Linfeng Zhao

Preceding vehicles are easily lost due to the limited view of onboard sensors on roads with large curvature, resulting in the functional failure of the environmental perception system. This makes intelligent vehicles unable to obtain reliable information to make reasonable decisions, threatening the safety of car-following. Therefore, this paper proposes a method to improve car-following safety on blind curved roads. By analyzing the cautious mindset of drivers during cornering, an improved Markov model based on long-term prediction decay is proposed, combined with a Long Short-Term Memory (LSTM) model, to effectively predict the state of the preceding vehicle and address the intended functional safety issues arising from sensor functional failures. Subsequently, different strategies are designed for various states of the preceding vehicle, such as acceleration and deceleration, and a sensor field-of-view distance model is established to provide effective distance parameters for vehicle control. Notably, the proposed strategy has undergone rigorous simulation and hardware-in-the-loop (HiL) tests, proving its effectiveness in improving car-following safety on large curvature curves and instilling confidence in its potential.

Aiming at the problem that the traditional visual and radar methods are easily interfered by the dynamic changes of the environment and have insufficient accuracy in the identification of complex soft and muddy pavements, a pavement identification method based on the online estimation of soil mechanical characteristic parameters is proposed. By analyzing the sensitivity of wheel-ground coupling force to soil parameters, the dominant parameters are screened for dynamic identification, and the rest of the parameters are simplified based on the empirical values; the triangular approximation method is adopted to deal with the stress expression, avoiding the complex solution of the closed-form force/moment integral equation. Aiming at the time-varying characteristics of soil parameters, a strong tracking traceless Kalman filter is designed to realize online estimation of parameters. Simulation and real vehicle tests show that the proposed method can accurately recognize the soft pavement type without additional sensing equipment, and the dominant parameter estimation errors are less than 5%.

The hub motor of a distributed drive vehicle, as the main executing structure of the drive system, may produce varying degrees of torque loss during operation, which directly lead to the deterioration in the yaw stability of the vehicle and wheel slip especially when the vehicle is driving on low adhesion road. In this paper, a coordinated torque distribution control strategy considering wheel slip is proposed in the case of hub motor torque loss. By designing a torque constraint to prevent wheel slip and a torque distribution strategy considering torque loss, the four-wheel torque is distributed coordinately. Among the torque constraints, the fuzzy threshold method and the nonlinear feedback method are used to calculate the desired wheel torque, and then the torque constraint method considering wheel slip is designed based on particle swarm extension control theory, which can switch the torque constraint mode adaptively. To ensure the stability of the vehicle, the stability criterion is designed based on the phase plane and the yaw moment controller is designed by the sliding mode algorithm. The torque distribution strategy considering the torque loss is combined with the torque loss of the hub motor and the normal wheel torque distribution is reconstructed by the quadratic programming method. Finally, the proposed torque coordination distribution strategy is verified by simulation and hardware-in-the-loop test to prove the effectiveness of the control strategy.

Distributed drive electric vehicles can reduce range anxiety through regenerative braking. However, if the wheel motor torque output fails, it will form an additional yaw moment to the vehicle, causing instability, or deviation and threatening its safety. To solve this problem, the research object is an electric vehicle driven by a four-wheel hub motor. A braking force compensation distribution strategy for front and rear axles is proposed, which combines electronic hydraulic braking (EHB) system compensation control and deviation auxiliary control. Firstly, a fault detection module is established, and the motor’s output torque is estimated by designing a torque observer to obtain the fault degree information of the motor. Secondly, to fully use the motor’s regenerative braking force, the fault-free and faulty electro-hydraulic braking force distribution strategies are designed in the coordinated distribution layer of the electro-hydraulic braking system. The corresponding electro-hydraulic braking force compensation method is selected according to the fault degree of the regenerative braking function, the position of the faulty wheel, and the braking strength. Then, a deviation auxiliary controller is designed based on the model predictive control, and the intervention time of the auxiliary controller is determined according to the vehicle’s state. Finally, the control method is verified based on CarSim/Simulink co-simulation and hardware-in-the-loop (HIL) platform. The test results show that the designed control method can effectively compensate for the regenerative braking failure of random wheel and ensure the braking safety of the vehicle.

All-terrain forklift trucks often work on complex terrain such as soft and muddy terrain in the wild, making them prone to rolling and rollover. In this work, a seven-degree-of-freedom nonlinear dynamic model and a wheel-ground interaction model between resilient wheels and soft/hard road surfaces based on the structural characteristics of the articulated steering + swinging bridge of an all-terrain forklift truck are established. The mechanism of all-terrain forklift trucks’ roll instability and a simulation comparison of four types of road models (rigid, sandy, loam, and clay) are analyzed. All-terrain forklift trucks are least prone to rolling when being driven on rigid roads. On soft roads, sand brings the greatest risk of rollover, followed by loam and clay. The roll stability indexes HR-LTR and SR-LTR considering different road surfaces are proposed. The safety limit of roll stability based on the indexes is divided, and an anti-rollover control strategy based on road surface recognition is proposed. On the basis of the road surface recognition results, corresponding stability index calculation methods and actuator control algorithms are selected. Simulation and experimental results show that with the use of no control and TTR-based hierarchical control, the forklift truck yaw velocity is reduced by up to 73.31% and 63.52%, respectively, and the roll angle is reduced by up to 72.7% and 48.27%, respectively. The proposed roll stability index and anti-rollover control strategy for different road surfaces can effectively improve the roll stability of all-terrain forklift trucks when being driven on different complex road surfaces, reduce the risk of rollover, and improve the rollover stability of all-terrain forklift trucks on different complex roads, thereby improving the safety of all-terrain forklift trucks.

In this paper, an intelligent distributed drive electric vehicle is used as the research object. The research aims to solve the problem of path-tracking control of intelligent vehicles when the single-side in-wheel motor of the front axle fails. A fault-tolerant control method based on sliding mode observer is proposed. Firstly, an overall vehicle dynamics model considering the failure factor is established, and the failure of the in-wheel motor is monitored by the sliding mode observer. Torque coordination compensation control is activated according to the observed failure. The control system mainly consists of two parts: the torque-coordinated lateral stability controller and the prediction fault tolerant deviation compensation controller. The torque-coordinated lateral stability controller controls the torque of the other wheels to balance the front wheel torque, and the prediction tolerant deviation compensation controller uses Model Predictive Control (MPC) to control the front wheel angle and rear wheel torque of the vehicle to reduce the lateral deviation. Finally, under double-lane change conditions, dynamics simulations and hardware-in-loop experiments are performed to validate the effectiveness of the proposed control method.