Evaluating the Ratcheting Performance of Park Lock Mechanism for Electric Drive Unit Using Multibody Dynamics Approach

<div class="section abstract"><div class="htmlview paragraph">The Electric Drive Unit (EDU) is an essential system in electric vehicles. One of the important features in an EDU is the Park Lock. This safety device prevents the vehicle from unwanted rolling on grades, by locking the transmission. To prevent catastrophic damage to the mechanism, the park lock must also be designed so that it does not inadvertently engage above a specified maximum speed. These two objectives are accomplished by means of a pawl and ratchet gear mechanism which completely engages below a critical speed. Above that speed, the ratchet gear will continue to rotate freely while the pawl will maintain intermittent dynamic contact, but not fully engage (i.e., “ratcheting”).</div><div class="htmlview paragraph">Although physical testing is used to verify the performance of a park lock, this expensive and time-consuming approach is infeasible at the design stage, when test results might be most useful. To address this, various computational methods are employed to predict the critical engagement speed early in the design process. One such method is multibody dynamic (MBD) analysis. Typically, an MBD analysis is performed by simulating an inadvertent park lock actuation while the vehicle is moving well above the maximum specified engagement speed. The vehicle is then gradually decelerated while ratcheting occurs, until finally, the pawl locks into the ratchet gear at the critical speed, causing the vehicle to stop.</div><div class="htmlview paragraph">Experience has shown that a single MBD simulation or a single physical test is inadequate to validate a park lock design. In practice, small variations in input parameters or operating conditions can cause large variations in results. This is due to the extreme nonlinearity of numerous dynamic impact events between the pawl and ratchet gear geometries. Examples of such input variations include key parameters like contact stiffness, friction coefficients, geometrical tolerances, part initial conditions, and even computational solver settings.</div><div class="htmlview paragraph">To address this limitation, the present work uses the ADAMS<sup>TM</sup> MBD software in conjunction with a Design of Experiments approach, to perform hundreds of simulations that determine a comprehensive range of engagement speeds. Key input parameters are automatically varied using a Monte Carlo strategy. The proposed method is validated with physical testing wherein a range of predicted engagement speeds are well correlated. It shows the reliability and applicability of proposed approach to reduce development time and cost with improving safety in electric vehicles.</div></div>