In-wheel-motor-driven electric vehicles (IWM-EVs) provide more potential to enhance vehicle stability performance. However, traditional stability control relies on the current status fed back by sensors for stability judgment and control, only taking effect after the vehicle has already become unstable. In response to this issue, this paper proposes a pre-stability control strategy based on a hybrid dynamic state prediction method to predict dangerous driving conditions and intervene in vehicle stability control in advance. First, a driver-vehicle model is established to characterize the driver's driving intention and obtain the vehicle's ideal motion responses. Then, the methodology for implementing vehicle pre-stability control is introduced, which mainly includes sideslip angle estimation utilizing the extended Kalman filter, a hybrid dynamic state prediction approach based on vehicle model and data trends, and a vehicle pre-stability judgment method. Subsequently, a vehicle hierarchical controller is designed to achieve pre-stability control. The upper-level controller focuses on calculating the required additional yaw moment, and the lower-level controller aims to optimize torque distribution among the four wheels. Finally, the proposed pre-stability control strategy is validated by the hardware-in-the-loop test bench. The results show that the proposed control strategy can intervene in dangerous driving conditions in advance, and its mean errors of the yaw rate and sideslip angle are reduced by over 17.1% and 23.5%, respectively, compared with the traditional method, which significantly enhances vehicle stability and driving safety.

In order to effectively reduce the energy consumption of the vehicle, an optimal torque distribution control for multi-axle electric vehicles (EVs) with in-wheel motors is proposed. By analyzing the steering dynamics, the formulas of additional steering resistance are given. Aiming at the multidimensional continuous system that cannot be solved by traditional optimization methods, the deep deterministic policy gradient (DDPG) algorithm for deep reinforcement learning is adopted. Each wheel speed and deflection angle are selected as the state, the distribution ratio of drive torque is the optimized action and the state of charge (SOC) is the reward. After completing a large number of training for vehicle model, the algorithm is verified under conventional steering and extreme steering conditions. The maximum SOC decline of the vehicle can be reduced by about 5% under conventional steering conditions based on the motor efficiency mapused. The combination of artificial intelligence technology and actual situation provides an innovative solution to the optimization problem of the multidimensional state input and the continuous action output related to vehicles or similar complex systems.

As the core input parameters of various control systems, the real-time and accurate acquisition of reference speed, mass and road slope is the key factor to improve the performance of intelligent vehicle dynamics control. Therefore, the parameter estimation method based on multi-dimensional information fusion is proposed in this paper. A comprehensive evaluation of wheel dynamics state is realized by information fusion, which is quantified in terms of wheel speed credibility. Then the calculated dynamic speed and auxiliary speed are weighted coupled to achieve accurate estimation of reference speed, which avoids the influence of unstable wheels. Similarly, the method to calculate the confidence factor of mass estimation is established in order to screen the vehicle state suitable for estimation. And the online estimation of mass is realized based on recursive least square method. Meanwhile, the road slope estimation algorithm based on interactive multiple model has been designed, which achieves the weighted fusion of Kalman filter observer based on kinematics and unscented Kalman filter observer based on dynamics. Finally, the road tests were carried out on representative working conditions. The maximum error between the actual speed and the reference speed does not exceed 0.68m/s. The relative error of mass estimation is not more than 1.95%, and the absolute error of slope estimation is less than 1.84%, which proves that the proposed estimation algorithm has high comprehensive performance. More importantly, it is not limited to specific working conditions, which means a great significance for the development of intelligent vehicles.

In this paper, an optimal longitudinal slip ratio system for real-time identification of electric vehicle (EV) with motored wheels is proposed based on the adhesion between tire and road surface. First and foremost, the optimal longitudinal slip rate torque control can be identified in real time by calculating the derivative and slip rate of the adhesion coefficient. Secondly, the vehicle speed estimation method is also brought. Thirdly, an ideal vehicle simulation model is proposed to verify the algorithm with simulation, and we find that the slip ratio corresponds to the detection of the adhesion limit in real time. Finally, the proposed strategy is applied to traction control system (TCS). The results showed that the method can effectively identify the state of wheel and calculate the optimal slip ratio without wheel speed sensor; in the meantime, it can improve the accelerated stability of electric vehicle with traction control system (TCS).

We explored a vehicle hill descent control (HDC) system based on an electronic stability program (ESP) and applied this system to brake cars. The experimental results reveal that our system can effectively reduce the workload of a driver during a downhill journey. In the first phase of our work, the control strategy of the HDC system based on fuzzy-PID (Proportion Integral Differential) was built by MATLAB/Simulink. Then, the co-simulation based on MATLAB/Simulink, CarSim and AMESim was carried out. Finally, a real vehicle test was conducted to further verify the feasibility of the strategy. A series of simulation experiments and real vehicle tests show that the HDC system can assist the driver to control the vehicle while driving downhill at low speed, thus effectively improving the safety of the vehicle and reducing the workload of driver.

Under complicated situations, such as the low slippery road surface and split-μroad surface, traction control system is the key issue to improve the performance of vehicle acceleration and stability. In this paper, a novel control strategy with engine controller and active pressure controller is presented. First and foremost, an ideal vehicle model is proposed for simulation; then a method for the calculation of optimal slip ratio is also brought. Finally, the scheme of control method with engine controller and active brake controller is presented. From the results of simulation and road tests, it can be concluded that the acceleration performance and stability of a vehicle equipped with traction control system (TCS) can be improved.