vehicle. The performance of the algorithms has been evaluated in the real line network, which has multiple stations with different characteristics. The energy consumption has been reduced by 10.47% in GA and 8.92% in ABC compared to the actual driving values. Moreover, the results of the study have been analyzed in terms of passenger comfort, cost, and emission values. [ABSTRACT FROM AUTHOR]

This paper proposes an autonomous vehicle trajectory tracking system that fully considers road friction. When an intelligent vehicle drives at high speed on roads with different friction coefficients, the difficulty of its trajectory tracking control lies in the fast and accurate identification of road friction coefficients. Therefore, an improved strategy is designed based on traditional recursive least squares (RLS), which is utilized for accurate identification of the friction coefficient. First, the tire force and slip rate required for the estimation of the road friction coefficient by constructing the vehicle dynamics model and tire effective model are calculated. In this paper, a variable forgetting factor recursive least squares (VFF-RLS) method is proposed for the construction of the friction coefficient estimator. Second, the identified results are output to the model predictive controller (MPC) constructed in this paper as a way to improve tire slip angle constraints, to realize the trajectory tracking of the intelligent vehicle. Finally, the joint simulation test results of Carsim and Matlab/Simulink show that the trajectory tracking system based on the VFF-RLS friction coefficient estimator has outstanding tracking performance. [ABSTRACT FROM AUTHOR]

To improve the multidirectional motion control accuracy and driving stability of Distributed Drive Autonomous Vehicles (DDAVs) under extreme conditions, the extreme speed estimation method based on dynamic boundary and the multidirectional motion coupling control law design method based on multi degrees of freedom vehicle dynamic model are proposed. The extreme speed estimation method identifies the stable state of DDAVs by the dynamic boundary composed of yaw rate, sideslip angle and roll angle, and then estimates the extreme speed of the vehicle. The design method of multidirectional motion coupling control law adopts eight-degrees-of-freedom (8-DOF) vehicle dynamic model to design path tracking control law, speed tracking control law, yaw stability control law, and active suspension control law at the same time, so as to realize multidirectional motion coupling control. Based on the above method, the Multidirectional Motion Coupling Control System (MMCCS) of DDAVs is designed. The effectiveness of the proposed method is verified by double-line-shifting and serpentine driving simulations under different road adhesion conditions. The superiority of the method is proved by comparing the existing integrated control method. [ABSTRACT FROM AUTHOR]

This paper studies the problem of secure control of automated vehicles in a platoon-based driving pattern over a vehicular ad-hoc network (VANET) subject to various cyber attacks. The platoon under consideration is a convoy of a leader vehicle whose control input is unknown to its following vehicles and some follower vehicles with uncertain heterogeneous engine time constants, bounded disturbance and noise. First, a local estimator is developed for each follower vehicle so as to construct some confidence ellipsoidal estimation region always enclosing vehicular true state regardless of uncertain heterogenous engine time constants, bounded disturbance and noise. A convex optimization algorithm is proposed to find some optimal ellipsoidal sets and recursively solve out gain matrices of the local estimators. Then, a scalable control protocol employing the state estimates from its local and underlying neighboring estimators is designed to accomplish secure platooning control. Under the derived design technique, the resulting closed-loop platooning tracking errors are proven to remain in the vicinity of zero. Comparative studies are conducted to validate the efficacy of the proposed control method on achieving the satisfactory platooning performance by handling different attack strategies. • A scalable secure platooning control protocol is designed for automated vehicles. • A class of amplitude-bounded attacks is considered to represent various attacks. • A local set-membership estimator is designed for each follower vehicle. • A general model of vehicle longitudinal dynamics is considered. [ABSTRACT FROM AUTHOR]

To improve the overall performance of four-wheel steering vehicles under different working conditions and solve the problem of the unbalanced comprehensive control effect of a single controller, a multi-mode optimal decision control system is proposed based on the linear 2-DOF dynamic model of the vehicle, which can make the vehicle have better performance under different working conditions. The system is composed of multiple controllers, in which the self-tuning dual yaw rate feedback controller can create timely feedback and make adjustments according to the driving state of the vehicle, which is optimal under the condition of a small rotation angle at medium and high speeds. The system can match the corresponding controller according to the optimal performance of different control strategies at different speeds and angles. The simulation results show that the multi-mode optimal selection system can make the vehicle perform optimally under different working conditions, and that comprehensive performance is more prominent than that of a single controller. [ABSTRACT FROM AUTHOR]

