Integrated Traction Control Strategy for Distributed Drive Electric Vehicles with Improvement of Economy and Longitudinal Driving Stability

Abstract

This paper presents an integrated traction control strategy (ITCS) for distributed drive electric vehicles. The purpose of the proposed strategy is to improve vehicle economy and longitudinal driving stability. On high adhesion roads, economy optimization algorithm is applied to maximize motors efficiency by means of the optimized torque distribution. On low adhesion roads, a sliding mode control (SMC) algorithm is implemented to guarantee the wheel slip ratio around the optimal slip ratio point to make full use of road adhesion capacity. In order to avoid the disturbance on slip ratio calculation due to the low vehicle speed, wheel rotational speed is taken as the control variable. Since the optimal slip ratio varies according to different road conditions, Bayesian hypothesis selection is utilized to estimate the road friction coefficient. Additionally, the ITCS is designed for combining the vehicle economy and stability control through three traction allocation cases: economy-based traction allocation, pedal self-correcting traction allocation and inter-axles traction allocation. Finally, simulations are conducted in CarSim and Matlab/Simulink environment. The results show that the proposed strategy effectively reduces vehicle energy consumption, suppresses wheels-skid and enhances the vehicle longitudinal stability and dynamic performance.

Longitudinal Vehicle Dynamics: A Comparison of Physical and Data-Driven Models Under Large-Scale Real-World Driving Conditions

“…Another use for forward models is in online model selection. If there is significant mismatch between the model's prediction and sensory data, the control system could select between alternative inverse models [42]. For example if the car runs on a slippery road the predictions of a model trained on a dry, well-maintained road will fail.…”

“…For the physical model, it would be necessary to re-engineer the model, introducing additional terms and refined models for tyre forces, which in turn might call for complex and more costly experiments [45]. In more extreme driving, it would also be necessary to consider selecting between prediction models (and their inverted control counterparts), depending on conditions [42]. Also, the assumption of de-coupled lateral-longitudinal dynamics would need to be examined carefully; the introduction of a lateral physical model significantly increases the complexity of physical vehicle dynamic modelling.…”

Steering Stability Control for a Four Hub-Motor Independent-Drive Electric Vehicle with Varying Adhesion Coefficient

“…With the rapid development of motor technology, the four hub-motor independent-drive electric vehicle (4MIDEV) concept has emerged [1,2]. Different from the traditional centralized drive vehicle, the 4MIDEV is directly driven by four hub motors without the need for clutches, drive shafts and other mechanical components.…”

Continuous Steering Stability Control Based on an Energy-Saving Torque Distribution Algorithm for a Four in-Wheel-Motor Independent-Drive Electric Vehicle

“…(2) In the second-hierarchy torque distribution algorithm, the equality constraint Bu = v is converted to min W v (Bu − v) 2 and introduced into (20) as a penalty item to reduce the constraint intensity of the control error. The optimization problem can be reformulated as:…”

“…With the increasing use of electric vehicles (EVs) and the rapid development of motor-integration technology, a four in-wheel-motor independent-drive electric vehicle (4MIDEV) emerged [1][2][3]. The 4MIDEV, unlike the traditional centralized-drive vehicles, is driven by four motors integrated into four wheel hubs.…”