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One purpose of the control strategy for a parallel hybrid electric vehicle (PHEV) is to control the state of charge (SOC) of the battery to achieve a maximum powertrain efficiency. Because of the nonlinearity of the powertrain, the control strategy should implement nonlinear optimization in real time. This paper presents a new hierarchical optimal control design to execute real-time optimization on the basis of a model predictive control concept. The proposed control architecture suggests a two-layer control subjected to the related constraints according to the changing rate of different parameters. The prime controller optimizes the best trajectory of the state of charge according to the load detected by a load prediction module, while the second controller will apply the output signal from the prime controller along with the signal from the prediction module and the driver inputs to control the powertrain. Additionally, a new methodology to predict the future load imposed on the wheels is introduced. Through simulation and experiment results, it is shown that the proposed prediction control can effectively reduce fuel consumption and exhaust emission.

The neural‐point‐clamped (NPC) three‐level inverters exhibit a midpoint potential unbalance problem. With the analysis on the midpoint potential fluctuation, its voltage vectors are discussed, and the mathematical model of the midpoint current in each sector is established. Based on virtual space vector pulse‐width modulation (VSPWM) calculated in the gh coordinate, a balance control method is proposed to compensate the midpoint potential. The virtual medium vector and voltage adjustment factor are introduced. Different positive and negative short vectors are distributed to different adjustable parameters to improve the influence of short vectors on the midpoint potential. The experimental results show that the proposed method balances the midpoint potential effectively, and VSVPWM with the gh coordinate can reduce the complexity of the calculation.

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Hybrid electric vehicles (HEVs) require the power to drive the vehicle via a combination of internal combustion engine (ICE) and electric machine (EM). To improve the drivability, the smooth torque change during the driving mode switching is essential. This task can be achieved by using the coordinated control strategy. This paper presents a coordinated control strategy based on considering the different dynamic response characteristics of the ICE and the EM, which can effectively suppress the torque surge during the driving mode switching processes. The novelty lies in the proposed control is a motor active synchronization control strategy without clutch disengagement based on the mode switching classification. The coordinated control strategy is designed according to the classification of the driving modes. The objective is to minimize torque fluctuation and maintain or improve the driving performance of the vehicle. Results from the computer simulation demonstrate the effectiveness of this approach in reducing the torque surge without sacrificing vehicle performance.

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During the operation of speed-sensorless control system for induction motor, the stator and rotor resistance varies greatly with the change of temperature and the frequency of the rotor side, which affects the estimation of the stator flux and leads to the low accuracy of the speed estimation. A speed-sensorless vector control method based on parameters identification with the full-order adaptive state observer is proposed in this paper. In the model reference adaptive system of AC motor, the stator resistance and rotor flux are assigned as state variables to build the reference model, and a full-order flux observer is introduced to adjustable model. Lyapunov theory and Popov superstability theory are used to deduce the speed and rotor resistance adaptive rate. The feedback gain matrix is simplified to speed up the convergence rate of the system. The estimation values of speed and rotor resistance are taken as the proportional integral form, so that an interactive model reference adaptive system is constructed by speed and rotor resistance identification. While observing the rotor flux, it can not only ensure the accuracy of the reference model but also eliminate the disadvantages of the voltage model with integral terms, and the rotor speed can be estimated at the same time. The experimental results show that the accurate performance of speed and flux identification can meet the requirements of application; the proposed control method with the identification of speed and rotor resistance has little fluctuations phenomenon on motor torque in low speed and achieves better performance.