Xinxin Zhao

A condition-dependent recoverable stability boundary is proposed for risk-supervised direct yaw-moment control of distributed-drive electric vehicles. Recoverable states are identified through long-horizon nonlinear trajectories converging to a stable equilibrium without yaw-moment intervention. Stable-manifold information is used only when consistent with numerical classification. Conservative convex inner approximations with common half-space normals enable boundary updates through database lookup and bilinear interpolation. A boundary-utilization index continuously regulates yaw-moment participation within model predictive control. Compared with a geometric phase-plane boundary, enclosed areas increase by 53.3% and 145.2% at 110 and 80 km/h with adhesion coefficients of 0.60 and 0.50, respectively. At 110 and 70 km/h with adhesion coefficients of 0.80 and 0.50, the increases are 58.0% and 158.2%. No overestimation is observed on the evaluation grid. In the first two conditions, peak sideslip-angle errors decrease by 36.4% and 45.2% relative to permissive fixed-boundary control, while peak additional yaw moments are 72.7% and 63.2% lower than with conservative fixed-boundary control. At 80 km/h with adhesion coefficient of 0.35, peak sideslip-angle error decreases by 55.8% relative to no yaw-moment control. Ten co-simulations yield an aggregate equivalent mean simulation execution time of 1.54 ms per 10 ms control interval.

An effective energy management strategy (EMS) is crucial to improve the energy efficiency of hybrid vehicles, especially for heavy-duty mining trucks. An energy management strategy based on a proximal policy optimization algorithm with mask layer and novel reward functions (PPO-MASK-NR) is proposed for hybrid electric mining trucks (HEMTs) with multi-planetary systems. This algorithm fundamentally avoids irrational exploration by an intelligent agent by incorporating a real-time mask layer, and it accelerates learning efficiency by suppressing the backward propagation of gradients for irrational actions. A universally designed reward function is applied to ensure the achievement of the correct final state of charge (SOC) value and the expansion of the SOC's exploration range. Finally, the generalization performance of the proposed algorithm is validated through new driving cycles, and its authenticity is confirmed through hardware-in-the-loop (HiL) testing. The simulation results show that within the selected training cycles, the proposed algorithm achieves 98% compared with the dynamic programming algorithm (DP). The proposed algorithm has an improvement of 11% and 5% in online applications for a new driving cycle compared to a rule-based technique (RB) and the equivalent fuel consumption minimization approach (ECMS), respectively.

To enhance the path-tracking accuracy and control stability of articulated underground vehicles navigating high-curvature tunnels, this paper proposes a novel Multi-Time-Scale Nonlinear Model Predictive Control (MTS-NMPC) strategy. The core innovation lies in its dynamic adaptation of the prediction horizon to simultaneously compensate for the body torsion effects and yaw deviations induced by high-speed cornering. A high-fidelity vehicle dynamics model is first established. Subsequently, an adaptive mechanism is designed to adjust the prediction horizon based on the reference speed and road curvature. Experimental results demonstrate that the proposed MTS-NMPC achieves remarkable reductions of 35% and 17% in the maximum lateral tracking error and heading deviation, respectively, compared to conventional NMPC. It also improves stability by suppressing the velocity fluctuations of the articulated joint. The superior control performance and robustness of our method are further validated through field tests in an underground mine.

To address the poor trajectory tracking of mining trucks in narrow, high-curvature paths, this study explores the impact of four-wheel steering (4WS) and direct yaw moment control (DYC) on vehicle stability. A validated two-degree-of-freedom 4WS vehicle model was developed. A fuzzy logic controller with dual inputs (yaw rate and yaw angular acceleration) and a single output (compensatory yaw moment) was designed, alongside an optimal torque distribution controller based on tire friction circle theory to allocate the resultant yaw moment. A co-simulation platform integrating TruckSim and MATLAB/Simulink was established, and experiments were conducted under steady-state and double-lane-change conditions. Comparative analysis with traditional front-wheel steering and alternative control methods reveals that the 4WS mining truck with fuzzy-controlled optimal torque distribution achieves a reduced turning radius, enhancing maneuverability and stability. Hardware-in-the-loop (HIL) testing further validates the controller’s effectiveness in real-time applications.

