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This paper designs the important components and structure of the integrated electro-hydraulic brake system (I-EHB). Firstly, the simplified linear system is modeled, and the transfer function without considering the nonlinear disturbance such as system friction is derived, and the correctness of the linear system is preliminarily verified by AMESim. Then set up the I-EHB system test bench, and use the Stribeck friction model to identify the friction torque parameters in the static and kinetic friction stages of the system to obtain a more accurate friction model. Finally, based on the I-EHB system model of friction disturbance, a pressure-speed-current three-loop cascade PID controller is designed, and a feedforward controller based on the system model is added to form the control structure of “pressure feedforward compensation + pressure-speed-current closed-loop cascade PID”.

The vehicle will produce certain shock and vibration during the braking process, which will affect the driving experience of the driver. Aiming at the problems of pitch vibration, longitudinal vibration and shock during the braking process, this paper proposes a planning and following control method for target longitudinal acceleration in post-braking phase, and designs control trigger strategies. Target longitudinal acceleration planning takes minimizing longitudinal shock as the design goal. The following control takes the brake pressure as the control object, and adopts the “feedforward +PID feedback” method to follow the target longitudinal acceleration. Besides, considering the safety of braking process, the trigger condition of control is designed which utilizes BP neural network method to judge whether the control has to be triggered. Based on Simulink software, the simulation model of straight-line braking is established.

For the improvement of the vehicle yaw stability, this paper studies the control problem of the active front steering (AFS) system with actuator input delay. A novel sliding mode predictive control method to handle actuator input delay is proposed for the AFS system. Firstly, considering the nonlinearities of the vehicle system, a linear parameter varying vehicle system model with two-level structure is proposed to capture the vehicle dynamic behaviors. Secondly, to deal with the issues of actuator input delay and system constraints, a novel sliding mode predictive control method is put forward. In the process of controller design, a sliding mode control algorithm is employed for the improvement of the robustness of the control system, and then a model predictive control algorithm is employed to deal with system constraints.

Electronic expansion valve as a throttle element is widely used in heat pump systems and flow characteristics are its most important parameter. The flow characteristics of the electronic expansion valve (EXV) with a valve port diameter of 3mm are studied, when the refrigerant R134a is used as the working fluid. The main factors affecting the flow characteristics are researched by adopting the orthogonal experiment method and single factor control method, for example, inlet pressure, inlet temperature, outlet pressure and valve opening. The results show that the expansion valve opening degree has the greatest influence on mass flow rate. In view of the complicated phase change of the refrigerant passing through electronic expansion valve, it is difficult to model the flow characteristics accurately.

Uniaxial fatigue tests of rubber dumbbell specimens under different mean and amplitude of strain are carried out. An Extreme Learning Machine (ELM) model optimized by Dragonfly Algorithm (DA) is proposed to predict the fatigue life of rubber based on measured rubber fatigue life data. Mean and amplitude of strain and measured rubber fatigue life are taken as input variables and output variables respectively in DA-ELM model. For comparison, genetic algorithm (GA) and particle swarm optimization (PSO) are used to optimize ELM parameters, and GA-ELM and PSO-ELM models are established. The comparison results show that DA-ELM model performs better in predicting the fatigue life of rubber with least dispersion. The coefficients of determination for the training set and test set are 99.47% and 99.12%, respectively. In addition, a life prediction model equivalent strain amplitude as damage parameter is introduced to further highlight the superiority of DA-ELM model.

As an important vibration damping element in automobile, the rubber mount can effectively reduce the vibration transmitted from the engine to the frame. In this study, a method of parameters identification of Mooney-Rivlin model by using surrogate model was proposed to more accurately describe the mechanical behavior of mount. Firstly, taking the rubber mount as the research object, the stiffness measurement was carried out. And then the calculation model of the rubber mount was established with Mooney-Rivlin model. Latin hypercube sampling was used to obtain the force and displacement calculation data in different directions. Then, the parameters of the Mooney-Rivlin model were taken as the design variables. And the error of the measured force-displacement curve and the calculated force-displacement curve was taken as the system response. Two surrogate models, the response surface model and the back-propagation neural network, were established.

Robust data association is a core problem of visual odometry, where image-to-image correspondences provide constraints for camera pose and map estimation. Current state-of-the-art visual semantic odometry uses local map points semantics, building semantic residuals associated with all classes to realize medium-term tracking of points. Considering the problem of inefficient semantic data associations and redundant semantic observation likelihood model in the visual semantic odometry, we propose a visual odometry, Local Semantic Odometry (LVSO), which is integrated with medium-term semantic constraints based on local nearest neighbor distance model.

To solve the contradiction between model complexity and the warning accuracy of the algorithm of the vehicle rollover warning, a rollover state warning method based on the secondary predictive zero-moment point position for vehicles is proposed herein. Taking a sport utility vehicle(SUV) as the research object, a linear three-degrees-of-freedom vehicle rollover dynamics model is established. On the basis of the model, the lateral position of the zero-moment point and its primary and secondary rates of change are calculated. Then, the theoretical solution of time-to-rollover of the vehicles is deduced from the lateral position of the secondary predictive zero-moment point. When the rollover warning index, the lateral position of the zero-moment point, is greater than the set threshold, the active anti-rollover control system will be triggered. The active anti-rollover braking control system adopts a hierarchical control strategy.

Air springs with auxiliary chambers (ASAC) are widely used in automotive suspension systems. The introducing of the auxiliary chamber and the connecting flow passage makes the system more complex, especially in which case an additional resonance peak caused by the air inertia in a connecting pipe appears. To characterize the nonlinear dynamic characteristics, this paper proposes a novel physical-neural network hybrid modeling method for ASACs. Firstly, experiments are carried out to measure the dynamic characteristics of ASACs. Then, based on the thermodynamic principle, a nonlinear dynamic characteristic model for the ASAC is developed and a linearized process is performed to obtain a linearized physical model. Due to the amplitude dependence and frequency dependence in the dynamic characteristics of ASACs, the physical model cannot accurately characterize these nonlinearities.

Battery Run-down under the Electric Vehicle Operation (BREVO) model is a model that links the driver’s travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. This study implements reinforcement learning (RL) to the simplified BREVO model to optimize drivers’ decisions on charging such as charging rate, charging time, and charging capacity needed. This is done by a reward function that considers both the driver’s daily travel demands and the minimization of battery degradation over a year. It shows that using appropriate charger type (No Charge, Level 1, Level 2, direct-current Fast Charge [DCFC], extreme Fast Charging [xFC]) with an appropriate charging time can reduce battery degradation and total charging cost at the end of the year while satisfying driver’s daily travel demand.

This paper presents an adaptive H2/H∞ control strategy for a semi-active suspension system with unknown suspension parameters. The proposed strategy takes into account the damping force characteristics of continuous damping control (CDC) damper. Initially, the external characteristics of CDC damper were measured, and a forward model and a back propagation (BP) neural network inverse model of CDC damper were proposed using the measured data. Subsequently, a seven-degree-of-freedom vehicle with semi-active suspension system and H2/H∞ controller was designed. Multiple feedback control matrices corresponding to different sprung mass parameter values were determined by analyzing time and frequency domain performance. Finally, a dual observer system combining suspension state and parameter estimation based on the Kalman filter algorithm was established.