As an important safety critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer Artificial Neural Networks (ANN) with Levenberg-Marquardt Backpropagation (LMBP) training algorithm. Firstly, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of backpropagation, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.

The driving motor of the electric vehicle (EV) can recover kinetic energy during normal braking maneuvers by regenerative function. At the same time, its dynamic torque response proves to be accurate and fast for emergency braking, namely anti-lock braking, with coordinated control of the frictional braking system. However, vehicle transmission properties will deteriorate control performance of the motor, especially in the anti-lock braking process. A novel PMSM control method is proposed considering the transmission influence on this high-dynamic braking process of the pure electric vehicle. Firstly, the EV's dynamic model which includes the PMSM field oriented control (FOC) model, the transmission dynamic model and the hydraulic braking system is built, and the influences of transmission elasticity and backlash non-linearity on motor braking torque are analyzed. Then, based on the wheel slip ratio target of the anti-lock braking, the novel mode-switching method for motor torque control between the backlash sliding-mode compensation and the elasticity double-closed-loop PID compensation is put forward. Two state-of-the-art anti-lock braking algorithms which simplify transmission properties, slip ratio phase plane theory and sliding-mode control, are compared with the proposed method. Simulation and test-bench experiment results show that, on different test road surfaces, the mode-switching PMSM control can effectively compensate for transmission effects and significantly improve the EV's anti-lock braking comfort, stability and maneuverability with fast and accurate motor torque regulating.

Due to the numerical subsystems and functionalities among the platoon, failures easily occur and lead vehicle safety and stability to deteriorate. With the surge of platoon connectivity, the abnormal states arising in faulty vehicles will have an impact on adjacent healthy vehicles, potentially resulting in severe traffic accidents. To address the aforementioned problems and improve the functional safety of platoons, a systemic anti-fault safety consensus strategy is proposed in this paper to deal with sensor faults, LOE actuator faults, and bias actuator faults simultaneously. To tackle the necessary defects, this safety consensus technique can be split into fault detection tasks and fault-tolerant control tasks. Initially, a distributed finite-time observer is employed to detect sensor faults through the index between the reconstructed states and received states. In the presence of actuator faults, a novel adaptive finite-time fault parameter estimation law collaboration with a feasible differential observer is developed to assess the precise fault extent. In addition, to improve the estimation performance of the adaptive fault parameter estimation law, an integral discounted cost function is constructed in this paper to obtain the optimal learning gain. Via theoretical analysis, the solution for the minimal value of the cost function and the finite-time convergence property are demonstrated in this paper. Furthermore, by utilizing this novel adaptive optimal finite-time estimation law, a novel distributed adaptive finite-time fault-tolerant controller is constructed to compensate for failures in platoon. Finally, the effectiveness and feasibility of our safety consensus strategy are verified by real-time simulations through the dSPACE platform.

Because of the damping and elastic properties of an electrified powertrain, the regenerative brake of an electric vehicle (EV) is very different from a conventional friction brake with respect to the system dynamics. The flexibility of an electric drivetrain would have a negative effect on the blended brake control performance. In this study, models of the powertrain system of an electric car equipped with an axle motor are developed. Based on these models, the transfer characteristics of the motor torque in the driveline and its effect on blended braking control performance are analysed. To further enhance a vehicle's brake performance and energy efficiency, blended braking control algorithms with compensation for the powertrain flexibility are proposed using an extended Kalman filter. These algorithms are simulated under normal deceleration braking. The results show that the brake performance and blended braking control accuracy of the vehicle are significantly enhanced by the newly proposed algorithms.

Regenerative braking provided by an electric powertrain is very different from conventional friction braking with respect to the system dynamics. During regenerative decelerations, the powertrain backlash and flexibility excite driveline oscillations, causing the vehicle driveability and the blended brake performance to deteriorate. In this article, system models, including a powertrain model with non-linear backlash and flexibility, and a hydraulic brake system model, are developed. The effects of the powertrain backlash and the flexibility on the vehicle driveability during regenerative deceleration are analysed. To improve the driveability and the blended braking performance of an electric vehicle further, a mode-switching-based active control algorithm with a hierarchical architecture is developed providing compensation for the backlash and the flexibility. The proposed control algorithms are simulated and compared with the baseline strategy under the regeneration braking process. The simulation results show that the vehicle driveability and the blended braking performance can be significantly enhanced by the developed active control algorithm.

The development of a novel electrohydraulic brake system named REHB is presented in this paper. Neither high-pressure accumulator nor linear valves are adopted to reduce the system cost, the difficulty of manufacturing, assembling, and the risk of leakage. Stroke simulators are implemented to elevate the quality of pedal feel. The braking force blending control strategy is well designed to coordinate regenerative and friction brake force, which has ensured maximized regenerative brake force. In the aspect of underlying control, Current Amplitude Modulation control is adopted to improve the accuracy of hydraulic pressure modulation and eliminate vibration noise. High fidelity models of vehicle and brake system are built based on MATLAB-AMESim co-simulation platform. Three demand levels of one-step-brake scenarios are simulated. Simulation results show the reasonability of proposed hydraulic structure and designed control strategy. A regenerative brake test bench is set up based on a prototype of the developed system. Three identical one-step-brake experiments are conducted on the test bench. According to the bench test results, the blending of regenerative and friction brake force tracks driver brake demand adequately well. Regenerative efficiencies are calculated as 46.32%, 43.93%, and 31.88% respectively in three bench tests. Compared with the previous generation, no obvious valve buzzing noise is observed, and jerks are whittled significantly. REHB has provided a brake-by-wire solution of safety, high energy recuperation efficiency with low cost and uncompromising performance.