Active chassis systems are pivotal in modern automotive design, particularly for high-performance vehicles where handling, stability, ride comfort, and driver engagement significantly impact consumer choices. However, control strategies often lack justification for the chosen vehicle architecture and fail to define achievable performance limits—key considerations in early development for optimal hardware and software design. Typically, active chassis systems follow a V-cycle development process, starting with simulations to evaluate the performance envelope or feasibility region of the actuation system, ensuring it meets vehicle dynamics requirements. These simulations are essential for guiding control strategies and testing both virtual and physical prototypes, helping reduce development costs from the proof-of-concept to pre-production stages. This thesis focuses on the tools and methodologies necessary for designing vehicles and chassis controllers, concentrating on (i) handling and (ii) ride. It emphasizes selecting the right simulation tools for early-stage evaluation and applying suitable performance assessment methods in both simulation and experimental phases. For handling dynamics, the research investigates the minimum model complexity required to evaluate active control systems like front and rear active camber, rear-wheel steering, active roll moment distribution, and torque vectoring. A significant contribution is the introduction of the handling stability ratio, a novel metric that quantifies stability across various conditions, facilitating the integration of multiple actuators. This enables clear performance targets for early vehicle and control system design, particularly for actuator selection and understanding vehicle limitations. The study also developed a model-based feed-forward controller for active camber systems, enhancing handling in both steady-state and transient conditions, providing an engaging and non-intrusive driving experience. Additionally, a pre-emptive braking strategy using nonlinear model predictive control is proposed to improve vehicle stability by managing longitudinal dynamics, with minimal intrusive interventions, even in emergency situations. In ride quality, the thesis offers a comprehensive evaluation of objective and subjective metrics for assessing primary and secondary ride dynamics. It outlines the necessary tools, instrumentation, and methods for accurate assessment, and introduces a systematic approach to correlating objective data with driver perception. This enables prediction of subjective scores from objective metrics, improving the driving experience and saving time and costs during the design and pre-production phases. The study also highlights the need for appropriate model complexity in vehicle, suspension, and tire simulations to ensure accurate results. In conclusion, this research enhances the understanding of active chassis systems, providing new tools and methodologies for the design and control of high-performance vehicles. The findings bridge the gap between theoretical models and practical applications, contributing to improved vehicle performance and driver satisfaction.