Steering Stability Control Strategy Applied to Distributed Electric Drive Vehicles: Energy Optimization Considering Multi-objective Demands

Abstract

This article presents a cooperative controller that is specifically designed to enhance the stability of a distributed-drive vehicle during steering. The controller focuses on improving lateral stability during steering and achieving optimal torque allocation to meet numerous objectives. The article proposes a novel approach to improve the performance of the sliding mode controller for transverse stability control during steering. This is achieved by designing a fractional-order non-singular fast terminal sliding mode surface function, a fractional-order double-power exponential convergence law, and introducing a weighted integration term. Furthermore, the vehicle’s torque was fine-tuned by employing an ant colony optimization (ACO) technique within the acceptable range defined by the lateral and longitudinal control requirements. To prevent the ACO algorithm from being stuck in local optima, a pseudo-random rule was implemented based on the original state transfer probability. This rule helps accelerate the convergence of the algorithm. Additionally, an elite approach and a dynamic change strategy for pheromone concentration were devised. Ultimately, the performance of the co-controller that was built is evaluated by simulation experiments conducted under both accelerated and decelerated driving situations. The test findings indicate that the technique effectively improves the lateral stability, tracking control, and energy economy of electric cars, with promising potential for practical use.