Energy conservation and emission reduction have become a global development consensus. Traditional construction machinery driven by an engine has high energy consumption and poor emission. Electric construction machinery is considered to be one of the main trends in the future due to its zero emissions by canceling the engine and using the motor-driven hydraulic system. However, most of the existing electric construction machinery works through the motor to simulate the engine without considering the regulation characteristics of the motor. Therefore, although the existing electric construction machinery improves the emission and the energy efficiency of the power system to a certain extent, the control of the motor and hydraulic systems can still be further optimized. The energy efficiency of the whole machine can be maximized. Based on the LUDV system of construction machinery, a control strategy based on motor speed classification and variable constant power can automatically identify the actual working conditions of the electric excavator and adjust the starting pressure of the constant power valve, to change the constant power range of hydraulic pump and achieve the goal of adapting to the working conditions of power system, is proposed. Simulation and experiments are carried out to verify the feasibility of the proposed control strategy. The results show that the speed classification and variable constant power control system can effectively realize the hierarchical regulation of motor speed and provide relatively stable speed input for the hydraulic system. Moreover, the current working condition can be identified through the pump outlet pressure. The adaption of the working conditions can be realized through the proportional reducing valve by adjusting the starting pressure of the variable constant power valve.

With the escalation of global warming and environmental pollution, electric products characterized by zero emissions, low vibration, and minimal pollution are increasingly favored by consumers. As a pivotal loading and transportation tool, the electrification of forklifts progressed earlier and is relatively mature. However, the prevalent low-voltage systems (72 V or 80 V) in current electric forklifts exhibit issues such as elevated heat loss, restricted motor instantaneous power due to voltage constraints, susceptibility to electrical erosion, and challenges in achieving rapid charging. To address these challenges, a powertrain solution employing high-voltage lithium batteries (320 V) as energy storage units for electric forklifts is proposed. The key parameters of the high-voltage lithium battery were meticulously calculated and selected. The powertrain architecture of the high-voltage lithium battery electric forklift was analyzed, and operational conditions were thoroughly examined. To verify the superior energy efficiency performance of the proposed high-voltage electric forklift in comparison to its low-voltage counterparts, a test prototype was constructed, and comprehensive tests, including average energy consumption and thermal equilibrium assessments, were conducted. The test results demonstrated that under average energy consumption conditions, the operational duration ranged from 8.89 to 13.34 h, surpassing the 7.5 h achieved by low-voltage electric forklifts. The thermal equilibrium temperatures of all electrical control units remained below 43 °C, significantly lower than the 80 °C shutdown protection threshold allowed for low-voltage forklifts. These findings indicate that the proposed high-voltage lithium battery electric forklift exhibits relatively low energy consumption, significantly enhances overall operational efficiency, and ensures stable operation, providing a viable solution and reference for the electrification of forklifts and other construction machinery.

Electric construction machinery with zero emission and high efficiency is considered to be a main development trend. An electric motor (EM) in electric construction machinery often needs to work at low speed or even in stalling conditions with large torque value and high work efficiency. In this paper, based on vector control of the maximum torque per ampere (MTPA) of an internal permanent magnet synchronous motor (IPMSM), a voltage and current composite observation algorithm was studied to improve IPMSM control performance at low speed. By establishing the observer model, the amplitude limited compensation for the voltage flux observation method was used to observe the EM stator flux. After being combined with the current observation method and the stator current obtained by coordinate transformation, the EM parameters in real-time can be identified for MTPA. Experimental research was carried out. The results show that the algorithm improved the speed control characteristic and output torque per unit current of the EM under low-speed working conditions.

With the increasingly serious environmental pollution and stricter emission standards, energy saving and emission reduction in construction machinery (CM) are imminent. Electrification of CM is an inevitable trend. Due to the complicated working conditions and the violent load fluctuations, the existing motor controller cannot adapt well to the operating characteristics of the electric CM. In this paper, the work requirements of the positive flow system are analyzed. Considering the robustness and resistance to load disturbance of the control system, a vector control strategy through online tuning of speed PI parameters based on fuzzy control is proposed. The characteristics of the control system are analyzed with the co-simulation of AMESim and MATLAB/Simulink. A test rig was set up to verify the feasibility of the control strategy. The results show that when the load is variable, the fluctuation of rotation speed is approximately 1.5% with fuzzy PI, and 2.5% with traditional PI. Furtherly, the vector control algorithm based on fuzzy PI is tested on a positive flow system. The test results show that when the load fluctuates drastically, the electric motor speed fluctuation is within 3.5%, and the steady-state error is only approximately 0.3%.