To advance the active safety performance for vehicles, especially in extreme conditions, an active rear steering (ARS) control system is designed in this paper. A driver model is established to simulate the driving behaviour of a human driver who is in charge of the front steering control. In the ARS control system, the sliding mode predictive control (SMPC) approach is applied to the ARS controller design based on a 3 degrees of freedom (DoF) nonlinear vehicle model. In the ARS controller design, four kinds of active safety performances are considered, namely, path-tracking performance, handling performance, lateral stability, and rollover prevention. Furthermore, the priority of the four kinds of active safety performance is defined. According to the control priority, an event-triggered mechanism (ETM) is designed to adjust the SMPC controller of the ARS system to address different driving conditions. Finally, two simulation cases are conducted to evaluate the performance of the proposed ARS system. The results show that the ARS system is in favour of the active safety performance advancement for human drivers. Additionally, the comparative simulation indicates that the SMPC algorithm is superior to the fast terminal sliding mode control (FTSMC) algorithm. [ABSTRACT FROM AUTHOR]

It is well known that tracked vehicles can adapt well to all kinds of terrain. However, the safety of tracked vehicles should be considered during steering on sloped terrain. This paper focuses on the differential steering control of tracked vehicles independently driven by a hydraulic motor. Firstly, the dynamic model of hydrostatic drive system was built and the kinematics and dynamics of differential steering driving were analyzed theoretically. Secondly, in order to prevent rollover of the tracked vehicle, the method of vehicle speed constraint was proposed. The constraint conditions of vehicle speed and steering angular velocity were analyzed under different slope conditions. Thirdly, based on the analysis results, differential steering control rules for tracked vehicles were formulated. To verify the effectiveness of the control rules, the models of vehicle driving dynamics and Fuzzy PID control simulation were established in MATLAB/Simulink. Longitudinal steering simulation was carried out on a slope (0°, 30°), and an analysis of the simulation of lateral steering along the contour line was carried out. The simulation results showed that this steering control strategy was able to automatically adjust the target vehicle speed to avoid rollover while the driver was inputting steering signals. [ABSTRACT FROM AUTHOR]

Appropriate vehicle lateral stability control is the key to ensure vehicle driving safety, whereas accurate lateral stability recognition can help improve the performance of vehicle control. In this article, the vehicle stability recognition and coordinated control are studied. Firstly, the vehicle dynamic model is established, through vehicle simulation software Carsim, the attribute dataset representing the vehicle lateral stability is further obtained. Then, clustering by fast search and find of density peaks method (CFSFDP) based procedure for the classification of the lateral stability as ‘Absolutely stability’, ‘Nearly stability’ and ‘Hardly stability’ is applied. The brain emotional learning network combined with genetic algorithm (GA-BEL) model is used to train datasets to recognize vehicle stability categories during driving. For the different recognition results, a coordinated control strategy based on active front steering control (AFS) and direct yaw moment control (DYC) is designed finally. Through the Carsim/Simulink co-simulations and Hardware-in-the-Loop tests under several typical driving conditions, the superiorities of the stability recognition method and the coordinated control strategy proposed in this paper are verified. [ABSTRACT FROM AUTHOR]

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

A multi-layer controller of direct yaw moment for electric vehicles is developed in this study. In the upper layer, the yaw moment are obtained using Adaptive Sliding Mode Control (ASMC) with adaptation gain to track the desired vehicle yaw rate. The corrective yaw moments are applied by four in-wheel electric motors. The lower layer controller consists of a torque distribution algorithm and inwheel motor torque controllers as well. The proposed torque distribution algorithm is intended to distribute the reference torques of each in-wheel motor controller appropriately based on both total longitudinal force and corrective yaw moment. To elucidate the effectiveness and robustness of the above control method, the simulation under various manoeuvres was carried out. A 7-DOF non-linear vehicle model is used for simulations and their results signify that the proposed control algorithm accomplishes a proper distribution of longitudinal force among four individual wheels, in turn, enhancing the yaw stability of the vehicle. [ABSTRACT FROM AUTHOR]

