This paper addresses the fault problem in distributed-four-wheel-drive electric vehicle drive systems. First, a fault-factor-based active fault diagnosis strategy is proposed. Second, a fault-tolerant controller is designed to reconstruct motor drive torque based on vehicle stability. This controller ensures that the vehicle maintains stability by providing fault-free motor output torque based on fault diagnosis results. To validate the effectiveness of the fault diagnosis and fault-tolerant control, SIL simulation is conducted using MATLAB/Simulink and CarSim. A hardware-in-the-loop (HIL) simulation platform with the highest confidence level is established based on NI PXI and CarSim RT. Through the HIL simulation experiments, it is shown that the proposed control strategy can accurately diagnose the operating state of the motor, rebuild the motor torque based on stability, and demonstrate robust stability when the drive system fails. Under various fault conditions, the maximum error in the vehicle lateral angular velocity is less than 0.017 rad/s and the maximum deviation in the lateral direction is less than 0.7 m. These findings substantiate the highly robust stability of the proposed method.

Direct yaw moment control (DYC) and differential drive-assist steering (DDAS) for distributed-drive vehicles are both realized by allocating the in-wheel motor torque. To address the interference caused by overlapping control objectives, this paper proposes a multilayer control strategy that integrates DYC and DDAS, consisting of an upper controller, a coordinated decision layer, and a torque distribution layer. The upper controller, designed based on the vehicle’s dynamic characteristics, incorporates an adaptive fuzzy control DYC system and a dual PID control DDAS system. The coordinated decision layer is developed utilizing a phase-plane dynamic weighting method, delineating region boundaries by applying the double-line and limit cycle methods. The torque distribution strategy is formulated considering motor peak torque and road adhesion conditions. Multi-condition joint simulation experiments indicate that the proposed multilayer control strategy, integrating the advantages of DYC and DDAS, reduces peak steering wheel torque by approximately 10%, peak yaw rate by around 25%, peak sideslip angle by roughly 29%, and peak sideslip angle rate by about 19%, significantly improving driving stability and maneuvering flexibility.

The main challenges for Finite Set-model Predictive Torque and Flux Control (MPTC) include huge calculation amount, large torque ripple, and the selection of weighting factor. To deal with these challenges and achieve the fastest torque response, a novel MPTC strategy with virtual voltage vector-based pattern is proposed in this paper. In each control period, a desired voltage vector is computed based on the maximum torque variation rate. The virtual desired voltage vector is combined by using basic voltage vectors with the minimum switching times. The switching pattern between two adjacent control periods is optimized by using a cost function in realtime. Compared with the traditional MPTC approach, the novel strategy has multifaceted improved performances: faster torque response, less calculation amount, lower switching frequency, reduced torque ripple, higher efficiency and no weighting factor. The proposed method is contrasted with traditional MPTC method and a well-known generalized multiple-vector-based MPC method via experiments.

The existing double-vector (DV) model-predictive control for a permanent magnet synchronous motor (PMSM) selects the optimal voltage vector (OVV) combination by traversing 12 alternative voltage vector combinations, which requires calculating the cost function 24 times. Also, the switching states between adjacent cycles are not optimized. To address these issues, a new DV model-predictive current control method is proposed in this article. First, the first OVV is selected from eight basic voltage vectors by a cost function. The predicted current errors between the predicted currents corresponding to the first OVV and the actual currents are used as the basis for selecting the second OVV. According to the principle of minimizing the number of switchings, there are only three alternative vectors for the second OVV. Therefore, the number of calculations of the cost function is decreased to 11, greatly reducing the computational burden. In addition, this article incorporates the objective of reducing the switching frequency into the cost function, resulting in a significant reduction of the switching frequency. The effectiveness of the proposed method is verified by experimental results on a 2.7-kW PMSM drive platform.

A predictive torque control strategy with parallel structure (P-PTC) was recently proposed to overcome the tedious tuning of weighting factor in the conventional predictive torque control (C-PTC). However, P-PTC still has some problems, such as the introduction of other parameters to be tuned and the lack of theoretical support, et al. To solve the above problems, an improved P-PTC without any extra parameters tuning (IP-PTC) is proposed for permanent magnet synchronous motor (PMSM). Firstly, two parallel cost functions are respectively constructed to select three voltage vectors that have the best control effect on electromagnetic torque and stator flux to form two new finite control sets (FCSs). Secondly, three different cases are divided according to the intersection number of these two FSCs. Finally, the corresponding optimal voltage vector selection scheme is proposed for these three cases. C-PTC, P-PTC and the proposed IP-PTC are studied through theory and experiments. The results show that when compared with C-PTC and P-PTC, the proposed IP-PTC has neither weighting factor nor any extra parameters, and it is well supported by theory. Furthermore, the proposed IP-PTC has the best static and dynamic performance among the three.