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Electromechanical Braking System (EMB) stops the wheel by motor and related enforce mechanism to drive braking pads to clamp the friction plate. It is compact in sized as well as faster in response, which solves the issue of potential leakage and slows response of traditional hydraulic brake system. The institutions at home and abroad have put forward all kinds of new structural schemes of EMB. At present, there are various EMB structural schemes, but the analysis and evaluation of these schemes are relatively few. In this paper, on the basis of a large number of research, the EMB actuator is modular decomposed according to function ,then the parametric 3D model library of each function module is established. According to brake requirements of the target vehicle, a development platform is set up to match EMB actuator structure scheme quickly.

Micro electric vehicle has gained increasingly popularity among the public due to its compact size and reasonable price in China in recent years. Since design factors that influence the power of electric vehicle drive-motor like maximum speed, acceleration time and so on are not fixed but varies in certain scopes. Therefore, to optimize the process of matching drive-motor’s power, qualitatively and quantitatively studies should be done to determine the optimal parameter combination and improve the design efficiency. In this paper, three basic operating conditions including driving at top speed, ascending and acceleration are considered in the matching process. And the Sobol’ method of global sensitivity analysis (GSA) is applied to evaluate the importance of design factors to the drive-motor’s power in each working mode.

The unscented Kalman filter (UKF) is applied to estimate the real vehicle velocity. The velocity estimation algorithm uses lateral acceleration, longitudinal acceleration and yaw rate as inputs. The non-linear vehicle model and Dugoff tire model are built as the estimation model of UKF. Some parameters of Dugoff tire model and vehicle, which can't be measured directly, are identified by the particle swarm optimization (PSO). For the purpose of evaluating the algorithm, the estimation values of UKF are compared with measurements of the Inertial and GPS Navigation system. Besides, the real time property of UKF is tested by xPC Target, which is a real-time software environment from MathWorks. The result of the real vehicle experiment demonstrates the availability of the UKF and PSO in vehicle velocity estimation.

Indirect rotary transducer for an automotive screen interface is an innovative solution for the smart cockpit. The primary objective of this study is to design an indirect rotary transducer system, and study its feasibility in the smart cockpit. The working theory of this designed system is that the magnetic induction hall electronic chip can detect changes in the magnetic field. Several tests have been conducted, which show that the hypothesis of dangling operating system achieves the same effect as a hard-wired operating system. The results of the experiment indicate that the magnetic induction hall sensor can meet the specification of traditional hard-wired operating system. This system is a good concept for intelligent cab driving, which can fully meet the needs of the current market.

In the process of ABS control, the Anti-lock braking system (ABS) of the vehicle adjusts the wheel cylinder brake pressure through the hydraulic actuator so as to control the movement of the wheel. The high-speed on-off valve (HSV) is the key components of the Anti-lock braking system. HSV affects the performance of the hydraulic actuator and the valve response characteristics affects the Anti-lock braking system pressure response as well as braking effect. In this paper, the electromagnetic field theory and flow field theory of HSV are analyzed, and simulation analysis of electromagnetic field characteristics of HSV is done by ANSYS. Combined with the ANSYS analysis results, a precise physical model of HSV is constructed in AMESim. Meanwhile, the valve response characteristics are analyzed. Moreover, the influence of different wheel cylinder diameter and PWM carrier frequency on hydraulic braking force characteristics are analyzed.

In this paper, a novel Electromechanical Brake actuator (EMB) is redesigned aimed at an electric vehicle driven by wheel hub motor. The two way ball screw is adopted in this mechanism. Clearance automatic adjustment and parking braking function is added in this mechanism. As a consequence, fast braking response is achieved and the wear difference of the inner and outer pads can be minimized and the initial braking force can also be improved. The electric vehicle is based on a traditional chassis. In this electric vehicle which driven by wheel hub motor, the brake disc and brake actuator will be correspondingly moved inside because wheel hub motor will take up inner space of wheel hub. As a result, the actuator might interfere with the suspension and steering systems and influence hard spot of chassis design. To solve this problem, conversely installed caliper concept is used in this paper.

Accurate parameters of vehicle motion state are very important to the active safety of a vehicle. Currently the extended Kalman filter and unscented Kalman filter are widely used in estimation of the key state parameters, such as speed. In this situation, tire model must be used. The Magic Formula Tire Model is widely used in vehicle dynamics simulation because of its high versatility and accuracy. However, it requires a large number of parameters, which make the key state parameters of a real vehicle difficult to accurately obtain. Therefore, it is limited in real-time control of a vehicle. Firstly, the original Magic Formula Tire Model is simplified in this paper; then Jin Chi's Tire Model is introduced; thirdly, parameters of both the simplified Magic Formula and Jin Chi's Tire Model are identified using PSO (Particle Swarm Optimization) algorithm. Finally, Jin Chi's Tire Model is also used in parameters identification of experimental data.

With the objective to regulate hydraulic pressure accurately by controlling high speed on-off valve (HSV), finite element models are parameterized based on measured parameters of an ABS hydraulic actuator unit (HCU). The data that reflects transient electromagnetic characteristics of HSV is selected with finite element numerical simulation. Taking full advantage of those data, accurate physical models of HSV are built with other parts of hydraulic braking system. Then a new system structure is proposed to control hydraulic pressure. Not only do simulation results show ideal control effect, but also hydraulic braking system can be controlled under arbitrary input signal. Accordingly, hydraulic braking force can achieve fine regulation. Finally, the hydraulic braking system is utilized to design antilock brake control system for four-wheel-drive electric vehicle with electro-hydraulic braking. That kind of system is established on the basis of hierarchical control structure.

Many learning-based methods estimate ego-motion using visual sensors. However, visual sensors are prone to intense lighting variations and textureless scenarios. 4D radar, an emerging automotive sensor, complements visual sensors effectively due to its robustness in adverse weather and lighting conditions. This paper presents an end-to-end 4D radar-visual odometry (4DRVO) approach that combines sparse point cloud data from 4D radar with image information from cameras. Using the Feature Pyramid, Pose Warping, and Cost Volume (PWC) network architecture, we extract 4D radar point features and image features at multiple scales. We then employ a hierarchical iterative refinement approach to supervise the estimated pose. We propose a novel Cross-Modal Transformer (CMT) module to effectively fuse the 4D radar point modality, image modality, and 4D radar point-image connection modality at multiple scales, achieving cross-modal feature interaction and multi-modal feature fusion.

SLAM (Simultaneous Localization and Mapping) plays a key role in autonomous driving. Recently, 4D Radar has attracted widespread attention because it breaks through the limitations of 3D millimeter wave radar and can simultaneously detect the distance, velocity, horizontal azimuth and elevation azimuth of the target with high resolution. However, there are few studies on 4D Radar in SLAM. In this paper, RI-FGO, a 4D Radar-Inertial SLAM method based on Factor Graph Optimization, is proposed. The RANSAC (Random Sample Consensus) method is used to eliminate the dynamic obstacle points from a single scan, and the ego-motion velocity is estimated from the static point cloud. A 4D Radar velocity factor is constructed in GTSAM to receive the estimated velocity in a single scan as a measurement and directly integrated into the factor graph. The 4D Radar point clouds of consecutive frames are matched as the odometry factor.