An accelerated second order super twisting sliding mode observer with an adaptive gain is proposed for a typical nonlinear system. The key contribution of this algorithm is that the rate of convergence of observation error is accelerated remarkably by introducing "system damping". Chattering issue is attenuated with satisfactory performance compared to conventional sliding mode observer. Furthermore adaptive gain can vary with deviation between the trajectory and sliding mode switching manifold dynamically so that overshoot can be reduced. The novel observer is proven mathematically to be convergent in a finite time. Finally, an example of nonlinear system is given to verify the performance.

As there is no clutch or hydraulic torque converter in electric vehicles to buffer and absorb torsional vibrations. Oscillation will occur in electric vehicle drivetrains when drivers tip in/out or are shifting. In order to improve vehicle response to transients, reduce vehicle jerk and reduce wear of drivetrain parts, torque step changes should be avoided. This article mainly focuses on drivetrain oscillations caused by torque interruption for shifting in a Motor-Transmission Integrated System. It takes advantage of the motor responsiveness, an optimal active control method is presented to reduce oscillations by adjusting motor torque output dynamically. A rear-wheel-drive electric vehicle with a two gear automated manual transmission is considered to set up dynamic differential equations based on Newton’s law of motion. By linearization of the affine system, a joint genetic algorithm and linear quadratic regulator method is applied to calculate the real optimal motor torque. In order to improve immediacy of the control system, time consuming optimization process of parameters is completed off-line. The active control system is tested in AMEsim^® and limitation of motor external characteristics are considered. The results demonstrate that, compared with the open-loop system, the proposed algorithm can reduce motion oscillation to a satisfied extent when unloading torque for shifting.

Vehicle mass is one of the most critical parameters in the vehicle control system, based on the discrete vehicle longitudinal dynamic equation after the forward Euler approximation, non‐linear particle filter is introduced to estimate the vehicle mass intelligently, and it gains a competitive advantage that statistical characteristics of noise and uncertainties in the system are not necessary to be known or supposed in advance. As a sort of recursive, Bayesian state estimator, vehicle mass is regarded as a constant state variable to constitute the discrete state‐space equation, motor torque is selected as input signal, and the measurable vehicle speed is selected to constitute the observation equation, parameters such as rolling resistance coefficient, air drag coefficient and road slop are considered as high‐power noise and uncertainties. The performance of the proposed vehicle mass estimator is tested by several groups of load and the results demonstrate that the output of the particle filter based vehicle mass estimator can converge to the real value and keep steady.

The suddenly released torque that accumulated in the elastic drive shaft will bring torsional vibration and jerking feel at the shifting moment. A novel sliding mode observer is proposed to estimate the torque in drive shaft for a motor‐transmission integrated powertrain system. Non‐linear external characteristics of a driving motor and non‐linear drag torque are considered in the electric powertrain system. In order to attenuate the chatting problem, the second‐order super twisting sliding mode algorithm with an adaptive gain is adopted. Furthermore, a term ‘system damping’ is introduced to accelerate the estimation error convergence. The proposed estimation algorithm is tested on test rig for typical operating conditions. The results show that the torque in drive shaft can be estimated satisfactorily and the tracking error converges to 0 in a short time.

Promotion of pure electric vehicle (EV) has become a trend, of which the braking performance is very important. In this paper, by using a family of curves of the relationship between adhesion coefficient and sliding rate of an electric vehicle on different roads, to elect the sliding rate corresponding to the peak value of adhesion coefficient, and then the mathematical models of EV braking process are established and the dynamics differential equation is obtained. According to the optimal control theory, appropriate state vector and control vector based on the dynamic mathematical model of vehicle-road system are selected, and a quadratic performance index of the minimum error between actual output and expected output with the minimum energy consumption of the hydraulic braking system in time domain are proposed as well. Typical working condition parameters are selected as the initial condition to calculate series of control parameters of the optimal control model by Riccati matrix algebra equation. In MATLAB/simulink vehicle anti-lock braking system model is established, and by simulation graphs of wheel velocity response show that, the wheel velocity can satisfy the slide requirements well by using optimal feedback control algorithm for EV with more advanced ECU system.