With the aims of regeneration efficiency and brake comfort, three different control strategies, namely the maximum-regeneration-efficiency strategy, the good-pedal-feel strategy and the coordination strategy for regenerative braking of an electrified passenger car are researched in this paper. The models of the main components related to the regenerative brake and the frictional blending brake of the electric passenger car are built in MATLAB/Simulink. The control effects and regeneration efficiencies of the control strategies in a typical deceleration process are simulated and analysed. Road tests under normal deceleration braking and an ECE driving cycle are carried out. The simulation and road test results show that the maximum-regeneration-efficiency strategy, which causes issues on brake comfort and safety, could hardly be utilized in the regenerative braking system adopted. The good-pedal-feel strategy and coordination strategy are advantageous over the first strategy with respect to the brake comfort and regeneration efficiency. The fuel economy enhanced by the regenerative braking system developed is more than 25% under the ECE driving cycle.

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 highlevel architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feedforward 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.

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Because of its significant impact on the cooperative regenerative braking performance of electrified vehicles, the modulation effect of a hydraulic brake is of great importance. To improve the hydraulic brake control performance further, a novel pressure-difference-limiting control method for hydraulic pressure modulation based on on-off solenoid valves is proposed. The linear relationship between the coil current and the pressure difference across the valve is obtained. The characteristics of pressure-difference-limiting modulation are simulated and analysed. Then, a cooperative regenerative braking control algorithm based on the pressure-difference-limiting modulation of the hydraulic brake is designed. Hardware-in-the-loop tests of the algorithm under typical braking procedures are carried out. The test results demonstrate the validity and feasibility of the developed regenerative braking control algorithm and indicate that the proposed pressure-difference-limiting modulation method, which has an advantage over the conventional control based on a pulse-width-modulated signal with respect to the control accuracy of the hydraulic brake pressure, has great potential to improve the braking performance of a vehicle.

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