This paper proposes a novel adaptive dynamic surface control scheme for car-following tasks based on the probabilistic prediction of the preceding vehicle's future states. Firstly, the vehicle driving process in a short time is regarded as a discrete Markov chain, and the preceding vehicle's potential speed and acceleration for the car-following control system are predicted for a preset prediction time horizon via the Markov Chain Monte Carlo (MCMC) technique. Then, an adaptive dynamic surface control system for car-following tasks is constructed to handle the characteristics of parametric uncertainties, external disturbances and nonlinearities of vehicles, in which the certain term is estimated by the fuzzy logic technique in real-time, and the stability of the proposed adaptive car-following control system is proven by the Lyapunov theory. Finally, the performance of the proposed adaptive dynamic surface control scheme for car-following tasks is evaluated by experimental tests, and the results show that the proposed control scheme can achieve preferable performance and high control precision.
In this paper, we propose a stator and rotor temperature prediction for a water-cooled electric vehicle motor based on a back propagation (BP) neural network. Firstly, a computational model of the permanent magnet synchronous motor (PMSM) is established based on the main parameters of the PMSM, and the PMSM losses are calculated and analysed. Then, a three-dimensional field computational model of the PMSM is established, the temperature field simulation boundary conditions are solved, and the temperature field analysis is carried out. Moreover, the computational fluid dynamics (CFD) verifies the accuracy of the one-dimensional thermal network model. Finally, the technique uses the one-dimensional thermal network model to construct a training set and builds the PMSM stator and rotor temperature prediction model based on BP neural network training. Compared with the CFD, the computation time of the model is greatly reduced while maintaining sufficient accuracy.
Vehicle handling requires a careful balance between agility at low speeds and stability at high speeds, a trade-off that traditional fixed-link steering systems struggle to address. Steer-by-wire (SBW) technology removes the mechanical connection between the steering wheel and wheels, enabling real-time, independent adaptation of the steering ratio at both front and rear axles. This work presents a preliminary simulation-based evaluation of a speed-adaptive variable steering ratio strategy applied to both axles, aimed at improving handling under diverse conditions. Validation was performed through offline ISO standardised open-loop manoeuvres and driver-in-the-loop (DiL) simulations, providing early subjective insights. While results indicate potential improvements in manoeuvrability and stability, the current validation underscores the need for further research and broader experimental evaluation to confirm these findings. This study lays the groundwork for future investigations into optimising speed-adaptive steering in four-wheel SBW systems.
The tyre-road friction coefficient (TRFC) is pivotal for ensuring vehicle-following safety and optimising road utilisation in adaptive cruise control (ACC) applications. Therefore, based on real-time TRFC estimation, this paper introduces a new dynamic variation safe distance (DVSD) strategy and an innovative multiple-objective ACC algorithm. The hierarchical structure is utilised in which the lower layer controller compensates for nonlinear vehicle dynamics, ensuring precise tracking of the desired acceleration. In the upper layer controller, employing the model predictive control (MPC) algorithm and considering the impact of road conditions on vehicle dynamic control, we introduce TRFC as a variable constraint to enhance its suitability for real-world scenarios. Simulation results demonstrate that the proposed multi-objective MPC-based ACC controller with DVSD achieves superior control effects and reduces tracking errors compared to the conventional MPC controller. Furthermore, compared with the traditional (CTH) spacing strategy, it significantly enhances vehicle-following safety and increases road utilisation rates.
The integration of multiple energy storage systems in electric vehicles (EVs) presents a promising approach to optimising operational efficiency. This research addresses the critical challenge of enhancing EV drive system efficiency through the novel application of a multi-source converter (MSC) and a comprehensive battery management system (BMS). By linking two distinct DC energy sources directly to the traction motor and eliminating the need for a DC/DC boost converter, this approach significantly improves overall efficiency and aligns with sustainable transportation goals. The study analyses various MSC topologies for EV applications, emphasising their environmental benefits, bidirectional charging capabilities, and the dynamic control facilitated by the BMS. Field-oriented control with space vector modulation ensures harmonised operation of the traction motor. Simulation results in MATLAB/Simulink validate the bidirectional capabilities and efficiency improvements, while a scaled-down experimental setup demonstrates the dynamic behaviour of the traction motor powered by an MSC, underscoring its practical applicability.
