Hybrid Coyote Optimization based Big Bang Big Crunch optimized FLC design for Active Suspension Control in IWM-EV

The move from Internal Combustion Engine (ICE) to Electric Vehicles (EVs) has implications for resolving environmental problems, increasing energy efficiency, and boosting economic and public health outcomes. Among the numerous EV architectures, In-Wheel-Motor (IWM) vehicles stand out for their higher energy efficiency, improved vehicle dynamics, and environmental advantages. However, the additional un-sprung mass from IWM reduces ride comfort and handling, necessitating improved suspension systems such as semi-active or completely active suspensions. This paper offers a Fuzzy Logic Control (FLC) solution for active suspension in IWM-powered EVs, with the goal of improving ride comfort and overall system performance. To strengthen the FLC's performance, a Hybrid Coyote Optimization-based Big Bang Big Crunch (HCOB<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>C) algorithm is used, to solve the computational imbalances and slow convergence speeds inherent in the individual techniques. Simulations demonstrate the proposed controller's practicality and efficacy, with an emphasis on ISO 2631–1 criteria for ride comfort, suspension deflection, and tyre deflection performance. The results show that the optimized FLC design has the potential to greatly improve handling, stability, and overall driving experience in IWM-driven EVs.