In general, battery packs are monitored by the battery management system (BMS) to ensure the efficiency and reliability of the energy storage system. SOC and SOH represent the battery's energy and lifetime, respectively. ey are the core aspects of the battery BMS. e traditional method assumes that the SOC is determined by the integral of the current input and output from the battery over time, which is an open-loop-based approach and often accompanies by poor estimation accuracy and the accumulation of sensor errors. e contribution of this work is to establish a new equivalent circuit model based on the lithium battery external characteristic, and the battery parameters are identified by considering the influence of capacity fade, voltage rebound, and internal capacitance-resistance performance. e correlation between the ohmic internal resistance and real capacity is obtained by degradation test. en, the dual extended Kalman filter (DEKF) is used to perform real-time prediction of the lithium battery state. And through the simulation analysis and experiments, the feasibility and precision of the estimation method are well proved.
Loaders are widely used in the construction of earthworks for construction projects. Due to the large volume and mass of these machines, they have shortcomings such as low driving efficiency and high energy consumption. To address these shortcomings, this work applied eclectic drive technology to a loader’s traction system. A wheel electric drive system with a switched reluctance driver (SRD) was developed. The operating principle and basic structure of the SRD system were analyzed. A new voltage PWM-controlled strategy with dynamic adjustable turn-on and turn-off angles and a single conducting phase in a fixed period was developed. Then, an SRD simulation model was established in MATLAB/Simulink. A simulation of the working condition for the loader was performed. A test bench for the SRD was built and accordingly the transportation and operation conditions were tested. The corresponding speed–time curve of transportation and operation was obtained. Simulation and experiment results showed that the electric drive system with SRD had excellent responses to changes in torque and speed and adapted well to the various working conditions of the loader. The system could effectively traction the loader and verified the feasibility and applicability of the wheel electric drive system.
Aeroengine mainshaft bearings are key components in modern aeroengines, and their main functions are to support the rotation of the main shaft of the aeroengine in harsh environments, such as high temperature, heavy load, high speed and oil break; reduce the friction coefficient during the high-speed rotation of the main shaft; and reliably ensure the rotation accuracy and power transmission of the aeroengine’s main shaft during operation. The manufacture of aeroengine mainshaft bearings requires complex processes and precise machining to ensure high performance and reliability, and how to intelligently complete the production and manufacture of mainshaft bearings and ensure the strength and accuracy of the bearings, quickly distinguish the fault types of the bearings and efficiently calculate, analyze and predict the life of the bearings are the current research hotspots. Therefore, building a high-fidelity and computationally efficient digital twin life cycle of aeroengine mainshaft bearings is a valuable solution. This paper summarizes the key manufacturing technology, manufacturing mode and manufacturing process based on digital twins in the life cycle of aeroengine mainshaft bearings, including the metallurgical process, heat treatment process and grinding process of aeroengine mainshaft bearings. It presents a fault diagnosis and life analysis of mainshaft bearings of aeroengines, discussing the key technologies and research directions of the life cycle of mainshaft bearings based on digital twins.
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