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<p>To meet the demands for efficient and stable operation of hybrid electric vehicles with multi-energy systems under complex driving conditions, this paper proposes an EMS that integrates a super-twisting sliding mode observer with model predictive control, termed STSMO-MPC EMS. This strategy overcomes the challenges of system nonlinearity, strong coupling, and dynamic environmental factors that conventional EMS methods often struggle with, providing a more robust and forward-thinking solution. By incorporating the rolling optimization mechanism of MPC, the proposed method enables accurate prediction and coordinated control of multiple energy sources. Simultaneously, the integration of STSMO enhances the observability and estimation accuracy of motor states, thereby enhancing the system’s real-time responsiveness to complex disturbances and uncertainties. This combined approach not only achieves deeper optimization of the electric drive system’s operation but also enhances energy flow distribution efficiency and the coordination of overall vehicle control. Simulation results demonstrate that, compared with traditional SMO-MPC EMS, the proposed STSMO-MPC EMS achieves significant improvements in battery energy consumption, driving smoothness, and overall system performance: energy utilization efficiency increases by 11.6%, torque ripple index decreases by 26.6%, and average energy decay rate improves by 41%. These findings indicate that the STSMO-MPC EMS offers an intelligent energy management solution that effectively balances stability, efficiency, and real-time performance for multi-source hybrid systems, demonstrating significant potential for engineering applications and future implementation.</p>

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Energy management strategy for electro-hydraulic hybrid vehicles based on an STSMO-MPC motor control algorithm

  • 期刊论文

  • 发表日期：

    2026年2月11日

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  • Qi Qiang Guan,
  • Hongxin Zhang,
  • Jian Yang

摘要

To meet the demands for efficient and stable operation of hybrid electric vehicles with multi-energy systems under complex driving conditions, this paper proposes an EMS that integrates a super-twisting sliding mode observer with model predictive control, termed STSMO-MPC EMS. This strategy overcomes the challenges of system nonlinearity, strong coupling, and dynamic environmental factors that conventional EMS methods often struggle with, providing a more robust and forward-thinking solution. By incorporating the rolling optimization mechanism of MPC, the proposed method enables accurate prediction and coordinated control of multiple energy sources. Simultaneously, the integration of STSMO enhances the observability and estimation accuracy of motor states, thereby enhancing the system’s real-time responsiveness to complex disturbances and uncertainties. This combined approach not only achieves deeper optimization of the electric drive system’s operation but also enhances energy flow distribution efficiency and the coordination of overall vehicle control. Simulation results demonstrate that, compared with traditional SMO-MPC EMS, the proposed STSMO-MPC EMS achieves significant improvements in battery energy consumption, driving smoothness, and overall system performance: energy utilization efficiency increases by 11.6%, torque ripple index decreases by 26.6%, and average energy decay rate improves by 41%. These findings indicate that the STSMO-MPC EMS offers an intelligent energy management solution that effectively balances stability, efficiency, and real-time performance for multi-source hybrid systems, demonstrating significant potential for engineering applications and future implementation.