Accurate detection and diagnosis battery faults are increasingly important to guarantee safety and reliability of battery systems. Developed methods for battery early fault diagnosis concentrate on short-term data to analyze the deviation of external features without considering the long-term latent period of faults. This work proposes a novel data-driven method to detect long-term latent fault and abnormality for electric vehicles based on real-world operation data. Specifically, the battery fault features are extracted from the incremental capacity curves, which are smoothed by advanced filter algorithms. Secondly, principal component analysis algorithm is utilized to reduce dimensionality and the cumulative percent variance is to determine the number of significant features. Based on the features, a cluster algorithm is employed to capture the battery potential failure information. Moreover, the cumulative root-mean-square-deviation is introduced to quantificationally analyze the degree of the battery failures using large-scale battery data to avoid the missing fault reports using short-term data. In addition, a battery system failure index is proposed to evaluate battery fault conditions. The results indicate that the proposed long-term feature analysis method can effectively detect and diagnose faults.

When a permanent magnet synchronous motor (PMSM) is designed according to the traditional motor design theory, the performance of the motor is often challenging to achieve the desired goal, and further optimization of the motor design parameters is usually required. However, the motor is a strongly coupled, non-linear, multivariate complex system, and it is a challenge to optimize the motor by traditional optimization methods. It needs to rely on reliable surrogate models and optimization algorithms to improve the performance of the PMSM, which is one of the problematic aspects of motor optimization. Therefore, this paper proposes a strategy based on a combination of a high-precision combined surrogate model and the optimization method to optimize the stator and rotor structures of interior PMSM (IPMSM). First, the variables were classified into two layers with high and low sensitivity based on the comprehensive parameter sensitivity analysis. Then, Latin hypercube sampling (LHS) is used to obtain sample points for highly sensitive variables, and various methods are employed to construct surrogate models for variables. Each optimization target is based on the acquired sample points, from which the most accurate combined surrogate model is selected and combined with non-dominated ranking genetic algorithm-II (NSGA-II) to find the best. After optimizing the high-sensitivity variables, a new finite element model (FEM) is built, and the Taguchi method is used to optimize the low-sensitivity variables. Finally, finite element analysis (FEA) was adopted to compare the performance of the initial model and the optimized ones of the IPMSM. The results showed that the performance of the optimized motor is improved to prove the effectiveness and reliability of the proposed method.

Significant nonlinearity of electronic hydraulic brake (EHB) systems often leads to complex hydraulic force control responses. This paper designs a motor-driven EHB system and analyzes nonlinear friction induced by the deceleration mechanism. To compensate this friction, a flutter signal is added to the controller input. In addition, this paper designs a fuzzy-PI (Proportional and Integral) controller for the cylinder hydraulic pressure of the EHB system based on the opening and closing characteristics of a solenoid valve. Response curves of cylinder hydraulic pressure are obtained under three different input signals: step, triangular, and sinusoidal. The co-simulation model is established by AMEsim™ and Simulink® ansofts. The study results indicate that the proposed hydraulic-force-following control method of the EHB system can follow different input signals well. A step response test and a sine-wave-following test are carried out, which correspond to the EHB response in the case of driver’s emergency braking and frequent braking, respectively. Stable and rapid pressure build-up is obtained under different step target hydraulic pressures. Therefore, the hydraulic-force-following control method of the EHB system based on a fuzzy-PI controller can satisfy the EHB system accuracy requirements for an electric vehicle, which is a certain valuable for the automobile industry.

To prevent the occurrence of the cavitation phenomenon in hydraulic shock absorber, the production process of the cavitation during the recovery process of shock absorber is studied, and the parameter model of cavitation production mechanism is built. Based on Computational Fluid Dynamics (CFD) numerical method, a high-precision mesh model of shock absorber is established and the simulation analysis is carried out by using FLUENT software. Therefore, the specific position and distribution of cavitation in the hydraulic shock absorber are obtained, and the measures to inhibit cavitation are put forward. Finally, the simulation results are verified by experimental study and the experimental results show that the cavitation is mainly distributed around the recovery valve of shock absorber, and the cavitation phenomenon becomes more obvious with increasing the piston speed; using low viscosity oil and increasing the diameter of piston rod can effectively inhibit cavitation phenomenon. This study provides a certain reference for preventing the cavitation phenomenon of hydraulic shock absorber.

Clamping force control is one of the key technologies in the algorithm design and implementation of electro-mechanical braking system, whose control effects directly affect the vehicle braking performance and safety performance. In order to improve the clamping force control performance of electro-mechanical braking (EMB) system, an EMB clamping force control method based on Variable universe adaptive fuzzy PID (VUF-PID) controller is proposed, and stretching factors are added to the fuzzy PID control. According to the operation of the controlled object, the fuzzy theory domain can be adjusted in real time to keep the system in the proper parameter value and improve the adaptive ability of the system. The response characteristics and effectiveness of clamping force under step braking condition, gear switching braking condition and sine braking condition are verified by simulation experiments using MATLAB/Simulink. The results show that the proposed VUF-PID control method has strong tracking characteristics and stability characteristics, and meet the braking requirements under different braking conditions.