In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle's cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated powertrain was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compared to the reference method from the literature. The results of this study provide an applicable vehicle modelling methodology with efficient optimization framework, which demonstrates 5.5% improvement in energy consumption compared to the reference optimization theory. [ABSTRACT FROM AUTHOR]

The evaluation of take-over performance and take-over safety performance is critical to improving the take-over performance of conditionally automated driving, and few studies have attempted to evaluate take-over safety performance. This study applied a binary logistic model to construct a take-over safety performance evaluation model. A take-over driving simulator was established, and a take-over simulation experiment was carried out. In the experiment, data were collected from 15 participants who took over the vehicle and performed emergency evasive maneuvers while performing non-driving-related task (NDRT). Then, to calibrate the abnormal trajectory, the Kalman filter is adopted to filter the disturbed vehicle positioning data and the belief rule-based (BRB) method is proposed to warn irregular driving behavior. The results revealed that the accident rate of male participants is higher than that of female participants in the three frequency take-over experiment, and the overall driving performance of female participants is higher than that of male participants. Meanwhile, medium and high take-over frequencies have a significant effect on the prevention of vehicle collisions. In the take-over safety performance evaluation model, the minimum time to collision (TTC) of 2.3 s is taken as the boundary between the dangerous group and the safety group, and the model prediction accuracy rate is 87.7%. In sum, this study enriches existing research on the safety performance evaluation of conditionally automated driving take-over and provides important implications for the design of driving simulators and the performance and safety evaluation of human-machine take-over. [ABSTRACT FROM AUTHOR]

This paper aims to study the torque optimization control of distributed drive electric vehicles in the cornering process and reduce the cornering energy consumption. The main energy consumption of the vehicle in the cornering process is analyzed clearly based on the 7-DOF vehicle dynamics model. The torque vectoring distribution (TVD) of a distributed drive electric vehicle in the process of turning was studied on the basis of the Pontryagin Minimum Principle (PMP). The Beetle Antenna Search–Particle Swarm Optimization (BAS-PSO) algorithm was used to optimize the torque distribution coefficient offline, and the algorithm was improved to improve the operation speed. Based on the vehicle dynamics characteristics, the table of torque distribution coefficient of minimum turning energy consumption and the optimal energy-saving degree of TVD control in different bending conditions were worked out. [ABSTRACT FROM AUTHOR]

Based on the demand of vehicle emission research and control, this paper presents the development of a portable vehicle measurement system (PEMS) based on SEMTECH-DS and ELPI+, the vehicle emission tests carried out on actual roads, and the data obtained for the establishment and validation of a vehicle emission model. Based on the results of the vehicle emission test, it was found that vehicle driving conditions (speed, acceleration, vehicle specific power (VSP), etc.) had a significant impact on the pollutant emission rate. In addition, local driving cycles were generated and the frequency distribution of VSP-bin under different cycles was analyzed. Then, through the establishment of an emission rate database, calculation of emission factors and validation of the emission model, a vehicle emission model based on actual road driving conditions was developed by taking VSP as the "surrogate variables". It showed that the emission factor model established in this study could better reflect the vehicle transient emissions on the actual road with high accuracy and local adaptability. Through this study, it could be found that due to the great differences in traffic development modes and vehicle driving conditions in different cities in China, the emission model based on driving conditions was a better choice to carry out the research on vehicle emission in Chinese cities. Compared with directly applying international models or quoting the recommended values of relevant macroscopic guidelines, the emission factor model established in this study, using actual driving conditions, could better reflect the vehicle transient emissions on the actual road with high accuracy and local adaptability. In addition, due to the rapid development of China's urban traffic and the rapid change of driving conditions, it was of great significance to regularly update China's urban conditions to improve the accuracy of the model, no matter which model was chosen. [ABSTRACT FROM AUTHOR]

