Vehicle Test Platform Research Articles

Automobile exhaust flexible thermoelectric harvester enabled by liquid metal-based heatsink

The softening thermoelectric generator based on a deformable heatsink is expected to facilitate achieving remarkable temperature differences and thermal harvesting capabilities while alleviating exhaust back pressure and engine operating perturbations. However, the available soft polymer materials with poor thermal characteristics face challenges in realizing efficient heat transfer. Herein, we reported a highly integrated automobile exhaust flexible thermoelectric generator (IAE-FTEG) enabled by a liquid metal (LM)-based stretchable heatsink for cylindrical thermal sources to achieve efficiently harnessing waste heat energy and continuously powering multiple vehicle-mounted sensors. IAE-FTEG exhibits excellent flexibility (the bending radius at the apex of 0.57 cm) and outstanding heat transfer capability by introducing the porous sandwich-based soft electrode films and LM-based thermal interface material, achieving an output power enhancement of 25.7 %. The flexible heatsink is prepared by embedding LM droplets and copper particles into the elastic matrix forming solid–liquid multiphase composites, which obtain considerable thermal conductivity (2.40 W/mK@200 % stretching deformation) and excellent geometric conformity. We built the bench and the real vehicle test platform of the IAE-FTEG waste heat recovery system. The simulation test results demonstrate that the maximum power density achieved by the IAE-FTEG waste heat recovery system is 244 kW/m3. The real vehicle tests show that the IAE-FTEG waste heat recovery system has a relatively stable output performance with an average power density of 117 kW/m3 under urban conditions and can realize a stable power supply for back-end loads. Our IAE-FTEG can offer new opportunities in enabling efficient curved-surface waste heat recovery, flexible wearable electronics, and personalized thermal management.

Learning-Based Vehicle State Estimation Using Gaussian Process Regression Combined with Extended Kalman Filter

In order to address the challenge of accurate vehicle state estimation, especially in highly nonlinear and complex operating conditions, this paper proposes a learning-based method for vehicle state estimation. To achieve this, a novel theoretical framework is constructed that combines the model-based Extended Kalman Filter (EKF) with Gaussian process regression (GPR). By establishing a dynamic model of the vehicle and describing the errors using machine learning techniques, the sources of state estimation errors are analyzed. Furthermore, by considering the model uncertainty, an error prediction model is developed through GPR learning from real-world vehicle data. Combined with EKF, the proposed method enables high-precision online estimation of vehicle state. To validate the proposed method, tests are performed on a real vehicle test platform equipped with high-precision sensors and data acquisition equipment, under two different operating conditions: emergency and normal. The results are compared with those obtained using EKF and State Observer, which demonstrates a more accurate and adaptable vehicle state estimation, and provides a method for quantitatively describing the confidence interval of vehicle state estimation result.

Game theory-based mandatory lane change model in intelligent connected vehicles environment

In the environment of intelligent connected vehicles, drivers are capable of making wiser and safer decisions. However, the interaction between drivers and vehicle systems has undergone changes in the intelligent connected vehicles environment, leading to a decrease in the applicability of existing microscopic driving models, such as the mandatory lane change model, which requires reevaluation or improvement. Therefore, to investigate the influence of different intelligent connected vehicles environments on the microscopic mandatory lane-changing model, this study developed three interaction systems to characterize different intelligent connected vehicles environments: the baseline, warning group, and guidance group. The Baseline provides basic information, the warning group adds icons of preceding vehicles and real-time headway information, while the guidance group further includes speed and voice guidance functions. The baseline describes the traditional environment, while the other two groups describe the intelligent connected vehicles environment. Using a self-developed intelligent connected vehicle testing platform, we conducted driving simulation experiments with 43 participants at the interchange merging area of a highway. This study, grounded in game theory, establishes function models for participants, strategies, and payoff functions in the mandatory lane-changing process. Utilizing data from driving simulation experiments, the parameters of the dual-layered planning model are calibrated. Evaluation of the constructed model is conducted through confusion matrices and lane-changing spatiotemporal characteristic indicators. The results demonstrate satisfactory predictive performance of the baseline group model, warning group model, and guidance group model across different intelligent connected vehicles environments. Specifically, compared to existing literature, the baseline group model exhibits improvements of 7 % and 2 % respectively in overall lane-changing detection accuracy by drivers. The warning group model shows improvements of 2.9 % and 1.7 %, while the guidance group model exhibits improvements of 5.1 % and 4.3 %. Additionally, the baseline group model reduces the mean absolute error in predicting different game strategies by 16.7 % and 5.6 % respectively compared to existing literature. Concerning lane-changing position prediction, the warning and guidance group models demonstrate minimal errors, whereas the baseline group model exhibits good consistency in predicting lane-changing duration. Furthermore, both the warning and guidance group models show some delay in predicting lane-changing duration. While intelligent connected vehicles environments significantly influence the prediction of lane-changing positions, they do not significantly affect the prediction of lane-changing duration. However, game strategies significantly impact the prediction of lane-changing duration but do not significantly affect the prediction of lane-changing positions. The study findings offer valuable insights into micro lane-changing behaviors of drivers in intelligent connected vehicles environments, bearing crucial significance for the in-depth investigation of real-time control and guidance strategies for vehicles in merge areas of highways under intelligent connected vehicles conditions.

