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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Model Predictive Control-Based Electronic Differential Control Strategy for Distributed-Drive Buses Considering the Reduction of Tire Wear

Chang Wang, Yingshi Guo, Qinyu Sun, Libo Zhou et al.
2 citations
IEEE/ASME Transactions on Mechatronics
Electric and Hybrid Vehicle Technologies
0.41
article

A Model Predictive Control-Based Electronic Differential Control Strategy for Distributed-Drive Buses Considering the Reduction of Tire Wear

Chang Wang, Yingshi Guo, Qinyu Sun, Libo Zhou, Bing Yang, Rui Fu
article en
2 citations

Abstract

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.

IEEE/ASME Transactions on MechatronicsVol. 30(3)
Chang'an University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 40%
Electric and Hybrid Vehicle Technologies
0.41
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Model Predictive Control-Based Electronic Differential Control Strategy for Distributed-Drive Buses Considering the Reduction of Tire Wear — Chang Wang, Yingshi Guo, et al. · IEEE/ASME Transactions on Mechatronics (2024) | TGRS Research Map | TGRS