With the rapid evolution of the electric vehicle (EV) industry, torque control has emerged as a critical aspect of EV technology, presenting unique challenges in achieving optimal vehicle performance and efficiency. This paper delves into the challenges of torque control in EVs, particularly during the critical transition phases between driving and braking. We introduce a novel torque filtering control method, meticulously designed to optimise torque rise and fall rates and implement effective torque zero-crossing management. This approach swiftly accommodates the driver's torque requests, while markedly diminishing the impact and noise caused by the reducer gear meshing when the driving and braking conditions switch to each other, thus elevating both vehicle comfort and safety. The method's efficacy was rigorously tested and validated under diverse conditions using a 10-metre pure electric bus, demonstrating notable improvements in vehicle stability and passenger comfort, especially in complex conditions. This research contributes a robust solution to torque control challenges in EVs, marking a significant stride in the technological evolution of electric mobility.
Aiming at the problem that the high-speed vehicle is susceptible to yaw instability and deviation from the predetermined path under crosswind disturbances, this paper establishes an eight-degree-of-freedom vehicle dynamics model considering crosswind action, and uses the phase plane method to identify the vehicle's state in the crosswind environment. The coordinated controller of active front-wheel steering (AFS) and direct yaw-moment control (DYC) has been designed to control the stability of the vehicle in the crosswind environment. The control algorithm was simulated and verified under the crosswind disturbance condition, and the results show that the lateral displacement of the vehicle with the coordinated controller of AFS and DYC is less than that of the vehicle without control or with a single controller, and the vehicle yaw rate, sideslip angle, and lateral acceleration are all significantly improved. The controller improves the driving safety and stability of the vehicle in the crosswind environment.
In the transmission system of heavy-duty vehicles, when the friction pair of the wet clutch is in the separation condition, due to the viscous effect of the oil, the relative speed difference between the friction plate and the steel disc will generate the drag torque in the clearance of the friction pair and the drag power loss in wet clutch, which will cause a decrease in the transmission efficiency of the power system and an increase in the failure rate. Therefore, the fluid model of friction pair was established with composite groove as the research object. Based on the Kriging approximation model, eight groove parameters were selected as optimisation variables, and the minimum drag torque was taken as the optimisation objective. The multi-island genetic algorithm was used to optimise the structure parameters of the friction pair groove, the results showed that the optimised friction plate effectively improved the oil circulation.
The dynamic behaviour of a stator system is crucial for the development of electric motors, as it plays a vital role in predicting and optimising the motor noise, which has emerged as a primary source of noise emissions in pure electric vehicles. Although the finite element method (FEM) is widely employed for modelling stator systems, existing FEM models typically focus on circular wire windings and fail to accurately represent stator systems with flat wire windings. Consequently, this paper aims to develop more precise FEM models for flat wire motor stator systems. The FEM models for the stator core, flat wire windings, insulating material, and the entire stator system are constructed by incorporating the unique characteristics of flat wire stator systems, particularly their radial stacking features. These proposed FEM models are validated through modal experiments conducted on an 8-pole 48-slot permanent magnet synchronous motor with flat wire windings.
Yaw stability control (YSC) is critical for intelligent heavy vehicles (IHVs) to ensure driving steering safety. This study introduces a novel prescribed performance PID (PPPID) control strategy to address yaw stability challenges in IHVs equipped with electro-hydraulic compound steering systems (EHCSS). Firstly, a mathematical framework is established that integrates the vehicle's two-degree-of-freedom (2-DOF) model with the physical model of the EHCSS. Subsequently, a PPPID controller is developed for steering stability, utilising the sideslip angle and yaw rate as target parameters. Finally, the proposed control strategy is validated through hardware-in-the-loop (HIL) testing under double-lane change (DLC) and sine wave conditions at varying speeds, with comparative analysis against the traditional PID control method. The results demonstrate that, in contrast to conventional PID, the PPPID control strategy can make errors converging within the designated performance range while avoiding issues such as overshoot and data instability.
