A Model Predictive Control-Based Electronic Differential Control Strategy for Distributed-Drive Buses Considering the Reduction of Tire Wear
The electronic differential control (EDC) system is an indispensable key technology for distributed-drive buses. However, compared to traditional buses with mechanical differentials, distributed-drive buses based on EDC have more severe tire wear and tire bias wear. To tackle these problems, we consider the reduction of tire wear, and propose an EDC strategy based on model predictive control (MPC) to improve the steering stability and extend the tire service life. First, nonlinear three-degree-of-freedom dynamics model and magic tire formula are established. Second, the slip ratio is selected as the observation of tire wear, and the underlying causes of tire wear are analyzed via simulation tests under different driving conditions. Third, a three-layer EDC strategy is designed. The upper layer is the EDC trigger condition and vehicle speed-following control; the middle layer is the calculation of control parameters such as the desired stability parameters, estimated sideslip angle, and slip state of the driving wheels; and the lower layer is the MPC-based driving torque controller to obtain the motor torque. Finally, the proposed MPC-based EDC strategy is validated and compared by simulation tests and real experiments. The comparative results demonstrate that the MPC-based EDC can achieve optimized differential torque control to effectively reduce the tire wear while improving the stability in different steering maneuvers.
Authors
- Chang Wang (ORCID: https://orcid.org/0000-0003-3531-1215)
- Yingshi Guo (ORCID: https://orcid.org/0000-0001-9384-7558)
- Qinyu Sun (ORCID: https://orcid.org/0000-0002-6446-0570)
- Libo Zhou (ORCID: https://orcid.org/0000-0002-7183-3371)
- Bing Yang
- Rui Fu (ORCID: https://orcid.org/0000-0002-5732-3068)
Institutions
- Chang'an University (CN)
Publication Details
- Journal
- IEEE/ASME Transactions on Mechatronics
- Published
- 2024-08-26
- DOI
- https://doi.org/10.1109/tmech.2024.3440312
- Citations
- 2
- Primary Topic
- Electric and Hybrid Vehicle Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.41
Funders
- National Natural Science Foundation of China