Incorporating hybrid electric technology in mining trucks efficiently allocates energy between the diesel engine and electric motors, significantly reducing fuel consumption and emissions. This paper introduces an advanced Transformer network model aimed at an adaptive Energy Management Strategy (EMS) to improve the fuel efficiency of Hybrid Electric Mining Trucks (HEMTs). Initially, we outline models representing various HEMT subsystems, including its complex powertrain. Subsequently, leveraging the established vehicle model, we employ Dynamic Programming (DP) to ascertain the globally optimal control strategy for a standard driving cycle. Utilizing the Transformer model with ProbSparse self-attention, we determine the state of charge (SOC) associated with this globally optimal control strategy. We then implement a Proportional-Integral (PI)-based SOC tracking controller to achieve a time-varying Equivalent Factor (EF). Additionally, we compare the proposed strategy across various driving cycles, underscoring the Transformer network's superior generalization capabilities over the LongShort Term Memory (LSTM) model. Compared to the rule-based (RB) strategy and the traditional Equivalent Consumption Minimization Strategy (ECMS), our proposed EMS with time-varying EF based on the Transformer model shows substantial performance improvements of 15% and 12%, respectively. Furthermore, our strategy exhibits a 2% fuel economy advantage over an LSTM-based EMS algorithm with time-varying EF

A hybrid mining dump truck contains multiple power sources and has a variety of operating modes. When the vehicle switches between different operating modes, inappropriate control strategies will result in insufficient power or excessive output torque ripple. The resulting vehicle shock and the dynamic characteristics of the dynamic clutch mechanism will affect ride comfort. In order to improve the performance of the hybrid mining dump truck in the mode switching process as much as possible, this paper takes power-split hybrid special transmission as the research object and proposes a hybrid model predictive control (HMPC) strategy. However, the simulation time of the HMPC algorithm is about 27% less than that of the MPC algorithm. HMPC can significantly shorten the control time while improving the vehicle ride comfort, reducing the sliding friction work during mode switching, and improving the real-time and robustness of mode switching.

The accurate estimation of battery state of charge (SOC) for modern electric vehicles is crucial for the range and performance of electric vehicles. This paper focuses on the historical driving data of electric buses and focuses on the extraction of driving condition feature parameters and data preprocessing. By selecting relevant parameters, a set of characteristic parameters for specific driving conditions is established, a process of constructing a battery SOC prediction model based on a Long short-term memory (LSTM) network is proposed, and different hyperparameters of the model are identified and adjusted to improve the accuracy of the prediction results. The results show that the prediction results can reach 1.9875% Root Mean Square Error (RMSE) and 1.7573% Mean Absolute Error (MAE) after choosing appropriate hyperparameters; this approach is expected to improve the performance of battery management systems and battery utilization efficiency in the field of electric vehicles.

In improving the efficiency of powertrain systems and ride comfort for electric vehicles (EVs), the transmission model is required to enable more accessible and more straightforward control of such vehicles. In this study, a wet-type, two-speed, dual...

The shift strategy of automatic transmission plays a vital role in the smoothness and economy of heavy-duty mining trucks. In this paper, an engine fuel consumption model, a 6 + 2 speed automatic transmission model, and a vehicle resistance model are built in MATLAB. Combined with the development of smart mining technology, the traditional two-parameter economic shift strategy is corrected based on vehicle load and road slope. The dynamic programming optimization algorithm is used to extract the best economical shift strategy under known working conditions to reduce the fuel consumption and the number of shifts. Finally, simulation experiments of the optimized shift strategy by dynamic programming in a typical mine work cycle are carried out. The simulation results show that the engine speed and output torque are maintained in a relatively stable and efficient working range by using the proposed shift strategy. Compared with the traditional two-parameter shift strategy, dynamic programming has advantages in reducing fuel consumption and shift numbers.

In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of control will also increase. In this paper, a dynamic model of a 6 + 2 speed automatic transmission and vehicle powertrain system are built based on the Lagrange method, and the dynamic analysis of the two sets of clutches that make up the double-transition shift are carried out. Since a simulation model of the double-transition shift process is built-in MATLAB/Simulink, the shift jerk and clutch energy loss are used as multi-objective, and the genetic algorithm is used to optimize the simulation. Five strategies for the overlapping time of the clutches are proposed, and simulation experiments and Pareto optimal analysis are carried out, respectively. The simulation results show that the non-overlapping of the two sets of clutch inertia phases in the double-transition shift can effectively reduce the shift jerk. The overlapping of the torque phase and the inertia phase of the other clutch set can control the clutch energy loss at a low level due to using less shift time.