On-road driving risk mitigation and safety assurance control framework for in-wheel motor electric vehicles

Abstract

The driving behavior characteristics of electric vehicles have been investigated extensively, much attention has been directed to vehicle stabilization and handling, and several works have been well introduced in the literature. However, integrating different control strategies to achieve various goals at potentially encountered extreme driving conditions is still an active research area. This research study introduces a combined control scheme for in-wheel motor electric vehicles (IWM-EVs) equipped with a path-tracking module. It considers multiple factors influencing vehicle motion and safety, such as wheel slip and rollover. Different control strategies are implemented to design four active front steering controllers. Due to the limitation of the steering controller, a yaw motion stability control module is established using three control strategies to compute the corrective yaw moment. Two torque distribution methods, rule-based and optimization-based, were contrasted for the three controllers. A torque coordination module introduces the allocated torque from yaw moment, speed tracking, and wheel slip controllers. The velocity tracking module is established by utilizing the whale optimization algorithm (WOA), and the tire slip control module is combined with a wheel-slipping identification module. In addition, the rollover propensity is mitigated by an individual module, and the capability to track a path in the autonomous driving mode is added to the vehicle based on the optimal control approach using the model predictive control (MPC). The conducted simulations under the activation of controllers via the emergency detection module prove the effectiveness of the proposed methodology in enhancing and ensuring vehicle behavior and safety with the highest improvement percentage in lateral displacement $40.4\%$ and $52.98\%$ in different driving scenarios.