Optimized energy management for plug-in hybrid vehicles with predicted driving cycles1

The traditional rule-based energy management strategy for plug-in hybrid vehicles has issues, such as difficulty in online correction and limited online optimization capabilities. In addition, the global optimization energy management strategy cannot be applied online or in real-time. Considering the above difficulties, this study proposes a real-time optimization energy management strategy based on the Markov chain for driving condition prediction and online optimization with the minimum principle. To verify the proposed control strategy, the plug-in hybrid vehicle dynamics model, driving condition prediction model, and online optimization control model were first established. The initial value of the battery state of charge was set to 0.4 under the UDDS (Urban Dynamometer Driving Schedule) standard cycle. The simulation results showed that the comprehensive fuel consumption cost was 1.66 yuan, which was 8.28% better than the energy economy of the traditional rule-based energy management strategy. At the same time, a complete vehicle test was also conducted based on a sample vehicle test platform. The experimental results indicated that the energy management strategy proposed herein exhibits better fuel economy compared to that exhibited by the traditional rule-based energy management strategy. Simulations and experiments have verified the effectiveness of the proposed control strategy in this study.

Evaluating the Efficacy of Real-Time Connected Vehicle Basic Safety Messages in Mitigating Aberrant Driving Behaviour and Risk of Vehicle Crashes: Preliminary Insights from Highway Scenarios

Connected vehicle (CV) technology has revolutionised the intelligent transportation management system by providing new perspectives and opportunities. To further improve risk perception and early warning capabilities in intricate traffic scenarios, a comprehensive field test was conducted within a CV framework. Initially, data for basic safety messages (BSM) were systematically gathered within a real-world vehicle test platform. Subsequently, an innovative approach was introduced that combined multimodal interactive filtering with an advanced vehicle dynamics model to integrate BSM vehicle motion data with observations from roadside units. In addition, a driving condition perception methodology was developed, leveraging rough sets and an enhanced support vector machine (SVM), to identify aberrant driver behaviours and potential driving risks effectively. Furthermore, this study integrated BSM data from various scenarios, including car-following, lane changes, and free driving within the CV environment, to formulate multidimensional driving state sequence patterns for short-term predictions (0.5 s) utilising the long short-term memory (LSTM) model framework. The results demonstrated the effectiveness of the proposed approach in accurately identifying potentially hazardous driving conditions and promptly predicting collision risks. The findings from this research hold substantial promise in advancing road traffic safety management.

Design and Verification of Deep Submergence Rescue Vehicle Motion Control System.

