Analysis of Optimum Lubricant Quantity of Electric Vehicle Reducer based on Moving Particle Semi-implicit Method （MPS）

Based on the analysis of the heat generation and heat transfer process of the internal components of an electric vehicle reducer， a thermal network simulation model of an electric vehicle reducer based on AMEsim software is established. It is assumed that under high lubricating conditions， the simulation of heat balance of the reducer under different condition of high-temperature and high-load different vehicle speed is completed. Then the moving particle semi-implicit method （MPS） is used to analyze the agitation flow field of the reduction gearbox at different lubricating oil quantity. Based on this， the convective heat transfer thermal resistance module of the thermal network model of the reducer is modified to obtain optimum oil quantity for reducer. The modified results indicate that under high-temperature and high-load conditions of electric vehicle reducers，a smaller amount of lubricating oil will cause the gears of reducer to be under-lubricated， resulting in higher tooth surface temperatures. Combining the thermal network model with the moving particle semi-implicit method （MPS） provides a new method for the lubrication effect analysis of electric vehicle reducers.