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<p>Distributed drive electric vehicles (DDEVs) show great potential in electric vehicle applications owing to their high transmission efficiency and precise driving control. Nevertheless, existing stability control methods suffer from insufficient proactivity and limited real-time regulation capability, making them unable to guarantee satisfactory longitudinal stability performance under complex operating conditions. To address this issue, this paper presents a vehicle state estimation and longitudinal stability control system for complex driving scenarios. A LiDAR-IMU fusion scheme is developed, which combines the adaptive unscented Kalman filter (AUKF) and time-series analysis (TSA) to improve state estimation accuracy. Furthermore, a multi-model model predictive control (MPC) framework is established to classify driving conditions and generate integrated control commands through weighted fusion, so as to achieve optimized longitudinal stability and smooth mode switching. The main novelty of this work lies in the integration of predictive state estimation and scenario-classified weighted-fusion multi-model MPC, which differs from conventional switching MPC and gain-scheduled MPC by avoiding abrupt mode switching and explicitly considering model differences under typical DDEV operating conditions. Both simulation and hardware-in-the-loop (HIL) results validate that the proposed system effectively enhances longitudinal stability and control performance under complex conditions. The root-mean-square error (RMSE) of yaw rate estimation is reduced to 0.111&#xa0;deg/s, and the control accuracy is improved by 21.7% compared with the conventional MPC method. This work lays a solid theoretical basis for the application of distributed drive electric vehicles.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月22日

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  • Meng Dang,
  • Chuanwei Zhang,
  • Zhongyu Guo,
  • Jianlong Wang

摘要

Distributed drive electric vehicles (DDEVs) show great potential in electric vehicle applications owing to their high transmission efficiency and precise driving control. Nevertheless, existing stability control methods suffer from insufficient proactivity and limited real-time regulation capability, making them unable to guarantee satisfactory longitudinal stability performance under complex operating conditions. To address this issue, this paper presents a vehicle state estimation and longitudinal stability control system for complex driving scenarios. A LiDAR-IMU fusion scheme is developed, which combines the adaptive unscented Kalman filter (AUKF) and time-series analysis (TSA) to improve state estimation accuracy. Furthermore, a multi-model model predictive control (MPC) framework is established to classify driving conditions and generate integrated control commands through weighted fusion, so as to achieve optimized longitudinal stability and smooth mode switching. The main novelty of this work lies in the integration of predictive state estimation and scenario-classified weighted-fusion multi-model MPC, which differs from conventional switching MPC and gain-scheduled MPC by avoiding abrupt mode switching and explicitly considering model differences under typical DDEV operating conditions. Both simulation and hardware-in-the-loop (HIL) results validate that the proposed system effectively enhances longitudinal stability and control performance under complex conditions. The root-mean-square error (RMSE) of yaw rate estimation is reduced to 0.111 deg/s, and the control accuracy is improved by 21.7% compared with the conventional MPC method. This work lays a solid theoretical basis for the application of distributed drive electric vehicles.