Integrated DYC and ASR Mixed Control of Distributed Drive Electric Vehicle Using Co-Simulation Methodology

The improvement of handling and stability performance of distributed drive electric vehicle (DDEV) is analyzed, visualized and designed by proposing and deploying the mixed control strategies in this paper including Direct Yaw Control (DYC), Anti-slip Regulation (ASR) and dual-mode switching control. The practicability and real time visualization of driving efficiency and timeliness of DDEV is achieved to reduce the margin of error for the desired torque value by employing the DYC strategy which uses fuzzy PID algorithm. Furthermore the ASR strategy which adopts the optimal slip rate algorithm to determine the requirement of desired torque value based on the different road conditions is used to reduce slip phenomenon effectively and to maintain handling control of DDEV. In response to different scenes especially conflict and coexistence between DYC and ASR, the dual-mode switching control strategy is applied to find more suitable slip rate range by using the root mean square error method (REME). Finally, co-simulation platform of ADAMS/Car and MATLAB/Simulink is built to simulate the mixed control strategies by integrating dual-mode switching control, DYC and ASR. The simulation results show that this strategy has a more significant control effect needed to meet the requirements of normal vehicle handling and stability. The mixed control strategy is adopted and downloaded into the electronic control unit of our student type formula vehicle called Flash V6 which was designed and developed by a team of students, the ZUST ATTACKER Team.

An improved estimation method for vehicle velocity of distributed drive electric vehicle

In this paper, Simulink and Carsim are combined to study the velocity estimation of distributed drive electric vehicles. Firstly, the minimum co-simulation system is established to complete the design and debugging of the algorithm. Then, a new algorithm combining unscented Kalman filter and strong tracking filter is proposed based on the vehicle estimation model. The accuracy and real-time performance of the velocity estimation algorithm are validated by simulation under snake-shaped driving conditions with different road adhesion coefficients. Finally, an experimental test is carried out to verify the effectiveness of the proposed algorithm in estimating vehicle velocity.

Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.