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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.