In automotive engineering, designing for optimal vehicle dynamics is challenging due to the complexities involved in analysing the behaviour of a multibody system. Typically, a simplified set of dynamics equations for only the key bodies of the vehicle such as the chassis and wheels are formulated based on reduced degrees of freedom [1]. In contrast, one could employ high-fidelity multibody dynamics simulation and include more intricate details such as the individual suspension components while considering full degrees of freedom for all bodies; however, this is more computationally demanding. Also, for gradient-based design optimization, computing adjoints for different objective functions can be more challenging for the latter approach [2], and often not feasible if an existing multibody dynamics solver is used. We propose a mixed-fidelity multidisciplinary approach, in which a simplified set of equations is used to model the key vehicle dynamics while incorporating a high-fidelity multibody suspension module as an additional coupled discipline. We then employ MAUD (modular analysis and unified derivatives) [3] to combine analytical derivatives based on the dynamics equations and finite differences obtained using an existing multibody solver. Also, we use a direct collocation method for time integration, which solves for both the system trajectory and optimal design variables simultaneously [4]. The benefits of our approach are shown in an experiment conducted to optimize vehicle parameters for both handling and ride comfort. In summary, the current work demonstrates that a gradient-based, mixed-fidelity multidisciplinary design optimization approach provides a scalable solution for tackling a complex vehicle dynamics application. REFERENCES [1] G. Rill and A.A. Castro. Road vehicle dynamics: Fundamentals and modeling with MATLAB. CRC Press, 2020. [2] M. Ebrahimi, A. Butscher, H. Cheong, and F. Iorio. Design optimization of dynamic flexible multibody systems using the discrete adjoint variable method. Computers & Structures, 213: 82-99, 2019. [3] J.T. Hwang and J.R. Martins. A computational architecture for coupling heterogeneous numerical models and computing coupled derivatives. ACM Transactions on Mathematical Software, 44(4): 1-39, 2018. [4] J.T. Allison, T. Guo, and Z. Han. Co-design of an active suspension using simultaneous dynamic optimization. Journal of Mechanical Design, 136(8): 081003, 2014.