Power-split hybrid electric vehicles (power-split-HEVs) exhibit significant engine torque fluctuations due to their mechanical coupling with the driveline, leading to pronounced torsional vibration issues in the drive shaft. This study investigates an active torsional vibration suppression strategy based on drive motor control. First, a dynamic model of the power-split-HEV driveline is established, and its intrinsic characteristics are analyzed. Subsequently, an engine excitation torque model is developed to identify the dominant response orders, while a vehicle dynamics model is constructed to elucidate the torsional vibration mechanisms in both hybrid and pure electric driving modes. Next, a torsional vibration feedback control framework is proposed, utilizing the electric motor as a secondary-channel torque disturbance compensator. Furthermore, a novel frequency-decoupled dual-loop control framework is proposed, with rigorous derivation of the sufficient conditions for decoupling. Based on this framework, two distinct vibration suppression algorithms are developed for the secondary-loop controller, each tailored for specific operational modes. Finally, the proposed algorithms are validated through simulation and hardware-in-the-loop (HIL) testing. The results demonstrate a torque fluctuation suppression ratio of up to 72.2%, confirming that the active suppression algorithm effectively mitigates driveline torsional vibration induced by engine harmonic torque disturbances.

The electromechanical transmission (EMT) systems of hybrid special vehicles are highly susceptible to severe transient torsional resonance under frequent start-stop operating conditions. Traditional entire-frequency domain H_∞ active vibration reduction strategies are often limited by insufficient gain, failing to achieve ultimate suppression within the core resonance frequency band. To address this issue, this paper proposes a finite-frequency H_∞ active torsional vibration suppression strategy based on a motor dual-loop control architecture. This strategy achieves a profound physical decoupling between torsional vibration suppression and steady-state driving tasks. Furthermore, by introducing the Generalized Kalman–Yakubovich–Popov (GKYP) lemma and Linear Matrix Inequalities (LMIs) into the secondary loop, the control degrees of freedom are precisely concentrated on the 8–30 Hz frequency band, where the transient resonance energy is highly localized. This thoroughly eliminates the conservatism inherent in entire-frequency designs. To mitigate the instability risks caused by unmeasurable states and actuator response lags in practical engineering applications, a robust controller integrating input time-delay compensation and dynamic output feedback is subsequently constructed. Numerical case studies and hardware-in-the-loop (HIL) test results based on a specific EMT configuration demonstrate that the proposed strategy effectively overcomes the instability induced by system delays. It achieves an outstanding resonance peak attenuation of up to 93% and strictly constrains output shaft torque fluctuations within a safe threshold of 50 N·m. Ultimately, this study provides an efficient and robust closed-loop engineering solution for the transient vibration management of high-power electromechanical transmission systems and the enhancement of overall vehicle NVH performance.

Farmland in the hilly and mountainous regions of southern China is characterized by complex terrain and highly variable operating loads. Conventional diesel-powered tracked harvesters are constrained by high crop losses, excessive impurity rates, frequent blockages, and low overall energy-use efficiency. Distributed electric drive provides a promising solution; however, threshing cylinder blockage, high-frequency load transients, and slope operation make it difficult for conventional energy-management strategies to simultaneously ensure dynamic responses, fuel economy, and battery state of charge (SOC) stability. This study therefore proposes a deep deterministic policy gradient (DDPG)-based reinforcement learning energy-management strategy (RL-EMS) for a range-extended, distributed-drive hybrid tracked combine harvester. First, a full-vehicle dynamic model incorporating eight electric-drive units and strong electromechanical coupling is established. Second, power allocation is formulated as a Markov decision process (MDP), with a multi-objective reward function that accounts for fuel consumption, SOC tracking, and boundary violations; the load-rate-of-change is introduced as a feedforward state. Finally, a supervisory physical layer comprising feasible power projection, safety filtering, and rate limiting is inserted between the policy network output and the physical plant so that the executed command satisfies range extender power, battery SOC, current, and power-slew constraints. Under the standard 1000 s cycle, SOC-corrected energy-equivalent comparison shows that the RL-EMS reduces fuel consumption by 1.5% relative to the adaptive equivalent consumption minimization strategy (A-ECMS) and by 26.6% relative to the constant-torque energy-management strategy (CT-EMS). Under an unseen complex random cycle, the RL-EMS reduces fuel consumption by 5.1% relative to A-ECMS. It also suppresses DC-bus voltage sag during a threshing cylinder blockage transient, demonstrating favorable electromechanical transient response. The proposed method provides a modeling and control reference for the intelligent energy management of range-extended, distributed-drive agricultural machinery.

As the core energy-replenishing unit of series hybrid electric vehicles (SHEVs), the range extender experiences inherent engine torque pulsations during start-stop and steady-state operating conditions. These pulsations induce intense lateral-torsional-pendulum (LTP) coupled vibrations through the mounting system, emerging as a critical bottleneck restricting overall vehicle NVH performance. Conventional isolated analysis methods for crankshaft torsional vibration and mounting isolation, alongside existing active torsional control strategies, frequently suffer from frequency-domain coupling conflicts between power generation and vibration attenuation. To address these issues, this study investigates an active torsional vibration suppression strategy for a specialized vehicle range-extender-mounting system, grounded in the underlying coupled vibration mechanism. First, a multi-degree-of-freedom (MDOF) lumped-parameter dynamic model of the system is established to elucidate the LTP coupled vibration mechanism, verifying the dynamic law that suppressing pure torsional vibration can synchronously mitigate lateral and vertical vibrations. Subsequently, based on a dual-loop motor control method oriented toward torsional vibration control, a parallel narrowband Filtered-x Least Mean Square (FxLMS) active torsional vibration controller with adjustable attenuation intensity is designed to specifically target the second and fourth dominant engine harmonic excitations. Finally, the performance of the proposed strategy is validated through numerical simulations and hardware-in-the-loop (HIL) semi-physical experiments. The results indicate that the proposed strategy achieves a maximum torque vibration amplitude attenuation of Change to 78.5% under steady-state conditions while maintaining superior convergence and robustness under dynamic variable-speed conditions. Concurrently, the lateral and vertical vibration amplitudes of the mounting system during start-stop phases are synchronously reduced by over 50%, effectively realizing the co-optimization of active torsional control and system-level NVH performance.