Engine Idle Creep Testing and Modeling of Vehicles Equipped with CVT, DCT, and Conventional Automatic Transmissions

Abstract

<div class="section abstract"><div class="htmlview paragraph">Determining impact speeds is an important factor in any accident reconstruction. Event data recorders are now commonplace in on-road vehicles and provide an added tool for the accident reconstructionist. However, in low-speed collisions where impact severity is often important, event data recorders fail to record data as the minimum threshold for impact severity sometimes is not met. Alternatively, damage-based methods may be ineffective in quantifying the severity of the impact due to a lack of defined vehicle crush damage. These types of scenarios oftentimes present themselves as a bullet vehicle in the beginning processes of accelerating from a stop or when a stopped target vehicle is rear-ended from behind by the bullet vehicle. A specific subset of this scenario might entail the foot of the driver of the bullet vehicle coming off the brake pedal, allowing the bullet vehicle to “creep” forward at engine idle speeds and impacting the target vehicle resulting in no visible crush damage to either vehicle. Eighteen vehicles with conventional automatic transmissions were tested, which included sedans, sport utility vehicles (SUVs), pickup trucks, and vans. Two vehicles (one sedan, one wagon) equipped with dual-clutch transmissions (DCTs) and three vehicles (one sedan, one hatchback, and one wagon) equipped with continuously variable transmissions (CVTs) were also tested. These vehicles were allowed to accelerate at idle with the brake pedals released. Acceleration, speed, distance, and engine speed data were collected for multiple vehicles runs in both forward and reverse directions over level ground. The data resulting from this study were then compared/contrasted among the different drivetrains and also previously published literature to determine similarities and differences. Previous study data sets were also incorporated with the authors’ data to improve predicted vehicle speed.</div></div>