A six degree-of-freedom (DOF) motion control system for docking with a deep submergence rescue vehicle (DSRV) test platform was the focus of this study. The existing control methods can meet the general requirements of underwater operations, but the complex structures or multiple parameters of some methods have prevented them from widespread use. The majority of the existing methods assume the heeling effect to be negligible and ignore it, achieving motion control in only four or five DOFs. In view of the demanding requirements regarding positions and inclinations in six DOFs during the docking process, the software and hardware architectures of the DSRV platform were constructed, and then sparse filtering technology was introduced for data smoothing. Based on the adaptive control strategy and with a consideration of residual static loads, an improved S-plane control method was developed. By converting the force (moment) calculated by the controller to the body coordinate system, the complexity of thrust allocation was effectively reduced, and the challenge of thrust allocation in the case of a high inclination during dynamic positioning was solved accordingly. The automatic control of the trimming angle and heeling angle was realized with the linkage system of the ballast tank and pump valve. A PID method based on an intelligent integral was proposed, which not only dealt with the integral "saturation" problem, but also reduced the steady-state error and overshooting. Water pool experiments and sea trials were carried out in the presence of water currents for six-DOF motion control. The responsiveness and precision of the control system were verified by the pool experiment and sea trial results and could meet the control requirements in engineering practice. The reliability and operational stability of the proposed control system were also verified in a long-distance cruise.

Design of Control Scheme for Integrated Thermal Management System of New Energy Bus

With the gradual promotion and popularization of new energy vehicles, the safety of new energy vehicles has attracted more and more attention. The thermal management system (TMS) is the key to whether the key assembly of new energy can work normally. In this paper an optimized control method is developed for the intelligent control of the TMS with the lowest overall energy consumption by taking into account the performance requirements of each subsystem. To realize this, the simulation of the integrated TMS is carried out along with a real vehicle test platform to provide practical and feasible solutions. This study has improved the design depth and level of system intelligence and digitalization.

Development of Experimental Teaching Platform for In-Wheel Motor Electric Vehicle On-Board Testing

Background: The on-board test system is the key technology of in-wheel motor electric vehicles, which plays a vital role in the safety of the driver and the efficient operation of the vehicle. Methods: Based on the requirements of the current in-wheel motor electric vehicle experiment teaching content, this research develops an in-wheel motor electric vehicle experimental teaching platform based on Matlab/Simulink and LabVIEW to meet the current needs of the in-wheel motor electric vehicle experiment teaching content. Results: The vehicle speed, sideslip angle, and road adhesion coefficient can be accurately estimated using the vehicle test platform developed in the article. Conclusion: With accurate experimental results, the functionality, value, and teaching value of the in-wheel motor electric vehicle experimental teaching platform were fully verified.

Research on automatic driving control strategy based on improved model predictive control

As a new technology direction in the automotive industry in recent years, automatic driving technology has played an important role in reducing traffic accidents, promoting energy conservation and emission reduction, and improving the efficiency of high-speed and urban roads. As one of the key technologies of autonomous vehicles, the performance of control algorithm greatly affects the smoothness of vehicle driving and the accuracy of track tracking. In order to consider the lateral control and longitudinal control accuracy at the same time, and ensure that the vehicle travels according to the given trajectory, this paper first establishes the vehicle kinematics model, and improves the traditional model predictive control algorithm, which realizes the high dynamic control of the desired trajectory while ensuring the vehicle stability and comfort. Finally, based on the built real vehicle test platform for automatic driving, the acceleration and deceleration test and the high-speed serpentine test are designed. By analyzing the test data, it can be seen that the control algorithm proposed in this paper can effectively achieve the vehicle trajectory tracking, and the control accuracy is improved.

Weight Adaptive Path Tracking Control for Autonomous Vehicles Based on PSO-BP Neural Network.

In order to improve the tracking adaptability of autonomous vehicles under different vehicle speeds and road curvature, this paper develops a weight adaptive model prediction control system (AMPC) based on PSO-BP neural network, which consists of a dynamics-based model prediction controller (MPC) and an optimal weight adaptive regulator. Based on the application of MPC to achieve high-precision tracking control, the optimal weight under different operating conditions obtained by automated simulation is used to train the PSO-BP neural network offline to achieve online adjustment of MPC weight. The validation results of the Prescan-Carsim-Simulink joint simulation platform show that the adaptive control system has better tracking adaptation capability compared with the original classical MPC control. The control strategy was also verified on an autonomous vehicle test platform, and the test results showed that the adaptive control strategy improved tracking accuracy while meeting the vehicle's requirements for real-time control and lateral stability.

