| Autor: |
Houzhong Zhang, Yiyun Qi, Weijian Si, Chengyin Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Machines, Vol 12, Iss 11, p 769 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2075-1702 |
| DOI: |
10.3390/machines12110769 |
| Popis: |
To optimize the acceleration performance of independently driven electric vehicles with four in-wheel motors, this paper proposes an anti-slip regulation (ASR) strategy based on dynamic road surface observer for more efficient tracking of the optimal slip ratio and enhanced vehicle acceleration. The method uses the Unscented Kalman Filter (UKF) observer to estimate vehicle speed and calculate the actual slip ratio, while a fuzzy controller based on the Burckhardt tire model identifies road surfaces. The road’s peak adhesion coefficient and optimal slip ratio curve are fitted using a Back Propagation Neural Network (BPNN) optimized by Particle Swarm Optimization (PSO). The control strategy further refines torque management through an adaptive sliding mode control (ASMC) that integrates adaptive laws and a super-twisting sliding mode approach to track the optimal slip ratio. Joint simulations with MATLAB/Simulink and Carsim on low-adhesion, joint, and split road surfaces demonstrate that the strategy quickly and accurately identifies the optimal slip ratio across various road surfaces. This enables the tire slip ratio to approach the optimal value in minimal time, significantly improving vehicle dynamic performance. Compared to conventional sliding mode controllers, the optimized ASMC reduces chattering and improves control precision. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
