Multidisciplinary and Integrated Approach to Predict Automotive Axle System Efficiency

<div class="section abstract"><div class="htmlview paragraph">In today’s competitive automotive market, customers are now looking for system efficiency as one of the important design parameters of system performance along with durability and reliability. It is essential to ensure products are designed to utilize maximum input power and have better system efficiency. In automotives, transmission and axle systems are power transmitting elements from prime mover to wheels and are one of the main contributors to overall vehicle efficiency. Hence, predicting and assessing overall system efficiency of these aggregates is of paramount importance. System efficiency is driven by component power losses for various speeds and torques, which are arising out of component design parameters, complex interaction within system, operating conditions, lubrication, temperatures etc.</div><div class="htmlview paragraph">To capture multi-physics of speed and torque dependent losses of automotive axle, multidisciplinary and integrated approach is proposed in this paper, Efficiency predictive model is developed in simulation tool by modelling detailed system consists of power transmitting components and flexible housings. Churning losses of hypoid gears, differential system, and auxiliary components etc. which are speed dependent, are evaluated by Computational Fluid Dynamics (CFD) principles. Frictional losses of hypoid gears and bearings are evaluated by different ISO methods. With these, total system losses are evaluated including oil seal losses, and overall system efficiency is calculated thereby. Developed methods are applied to commercial vehicle axle and validated with physical test. This helps to evaluate system loss components and their respective contributions.</div><div class="htmlview paragraph">Developed predictive methods can be easily extended to e-axle system efficiency prediction. Using these methods, system efficiency and power losses can be predicted and analyzed at design stage before prototypes are built and help to carry out required design changes to improve system efficiency. Actionable insights from predictive model helps to ensure axle system and components are designed for better efficiency right at initial stage of design.</div></div>