The increasing complexity and demanding performance requirement of modern automotive propulsion systems necessitate more intelligent and robust predictive controls. Due to the significant uncertainties from both unavoidable modeling errors and probabilistic environmental disturbances, the ability to quantify the effect of these uncertainties to the system behaviors is of crucial importance to enable advanced control designs for automotive propulsion systems. Furthermore, the quantification of uncertainty must be computationally efficient such that it can be conducted on board a vehicle in real-time. However, traditional uncertainty quantification methods for complicated nonlinear systems, such as Monte Carlo, often rely on sampling — a computationally prohibitive process for many applications. Previous research has shown promises of using spectral decomposition methods such as generalized Polynomial Chaos to reduce the online computational cost of uncertainty quantification. However, such method suffers from scalability and bias issues. This paper seeks to alleviate these computational bottlenecks by developing a multifidelity uncertainty quantification method that combines low-order generalized Polynomial Chaos with Monte Carlo estimation via Control Variates. Results on the mean and variance estimates of the axle shaft torque show that the proposed method can correct the bias of low-order Polynomial Chaos expansions while significantly reducing variance compared to the conventional Monte Carlo.