The iterative solution of spectral/hp element methods for complex geometries is challenging due to the typically large problem sizes, various length scales involved, anisotropic spatial discretisation and denser linear systems compared to the lower-order counterparts. These factors lead to ill-conditioned matrices, and efficient preconditioning techniques are needed to reduce the computational cost of these methods, facilitating their adoption in industrial applications. The current work compares the design and performance of various preconditioners within the incompressible Navier-Stokes equations solver of the open-source spectral/hp element method framework Nektar++. A new preconditioner is proposed within Nektar++, Lower-Order Refined (LOR) preconditioner, constructed on a low-order (P \(=\) 1) finite element discretisation spectrally equivalent to a given high-order discretisation. The LOR preconditioner provides advantages like cheap operator evaluations, constant memory requirement per degree of freedom, minimal sensitivity to high aspect-ratio elements and bounded iterative condition number with increasing problem size under certain conditions. The performance of the proposed preconditioner is compared to legacy implementations of preconditioners within Nektar++ and other existing out-of-the-box algebraic multigrid (AMG) methods to determine the best preconditioner for the current application. The chosen test cases are relevant in the context of race-car aerodynamics.