An advanced LKS (lane keeping system) for use on curving roads is presented to maintain autonomous vehicle driving within the target lane, without unintentional lane departure. There are the following two main objectives in designing this system: one is performing perfect lane keeping and the other is ensuring the dynamic stability of the vehicle, especially when driving on a curving and low-friction road with time-varying high speed. In this paper, a combined vehicle model, consisting of a lane keeping model and a vehicle lateral dynamic model, is firstly introduced. Then, a novel adaptive-weight predictive controller is used to calculate the desired steering angle and the additional yaw moment which provide coordinated control forlane keeping and dynamic stability control. Meanwhile, a square-root cubature Kalman filter-based vehicle sideslip angle observer, with a strong tracking theory modification (ST-SRCKF), is established to estimate the sideslip angle during the driving process. Finally, HIL (hardware-in-the-loop) tests and field tests are constructed, and the results show the effectiveness of our proposed LKS controller and ST-SRCKF sideslip angle estimation. [ABSTRACT FROM AUTHOR]

There is consensus in industry and academia that Highly Automated Vehicles (HAV) and Connected Automated Vehicles (CAV) will be launched into the market in the near future due to emerging autonomous driving technology. In this paper, a mixed traffic simulation framework that integrates vehicle models with different automated driving systems in the microscopic traffic simulation was proposed. Currently, some of the more mature Automated Driving Systems (ADS) functions (e.g., Adaptive Cruise Control (ACC), Lane Keeping Assistant (LKA), etc.) are already equipped in vehicles, the very next step towards a higher automated driving is represented by Level 3 vehicles and CAV which show great promise in helping to avoid crashes, ease traffic congestion, and improve the environment. Therefore, to better predict and simulate the driving behavior of automated vehicles on the motorway scenario, a virtual test framework is proposed which includes the Highway Chauffeur (HWC) and Vehicle-to-Vehicle (V2V) communication function. These functions are implemented as an external driver model in PTV Vissim. The framework uses a detailed digital twin based on the M86 road network located in southwestern Hungary, which was constructed for autonomous driving tests. With this framework, the effect of the proposed vehicle models is evaluated with the microscopic traffic simulator PTV Vissim. A case study of the different penetration rates of HAV and CAV was performed on the M86 motorway. Preliminary results presented in this paper demonstrated that introducing HAV and CAV to the current network individually will cause negative effects on traffic performance. However, a certain ratio of mixed traffic, 60% CAV and 40% Human Driver Vehicles (HDV), could reduce this negative impact. The simulation results also show that high penetration CAV has fine driving stability and less travel delay. [ABSTRACT FROM AUTHOR]

Aiming at the problem of vehicle model tracking error and overdependence in traditional path planning of intelligent driving vehicles, a path planning method of intelligent driving vehicles based on deep reinforcement learning is proposed. Firstly, the abstract model of real environment is extracted. The model uses deep reinforcement learning end-to-end strategy (DRL-ETE) and vehicle dynamics model to train the reinforcement learning model which approaches the optimal intelligent driving. Secondly, the real scene problem is transferred to the virtual abstract model through the model transfer strategy, and the control and trajectory sequences are calculated according to the trained deep reinforcement learning model in the environment. Finally, the optimal trajectory sequence is selected according to the evaluation function in real environment. Because the storage mode of experience playback mechanism of Deep Q-Network algorithm is FIFO, and the sampling mode of later playback training is average sampling, the efficiency of experience playback is low. These two problems lead to the slow process of intelligent driving vehicle to target and route finding. And because of greedy strategy, the information of exploration map is incomplete, and IDQNPER algorithm model is proposed. When storing samples, the samples are given weight, and sent to the network in priority order for sample training. Meanwhile, the importance data sequence is retained in the experience playback cache area, and the sequence with high similarity is removed. The total reward value is about 10% higher than the reward value of original Deep Q-network, which proves that the accuracy of intelligent driving vehicles tends to target points is higher. In order to further realize the autonomous decision-making of intelligent driving vehicles and solve the problem of relying too much on map information in the traditional human planning framework, an end-to-end path planning method is proposed based on the depth reinforcement learning theory, which maps the action instructions directly from the sensor information and then issues them to the intelligent driving vehicles. Firstly, CNN and LSTM are used to process radar and camera information. By comparing the advantages of DQ, Double DQN, Dueling DQN and PER algorithm, IDQNPER algorithm is used to train the automatic path planning of intelligent driving vehicles. Finally, the simulation and verification experiments are carried out in the static obstacle environment. The test results show that IDQNPER algorithm is adaptable to intelligent vehicles in different environments. The method can deal with the continuous input state and generate the continuous control sequence of the corner control, which can reduce the lateral tracking error. At the same time, the generalization performance of the model can be improved and the overdependence problem can be reduced by experience playback. [ABSTRACT FROM AUTHOR]

