As an efficient control strategy, model predictive current control (MPCC) has rapid response and simple calculation. This article proposes an improved MPCC scheme for permanent-magnet synchronous hub motor (PMSHM) drives. The mentioned control scheme uses the parameter values at the last moment to obtain the back electromotive force (EMF) and utilizes the obtained back EMF to obtain the predicted current value at the next moment. In the actual application of the motor, to enhance the robustness of the control system, a sliding mode controller is used to replace the conventional proportional-integral (PI) speed loop, and a finite position phase-locked loop based on the dichotomy is added to achieve sensorless speed control and provide an accurate rotor position angle. To improve the steady-state performance, the method of duty cycle is introduced, and the null vector and the actual vector are used together in the same control cycle. The simulation and experimental results both show the effectiveness of the proposed MPCC scheme, and the steady-state performance of MPCC is greatly improved compared with traditional MPCC.

In order to solve the problem of high fuel consumption and poor emission performance in high horsepower tractors, a parallel hybrid tractor system was designed using a dual power source of an engine and motor matched with a hydro-mechanical continuously variable transmission (HMCVT). An equivalent fuel consumption minimization strategy (ECMS) was used for power distribution of this hybrid system. To address the problem of poor adaptability of the equivalence factor to different working cycles in the conventional ECMS, a fuzzy adaptive equivalent fuel consumption minimization strategy (FA-ECMS) was proposed. A fuzzy PI controller based on battery SOC (State of Charge) feedback was designed to adjust the equivalence factor in real time, so as to achieve adaptive control of the equivalence factor. The physical model of the system was built by SimulationX, and the model of the control strategy was built using Matlab/Simulink. Two typical cycles of tractor plowing and road transportation were simulated. Under ECMS, the fuel consumption of the hybrid agricultural tractor was 14.3 L and 1.19 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.75% and 60.32%, respectively. Under FA-ECMS, the hybrid farm tractor consumed 13.34 L and 1.13 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.27% and 60.17%, respectively. The results showed that, with the introduction of a fuzzy PI controller to dynamically adjust the equivalence factor, the overall fuel consumption was reduced by 6.71% and 5.04%, respectively, and the battery power maintenance performance was improved. The designed control strategy could achieve a more reasonable power distribution between the engine and motor while maintaining the balance of the battery SOC.

A nonsingular terminal sliding mode controller (NTSMC) based on a direct torque control is presented for a switched reluctance motor (SRM) in this article. To guarantee dynamic stability, the nonsingular terminal sliding mode based on an improved reaching law (RL) is employed to design the speed controller. The torque ripple of the system can be suppressed, and the disturbance caused by uncertainties, such as load disturbance and parameter perturbation, can be suppressed by the proposed NTSMC. Moreover, the gray wolf optimization algorithm is applied to automatically adjust the parameters of the controllers and the value of given flux, thereby acquiring a satisfactory result. The NTSMC is validated by both simulation and experimental results with a six-phase 12/10 SRM. Compared with PI and conventional sliding mode control, NTSMC improves the convergence rate of state and exhibits better performance in torque ripple reduction and antidisturbance ability. The robustness and dynamic performance of the system can be ensured.

Vehicle fuel economy is greatly influenced by the driver’s driving style. To achieve remarkable promotion in the fuel economy, a novel predictive energy management strategy (EMS) with strong adaptability to driving styles is presented for plug-in hybrid electric buses (PHEBs) in this article. First, a combined unsupervised and supervised algorithm for the driving style identification is devised based on instant driving conditions. In the algorithm, a multidimensional Gaussian distribution (MGD)-based analysis on factors influencing driver’s driving style provides valuable inputs for local mean $K$ nearest neighbor (LMKNN) algorithm, and outstanding regression ability of the LMKNN returns exact recognition result and then incorporating the driving style recognition function into the model predictive control (MPC) to formulate a driver-oriented predictive EMS, where estimate distribution and particle swarm optimization (ED-PSO) algorithm is introduced to obtain optimal control sequence over a receding horizon. Finally, simulation validations in the MATLAB/Simulink environment show that the total cost of the proposed strategy is decreased by 12.53% compared to the charge-depleting and charge-sustaining (CD-CS) method, without sacrificing the real-time applicability.

Energy management strategies determine how much energy is consumed by the engine and electric motor of plug-in hybrid electric buses (PHEBs), which represent critical fuel-saving technologies. In this study, a model predictive control (MPC) method with the estimation of distribution algorithm (EDA) as the solver is proposed to optimize the energy flow of PHEBs. Inspired by the recursive mechanism, short-term velocity prediction is achieved based on a Markov chain model with online updates to greatly improve prediction accuracy. Then, the energy-flow control problem of PHEBs is formulated as a discrete-time nonlinear optimization problem. Due to its strong nonlinear multivariable and constrained nature, the control algorithm is implemented by using MPC. To obtain an optimal solution efficiently, the EDA algorithm is incorporated into the MPC-based control framework, in which the Gaussian distribution is selected as a probabilistic model to characterize the candidate solutions and make full use of the statistical information extracted from the search experience. All performance verifications were conducted by theoretical simulation and hardware-in–the-loop. The verification results show that the proposed strategy can greatly improve the fuel economy and the shorten computational time over cycle-based driving.