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The purposes of this paper are optimization of high speed reducer in electric vehicles based on the analysis of lubrication and verification of simulation accuracy and optimization results.

The traditional CFD method presents poor applicability to complex geometric problems due to grid deformity. Therefore, moving particle semi-implicit (MPS) method is applied in this study to simulate lubrication of the reducer and analyze the influence of input speed and lubrication system design on the distribution. According to the results, the reducer is optimized. Meanwhile, the experiments for lubrication and churning power loss is carried out to verify the accuracy of simulation and optimization effects.

The flow field of lubricant inside the reducer is obtained. The lubrication system of reducer needs to be improved. Simulation and experiment show that the optimization is sufficient and efficient.

According to the simulation of lubrication, the reducer is optimized. The lubrication experimental setup is established. The conclusion of paper can provide the method and tool for reducer in electric vehicle.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0123/

The lubrication of the high-speed reducer of an electric vehicle is investigated. The specific contents include visualization of the flow field inside reducer, lubrication evaluation of bearings and efficiency experiment.

The flow field inside reducer at five working conditions: straight, uphill, downhill, left lean and right lean is simulated by smoothed particle hydrodynamics (SPH). According to the instantaneous number of particles through bearings, the lubrication states of bearings are evaluated. The test platform is set up to measure the efficiency of the reducer.

The flow field inside the reducer is obtained, the lubrication of bearings needs to be improved, the efficiency of the electric vehicle reducer meets the requirement.

The SPH method is used to simulate lubrication instead of using the traditional grid-based finite volume method. A novel method to evaluate the lubrication of bearings is proposed. The method and conclusions can guide electric vehicle reducer design.

Autonomous robots must be able to understand long-term manipulation tasks described by humans and perform task analysis and planning based on the current environment in a variety of scenes, such as daily manipulation and industrial assembly. However, both classical task and motion planning algorithms and single data-driven learning planning methods have limitations in practicability, generalization and interpretability. The purpose of this work is to overcome the limitations of the above methods and achieve generalized and explicable long-term robot manipulation task planning.

The authors propose a planning method for long-term manipulation tasks that combines the advantages of existing methods and the prior cognition brought by the knowledge graph. This method integrates visual semantic understanding based on scene graph generation, regression planning based on deep learning and multi-level representation and updating based on a knowledge base.

The authors evaluated the capability of this method in a kitchen cooking task and tabletop arrangement task in simulation and real-world environments. Experimental results show that the proposed method has a significantly improved success rate compared with the baselines and has excellent generalization performance for new tasks.

The authors demonstrate that their method is scalable to long-term manipulation tasks with varying complexity and visibility. This advantage allows their method to perform better in new manipulation tasks. The planning method proposed in this work is meaningful for the present robot manipulation task and can be intuitive for similar high-level robot planning.