The driving state of a self-driving vehicle represents an important component in the self-driving decision system. To ensure the safe and efficient driving state of a self-driving vehicle, the driving state of the self-driving vehicle needs to be evaluated quantitatively. In this paper, a driving state assessment method for the decision system of self-driving vehicles is proposed. First, a self-driving vehicle and surrounding vehicles are compared in terms of the overtaking frequency (OTF), and an OTF-based driving state evaluation algorithm is proposed considering the future driving efficiency. Next, a decision model based on the deep deterministic policy gradient (DDPG) algorithm and the proposed method is designed, and the driving state assessment method is integrated with the existing time-to-collision (TTC) and minimum safe distance. In addition, the reward function and multiple driving scenarios are designed so that the most efficient driving strategy at the current moment can be determined by optimal search under the condition of ensuring safety. Finally, the proposed decision model is verified by simulations in four three-lane highway scenarios. The simulation results show that the proposed decision model that integrates the self-driving vehicle driving state assessment method can help self-driving vehicles to drive safely and to maintain good maneuverability. [ABSTRACT FROM AUTHOR]

In vehicle trajectory prediction, the difficulty in modeling the interaction relationship between vehicles lies in constructing the interaction structure between the vehicles in the traffic scene. Majority of existing models only focus on the interaction between the historical trajectory of the vehicle and the surrounding vehicles in the spatial domain, and do not pay attention to the interaction between the vehicle and the non-Euclidean correlation structure (graph structure) that exists in the environment. In order to overcome the deficiencies in the existing models, this paper proposes the Environment-Attention Network model (EA-Net) to obtain the full interactive information between the vehicle and its driving environment. In the proposed model, a new type of parallel structure consisting of Graph Attention network (GAT) and Convolutional social pooling containing Squeeze-and-Extraction mechanism (SE-CS), is constructed as the environmental feature extraction module and embedded in LSTM encoder-decoder. This structure solves the limitation of the dimension and structure influence when constructing the interaction model between the vehicle and the surrounding environment, making the extracted feature information comprehensive and effective. The prediction accuracy of the model with the RMSE loss function is tested on two public datasets– NGSIM and highD, and compared with several state-of-the-art trajectory prediction algorithm models. The results show that the prediction accuracy of the proposed Environment-Attention Network in the two datasets is more than 20% higher than that of the single structure model, which indicates that the proposed model proposed has superior performance and better adaptability to different traffic environments compared with the existing models. [ABSTRACT FROM AUTHOR]

Electric vehicles (EV) has been introduced as an alternative to the conventional vehicle as a mid-range vehicle to the world due to environmental considerations and technology development. Many electric motors can be used as the main driver of electric vehicles. Induction Motor (IM) is one of these types. This search uses MatLab/Simulink program for modeling and simulating MITSUBISHI I-MIEV type vehicle as a model of city vehicle based on an IM as the main engine. Four cases were studied by simulating a complete (EV + IM) model with different slopes of road, wind speeds, different road types, and different speeds of vehicle. Simulation tests indicate the IM's ability to drive the electric vehicle in high stability. Thus the specifications of EV depend upon the specifications of IM used as the main drive for EV and mechanical components for EV. [ABSTRACT FROM AUTHOR]

