Synergistic 1-D and 3-D Analysis to Optimize the Performance and Reliability of the Reed Valve Used in Reciprocating E-Compressor

Abstract

<div class="section abstract"><div class="htmlview paragraph">With the advent of electric and hybrid drivetrain in the commercial vehicle industry, electrically driven reciprocating compressors have gained widespread prominence. This compressor provides compressed air for key vehicle systems such as brakes, suspension systems and other auxiliary applications. To be a market leader, such an E-compressor needs to meet a myriad of design requirements. This includes meeting the performance by supplying air at required pressure and flow rate, durability requirements and having a compact design while maintaining cost competitiveness. The reed valve in such a compressor is a vital component, whose design is critical to meet the aforementioned requirements. The reed valves design has several key parameters such as the stiffness, natural frequency, equivalent mass, and lift distance which must be optimized. This reed valve also needs to open and close rapidly in response to the compressor operating speed. Since it is the order of milliseconds, the valve is subjected to high velocity and impact force during this short time. A 1-D AMESim representation of the compressor has the reed valve modeled as an equivalent spring mass system. 3-D static structural analysis is performed using FEA tool to predict the stiffness, natural frequency and equivalent mass which acts as the input to the 1-D model. The overall performance of the compressor is then predicted through the 1-D Model simulation. The pressure data from this 1-D model is fed back to FEA to perform a 3-D transient dynamic analysis. The impact velocity and dynamic stresses induced during valve operation is studied to ensure reed valve durability. Optimization of the design parameters of reed valve is performed by synergistically combining the key insights from the AMESim performance outputs as well as the stiffness and dynamic stress prediction from FEA.</div></div>