TY - JOUR
T1 - Adaptive torsional vibration suppression in power-split HEVs
T2 - Integrating BPNN-based secondary path estimation and variable-step FxLMS
AU - Yan, Qi
AU - Liu, Hui
AU - Gao, Pu
AU - Xie, Yunkun
AU - Yang, Dianzhao
AU - Jiao, Jiaxin
AU - Xiang, Changle
N1 - Publisher Copyright:
© IMechE 2026
PY - 2026
Y1 - 2026
N2 - In recent years, power-split Hybrid Electric Vehicles (HEVs) have attracted widespread attention for their superior fuel efficiency and performance. However, the planetary gear sets that mechanically couple the engine and drivetrain often induce undesirable torsional vibrations, degrading drivability and component durability. To mitigate this, an efficient multi-channel Active Vibration Control (AVC) strategy is proposed, utilizing a notch Filtered-x Least-Mean-Square (FxLMS) algorithm to leverage motor torque for compensating engine torque fluctuations. First, the system’s torsional vibration responses are analyzed to identify the input and output shafts as critical control targets. Second, a Back Propagation Neural Network (BPNN) is employed to model the secondary paths, which improves estimation accuracy and reduces errors by 65%–95% compared to the traditional Finite Impulse Response (FIR) filtering method. Third, a multi-channel AVC strategy is developed based on a modified notch FxLMS algorithm featuring variable step sizes. The proposed AVC strategy is then incorporated into the vehicle’s energy management system, forming an integrated control framework (ICF) for coordinated vibration suppression and energy optimization. Simulation and experimental results under various operation conditions verify that the proposed strategy effectively reduces torsional vibration amplitudes and improves powertrain smoothness, thereby enhancing overall drivability and comfort.
AB - In recent years, power-split Hybrid Electric Vehicles (HEVs) have attracted widespread attention for their superior fuel efficiency and performance. However, the planetary gear sets that mechanically couple the engine and drivetrain often induce undesirable torsional vibrations, degrading drivability and component durability. To mitigate this, an efficient multi-channel Active Vibration Control (AVC) strategy is proposed, utilizing a notch Filtered-x Least-Mean-Square (FxLMS) algorithm to leverage motor torque for compensating engine torque fluctuations. First, the system’s torsional vibration responses are analyzed to identify the input and output shafts as critical control targets. Second, a Back Propagation Neural Network (BPNN) is employed to model the secondary paths, which improves estimation accuracy and reduces errors by 65%–95% compared to the traditional Finite Impulse Response (FIR) filtering method. Third, a multi-channel AVC strategy is developed based on a modified notch FxLMS algorithm featuring variable step sizes. The proposed AVC strategy is then incorporated into the vehicle’s energy management system, forming an integrated control framework (ICF) for coordinated vibration suppression and energy optimization. Simulation and experimental results under various operation conditions verify that the proposed strategy effectively reduces torsional vibration amplitudes and improves powertrain smoothness, thereby enhancing overall drivability and comfort.
KW - Notch FxLMS algorithm
KW - back propagation neural network
KW - multi-channel active vibration control
KW - power-split hybrid electric vehicles
KW - variable convergence coefficients
UR - https://www.scopus.com/pages/publications/105026774454
U2 - 10.1177/09544070251408178
DO - 10.1177/09544070251408178
M3 - Article
AN - SCOPUS:105026774454
SN - 0954-4070
JO - Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
JF - Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
ER -