In order to improve the accuracy of steering control for corn combine harvesters, a proportional integral derivative (PID) algorithm based fully hydraulic steering control method for corn combine harvesters is proposed. Firstly, by analysing the steering process, a steering dynamics model is constructed. Secondly, clarify the relevant variables and parameters, and derive the calculation formula for the electro-hydraulic steering control model. Again, the PID algorithm was used to design the steering controller, calculate the angular velocity deviation value, and design proportional, integral, and derivative control functions. Finally, by transforming the transfer function through Laplace transform, optimising the parameters of the PID controller, and considering practical factors such as delay for correction, the optimisation of the electronic control full hydraulic steering control was ultimately achieved. The experimental results show that the steering control accuracy of the proposed method is always above 90%, with low steering angle control error and good stability.
The pedestrian-ground injuries in pedestrian-vehicle collision have always been a research focus. This study explores the prediction model of pedestrian landing mechanism in pedestrian-vehicle collision. First, seven important parameters before, during and after the collision were extracted from 1300 cases. The obtained parameters are converted into input parameters through principal component analysis (PCA). Finally, the pedestrian landing mechanism prediction model under default parameters is constructed based on back-propagation neural network (BPNN), genetic algorithm (GA) optimised BPNN (GA-BPNN), support vector machine (SVM), decision tree (DT) and Random Forest (RF). The results showed that the GA-BPNN model is the optimal model under default parameters; the performance of GA-BPNN was improved after hyperparameter optimisation, and the prediction accuracy of the improved GA-BPNN (IGA-BPNN) model was 76.78%, 94.86% and 95.09%, respectively. Considering the pedestrian landing mechanism can significantly reduce the risk of vehicle braking control method and improve the protection efficiency.
To improve the success rate of collision avoidance for autonomous vehicles and shorten response time, an intelligent obstacle avoidance control method based on an improved SAC algorithm is proposed. This method is based on a self-organising cluster model, integrating short-range repulsion, medium-range velocity calibration, and obstacle avoidance rules to achieve collision-free cluster collaboration. The conventional SAC algorithm adopts the AC framework to maximise the expected reward and entropy value while reducing the estimation bias of the value function through the value network component. On this basis, the PER-SAC method is proposed, which integrates priority experience replay (PER) and importance sampling weight strategy while optimising network structure, reward and punishment functions, continuous state and action space design. Additionally, transfer learning is incorporated. The experimental results demonstrate the effectiveness of this method, achieving a collision avoidance success rate of 97%, with a maximum response time of just 0.54 s.
Optimal control of a four-wheel steering car model is performed in this paper using two strategies: linear quadratic regulator (LQR) and state-dependent Riccati equation (SDRE). In such vehicles, the system is over-constrained, so the solution of inverse dynamics is not unique. Extracting the optimal control input satisfies the desired vehicle performance while minimising a specific objective function. The bicycle model with two steering inputs is used to model the four-wheel steering car and its nonlinear behaviour. A new approach for kinematic modelling is also proposed. Two closed-loop optimal control strategies are applied: one linear and valid near its operating point, and the other global and nonlinear. Their results are analysed and compared. Model correctness is investigated via comparison with CarSim. The effectiveness of the proposed optimal control strategies and the superiority of the nonlinear SDRE over traditional methods are validated using MATLAB simulations, showing accurate path tracking with minimal energy consumption across the its full workspace.
To address the challenge of jointly controlling the trajectory and attitude of a four-wheel independently actuated vehicle - specifically, the issue of how to appropriately allocate the steering angles of the front and rear wheels - this paper proposes a novel manipulation mechanism. By operating a joystick, the driver can generate the desired slip angle and yaw rate, thereby enabling the system to perceive and interpret the driver's intent regarding vehicle attitude control. Concurrently, a robust controller is developed, which utilises the slip angle and yaw rate derived from the joystick as control references, while employing the steering angles of the front and rear wheels as control inputs. This approach facilitates coordinated control of both vehicle trajectory and attitude. Simulation results demonstrate that the proposed joystick interface and control strategy effectively enhance the vehicle's maneuverability, stability, and overall safety.
