When the pure electric mining dump truck is working, it mainly ascends the slope at full load and descends the slope at no load. The loading state of the vehicle and the slope of the road will directly affect its axle load distribution and braking force distribution. In this paper, the slope dynamics analysis of the pure electric double-axle four-wheel drive mining dump truck was carried out. Based on the regenerative braking priority strategy, four regenerative braking control methods were developed based on the Matlab/Simulink platform and ADVISOR 2002 vehicle simulation software to study the ability of regenerative braking energy recovery and its impact on vehicle economic performance. The simulation results show that the regenerative braking priority control strategy used can maximize the regenerative braking force of the vehicle; the regenerative energy recovery capability of pure electric mining dump truck is proportional to the regenerative braking force that can be provided during braking; the two-axis braking strategy based on the I curve and the β line can make full use of the front and rear axle regenerative braking force when the braking intensity is large, and recover more braking energy; under road drive cycle, the single-axis braking force required to the braking strategy based on the maximized front axle braking force is the largest among all strategies, the motor braking efficiency is the highest, and the recovered braking energy is the most. For the studied drive cycle, the regenerative braking technology can reduce the vehicle energy consumption by 1.06%–1.56%. If appropriate measures are taken to improve the road surface condition and reduce the rolling resistance coefficient from f = 0.04 to f = 0.02, the regenerative braking technology can further reduce the vehicle energy consumption to 4.76%–5.73%. The economic performance of the vehicle is improved compared to no regenerative braking. In addition, the vehicle loading state and the driving motor working efficiency also directly affect the regenerative braking energy recovery capability of the pure electric mining truck.

Various erosion patterns generated through the cavitating water jet impacts under ambient pressure conditions were investigated in experiments and numerical simulations. A series of normalized stand-off distances $l_s/d_o$ ∈ [2.5 13.5] were studied during the erosion acceleration period. Two ring-like erosion areas were observed for comparatively low $l_s/d_o$ ∈ [2.5 6.5]. To gain insight into the pulse pressure loading on the erosion area, numerical calculations were performed using the volume of fluid (VOF) interface capturing methodology combined with the large-eddy simulation turbulence model. The erosion patterns are clarified based on the mass loss and distribution features of the eroded regions. The first ring, generated by the cavitation clouds impingement outside the central stagnation area, mainly contributes to the mass loss. The second ring moves inward, merges into the inner ring and eventually vanishes with increasing $l_s/d_o$. High pressure pulsation is found around the locations of the maximum erosion and the approximate intermediate radius of the second ring. The pressure pulsation in the first ring area are dominated by the frequency of the vortices shedding from the jet nozzle. Several higher frequencies are found as the spectral features of the eroded regions in each pattern.

Erosion experiments were performed to uncover the impact of organ-pipe chamber geometry on the frequency and erosion characteristics of self-excited cavitating waterjets. Jets emanating from self-excited nozzles with various organ-pipe geometries were investigated. The upstream and downstream contraction ratios of the organ-pipe resonator were changed respectively from 1.5 to 6 and 2 to 12. Pressure sensors and hydrophone were used to characterize jets’ frequency characteristics. Mass loss was also obtained in each of the configurations to assess the erosion performance. By tuning the self-excited frequency, the peak resonance was achieved using the nozzles with different geometries. Accordingly, the acoustic natural frequencies of various chamber geometries were obtained precisely. Results show that with increasing upstream and downstream contraction ratio of the organ-pipe chamber, the acoustic natural frequency increases monotonically due to the reduction of equivalent length, while the resonance amplitude and mass loss first increase and then decrease. There are optimum geometric parameters to reach the largest resonance amplitude and erosion mass loss: the upstream contraction ratio being between two and four, and downstream ratio being between four and seven. The effective length of the organ pipe can be calculated by the sum of the physical length and equivalent length to accurately obtain the acoustic natural frequency. Under the optimized parameters, the equivalent length can be estimated as 0.35D.

The automation and intelligence development of heavy-duty mobile machines and industrial equipment require the accurate control of electrical-hydraulic valve-controlled system (EHVS). This article presented a generic reinforcement learning with predictive target information control (RLPC) method for EHVS to improve the control accuracy, in which a distributional soft actor-critic (DSAC) iteration frame was established. First, a dynamic model considering nonlinearity, saturation, and uncertainty from a time-varying system perspective was established and validated through tests under typical conditions. Then the model-free RLPC method was designed for EHVS. As the baseline, the classical proportional-integral-derivative (PID) and an adaptive PID (APID) with RL-tuning were designed for comparison purposes. Finally, the corresponding iteration frame was established to improve the performance of RL-related works in the designed control methods with DSAC. The comparison results during typical operations show that the proposed RLPC method can handle dynamic response and steady errors better at different states of the system and actuator. The effects of the predictive horizon on control performance were also revealed. The stability of the method was also validated with a designed instantiating EHVS, specifically a hydraulic inverted pendulum system. The performance of the proposed methods was quantitatively analyzed with the normalizing performance indicator ρ. The mean value of the indicator was about 0.0028, which is lower than the first two methods and the existing control method for EHVS.

This paper presents a novel model-based method for estimating the attitude of underground articulated vehicles (UAV). We selected the Load–Haul–Dump (LHD) vehicle as our application object, as it is a typical UAV. First, we established the involved models of the LHD vehicle, including a kinematic model, the linear and angular constraints of a center articulation model, and a dynamic four degrees-of-freedom (DOF) yaw model. Second, we designed a Kalman filter (KF) to integrate the kinematic and constraint models with the data from an inertial measurement unit (IMU), overcoming gyroscope drift and disturbances in external acceleration. In addition, we designed another KF to estimate the yaw based on the dynamic yaw model. The accuracy of the estimations was further enhanced by data fusion. Then, the proposed method was validated by a simulation and a field test under different dynamic conditions. The errors in the estimation of roll, pitch, and yaw were 3.8%, 2.4%, and 4.2%, respectively, in the field test. The estimated longitudinal acceleration was used to obtain the velocity of the LHD vehicle; the error was found to be 1.2%. A comparison of these results to those of other methods showed that the proposed method has high precision. The proposed model-based method will greatly benefit the location, navigation, and control of UAVs without any artificial infrastructure in a global positioning system (GPS)-free environment.