To simultaneously monitor some electrical or mechanical faults of an in-wheel motor and intelligently evaluate the operation safety, in this article, a fuzzy system of operation safety assessment (OSA) is proposed in this article. This method first uses many symptom parameters (SPs), such as root mean square, crest factor, temperature rise, and current covariance to express the features of the electrical and mechanical faults from different perspectives, such as vibration, noise, temperature, current, and voltage; possibility theory is employed to translate the probability density function of each SP into the possibility function, and sample data are gradually updated to optimize the possibility function for obtaining the SPs’ membership functions that are evaluation models. Second, the probabilities of the current operation state, that is safety, attention, or danger, are obtained from each evaluation model in a stage. Picture fuzzy set (PFS) is used to define a basic picture fuzzy number (PFN); thus, many PFNs from multiple models and multiple stages are used to establish an OSA's decision matrix. Third, Mahalanobis distance is reintegrated into PFS's theory for objectively judging the real-time evaluation information, and the best–worst method is used to estimate subjectively the initial evaluation experience; thus, the multimodel linkage mechanism is designed. Finally, TODIM is modified to define the relative safety ratio, and prospect theory is employed to structure the global index for formulating the multistage collaboration approach; thus, a fuzzy OSA's system is established. The effectiveness of the proposed method was verified by the experimental analysis for the operation safety of in-wheel motor with electrical and mechanical faults.

A fault-tolerant collaborative control strategy for four-wheel-drive electric vehicles is proposed to address hidden safety issues caused by one or more in-wheel motor faults; the basic design scheme is that the control system is divided into two layers of motion tracking and torque distribution, and three systems, including driving, braking, and front-wheel steering are controlled collaboratively for four-wheel torque distribution. In the layer of motion tracking, a vehicle model with two-degree-of-freedom is employed to predict the control reference values of the longitudinal force and additional yaw moment required; four types of sensors, such as wheel speed, acceleration, gyroscope, and steering wheel angle, are used to calculate the actual values. At the torque distribution layer, SSOD and MSCD distribution schemes are designed to cope with two operating conditions, namely sufficient and insufficient output capacity after local hub motor failure, respectively, focusing on the objective function, constraints, and control variables of the MSCD control strategy. Finally, two operating environments, a straight-line track, and a DLC track, are set up to verify the effectiveness of the proposed control method. The results indicate that, compared with traditional methods, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 12.9% and 5.88%, respectively, in the straight-line track environment. In the DLC track environment, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 6% and 4.5%, respectively. The proposed fault-tolerant controller ensures that the four-wheel-drive electric vehicle meets the requirements of handling stability and safety under one or more hub motor failure conditions.