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Longitudinal stability control system for distributed drive electric vehicles based on multi-model MPC.

Meng Dang¹, Chuanwei Zhang², Zhongyu Guo²

¹School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China. dangmeng@xust.edu.cn.

Scientific Reports

|June 22, 2026

Summary

This summary is machine-generated.

This study introduces an advanced system for distributed drive electric vehicles (DDEVs) to enhance longitudinal stability. The integrated approach improves vehicle control and safety in complex driving scenarios.

Keywords:

Distributed drive Electric vehicles Model predictive control Multi-model fusion Stability control State estimation

Related Experiment Videos

Area of Science:

Automotive Engineering
Control Systems
Robotics

Background:

Existing stability control for distributed drive electric vehicles (DDEVs) lacks proactivity and real-time regulation.
This limitation hinders performance in complex driving conditions.

Purpose of the Study:

To develop a vehicle state estimation and longitudinal stability control system for DDEVs in complex scenarios.
To enhance proactive control and real-time regulation capabilities.

Main Methods:

Implemented a LiDAR-IMU fusion scheme using an adaptive unscented Kalman filter (AUKF) and time-series analysis (TSA) for accurate state estimation.
Developed a multi-model model predictive control (MPC) framework for driving condition classification and integrated control command generation.
Utilized scenario-classified weighted-fusion multi-model MPC to avoid abrupt mode switching and account for model differences.

Main Results:

Achieved a root-mean-square error (RMSE) of yaw rate estimation as low as 0.111 deg/s.
Improved control accuracy by 21.7% compared to conventional MPC methods.
Validated system effectiveness through simulation and hardware-in-the-loop (HIL) testing.

Conclusions:

The proposed system significantly enhances longitudinal stability and control performance in complex driving conditions for DDEVs.
The integration of predictive state estimation and scenario-classified MPC offers a novel approach to stability control.
This research provides a strong foundation for advanced DDEV applications.