Fasting the stabilization response for prevention of tractor rollover using active steering: Controller parameter optimization and real-vehicle dynamic tests

The rollover problem is prominent for a small tractor under complicated road conditions. The application of active steering (AS) technology is an effective approach to solve this problem. A lateral stability index based on the critical position is proposed and improved. Sliding mode control (SMC) technology based on an exponential terminal sliding mode surface is studied by considering the parameter changes and external disturbances in the rollover process of the tractor, which further accelerates the system’s response speed. A real vehicle test platform was built taking into account the limitations of the few real vehicle dynamic tests in current studies. Real vehicle dynamic tests with varying speeds, slopes, and obstacles were conducted for various road pavements and typical off-road pavements. For road pavements, the results demonstrated that the maximum roll angle of the tractor was 15.94 % less than with that under uncontrolled. For typical off-road pavements, AS control effectively restrained rollover accidents under non-extreme conditions, and SMC reduced the time the tractor was in the critical rollover region by 35.98 % on average compared with PID control. The stability region boundary of a small tractor was defined, and the stable driving region of a tractor on various slopes is obtained. This study provides a specific theoretical basis and implementation plan for guiding the research of tractor active safety technology.

Design and Verification of Crab Steering System for High Clearance Self-Propelled Sprayer

A crab steering system aiming at improving the steering flexibility and operation efficiency of the high clearance sprayer was designed. First, a steering transmission mechanism of the high clearance sprayer was designed according to the operational and structural characteristics of the sprayer. Meanwhile, a crab steering hydraulic system based on load sensing was designed, and a mathematical model of the steering transmission mechanism and hydraulic system was established to describe the working characteristics of the crab steering system. On the basis of analyzing the operating environment of the sprayer and the operating characteristics of crab steering mode, a control strategy and algorithm of the crab steering system were proposed. The simulation model of the crab steering control system was built according to the established mathematical model, and the simulation analysis of the crab steering system was carried out. The simulation results showed that under the excitation of step signal, the average deviation of the two front wheels is 0.063°, and the absolute value of the maximum deviation is 2.75°, which is within the allowable range of deviation and meets the crab steering demand of the sprayer. Additionally, in order to verify the effectiveness of the designed system, an actual vehicle test platform of the crab steering system was built based on the 3WPG-3000 high clearance self-propelled sprayer independently developed by the research group, and the field test results revealed that the average deviation of the four wheels was 0.285°, the maximum absolute deviation was 2.587°, the rotation deviation was small, within the allowable and reasonable range. Altogether, the results of the simulation and field test verify the accuracy, stability and practicability of the crab steering system, which effectively improved the maneuverability of the sprayer.

Design and Implementation of a Regenerative Mode Electric Vehicle Test Platform for Engineering Education

In engineering education, traditional teaching approaches cannot sufficiently help students to learn electric vehicle (EV) concepts. In this study, the design of an educational test setup including all the components and all dynamics of EVs is implemented, and case studies for engineering education with practical applications are discussed. The proposed test and training platform not only provides hands-on experience for engineering students, but also the opportunity for expert users to test their own designed algorithms on the test setup through a computer and human–machine interface device. The aim of the study is to show students the effects of road slope, vehicle weight and energy recovery parameters on a light EV. In this context, five case studies have been carried out by the students, and a survey was conducted with them. The survey results show that the test setup can help them better comprehend any EV system and develop their professional knowledge and skills.

Design and application of electromechanical flywheel hybrid device for electric vehicle