Intelligent‐driving technologies play crucial roles in reducing road‐traffic accidents and ensuring more convenience while driving. One of the significant challenges in developing an intelligent vehicle is how to operate it safely without causing fear in other human drivers. This paper presents a new behavioral decision‐making model to achieve both safety and high efficiency and also to reduce the adverse effect of autonomous vehicles on the other road users while driving. Moreover, we attempt to adapt the model for human drivers so that users can understand, adapt, and utilize intelligent‐driving technologies. Furthermore, this paper proposes a combined spring model for assessing driving risk. Thus, we analyze some driving characteristics of drivers and choose "safety" and "high efficiency" as the two main factors that are pursued by drivers while driving. Based on the principle of least action, a multiobjective optimization cost function is established for the decision‐making model. Finally, we design six unprotected left‐turn scenarios at a T‐intersection and three unprotected left‐turn scenarios at a standard two‐lane intersection for applying simulation algorithm and provide a decision‐making map for developing intelligent‐driving technologies. Based on the principle of least action, this paper demonstrates that optimization theory can give insight into drivers' behavior and can also contribute to the development of intelligent‐driving algorithms. The experimental results reveal that the behavioral decision‐making model can always avoid collision accidents on the premise of ensuring certain efficiency, and it can achieve 97.01%, 94.52%, 96.67%, 91.18%, 101.27%, 83.33%, 102.94%, 103.03%, and 105.77% of time to intersection's maximum pass rate in the considered nine scenarios. [ABSTRACT FROM AUTHOR]

All human drivers can be characterised by their habitual choice of driving behaviours, which results in a wide range of observed driving patterns and manoeuvres. Developing control strategies for autonomous vehicles that address this feature would increase the public acceptance of such vehicles. Therefore, this paper proposes a novel approach to developing rule-based fuzzy logic driver models that simulate different driving styles in the car-following regimes. These driver models were trained with the collected on-road driving data to capture corresponding human drivers' characteristics. The proposed approach consists of three main components: collecting on-road driving data, developing a vehicle model, and establishing the car-following driver models. Firstly, an instrumented vehicle was used to collect driving data over the same route for three consecutive months. Car-following scenarios during these journeys were extracted, and related data were processed accordingly. Afterwards, a representative model of the instrumented vehicle was created and evaluated. Finally, a fuzzy logic driver model that uses humanized inputs was developed and calibrated with the recorded data. The developed driver model's performance was assessed using the collected driving data and a baseline PID driver model. With the performance validated, models representing more aggressive and more defensive driving styles were derived following the same procedure. A cross-driver analysis was then implemented in a normalized car-following scenario with the established vehicle model to investigate the impacts of different driving styles further. The developed driver model can introduce driving styles into drive cycle experiments and replicate on-road real driving emission tests in the laboratory. Moreover, as the proposed method has high robustness to incomplete datasets, it can be a more cost-effective option to facilitate the development of humanized and customized vehicle control strategies for autonomous driving. [ABSTRACT FROM AUTHOR]

As the complex driving scenarios bring about an opportunity for application of deep learning in safe driving, artificial intelligence based on deep learning has become a heatedly discussed topic in the field of advanced driving assistance system. This paper focuses on analysing vehicle active safety control of collision avoidance for intelligent connected vehicles (ICVs) in a real driving risk scenario, and driving risk perception is based on the ICV technology. In this way, trajectories of surrounding vehicles can be predicted and tracked in a real-time manner. In this paper, vehicle dynamics based state-space equations conforming to model predictive controllers are set up to primarily explore and identify a safety domain of active collision avoidance. Furthermore, the model predictive controller is also designed and calibrated, thereby implementing the active collision avoidance strategy for vehicles based on the model predictive control method. At last, functional testing is conducted for the proposed active collision avoidance control strategy in a designed complex traffic scenario. The research findings here can effectively improve automatic driving, intelligent transportation efficiency and road traffic safety. [ABSTRACT FROM AUTHOR]

