Research demonstrated that the application of a multi-speed transmission could improve the dynamic and economic performance of electric vehicles. This paper deals with a novel multi-speed transmission for the electric dump truck (EDT) operating on the structure route (SR), which has a definite starting point and end point without complex traffic conditions. To optimize the gear ratio and shift schedule to reduce energy consumption in such conditions, the mathematical model of the transmission and the dynamic model of the EDT are initially required. Following this, the shift schedule is presented according to the motor efficiency map. After that, the gear ratio optimization is carried out by a particle swarm optimization (PSO) algorithm. Moreover, the proposed EDT is compared with an EDT with a single-speed transmission. The simulation results show that the energy consumption is reduced by 6.1%.

BEVs (Battery Electric Vehicles) have received widespread attention from various countries for their potential in combating global warming, the energy crisis, and environmental pollution. The driving range and energy consumption of BEVs vary significantly under different driving cycles, which often results in discrepancies between the values reported by manufacturers and real-world data. To address this issue, this paper establishes a modular simulation model of a BEV on the Matlab/Simulink platform and conducts simulation experiments and analyses of driving range and energy consumption under three different standard driving cycles, namely, the NEDC (New European Driving Cycle), WLTC (World Light Vehicle Test Cycle), and CLTC-P (China Light-duty Vehicle Test Cycle for Passenger Car), and compares the results with data from vehicle manufacturers and consumers. The results of the study show that the NEDC conditions are more ideal, the CLTC-P conditions are the most intense vehicle driving, and the WLTC conditions require the highest overall vehicle performance. Compared with other standard cycles, the WLTC conditions show better alignment with real-world driving range data. The two main factors affecting the energy consumption in each condition are driving range and acceleration. The energy recovery strategy, braking frequency, and average deceleration speed of the driving cycle conditions are important factors affecting the braking energy recovery. This study provides a theoretical basis for driving range and energy consumption testing and driving cycle condition improvement of BEVs.

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.

This paper proposes an integrated path tracking controller for articulated vehicles. A nonlinear model-predictive control (NMPC)-based reference state tracker is designed as an upper-level controller to solve the vehicle’s longitudinal velocity and steering rate. A terminal cost is introduced into the NMPC to improve the controller’s stability. A lower-level controller is developed to translate upper-level solutions into vehicle actuators’ signals, including steering and driving controllers. The steering controller translates the steering rate into the linear velocity of the cylinder to calculate the required fluid volume and ultimately into the rotation speed of the steering motor. The neural network method is applied in the driving controller to ensure accuracy under different loadings. In order to investigate the effects of the path tracking controller, an articulated dump truck is adapted for the field tests by adding the steering-by-wire system and driving-by-wire system, respectively. Experimental verifications of the lower-level controller are performed. The results show that the controller can accurately satisfy the demand. Finally, the tracking performance of the integrated path tracking controller is analyzed experimentally under different reference velocities. The results indicate that tracking accuracy can be guaranteed.

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.

This paper introduces a new two-speed planetary gear automatic transmission using an electronically controlled wedge clutch. In order to verify the feasibility of using this transmission in pure electric vehicles, the influencing factors of the two-speed transmission due to the increase in mass and the reduction in transmission efficiency are introduced. The vehicle simulation model was established on the MATLAB/Simulink platform, and the dynamic programming method was used to optimize the transmission ratio and shifting law. The simulation results show that the use of a two-speed automatic transmission can effectively improve the economic performance and dynamic performance of battery electric vehicles.