The electromechanical flywheel hybrid power device has the dual attributes of energy supply and power output, which can provide more design space for the optimization of vehicle energy utilization efficiency. It involves two key problems of device parameters design and energy management. Therefore, the optimization design of electromechanical flywheel device’s parameters and the energy management strategy are the main research objects in this paper. Firstly, based on the working principle of planetary gear mechanism, the dynamic characteristics of electromechanical flywheel device under different topological structure schemes are analyzed, and the design schemes of speed regulating motor connecting to the sun gear, flywheel connecting to the ring gear and output end connecting to the planet carrier are determined. Then, according to the dynamic characteristics of the vehicle, the parameters of flywheel, speed regulating motor and planetary gear mechanism were designed. Furthermore, considering the state of energy (SOE) of the electromechanical flywheel device, 15 working modes of the vehicle were designed, and the corresponding energy management strategies were proposed. Finally, a vehicle test platform was constructed to analyze the dynamic and economic performances of the electromechanical flywheel hybrid electric vehicle. Results show that, compared with the original vehicle driven by a single motor of the front axle, owing to the power intervention of the electromechanical flywheel hybrid device, the average acceleration from 0 km/h to 100 km/h is increased by 22.32% and the acceleration time is reduced by 18.25%, which effectively improves the vehicle dynamic performance. Moreover, the new hybrid system designed is conducive to the miniaturization design of the drive motor, increasing the load rate of the motor and improving the efficiency of powertrain system. With the advantage of short-term energy supply of electromechanical flywheel, the average discharge current of lithium battery can be reduced to improve its efficiency. Under J1015, NEDC and HWFET cycles, the average operating efficiencies of the powertrain system are increased by 8.2%, 5.6% and 4.3% respectively; the efficiency improvements of lithium battery also reach 6.7%, 4.2% and 4.1% respectively. The research of this paper is devoted to applying electromechanical flywheel to electric vehicles to help improve the performance of vehicles and promoting the development of hybrid electric vehicles in the future.

Method of voltage setting for power battery simulator using successive nearest-neighbor interpolation

The power battery simulator is important equipment in new energy vehicle test platform and other industrial fields, and the battery model is the key to emulate the battery characteristics accurately. A novel method of given voltage for the power battery simulator is proposed in the paper. According to the state of charge (SOC) of the power battery, three sub-tables with different resolutions are established corresponding to the initial, stationary, and final stage of SOC. To ensure the accuracy of a given voltage under small data capacity, a successive nearest-neighbor interpolation (SNNI) algorithm is proposed for processing battery model data and the iterative calculation is carried out for the data obtained by looking up the table until it approaches the required value. Simulations and experiments are done on various capacity data tables, which are obtained by employed different sampling ratios to reduce the data capacity, and the influence of iteration number selection on the accuracy of the algorithm is discussed. The results show that, after 4–5 iterations, the given voltage accuracy is controlled within 0.03 V by the SNNI algorithm with only 1.6% capacity of the standard model table, which is greatly improved compared with the traditional method, and the feasibility is verified by executing the proposed algorism in a DSP system.

Segment Drift Control with a Supervision Mechanism for Autonomous Vehicles

Stable maneuverability is extremely important for the overall safety and robustness of autonomous vehicles under extreme conditions, and automated drift is able to ensure the widest possible range of maneuverability. However, due to the strong nonlinearity and fast vehicle dynamics occurring during the drift process, drift control is challenging. In view of the drift parking scenario, this paper proposes a segmented drift parking method to improve the handling ability of vehicles under extreme conditions. The whole process is divided into two parts: the location approach part and the drift part. The model predictive control (MPC) method was used in the approach to achieve consistency between the actual state and the expected state. For drift, the open-loop control law was designed on the basis of drift trajectories obtained by professional drivers. The drift monitoring strategy aims to monitor the whole drift process and improve the success rate of the drift. A simulation and an actual vehicle test platform were built, and the test results show that the proposed algorithm can be used to achieve accurate vehicle drift to the parking position.

Design and verification of slip rate control system for straight line travel of high clearance self-propelled sprayer

A slip rate control system aimed at improving the working efficiency and driving stability of a high clearance sprayer was developed. First, the two-pump, two-anti-slip control (ASC) valve, four-motor “X” drive scheme hydraulic slip rate control system was designed, and a mathematical model of the system as well as a vehicle dynamics model were established. The system includes a slip rate control strategy, a proportional-integral-derivative control method and a fuzzy adaptive proportional-integral-derivative sprayer control method. To verify the performance of the system, a simulation model was developed using MATLAB/Simulink, and the performance of the two control methods were compared. Additionally, an actual vehicle test platform was built based on 3WPG-3000 high clearance self-propelled sprayer independently developed by the research group. The simulation results revealed that when a wheel slipped, the slip rate control system was able to control the wheel slip rate and keep it within the threshold value of 0.1, thus meeting the operating requirements of the sprayer. The field test results revealed that in field operations with a low adhesion coefficient, the system was able to maintain a nearly unchanged wheel speed in both fixed speed mode and variable speed mode, maintaining a slip rate below the target of 0.1 “when in a straight running mode” in both cases. Altogether, the results of the simulation and field test verify the stability, accuracy, and practicability of the system.