Vehicle technology development drives economic development but also causes severe mobile pollution sources. Eco-driving is an effective driving strategy for solving air pollution and achieving driving safety. The on-board diagnostics II (OBD-II) module is a common monitoring tool used to acquire sensing data from in-vehicle electronic control units. However, different vehicle models use different controller area network (CAN) standards, resulting in communication difficulties; however, relevant literature has not discussed compatibility problems. The present study researched and developed the universal OBD-II module, adopted deep learning methods to evaluate fuel consumption, and proposed an intuitive driving graphic user interface design. In addition to using the universal module to obtain data on different CAN standards, this study used deep learning methods to analyze the fuel consumption of three vehicles of different brands on various road conditions. The accuracy was over 96%, thus validating the practicability of the developed system. This system will greatly benefit future applications that employ OBD-II to collect various types of driving data from different car models. For example, it can be implemented for achieving eco-driving in bus driver training. The developed system outperforms those proposed by previous research regarding its completeness and universality. [ABSTRACT FROM AUTHOR]

Fuel cell (FC) technology has been identified as a technically attractive solution to decarbonize the transportation sector, especially for heavy-duty vehicles. In this context, the industry and the scientific community are in need of advanced fuel cell systems (FCS) models that are able to replicate real-world operating conditions. Due to the scarcity of said models in the open literature, this study aimed to develop a comprehensive methodology to calibrate and validate multi-physics dynamic FCS models. Therefore, the key contribution of this paper is the detailed description of the calibration process for each component and the calibration order. The specific focus here was to accurately describe the behavior of the FC stack as well as the cathode, anode, and cooling circuits of the balance of plant. The model was calibrated with the aid of experimental data from a Toyota Mirai FC electric vehicle, which was predominantly retrieved from the vehicle's Controller Area Network (CAN) bus system thereby negating the need for major intrusion into the powertrain system. The validation process was deemed successful with the model being able to truthfully replicate the characteristics of the FC vehicle operated on the World-wide harmonized Light duty Test Cycle (WLTC) 3b and US06 driving cycle. The time-resolved physical parameters such as the cathode pressure, mass flow, or the FC stack temperature were captured with high fidelity, while the overall performance parameters such as the H 2 consumption in the stack and the system, and the compressor energy consumption were predicted accurately with a deviation lower than 0.47%, 1.75% and 1.89% with respect to the experimental data, respectively. • A comprehensive methodology to calibrate multi-physics dynamic FCS models is proposed. • The calibration method can use data from the CAN bus of an FCV in real operation. • The methodology is compatible with both warm and cold realistic driving. • The transient evolution of the key physical variables was predicted accurately. • The key performance indicators were predicted with a deviation lower than 2%. [ABSTRACT FROM AUTHOR]

The vehicle is interfered by the lateral wind in the traditional anti-skid control method, which results in a bad anti-skid control effect of electric vehicle torque drive. For this reason, a method of torque adaptive drive anti-skid control for electric vehicle is proposed. In order to realize the driving force control of distributed driving electric vehicle, the driving motor is modeled and simplified. A battery model was established to analyze the influence of the bus voltage fluctuation on the motor torque response. The power system model is added to the existing vehicle model to complete the simulation model design of the distributed drive electric vehicle. According to the characteristics of the friction circle of the tire, the driving torque of the driving wheel is analyzed. In order to resist the influence of interference factors on vehicles during vehicle movement, through the concept of vehicle stability control, the left and right driving torque is dynamically adjusted by using the obtained ideal driving force distribution ratio of front and rear axles. The simulation results show that the proposed method can effectively increase the driving torque and improve the fitting coefficient of the driving anti-skid torque. [ABSTRACT FROM AUTHOR]

It is necessary to acquire the accurate information of vehicle driving states for the implementation of automobile active safety control. To this end, this paper proposes an effective co-estimation method based on an unscented Kalman filter (UKF) algorithm to accurately predict the sideslip angle, yaw rate, and longitudinal speed of a ground vehicle. First, a 3 degrees-of-freedom (DOFs) nonlinear vehicle dynamics model is established as the nominal control plant. Then, based on CarSim software, the simulation results of the front steer angle and longitudinal and lateral acceleration are obtained under a variety of working conditions, which are regarded as the pseudo-measured values. Finally, the joint simulation of vehicle state estimation is realized in the MATLAB/Simulink environment by using the pseudo-measured values and UKF algorithm concurrently. The results show that the proposed UKF-based vehicle driving state estimation method is effective and more accurate in different working scenarios compared with the EKF-based estimation method. [ABSTRACT FROM AUTHOR]