Adhesion identification and tire-force coupling based torque distribution strategy for enhanced stability of distributed driving electric vehicle by using fuzzy broad learning system algorithm

The dynamic variations of the tire-road friction coefficient (TRFC) and tire lateral force are critical factors to improve the stability performance of distributed driving electric vehicle (DDEV). However, traditional strategies often overlook the critical interaction between the TRFC and tire lateral force which leads to potential instability when tire forces approach adhesion limits. To address this issue, a novel adhesion identification and tire-force coupling based torque distribution strategy is proposed to improve the stability performance of DDEV explicitly account for tire-road interactions. The first is development of a TRFC recognition method using a fuzzy broad learning system (FBLS) algorithm to real-time evaluate the TRFC. Second, the tire lateral force estimated algorithm based on the square-root cubature Kalman filter (SRCKF) combined with vehicle dynamics model is designed to predict tire lateral force. And most critically, the identified TRFC and estimated lateral forces are integrated to dynamically update the constraints of a combined longitudinal-lateral tire force boundary in the proposed strategy. Finally, in combination with the above efforts, the adhesion identification and tire-force coupling based torque distribution strategy by using FBLS and SRCKF algorithm has been further developed accordingly. The numerical validation results demonstrate that the excellent performance of the proposed strategy in stability control, and the effectiveness of the proposed strategy is validated by Hardware-in-the-loop (HIL) experiments. Both the numerical validation and HIL results indicate that the implementation of the proposed strategy could significantly decrease the influence of interaction between TRFC and tire lateral force, further improving the stability control for DDEV.