Development of Online Adaptive Traction Control for Electric Robotic Tractors

Estimation and control of wheel slip is a critical consideration in preventing loss of traction, minimizing power consumptions, and reducing soil disturbance. An approach to wheel slip estimation and control, which is robust to sensor noises and modeling imperfection, has been investigated in this study. The proposed method uses a simplified form of wheels longitudinal dynamic and the measurement of wheel and vehicle speeds to estimate and control the optimum slip. The longitudinal wheel forces were estimated using a robust sliding mode observer. A straightforward and simple interpolation method, which involves the use of Burckhardt tire model, instantaneous values of wheel slip, and the estimate of longitudinal force, was used to determine the optimum slip ratio that guarantees maximum friction coefficient between the wheel and the road surface. An integral sliding mode control strategy was also developed to force the wheel slip to track the desired optimum value. The algorithm was tested in Matlab/Simulink environment and later implemented on an autonomous electric vehicle test platform developed by the Nanjing agricultural university. Results from simulation and field tests on surfaces with different friction coefficients (μ) have proved that the algorithm can detect an abrupt change in terrain friction coefficient; it can also estimate and track the optimum slip. More so, the result has shown that the algorithm is robust to bounded variations on the weight on the wheels and rolling resistance. During simulation and field test, the system reduced the slip from non-optimal values of about 0.8 to optimal values of less than 0.2. The algorithm achieved a reduction in slip ratio by reducing the torque delivery to the wheel, which invariably leads to a reduction in wheel velocity.

Autonomous Vehicles Sideslip Angle Estimation: Single Antenna GNSS/IMU Fusion With Observability Analysis

Taking advantage of available measurement in Internet of Things (IoT) for intelligent transportation systems, a sideslip angle estimation method for autonomous vehicles is presented and experimentally verified by fusing global navigation satellite system (GNSS) and inertial measurement unit (IMU), and by constructing an observability index (OI). The correlation between the vehicle sideslip error and the inertial navigation system (INS) heading error is presented first. Then, the observability for the heading error in a velocity-based Kalman filter is discussed and a novel index is defined to check the observability of the heading error. The course from a single antenna GNSS in an autonomous vehicle is augmented to estimate the heading error when the observability of the heading error is low. To reject the course measurement for scenarios that include sideslip movement, a binary hypothesis test approach is applied to indicate whether the vehicle is sidesliping. In addition, based on the OI and the sideslip indicator, a hybrid feedback strategy is designed for the heading error correction. To improve the convergence rate of the heading error in the velocity-based Kalman filter, a tuning strategy is presented. The stochastical observability of the designed Kalman observer is investigated for known and stochastic initial conditions. Finally, the proposed sideslip angle estimator is experimentally validated through a vehicle test platform in critical driving scenarios. The results confirm that the proposed OI can effectively identify when the heading error is observable, and also corroborate the effectiveness of the hybrid feedback strategy and adaptation method in the Kalman observer.

Vision-Assisted Landing Method for Unmanned Powered Parachute Vehicle Based on Lightweight Neural Network

With the development of airdrop technology, the intelligence degree of unmanned powered parachute vehicles (UPPVs) need to be improved. To achieve the accurate landing of UPPVs in complex environments, a landing runway recognition model based on a deep learning algorithm is trained and five actual flight tests are conducted. A six-degree-of-freedom (6-DOF) mathematical model of an unmanned powered parachute vehicle is established, and a landing runway offset controller is designed. The lightweight landing runway recognition model was trained by combining the YOLOv4 framework and the lightweight neural network MobileNet-V3 (Large) and validated in various scenarios. The runway recognition model was transplanted into the airborne image processor, and an unmanned powered parachute vehicle test platform was built for actual flight testing. The test results showed that the comprehensive accuracy of the runway recognition was 97.81% during visual landing and the offset correction was completed within 15s.